Repository: pepkit/geofetch (language: python)

Issue:
Incorrect path in PEP to the downloaded files
After downloading processed data with metadata, path to each file is incorrect. Project uploaded in peppy and pepr is have incorrect paths to the files. 
Initial yaml file has to be changed. 
bring in latest master changes

In series annotation file files are stored in the list
Geofetch is creating series annotation file where some of the columns show up as a python list. This list should be: 1) separated, or 2) stored as plain text
The argument --acc-anno is not working for processed data
For now, there is no possibility to produce annotation sheets for each accession. We should add it.


Discussion / hints:

Fix patch:
diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index 672acd6..0000000
--- a/.travis.yml
+++ /dev/null
@@ -1,17 +0,0 @@
-language: python
-python:
-  - "2.7"
-  - "3.5"
-  - "3.6"
-os:
-  - linux
-install:
-  - pip install --upgrade six
-  - pip install .
-  - pip install -r requirements/requirements-dev.txt
-  - pip install -r requirements/requirements-test.txt
-script: pytest --cov=geofetch
-branches:
-  only:
-    - dev
-    - master
diff --git a/MANIFEST.in b/MANIFEST.in
index ec7e74c..e704c91 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -2,3 +2,4 @@ include requirements/*
 include README.md
 include docs/img/geofetch_logo.svg
 include geofetch/config_template.yaml
+include geofetch/config_processed_template.yaml
diff --git a/README.md b/README.md
index 1f4072b..5c2b575 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
-# <img src="docs/img/geofetch_logo.svg" alt="geofetch logo" height="70"/>
+# <img src="docs/img/geofetch_logo.svg" alt="geofetch logo" height="70">
 
-[![Build Status](https://travis-ci.org/pepkit/geofetch.svg?branch=master)](https://travis-ci.org/pepkit/geofetch)
 [![PEP compatible](http://pepkit.github.io/img/PEP-compatible-green.svg)](http://pepkit.github.io)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 
 `geofetch` is a command-line tool that downloads sequencing data and metadata from GEO and SRA and creates [standard PEPs](http://pepkit.github.io). `geofetch` is hosted at [pypi](https://pypi.org/project/geofetch/) and documentation is hosted at [geofetch.databio.org](http://geofetch.databio.org) (source in the [/docs](/docs) folder).
 
-You can convert the result of geofetch into `bam` files with the [sra_convert](https://github.com/pepkit/sra_convert) pipeline.
+You can convert the result of geofetch into unmapped `bam` or `fastq` files with the included `sraconvert` command.
diff --git a/docs/README.md b/docs/README.md
index 7136d5e..3184d07 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -1,6 +1,7 @@
 # <img src="img/geofetch_logo.svg" class="img-header">
 
 [![PEP compatible](http://pepkit.github.io/img/PEP-compatible-green.svg)](http://pepkit.github.io)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 
 `geofetch` is a command-line tool that downloads and organizes data and metadata from GEO and SRA. When given one or more GEO/SRA accessions, `geofetch` will:
 
@@ -8,21 +9,26 @@
   - Produce a standardized [PEP](http://pepkit.github.io) sample table. This makes it really easy to run [looper](https://pepkit.github.io/docs/looper/)-compatible pipelines on public datasets by handling data acquisition and metadata formatting and standardization for you.
   - Prepare a project to run with [sraconvert](sra_convert.md) to convert SRA files into FASTQ files.
 
-## Quick demo
+## Quick example
 
-`geofetch` runs on the command line. This command will download the metadata for the given GSE number.
+`geofetch` runs on the command line. This command will download the raw data and metadata for the given GSE number.
 
 ```console
 geofetch -i GSE95654
 ```
 
-You can add `--just-metadata` if you don't want to download the raw SRA files.
+You can add `--processed` if you want to download processed files from the given experiment.
+
 
 ```console
-geofetch -i GSE95654 --just-metadata
+geofetch -i GSE95654 --processed
 ```
 
-For more details, check out the [usage](usage.md) reference, [installation instructions](install.md), or head on over to the [tutorial](tutorial.md) for a detailed walkthrough.
+You can add `--just-metadata` if you want to download metadata without the raw SRA files or processed GEO files.
 
+```console
+geofetch -i GSE95654 --just-metadata
+```
 
+For more details, check out the [usage](usage.md) reference, [installation instructions](install.md), or head on over to the [tutorial for raw data](raw-data-downloading.md) and [tutorial for processed data](processed-data-downloading.md) for a detailed walkthrough.
 
diff --git a/docs/changelog.md b/docs/changelog.md
index a83fbce..bdfd79a 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -1,5 +1,16 @@
 # Changelog
 
+## [0.8.0] -- 2022-03-10
+- Added `--filter-size` argument.
+- Added `--data-source` argument.
+- Removed `--tar_re` argument.
+- Added PEP for processed data.
+- Updated regex filter (case-insensitive update).
+- Changed way of downloading processed data (downloading each file separately).
+- Fixed code errors.
+- Separated sample and experiment processed data.
+
+
 ## [0.7.0] -- 2020-05-21
 - Fixed user interface for bam conversions
 - Added regex filter for processed data filenames, which will also auto-extract from tar archives
diff --git a/docs/metadata_output.md b/docs/metadata_output.md
index 0d28d50..3c2eace 100644
--- a/docs/metadata_output.md
+++ b/docs/metadata_output.md
@@ -6,7 +6,7 @@ For each GSE input accession (ACC), `geofetch` produces:
 - GSM_ACC####.soft a SOFT file (annotating the samples within the experiment)
 - SRA_ACC####.soft a CSV file (annotating each SRA Run, retrieved from GSE->GSM->SRA)
 
-In addition, a single combined metadata file ("annoComb") for the whole input,
+In addition, a single combined metadata file (.csv) for the whole input,
 including SRA and GSM annotations for each sample. Here, "combined" means that it will have
 rows for every sample in every GSE included in your input. So if you just gave a single GSE,
 then the combined file is the same as the GSE file. If any "merged" samples exist
@@ -17,3 +17,8 @@ SRX and SRR.
 The way this works: Starting from a GSE, select a subset of samples (GSM Accessions) provided, 
 and then obtain the SRX identifier for each of these from GEO. Now, query SRA for these SRX 
 accessions and get any associated SRR accessions. Finally, download all of these SRR data files.
+
+### The most important metadata in pep format will be stored in
+- NAME_annotation_sample_processed.csv - all metadata for sample processed data
+- NAME_annotation.csv - all metadata for series processed data
+- NAME_annotation_series_processed.csv file - all metadata for raw data
diff --git a/docs/sra_convert.md b/docs/sra_convert.md
index 918a96b..2ae7a14 100644
--- a/docs/sra_convert.md
+++ b/docs/sra_convert.md
@@ -15,4 +15,4 @@ This effectively makes it easier to interact with *project-level* management of
 
 ## Tutorial
 
-See the [tutorial](tutorial.md) for an example of how to use `sraconvert`.
\ No newline at end of file
+See the [tutorial](raw-data-downloading.md) for an example of how to use `sraconvert`.
\ No newline at end of file
diff --git a/docs/support.md b/docs/support.md
deleted file mode 100644
index e7ec205..0000000
--- a/docs/support.md
+++ /dev/null
@@ -1,3 +0,0 @@
-## Support
-
-Please use the [issue tracker](https://github.com/pepkit/geofetch/issues) at GitHub to file bug reports or feature requests.
diff --git a/docs/usage.md b/docs/usage.md
index 4f462c0..cc7c3d3 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -1,4 +1,4 @@
-# <img src="../img/geofetch_logo.svg" class="img-header">  usage reference
+# <img src="./img/geofetch_logo.svg" class="img-header">  Usage reference
 
 `geofetch` command-line usage instructions:
 
@@ -6,49 +6,55 @@
 
 `geofetch --help`
 ```{console}
-usage: geofetch [-h] -i INPUT [-n NAME] [-m METADATA_FOLDER] [-f]
-                [--just-metadata] [-r] [--acc-anno] [--use-key-subset] [-x]
-                [--config-template CONFIG_TEMPLATE] [-p] [-g GEO_FOLDER]
-                [-b BAM_FOLDER] [-P PIPELINE_INTERFACES]
+usage: geofetch [-h] [-V] -i INPUT [-n NAME] [-m METADATA_ROOT] [-u METADATA_FOLDER] [--just-metadata] [-r] [--config-template CONFIG_TEMPLATE] [-P PIPELINE_INTERFACES] [-k SKIP] [--acc-anno] [--use-key-subset] [-p]
+                [--data-source {all,samples,series}] [--filter FILTER] [--filter-size FILTER_SIZE] [-g GEO_FOLDER] [-x] [-b BAM_FOLDER] [-f FQ_FOLDER] [--silent] [--verbosity V] [--logdev]
 
-Automatic GEO SRA data downloader
+Automatic GEO and SRA data downloader
 
 optional arguments:
   -h, --help            show this help message and exit
+  -V, --version         show program's version number and exit
   -i INPUT, --input INPUT
-                        required: a GEO (GSE) accession, or a file with a list
-                        of GSE numbers
+                        required: a GEO (GSE) accession, or a file with a list of GSE numbers
   -n NAME, --name NAME  Specify a project name. Defaults to GSE number
-  -m METADATA_FOLDER, --metadata-folder METADATA_FOLDER
-                        Specify a location to store metadata [Default:
-                        $SRAMETA:]
-  -f, --no-subfolder    Don't automatically put metadata into a subfolder
-                        named with project name
-  --just-metadata       If set, don't actually run downloads, just create
-                        metadata
+  -m METADATA_ROOT, --metadata-root METADATA_ROOT
+                        Specify a parent folder location to store metadata. The project name will be added as a subfolder [Default: $SRAMETA:]
+  -u METADATA_FOLDER, --metadata-folder METADATA_FOLDER
+                        Specify an absolute folder location to store metadata. No subfolder will be added. Overrides value of --metadata-root [Default: Not used (--metadata-root is used by default)]
+  --just-metadata       If set, don't actually run downloads, just create metadata
   -r, --refresh-metadata
                         If set, re-download metadata even if it exists.
-  --acc-anno            Also produce annotation sheets for each accession, not
-                        just for the whole project combined
-  --use-key-subset      Use just the keys defined in this module when writing
-                        out metadata.
-  -x, --split-experiments
-                        Split SRR runs into individual samples. By default,
-                        SRX experiments with multiple SRR Runs will have a
-                        single entry in the annotation table, with each run as
-                        a separate row in the subannotation table. This
-                        setting instead treats each run as a separate sample
   --config-template CONFIG_TEMPLATE
                         Project config yaml file template.
+  -P PIPELINE_INTERFACES, --pipeline_interfaces PIPELINE_INTERFACES
+                        Optional: Specify one or more filepaths to pipeline interface yaml files. These will be added to the project config file to make it immediately compatible with looper. [Default: null]
+  -k SKIP, --skip SKIP  Skip some accessions. [Default: no skip].
+  --acc-anno            Also produce annotation sheets for each accession, not just for the whole project combined
+  --use-key-subset      Use just the keys defined in this module when writing out metadata.
+  --silent              Silence logging. Overrides verbosity.
+  --verbosity V         Set logging level (1-5 or logging module level name)
+  --logdev              Expand content of logging message format.
+
+processed:
   -p, --processed       Download processed data [Default: download raw data].
+  --data-source {all,samples,series}
+                        Optional: Specifies the source of data on the GEO record to retrieve processed data, which may be attached to the collective series entity, or to individual samples. Allowable values are: samples,
+                        series or both (all). Ignored unless 'processed' flag is set. [Default: all]
+  --filter FILTER       Optional: Filter regex for processed filenames [Default: None].Ignored unless 'processed' flag is set.
+  --filter-size FILTER_SIZE
+                        Optional: Filter size for processed files that are stored as sample repository [Default: None]. Supported input formats : 12B, 12KB, 12MB, 12GB. Ignored unless 'processed' flag is set.
   -g GEO_FOLDER, --geo-folder GEO_FOLDER
-                        Optional: Specify a location to store processed GEO
-                        files [Default: $GEODATA:]
+                        Optional: Specify a location to store processed GEO files. Ignored unless 'processed' flag is set.[Default: $GEODATA:]
+
+raw:
+  -x, --split-experiments
+                        Split SRR runs into individual samples. By default, SRX experiments with multiple SRR Runs will have a single entry in the annotation table, with each run as a separate row in the subannotation
+                        table. This setting instead treats each run as a separate sample
   -b BAM_FOLDER, --bam-folder BAM_FOLDER
-                        Optional: Specify folder of bam files. Geofetch will
-                        not download sra files when corresponding bam files
-                        already exist. [Default: $SRABAM:]
-  -P PIPELINE_INTERFACES, --pipeline_interfaces PIPELINE_INTERFACES
-                        Optional: Specify the filepath of a pipeline interface
-                        yaml file. [Default: null]
+                        Optional: Specify folder of bam files. Geofetch will not download sra files when corresponding bam files already exist. [Default: $SRABAM:]
+  -f FQ_FOLDER, --fq-folder FQ_FOLDER
+                        Optional: Specify folder of fastq files. Geofetch will not download sra files when corresponding fastq files already exist. [Default: $SRAFQ:]
+
+
+
 ```
diff --git a/docs/usage_template.md b/docs/usage_template.md
index ac6a28f..81e6348 100644
--- a/docs/usage_template.md
+++ b/docs/usage_template.md
@@ -1,4 +1,4 @@
-# <img src="../img/geofetch_logo.svg" class="img-header">  usage reference
+# <img src="./img/geofetch_logo.svg" class="img-header">  usage reference
 
 `geofetch` command-line usage instructions:
 
diff --git a/docs_jupyter/build/.gitignore b/docs_jupyter/build/.gitignore
deleted file mode 100644
index d6b7ef3..0000000
--- a/docs_jupyter/build/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-*
-!.gitignore
diff --git a/docs_jupyter/processed-data-downloading.ipynb b/docs_jupyter/processed-data-downloading.ipynb
new file mode 100644
index 0000000..dfcf672
--- /dev/null
+++ b/docs_jupyter/processed-data-downloading.ipynb
@@ -0,0 +1,545 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# geofetch tutorial for processed data\n",
+    "\n",
+    "The [GSE185701 data set](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE185701) has about 355 Mb of processed data that contains 57 Supplementary files, so it's a quick download for a test case. Let's take a quick peek at the geofetch version:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "geofetch 0.8.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "geofetch -V"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To see your CLI options, invoke `geofetch -h`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "usage: geofetch [-h] [-V] -i INPUT [-n NAME] [-m METADATA_ROOT]\n",
+      "                [-u METADATA_FOLDER] [--just-metadata] [-r]\n",
+      "                [--config-template CONFIG_TEMPLATE] [-P PIPELINE_INTERFACES]\n",
+      "                [-k SKIP] [--acc-anno] [--use-key-subset] [-p]\n",
+      "                [--data-source {all,samples,series}] [--filter FILTER]\n",
+      "                [--filter-size FILTER_SIZE] [-g GEO_FOLDER] [-x]\n",
+      "                [-b BAM_FOLDER] [-f FQ_FOLDER] [--silent] [--verbosity V]\n",
+      "                [--logdev]\n",
+      "\n",
+      "Automatic GEO and SRA data downloader\n",
+      "\n",
+      "optional arguments:\n",
+      "  -h, --help            show this help message and exit\n",
+      "  -V, --version         show program's version number and exit\n",
+      "  -i INPUT, --input INPUT\n",
+      "                        required: a GEO (GSE) accession, or a file with a list\n",
+      "                        of GSE numbers\n",
+      "  -n NAME, --name NAME  Specify a project name. Defaults to GSE number\n",
+      "  -m METADATA_ROOT, --metadata-root METADATA_ROOT\n",
+      "                        Specify a parent folder location to store metadata.\n",
+      "                        The project name will be added as a subfolder\n",
+      "                        [Default: $SRAMETA:]\n",
+      "  -u METADATA_FOLDER, --metadata-folder METADATA_FOLDER\n",
+      "                        Specify an absolute folder location to store metadata.\n",
+      "                        No subfolder will be added. Overrides value of\n",
+      "                        --metadata-root [Default: Not used (--metadata-root is\n",
+      "                        used by default)]\n",
+      "  --just-metadata       If set, don't actually run downloads, just create\n",
+      "                        metadata\n",
+      "  -r, --refresh-metadata\n",
+      "                        If set, re-download metadata even if it exists.\n",
+      "  --config-template CONFIG_TEMPLATE\n",
+      "                        Project config yaml file template.\n",
+      "  -P PIPELINE_INTERFACES, --pipeline_interfaces PIPELINE_INTERFACES\n",
+      "                        Optional: Specify one or more filepaths to pipeline\n",
+      "                        interface yaml files. These will be added to the\n",
+      "                        project config file to make it immediately compatible\n",
+      "                        with looper. [Default: null]\n",
+      "  -k SKIP, --skip SKIP  Skip some accessions. [Default: no skip].\n",
+      "  --acc-anno            Also produce annotation sheets for each accession, not\n",
+      "                        just for the whole project combined\n",
+      "  --use-key-subset      Use just the keys defined in this module when writing\n",
+      "                        out metadata.\n",
+      "  --silent              Silence logging. Overrides verbosity.\n",
+      "  --verbosity V         Set logging level (1-5 or logging module level name)\n",
+      "  --logdev              Expand content of logging message format.\n",
+      "\n",
+      "processed:\n",
+      "  -p, --processed       Download processed data [Default: download raw data].\n",
+      "  --data-source {all,samples,series}\n",
+      "                        Optional: Specifies the source of data on the GEO\n",
+      "                        record to retrieve processed data, which may be\n",
+      "                        attached to the collective series entity, or to\n",
+      "                        individual samples. Allowable values are: samples,\n",
+      "                        series or both (all). Ignored unless 'processed' flag\n",
+      "                        is set. [Default: all]\n",
+      "  --filter FILTER       Optional: Filter regex for processed filenames\n",
+      "                        [Default: None].Ignored unless 'processed' flag is\n",
+      "                        set.\n",
+      "  --filter-size FILTER_SIZE\n",
+      "                        Optional: Filter size for processed files that are\n",
+      "                        stored as sample repository [Default: None]. Supported\n",
+      "                        input formats : 12B, 12KB, 12MB, 12GB. Ignored unless\n",
+      "                        'processed' flag is set.\n",
+      "  -g GEO_FOLDER, --geo-folder GEO_FOLDER\n",
+      "                        Optional: Specify a location to store processed GEO\n",
+      "                        files. Ignored unless 'processed' flag is\n",
+      "                        set.[Default: $GEODATA:]\n",
+      "\n",
+      "raw:\n",
+      "  -x, --split-experiments\n",
+      "                        Split SRR runs into individual samples. By default,\n",
+      "                        SRX experiments with multiple SRR Runs will have a\n",
+      "                        single entry in the annotation table, with each run as\n",
+      "                        a separate row in the subannotation table. This\n",
+      "                        setting instead treats each run as a separate sample\n",
+      "  -b BAM_FOLDER, --bam-folder BAM_FOLDER\n",
+      "                        Optional: Specify folder of bam files. Geofetch will\n",
+      "                        not download sra files when corresponding bam files\n",
+      "                        already exist. [Default: $SRABAM:]\n",
+      "  -f FQ_FOLDER, --fq-folder FQ_FOLDER\n",
+      "                        Optional: Specify folder of fastq files. Geofetch will\n",
+      "                        not download sra files when corresponding fastq files\n",
+      "                        already exist. [Default: $SRAFQ:]\n"
+     ]
+    }
+   ],
+   "source": [
+    "geofetch -h"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calling geofetch will do 4 tasks: \n",
+    "\n",
+    "1. download all or filtered processed files from `GSE#####` into your geo folder.\n",
+    "2. download all metadata from GEO and store in your metadata folder.\n",
+    "2. produce a PEP-compatible sample table, `PROJECT_NAME_sample_processed.csv` and `PROJECT_NAME_series_processed.csv`, in your metadata folder.\n",
+    "3. produce a PEP-compatible project configuration file, `PROJECT_NAME_sample_processed.yaml` and `PROJECT_NAME_series_processed.yaml`, in your metadata folder.\n",
+    "\n",
+    "Complete details about geofetch outputs is cataloged in the [metadata outputs reference](metadata_output.md)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download the data\n",
+    "\n",
+    "First, create the metadata for processed data (by adding --processed and --just-metadata):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Metadata folder: /home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test\n",
+      "Trying GSE185701 (not a file) as accession...\n",
+      "Skipped 0 accessions. Starting now.\n",
+      "\u001b[38;5;228mProcessing accession 1 of 1: 'GSE185701'\u001b[0m\n",
+      "--2022-03-10 11:32:18--  https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?targ=gse&acc=GSE185701&form=text&view=full\n",
+      "Resolving www.ncbi.nlm.nih.gov (www.ncbi.nlm.nih.gov)... 2607:f220:41e:4290::110, 130.14.29.110\n",
+      "Connecting to www.ncbi.nlm.nih.gov (www.ncbi.nlm.nih.gov)|2607:f220:41e:4290::110|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: unspecified [geo/text]\n",
+      "Saving to: ‘/home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/GSE185701_GSE.soft’\n",
+      "\n",
+      "/home/bnt4me/Virgin     [ <=>                ]   2.79K  --.-KB/s    in 0s      \n",
+      "\n",
+      "2022-03-10 11:32:18 (262 MB/s) - ‘/home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/GSE185701_GSE.soft’ saved [2855]\n",
+      "\n",
+      "--2022-03-10 11:32:18--  https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?targ=gsm&acc=GSE185701&form=text&view=full\n",
+      "Resolving www.ncbi.nlm.nih.gov (www.ncbi.nlm.nih.gov)... 2607:f220:41e:4290::110, 130.14.29.110\n",
+      "Connecting to www.ncbi.nlm.nih.gov (www.ncbi.nlm.nih.gov)|2607:f220:41e:4290::110|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: unspecified [geo/text]\n",
+      "Saving to: ‘/home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/GSE185701_GSM.soft’\n",
+      "\n",
+      "/home/bnt4me/Virgin     [  <=>               ]  39.51K   139KB/s    in 0.3s    \n",
+      "\n",
+      "2022-03-10 11:32:19 (139 KB/s) - ‘/home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/GSE185701_GSM.soft’ saved [40454]\n",
+      "\n",
+      "\u001b[38;5;242m\n",
+      "--2022-03-10 11:32:19--  ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE185nnn/GSE185701/suppl/filelist.txt\n",
+      "           => ‘/home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/GSE185701_file_list.txt’\n",
+      "Resolving ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)... 2607:f220:41e:250::10, 2607:f220:41f:250::228, 130.14.250.11, ...\n",
+      "Connecting to ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)|2607:f220:41e:250::10|:21... connected.\n",
+      "Logging in as anonymous ... Logged in!\n",
+      "==> SYST ... done.    ==> PWD ... done.\n",
+      "==> TYPE I ... done.  ==> CWD (1) /geo/series/GSE185nnn/GSE185701/suppl ... done.\n",
+      "==> SIZE filelist.txt ... 794\n",
+      "==> EPSV ... done.    ==> RETR filelist.txt ... done.\n",
+      "Length: 794 (unauthoritative)\n",
+      "\n",
+      "filelist.txt        100%[===================>]     794  --.-KB/s    in 0.04s   \n",
+      "\n",
+      "2022-03-10 11:32:20 (18.3 KB/s) - ‘/home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/GSE185701_file_list.txt’ saved [794]\n",
+      "\n",
+      "\u001b[38;5;242m0\u001b[0m\n",
+      "\u001b[0m\n",
+      "Total number of processed SAMPLES files found is: 8\n",
+      "Total number of processed SERIES files found is: 1\n",
+      "Finished processing 1 accession(s)\n",
+      "Expanding metadata list...\n",
+      "Unifying and saving of metadata... \n",
+      "\u001b[92mFile /home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/bright_test_annotation_sample_processed.csv has been saved successfully\u001b[0m\n",
+      "  Config file: /home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/bright_test_annotation_sample_processed.yaml\n",
+      "Unifying and saving of metadata... \n",
+      "\u001b[92mFile /home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/bright_test_annotation_series_processed.csv has been saved successfully\u001b[0m\n",
+      "  Config file: /home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/bright_test_annotation_series_processed.yaml\n"
+     ]
+    }
+   ],
+   "source": [
+    "geofetch -i GSE185701 --processed -n bright_test --just-metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bright_test_annotation_sample_processed.csv   GSE185701_file_list.txt\n",
+      "bright_test_annotation_sample_processed.yaml  GSE185701_GSE.soft\n",
+      "bright_test_annotation_series_processed.csv   GSE185701_GSM.soft\n",
+      "bright_test_annotation_series_processed.yaml\n"
+     ]
+    }
+   ],
+   "source": [
+    "ls bright_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `.soft` files are the direct output from GEO, which contain all the metadata as stored by GEO, for both the experiment (`_GSE`) and for the individual samples (`_GSM`). Geofetch also produces a `csv` file with the SRA metadata. The filtered version (ending in `_filt`) would contain only the specified subset of the samples if we didn't request them all, but in this case, since we only gave an accession, it is identical to the complete file. Additionally, file_list.txt is downloaded, that contains information about size, type and creation date of all sample files.\n",
+    "\n",
+    "Finally, there are the 2 files that make up the PEP: the `_config.yaml` file and the `_annotation.csv` file (for samples and series). Let's see what's in these files now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pep_version: 2.0.0\n",
+      "project_name: bright_test\n",
+      "sample_table: /home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/bright_test_annotation_sample_processed.csv\n",
+      "\n",
+      "sample_modifiers:\n",
+      "  append:\n",
+      "    output_file_path: FILES\n",
+      "  derive:\n",
+      "    attributes: [output_file_path]\n",
+      "    sources:\n",
+      "      FILES: /{SRA}/{sample_name}\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "cat bright_test/bright_test_annotation_sample_processed.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are two important things to note in his file: First, see in the PEP that `sample_table` points to the csv file produced by geofetch. Second: output_file_path is location of all the files.\n",
+    "\n",
+    "Now let's look at the first 100 characters of the csv file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GSE,Sample_title,Sample_geo_accession,Sample_status,Sample_submission_date,Sample_last_update_date,S\n",
+      "GSE185701,Huh7_siNC_H3K27ac,GSM5621756,Public on Mar 01 2022,Oct 12 2021,Mar 03 2022,SRA,1,Huh 7,Hom\n",
+      "GSE185701,Huh7_siNC_H3K27ac,GSM5621756,Public on Mar 01 2022,Oct 12 2021,Mar 03 2022,SRA,1,Huh 7,Hom\n",
+      "GSE185701,Huh7_siDHX37_H3K27ac,GSM5621758,Public on Mar 01 2022,Oct 12 2021,Mar 03 2022,SRA,1,Huh 7,\n",
+      "GSE185701,Huh7_siDHX37_H3K27ac,GSM5621758,Public on Mar 01 2022,Oct 12 2021,Mar 03 2022,SRA,1,Huh 7,\n",
+      "GSE185701,Huh7_DHX37,GSM5621760,Public on Mar 01 2022,Oct 12 2021,Mar 03 2022,SRA,1,Huh 7,Homo sapie\n",
+      "GSE185701,Huh7_DHX37,GSM5621760,Public on Mar 01 2022,Oct 12 2021,Mar 03 2022,SRA,1,Huh 7,Homo sapie\n",
+      "GSE185701,Huh7_PLRG1,GSM5621761,Public on Mar 01 2022,Oct 12 2021,Mar 03 2022,SRA,1,Huh 7,Homo sapie\n",
+      "GSE185701,Huh7_PLRG1,GSM5621761,Public on Mar 01 2022,Oct 12 2021,Mar 03 2022,SRA,1,Huh 7,Homo sapie\n"
+     ]
+    }
+   ],
+   "source": [
+    "cut -c -100 bright_test/bright_test_annotation_sample_processed.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's download the actual data. This time we will will be downloading data from the [GSE185701 data set](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE185701) .\n",
+    "Let's additionally add few arguments:\n",
+    "- _geo-folder_ (required) - path to the location where processed files have to be saved\n",
+    "- _filter_ argument, to download only _bed_ files  (--filter \".Bed.gz$\") \n",
+    "- _data-source_ argument, to download files only from sample location (--data-source samples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Metadata folder: /home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test\n",
+      "Trying GSE185701 (not a file) as accession...\n",
+      "Skipped 0 accessions. Starting now.\n",
+      "\u001b[38;5;228mProcessing accession 1 of 1: 'GSE185701'\u001b[0m\n",
+      "Found previous GSE file: /home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/GSE185701_GSE.soft\n",
+      "Found previous GSM file: /home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/GSE185701_GSM.soft\n",
+      "\u001b[38;5;242mFile /home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/GSE185701_file_list.txt exists.\u001b[0m\n",
+      "Total number of processed SAMPLES files found is: 8\n",
+      "\u001b[32mTotal number of files after filter is: 4 \u001b[0m\n",
+      "Total number of processed SERIES files found is: 1\n",
+      "\u001b[32mTotal number of files after filter is: 0 \u001b[0m\n",
+      "\u001b[38;5;242m\n",
+      "--2022-03-10 11:35:36--  ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5621nnn/GSM5621756/suppl/GSM5621756_ChIPseq_Huh7_siNC_H3K27ac_summits.bed.gz\n",
+      "           => ‘/home/bnt4me/Virginia/for_docs/geo/GSE185701/GSM5621756_ChIPseq_Huh7_siNC_H3K27ac_summits.bed.gz’\n",
+      "Resolving ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)... 2607:f220:41e:250::12, 2607:f220:41e:250::11, 165.112.9.228, ...\n",
+      "Connecting to ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)|2607:f220:41e:250::12|:21... connected.\n",
+      "Logging in as anonymous ... Logged in!\n",
+      "==> SYST ... done.    ==> PWD ... done.\n",
+      "==> TYPE I ... done.  ==> CWD (1) /geo/samples/GSM5621nnn/GSM5621756/suppl ... done.\n",
+      "==> SIZE GSM5621756_ChIPseq_Huh7_siNC_H3K27ac_summits.bed.gz ... 785486\n",
+      "==> EPSV ... done.    ==> RETR GSM5621756_ChIPseq_Huh7_siNC_H3K27ac_summits.bed.gz ... done.\n",
+      "Length: 785486 (767K) (unauthoritative)\n",
+      "\n",
+      "GSM5621756_ChIPseq_ 100%[===================>] 767.08K  1.90MB/s    in 0.4s    \n",
+      "\n",
+      "2022-03-10 11:35:37 (1.90 MB/s) - ‘/home/bnt4me/Virginia/for_docs/geo/GSE185701/GSM5621756_ChIPseq_Huh7_siNC_H3K27ac_summits.bed.gz’ saved [785486]\n",
+      "\n",
+      "\u001b[38;5;242m0\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[92mFile /home/bnt4me/Virginia/for_docs/geo/GSE185701/GSM5621756_ChIPseq_Huh7_siNC_H3K27ac_summits.bed.gz has been downloaded successfully\u001b[0m\n",
+      "\u001b[38;5;242m\n",
+      "--2022-03-10 11:35:38--  ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5621nnn/GSM5621758/suppl/GSM5621758_ChIPseq_Huh7_siDHX37_H3K27ac_summits.bed.gz\n",
+      "           => ‘/home/bnt4me/Virginia/for_docs/geo/GSE185701/GSM5621758_ChIPseq_Huh7_siDHX37_H3K27ac_summits.bed.gz’\n",
+      "Resolving ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)... 2607:f220:41e:250::11, 2607:f220:41e:250::12, 130.14.250.12, ...\n",
+      "Connecting to ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)|2607:f220:41e:250::11|:21... connected.\n",
+      "Logging in as anonymous ... Logged in!\n",
+      "==> SYST ... done.    ==> PWD ... done.\n",
+      "==> TYPE I ... done.  ==> CWD (1) /geo/samples/GSM5621nnn/GSM5621758/suppl ... done.\n",
+      "==> SIZE GSM5621758_ChIPseq_Huh7_siDHX37_H3K27ac_summits.bed.gz ... 784432\n",
+      "==> EPSV ... done.    ==> RETR GSM5621758_ChIPseq_Huh7_siDHX37_H3K27ac_summits.bed.gz ... done.\n",
+      "Length: 784432 (766K) (unauthoritative)\n",
+      "\n",
+      "GSM5621758_ChIPseq_ 100%[===================>] 766.05K  2.48MB/s    in 0.3s    \n",
+      "\n",
+      "2022-03-10 11:35:39 (2.48 MB/s) - ‘/home/bnt4me/Virginia/for_docs/geo/GSE185701/GSM5621758_ChIPseq_Huh7_siDHX37_H3K27ac_summits.bed.gz’ saved [784432]\n",
+      "\n",
+      "\u001b[38;5;242m0\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[92mFile /home/bnt4me/Virginia/for_docs/geo/GSE185701/GSM5621758_ChIPseq_Huh7_siDHX37_H3K27ac_summits.bed.gz has been downloaded successfully\u001b[0m\n",
+      "\u001b[38;5;242m\n",
+      "--2022-03-10 11:35:39--  ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5621nnn/GSM5621760/suppl/GSM5621760_CUTTag_Huh7_DHX37_summits.bed.gz\n",
+      "           => ‘/home/bnt4me/Virginia/for_docs/geo/GSE185701/GSM5621760_CUTTag_Huh7_DHX37_summits.bed.gz’\n",
+      "Resolving ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)... 2607:f220:41e:250::11, 2607:f220:41e:250::12, 130.14.250.12, ...\n",
+      "Connecting to ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)|2607:f220:41e:250::11|:21... connected.\n",
+      "Logging in as anonymous ... Logged in!\n",
+      "==> SYST ... done.    ==> PWD ... done.\n",
+      "==> TYPE I ... done.  ==> CWD (1) /geo/samples/GSM5621nnn/GSM5621760/suppl ... done.\n",
+      "==> SIZE GSM5621760_CUTTag_Huh7_DHX37_summits.bed.gz ... 163441\n",
+      "==> EPSV ... done.    ==> RETR GSM5621760_CUTTag_Huh7_DHX37_summits.bed.gz ... done.\n",
+      "Length: 163441 (160K) (unauthoritative)\n",
+      "\n",
+      "GSM5621760_CUTTag_H 100%[===================>] 159.61K   862KB/s    in 0.2s    \n",
+      "\n",
+      "2022-03-10 11:35:40 (862 KB/s) - ‘/home/bnt4me/Virginia/for_docs/geo/GSE185701/GSM5621760_CUTTag_Huh7_DHX37_summits.bed.gz’ saved [163441]\n",
+      "\n",
+      "\u001b[38;5;242m0\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[92mFile /home/bnt4me/Virginia/for_docs/geo/GSE185701/GSM5621760_CUTTag_Huh7_DHX37_summits.bed.gz has been downloaded successfully\u001b[0m\n",
+      "\u001b[38;5;242m\n",
+      "--2022-03-10 11:35:41--  ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5621nnn/GSM5621761/suppl/GSM5621761_CUTTag_Huh7_PLRG1_summits.bed.gz\n",
+      "           => ‘/home/bnt4me/Virginia/for_docs/geo/GSE185701/GSM5621761_CUTTag_Huh7_PLRG1_summits.bed.gz’\n",
+      "Resolving ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)... 2607:f220:41e:250::11, 2607:f220:41e:250::12, 130.14.250.12, ...\n",
+      "Connecting to ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)|2607:f220:41e:250::11|:21... connected.\n",
+      "Logging in as anonymous ... Logged in!\n",
+      "==> SYST ... done.    ==> PWD ... done.\n",
+      "==> TYPE I ... done.  ==> CWD (1) /geo/samples/GSM5621nnn/GSM5621761/suppl ... done.\n",
+      "==> SIZE GSM5621761_CUTTag_Huh7_PLRG1_summits.bed.gz ... 117250\n",
+      "==> EPSV ... done.    ==> RETR GSM5621761_CUTTag_Huh7_PLRG1_summits.bed.gz ... done.\n",
+      "Length: 117250 (115K) (unauthoritative)\n",
+      "\n",
+      "GSM5621761_CUTTag_H 100%[===================>] 114.50K   644KB/s    in 0.2s    \n",
+      "\n",
+      "2022-03-10 11:35:41 (644 KB/s) - ‘/home/bnt4me/Virginia/for_docs/geo/GSE185701/GSM5621761_CUTTag_Huh7_PLRG1_summits.bed.gz’ saved [117250]\n",
+      "\n",
+      "\u001b[38;5;242m0\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[92mFile /home/bnt4me/Virginia/for_docs/geo/GSE185701/GSM5621761_CUTTag_Huh7_PLRG1_summits.bed.gz has been downloaded successfully\u001b[0m\n",
+      "Finished processing 1 accession(s)\n",
+      "Expanding metadata list...\n",
+      "Unifying and saving of metadata... \n",
+      "\u001b[92mFile /home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/bright_test_annotation_sample_processed.csv has been saved successfully\u001b[0m\n",
+      "  Config file: /home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/bright_test_annotation_sample_processed.yaml\n"
+     ]
+    }
+   ],
+   "source": [
+    "geofetch -i GSE185701 --processed -n bright_test --filter \".bed.gz$\" --data-source samples \\\n",
+    "--geo-folder /home/bnt4me/Virginia/for_docs/geo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now lets list the folder to see what data is there. And let's see what's in pep files now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[0m\u001b[01;31mGSM5621756_ChIPseq_Huh7_siNC_H3K27ac_summits.bed.gz\u001b[0m\n",
+      "\u001b[01;31mGSM5621758_ChIPseq_Huh7_siDHX37_H3K27ac_summits.bed.gz\u001b[0m\n",
+      "\u001b[01;31mGSM5621760_CUTTag_Huh7_DHX37_summits.bed.gz\u001b[0m\n",
+      "\u001b[01;31mGSM5621761_CUTTag_Huh7_PLRG1_summits.bed.gz\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "ls /home/bnt4me/Virginia/for_docs/geo/GSE185701"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GSE,Sample_title,Sample_geo_accession,Sample_status,Sample_submission_date,Sample_last_update_date,S\n",
+      "GSE185701,Huh7_siNC_H3K27ac,GSM5621756,Public on Mar 01 2022,Oct 12 2021,Mar 03 2022,SRA,1,Huh 7,Hom\n",
+      "GSE185701,Huh7_siDHX37_H3K27ac,GSM5621758,Public on Mar 01 2022,Oct 12 2021,Mar 03 2022,SRA,1,Huh 7,\n",
+      "GSE185701,Huh7_DHX37,GSM5621760,Public on Mar 01 2022,Oct 12 2021,Mar 03 2022,SRA,1,Huh 7,Homo sapie\n",
+      "GSE185701,Huh7_PLRG1,GSM5621761,Public on Mar 01 2022,Oct 12 2021,Mar 03 2022,SRA,1,Huh 7,Homo sapie\n"
+     ]
+    }
+   ],
+   "source": [
+    "cut -c -100 bright_test/bright_test_annotation_sample_processed.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pep_version: 2.0.0\n",
+      "project_name: bright_test\n",
+      "sample_table: /home/bnt4me/Virginia/repos/geof2/geofetch/docs_jupyter/bright_test/bright_test_annotation_sample_processed.csv\n",
+      "\n",
+      "sample_modifiers:\n",
+      "  append:\n",
+      "    output_file_path: FILES\n",
+      "  derive:\n",
+      "    attributes: [output_file_path]\n",
+      "    sources:\n",
+      "      FILES: /home/bnt4me/Virginia/for_docs/geo/{SRA}/{sample_name}\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "cat bright_test/bright_test_annotation_sample_processed.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we have easy access to this data by using [peppy](http://peppy.databio.org/en/latest/) package in python or [pepr](https://code.databio.org/pepr/) in r in further analysis "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Bash",
+   "language": "bash",
+   "name": "bash"
+  },
+  "language_info": {
+   "codemirror_mode": "shell",
+   "file_extension": ".sh",
+   "mimetype": "text/x-sh",
+   "name": "bash"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs_jupyter/tutorial.ipynb b/docs_jupyter/raw-data-downloading.ipynb
similarity index 94%
rename from docs_jupyter/tutorial.ipynb
rename to docs_jupyter/raw-data-downloading.ipynb
index 83bd784..734faa5 100644
--- a/docs_jupyter/tutorial.ipynb
+++ b/docs_jupyter/raw-data-downloading.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# geofetch tutorial\n",
+    "# geofetch tutorial for row data\n",
     "\n",
     "The [GSE67303 data set](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE67303) has about 250 mb of data across 4 samples, so it's a quick download for a test case. Let's take a quick peek at the geofetch version:"
    ]
@@ -18,7 +18,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "geofetch 0.7.0\n"
+      "geofetch 0.8.0\n"
      ]
     }
    ],
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 2,
    "metadata": {
     "scrolled": true
    },
@@ -45,13 +45,15 @@
      "output_type": "stream",
      "text": [
       "usage: geofetch [-h] [-V] -i INPUT [-n NAME] [-m METADATA_ROOT]\n",
-      "                [-u METADATA_FOLDER] [--just-metadata] [-r] [--acc-anno]\n",
-      "                [--use-key-subset] [-x] [--config-template CONFIG_TEMPLATE]\n",
-      "                [-p] [-k SKIP] [--filter FILTER] [-g GEO_FOLDER]\n",
-      "                [-b BAM_FOLDER] [-f FQ_FOLDER] [-P PIPELINE_INTERFACES]\n",
-      "                [--silent] [--verbosity V] [--logdev]\n",
+      "                [-u METADATA_FOLDER] [--just-metadata] [-r]\n",
+      "                [--config-template CONFIG_TEMPLATE] [-P PIPELINE_INTERFACES]\n",
+      "                [-k SKIP] [--acc-anno] [--use-key-subset] [-p]\n",
+      "                [--data-source {all,samples,series}] [--filter FILTER]\n",
+      "                [--filter-size FILTER_SIZE] [-g GEO_FOLDER] [-x]\n",
+      "                [-b BAM_FOLDER] [-f FQ_FOLDER] [--silent] [--verbosity V]\n",
+      "                [--logdev]\n",
       "\n",
-      "Automatic GEO SRA data downloader\n",
+      "Automatic GEO and SRA data downloader\n",
       "\n",
       "optional arguments:\n",
       "  -h, --help            show this help message and exit\n",
@@ -62,8 +64,8 @@
       "  -n NAME, --name NAME  Specify a project name. Defaults to GSE number\n",
       "  -m METADATA_ROOT, --metadata-root METADATA_ROOT\n",
       "                        Specify a parent folder location to store metadata.\n",
-      "                        The project name will be added as a subfolder[Default:\n",
-      "                        $SRAMETA:]\n",
+      "                        The project name will be added as a subfolder\n",
+      "                        [Default: $SRAMETA:]\n",
       "  -u METADATA_FOLDER, --metadata-folder METADATA_FOLDER\n",
       "                        Specify an absolute folder location to store metadata.\n",
       "                        No subfolder will be added. Overrides value of\n",
@@ -73,24 +75,51 @@
       "                        metadata\n",
       "  -r, --refresh-metadata\n",
       "                        If set, re-download metadata even if it exists.\n",
+      "  --config-template CONFIG_TEMPLATE\n",
+      "                        Project config yaml file template.\n",
+      "  -P PIPELINE_INTERFACES, --pipeline_interfaces PIPELINE_INTERFACES\n",
+      "                        Optional: Specify one or more filepaths to pipeline\n",
+      "                        interface yaml files. These will be added to the\n",
+      "                        project config file to make it immediately compatible\n",
+      "                        with looper. [Default: null]\n",
+      "  -k SKIP, --skip SKIP  Skip some accessions. [Default: no skip].\n",
       "  --acc-anno            Also produce annotation sheets for each accession, not\n",
       "                        just for the whole project combined\n",
       "  --use-key-subset      Use just the keys defined in this module when writing\n",
       "                        out metadata.\n",
+      "  --silent              Silence logging. Overrides verbosity.\n",
+      "  --verbosity V         Set logging level (1-5 or logging module level name)\n",
+      "  --logdev              Expand content of logging message format.\n",
+      "\n",
+      "processed:\n",
+      "  -p, --processed       Download processed data [Default: download raw data].\n",
+      "  --data-source {all,samples,series}\n",
+      "                        Optional: Specifies the source of data on the GEO\n",
+      "                        record to retrieve processed data, which may be\n",
+      "                        attached to the collective series entity, or to\n",
+      "                        individual samples. Allowable values are: samples,\n",
+      "                        series or both (all). Ignored unless 'processed' flag\n",
+      "                        is set. [Default: all]\n",
+      "  --filter FILTER       Optional: Filter regex for processed filenames\n",
+      "                        [Default: None].Ignored unless 'processed' flag is\n",
+      "                        set.\n",
+      "  --filter-size FILTER_SIZE\n",
+      "                        Optional: Filter size for processed files that are\n",
+      "                        stored as sample repository [Default: None]. Supported\n",
+      "                        input formats : 12B, 12KB, 12MB, 12GB. Ignored unless\n",
+      "                        'processed' flag is set.\n",
+      "  -g GEO_FOLDER, --geo-folder GEO_FOLDER\n",
+      "                        Optional: Specify a location to store processed GEO\n",
+      "                        files. Ignored unless 'processed' flag is\n",
+      "                        set.[Default: $GEODATA:]\n",
+      "\n",
+      "raw:\n",
       "  -x, --split-experiments\n",
       "                        Split SRR runs into individual samples. By default,\n",
       "                        SRX experiments with multiple SRR Runs will have a\n",
       "                        single entry in the annotation table, with each run as\n",
       "                        a separate row in the subannotation table. This\n",
       "                        setting instead treats each run as a separate sample\n",
-      "  --config-template CONFIG_TEMPLATE\n",
-      "                        Project config yaml file template.\n",
-      "  -p, --processed       Download processed data [Default: download raw data].\n",
-      "  -k SKIP, --skip SKIP  Skip some accessions. [Default: no skip].\n",
-      "  --filter FILTER       Filter regex for processed filenames [Default: None].\n",
-      "  -g GEO_FOLDER, --geo-folder GEO_FOLDER\n",
-      "                        Optional: Specify a location to store processed GEO\n",
-      "                        files [Default: $GEODATA:]\n",
       "  -b BAM_FOLDER, --bam-folder BAM_FOLDER\n",
       "                        Optional: Specify folder of bam files. Geofetch will\n",
       "                        not download sra files when corresponding bam files\n",
@@ -98,15 +127,7 @@
       "  -f FQ_FOLDER, --fq-folder FQ_FOLDER\n",
       "                        Optional: Specify folder of fastq files. Geofetch will\n",
       "                        not download sra files when corresponding fastq files\n",
-      "                        already exist. [Default: $SRAFQ:]\n",
-      "  -P PIPELINE_INTERFACES, --pipeline_interfaces PIPELINE_INTERFACES\n",
-      "                        Optional: Specify one or more filepaths to pipeline\n",
-      "                        interface yaml files. These will be added to the\n",
-      "                        project config file to make it immediately compatible\n",
-      "                        with looper. [Default: null]\n",
-      "  --silent              Silence logging. Overrides verbosity.\n",
-      "  --verbosity V         Set logging level (1-5 or logging module level name)\n",
-      "  --logdev              Expand content of logging message format.\n"
+      "                        already exist. [Default: $SRAFQ:]\n"
      ]
     }
    ],
diff --git a/geofetch/__init__.py b/geofetch/__init__.py
index 52a0079..042fc8d 100644
--- a/geofetch/__init__.py
+++ b/geofetch/__init__.py
@@ -1,4 +1,3 @@
 """ Package-level data """
 from .geofetch import *
 from ._version import __version__
-
diff --git a/geofetch/_version.py b/geofetch/_version.py
index 49e0fc1..b4e087d 100644
--- a/geofetch/_version.py
+++ b/geofetch/_version.py
@@ -1,1 +1,1 @@
-__version__ = "0.7.0"
+__version__ = "0.8.0-dev"
diff --git a/geofetch/config_processed_template.yaml b/geofetch/config_processed_template.yaml
new file mode 100644
index 0000000..74af190
--- /dev/null
+++ b/geofetch/config_processed_template.yaml
@@ -0,0 +1,11 @@
+pep_version: 2.1.0
+project_name: {project_name}
+sample_table: {sample_table}
+
+sample_modifiers:
+  append:
+    output_file_path: FILES
+  derive:
+    attributes: [output_file_path]
+    sources:
+      FILES: {geo_folder}/{GSE}/{file}
diff --git a/geofetch/config_template.yaml b/geofetch/config_template.yaml
index 2e6621c..bf7b5ba 100644
--- a/geofetch/config_template.yaml
+++ b/geofetch/config_template.yaml
@@ -1,7 +1,7 @@
 # Autogenerated by geofetch
 
 name: {project_name}
-pep_version: 2.0.0
+pep_version: 2.1.0
 sample_table: {annotation}
 subsample_table: {subannotation}
 
diff --git a/geofetch/geofetch.py b/geofetch/geofetch.py
index f657208..897a68a 100755
--- a/geofetch/geofetch.py
+++ b/geofetch/geofetch.py
@@ -1,7 +1,6 @@
-#!/usr/bin/env python
-
-__author__ = "Nathan Sheffield"
+#!/usr/bin/env python3
 
+__author__ = ["Vince Reuter", "Nathan Sheffield", "Oleksandr Khoroshevskyi"]
 
 # Outline:
 # INPUT: A list of GSE ids, optionally including GSM ids to limit to.
@@ -17,884 +16,1752 @@
 # 4. Parse the SRA RunInfo csv file and use the download_link field to grab the .sra file
 
 import argparse
-from collections import OrderedDict
 import copy
 import csv
 import os
 import re
 import subprocess
 import sys
-import tarfile
-import time
 
-if sys.version_info[0] == 2:
-    _STRING_TYPES = basestring
-else:
-    _STRING_TYPES = str
+# import tarfile
+import time
 
-from .utils import Accession, parse_accessions, parse_SOFT_line
+from .utils import Accession, parse_accessions, parse_SOFT_line, convert_size
 from ._version import __version__
 
-from logmuse import add_logging_options, logger_via_cli
+from logmuse import add_logging_options, init_logger
 from ubiquerg import expandpath, is_command_callable
 
-
+_STRING_TYPES = str
 _LOGGER = None
 
-
 # A set of hard-coded keys if you want to limit to just a few instead of taking
 # all information provided in GEO. Use with `--use-key-subset`
 ANNOTATION_SHEET_KEYS = [
-    "sample_name", "protocol", "read_type", "organism", "data_source",
-    'Sample_title', 'Sample_source_name_ch1', 'Sample_organism_ch1', 
-    "Sample_library_selection", "Sample_library_strategy", 
-    'Sample_type',  "SRR", "SRX", 'Sample_geo_accession', 'Sample_series_id', 
-    'Sample_instrument_model']
-
+    "sample_name",
+    "protocol",
+    "read_type",
+    "organism",
+    "data_source",
+    "Sample_title",
+    "Sample_source_name_ch1",
+    "Sample_organism_ch1",
+    "Sample_library_selection",
+    "Sample_library_strategy",
+    "Sample_type",
+    "SRR",
+    "SRX",
+    "Sample_geo_accession",
+    "Sample_series_id",
+    "Sample_instrument_model",
+]
 
 # Regex to parse out SRA accession identifiers
-PROJECT_PATTERN = re.compile("(SRP\d{4,8})")
-EXPERIMENT_PATTERN = re.compile("(SRX\d{4,8})")
-GSE_PATTERN = re.compile("(GSE\d{4,8})")
+PROJECT_PATTERN = re.compile(r"(SRP\d{4,8})")
+EXPERIMENT_PATTERN = re.compile(r"(SRX\d{4,8})")
+GSE_PATTERN = re.compile(r"(GSE\d{4,8})")
 SUPP_FILE_PATTERN = re.compile("Sample_supplementary_file")
 SER_SUPP_FILE_PATTERN = re.compile("Series_supplementary_file")
 
+SAMPLE_SUPP_METADATA_FILE = "_samples.csv"
+EXP_SUPP_METADATA_FILE = "_series.csv"
+
 # How many times should we retry failing prefetch call?
 NUM_RETRIES = 3
 
 
-def _parse_cmdl(cmdl):
-    parser = argparse.ArgumentParser(description="Automatic GEO and SRA data downloader")
+class Geofetcher:
+    def __init__(
+        self,
+        name="",
+        metadata_root="",
+        metadata_folder="",
+        just_metadata=False,
+        refresh_metadata=False,
+        config_template=None,
+        pipeline_interfaces=None,
+        skip=0,
+        acc_anno=False,
+        use_key_subset=False,
+        processed=True,
+        supp_by="samples",
+        filter=None,
+        filter_size=None,
+        geo_folder=".",
+        split_experiments=False,
+        bam_folder="",
+        fq_folder="",
+        sra_folder="",
+        bam_conversion=False,
+        picard_path="",
+        silent=False,
+        verbosity=None,
+        logdev=False,
+        input=None,
+    ):
+
+        # self.args = args
+        global _LOGGER
+        #  _LOGGER = logger_via_cli(args, name="geofetch")
+        _LOGGER = init_logger(name="geofetch", verbosity=verbosity, logfile=None)
+        self._LOGGER = _LOGGER
+
+        if name:
+            self.project_name = name
+        else:
+            self.project_name = os.path.splitext(os.path.basename(input))[0]
 
-    parser.add_argument(
-            "-V", "--version",
-            action="version",
-            version="%(prog)s {v}".format(v=__version__))
+        if metadata_folder:
+            self.metadata_expanded = expandpath(metadata_folder)
+            if os.path.isabs(self.metadata_expanded):
+                self.metadata_raw = metadata_folder
+            else:
+                self.metadata_expanded = os.path.abspath(self.metadata_expanded)
+                self.metadata_raw = os.path.abspath(metadata_root)
+            self.metadata_raw = metadata_folder
+        else:
+            self.metadata_expanded = expandpath(metadata_root)
+            if os.path.isabs(self.metadata_expanded):
+                self.metadata_raw = metadata_root
+            else:
+                self.metadata_expanded = os.path.abspath(self.metadata_expanded)
+                self.metadata_raw = os.path.abspath(metadata_root)
 
-    # Required
-    parser.add_argument(
-            "-i", "--input", dest="input", required=True,
-            help="required: a GEO (GSE) accession, or a file with a list of GSE numbers")
-    
-    # Optional
-    parser.add_argument(
-            "-n", "--name",
-            help="Specify a project name. Defaults to GSE number")
+        self.just_metadata = just_metadata
+        self.refresh_metadata = refresh_metadata
+        self.config_template = config_template
 
-    parser.add_argument(
-            "-m", "--metadata-root",
-            dest="metadata_root",
-            default= safe_echo("SRAMETA"),
-            help="Specify a parent folder location to store metadata. "
-                "The project name will be added as a subfolder "
-                "[Default: $SRAMETA:" + safe_echo("SRAMETA") + "]")
+        # if user specified a pipeline interface path, add it into the project config
+        self.pipeline_interfaces = pipeline_interfaces
+        if self.pipeline_interfaces:
+            self.file_pipeline_interfaces = self.pipeline_interfaces
+        else:
+            self.file_pipeline_interfaces = "null"
 
-    parser.add_argument(
-            "-u", "--metadata-folder",
-            help="Specify an absolute folder location to store metadata. "
-            "No subfolder will be added. Overrides value of --metadata-root "
-            "[Default: Not used (--metadata-root is used by default)]")
+        self.skip = skip
+        self.acc_anno = acc_anno
+        self.use_key_subset = use_key_subset
+        self.processed = processed
+        self.supp_by = supp_by
 
-    parser.add_argument(
-            "--just-metadata", action="store_true",
-            help="If set, don't actually run downloads, just create metadata")
+        if filter:
+            self.filter_re = re.compile(filter.lower())
+        else:
+            self.filter_re = None
 
-    parser.add_argument(
-            "-r", "--refresh-metadata", action="store_true",
-            help="If set, re-download metadata even if it exists.")
+            # Postpend the project name as a subfolder (only for -m option)
+            self.metadata_expanded = os.path.join(
+                self.metadata_expanded, self.project_name
+            )
+            self.metadata_raw = os.path.join(self.metadata_raw, self.project_name)
 
-    parser.add_argument(
-            "--acc-anno", action="store_true",
-            help="Also produce annotation sheets for each accession, not just"
-            " for the whole project combined")
+        if filter_size is not None:
+            self.filter_size = convert_size(filter_size.lower())
+        else:
+            self.filter_size = filter_size
+
+        self.geo_folder = geo_folder
+        self.split_experiments = split_experiments
+        self.bam_folder = bam_folder
+        self.fq_folder = fq_folder
+        self.sra_folder = sra_folder
+        self.bam_conversion = bam_conversion
+        self.picard_path = picard_path
+        self.silent = silent
+        self.verbosity = verbosity
+        self.logdev = logdev
+
+        self._LOGGER.info(f"Metadata folder: {self.metadata_expanded}")
+
+        # check to make sure prefetch is callable
+        if not just_metadata and not processed:
+            if not is_command_callable("prefetch"):
+                raise SystemExit(
+                    "You must first install the sratoolkit, with prefetch in your PATH."
+                )
+
+        # Some sanity checks before proceeding
+        if bam_conversion and not just_metadata and not self.which("samtools"):
+            raise SystemExit("For SAM/BAM processing, samtools should be on PATH.")
+
+    def fetch_all(self, input, name=None):
+        """Main script driver/workflow"""
+
+        if name:
+            self.project_name = name
+        else:
+            self.project_name = os.path.splitext(os.path.basename(input))[0]
+
+        acc_GSE_list = parse_accessions(
+            input, self.metadata_expanded, self.just_metadata
+        )
+
+        # Loop through each accession.
+        # This will process that accession, produce metadata and download files for
+        # the GSM #s included in the list for each GSE#.
+        # acc_GSE = "GSE61150" # example
+
+        # This loop populates a list of metadata.
+        metadata_dict = {}
+        subannotation_dict = {}
+        failed_runs = []
+        processed_metadata_samples = []
+        processed_metadata_exp = []
+
+        acc_GSE_keys = acc_GSE_list.keys()
+        nkeys = len(acc_GSE_keys)
+        ncount = 0
+        for acc_GSE in acc_GSE_list.keys():
+            ncount += 1
+            if ncount <= self.skip:
+                continue
+            elif ncount == self.skip + 1:
+                self._LOGGER.info(f"Skipped {self.skip} accessions. Starting now.")
+            self._LOGGER.info(
+                f"\033[38;5;228mProcessing accession {ncount} of {nkeys}: '{acc_GSE}'\033[0m"
+            )
 
-    parser.add_argument(
-            "--use-key-subset", action="store_true",
-            help="Use just the keys defined in this module when writing out metadata.")
+            if len(re.findall(GSE_PATTERN, acc_GSE)) != 1:
+                self._LOGGER.debug(len(re.findall(GSE_PATTERN, acc_GSE)))
+                self._LOGGER.warning(
+                    "This does not appear to be a correctly formatted GSE accession! "
+                    "Continue anyway..."
+                )
+
+            if len(acc_GSE_list[acc_GSE]) > 0:
+                self._LOGGER.info(
+                    f"Limit to: {list(acc_GSE_list[acc_GSE])}"
+                )  # a list of GSM#s
+
+            if self.refresh_metadata:
+                self._LOGGER.info("Refreshing metadata...")
+
+            # For each GSE acc, produce a series of metadata files
+            file_gse = os.path.join(self.metadata_expanded, acc_GSE + "_GSE.soft")
+            file_gsm = os.path.join(self.metadata_expanded, acc_GSE + "_GSM.soft")
+            file_sra = os.path.join(self.metadata_expanded, acc_GSE + "_SRA.csv")
+            file_srafilt = os.path.join(
+                self.metadata_expanded, acc_GSE + "_SRA_filt.csv"
+            )
 
-    parser.add_argument(
-        "-x", "--split-experiments", action="store_true",
-        help="""Split SRR runs into individual samples. By default, SRX
-            experiments with multiple SRR Runs will have a single entry in the
-            annotation table, with each run as a separate row in the
-            subannotation table. This setting instead treats each run as a
-            separate sample""")
+            # Grab the GSE and GSM SOFT files from GEO.
+            # The GSE file has metadata describing the experiment, which includes
+            # The SRA number we need to download the raw data from SRA
+            # The GSM file has metadata describing each sample, which we will use to
+            # produce a sample annotation sheet.
+            if not os.path.isfile(file_gse) or self.refresh_metadata:
+                Accession(acc_GSE).fetch_metadata(file_gse)
+            else:
+                self._LOGGER.info(f"Found previous GSE file: {file_gse}")
 
-    parser.add_argument(
-            "--config-template", default=None,
-            help="Project config yaml file template.")
+            if not os.path.isfile(file_gsm) or self.refresh_metadata:
+                Accession(acc_GSE).fetch_metadata(file_gsm, typename="GSM")
+            else:
+                self._LOGGER.info(f"Found previous GSM file: {file_gsm}")
 
-    parser.add_argument(
-            "-p", "--processed",
-            default=False,
-            action="store_true",
-            help="Download processed data [Default: download raw data].")
+            # if not os.path.isfile(file_gsm) or not os.path.isfile(file_gse):
 
-    parser.add_argument(
-            "-k", "--skip",
-            default=0,
-            type=int,
-            help="Skip some accessions. [Default: no skip].")
+            # download processed data
+            if self.processed:
+                try:
+                    (
+                        meta_processed_samples,
+                        meta_processed_series,
+                    ) = self.get_list_of_processed_files(file_gse, file_gsm)
+
+                    # taking into account list of GSM that is specified in the input file
+                    gsm_list = acc_GSE_list[acc_GSE]
+                    meta_processed_samples = self.filter_gsm(
+                        meta_processed_samples, gsm_list
+                    )
+                    # Unify keys:
+                    meta_processed_samples = self.unify_list_keys(
+                        meta_processed_samples
+                    )
+                    meta_processed_series = self.unify_list_keys(meta_processed_series)
+
+                    list_of_keys = self.get_list_of_keys(meta_processed_samples)
+                    self._LOGGER.info("Expanding metadata list...")
+                    for key_in_list in list_of_keys:
+                        meta_processed_samples = self.expand_metadata_list(
+                            meta_processed_samples, key_in_list
+                        )
+
+                    list_of_keys_series = self.get_list_of_keys(meta_processed_series)
+                    self._LOGGER.info("Expanding metadata list...")
+                    for key_in_list in list_of_keys_series:
+                        meta_processed_series = self.expand_metadata_list(
+                            meta_processed_series, key_in_list
+                        )
+
+                    # adding metadata from current experiment to the project
+                    processed_metadata_samples.extend(meta_processed_samples)
+                    processed_metadata_exp.extend(meta_processed_series)
+
+                    # save PEP for each accession
+                    if self.acc_anno and len(acc_GSE_list.keys()) > 1:
+                        if self.supp_by == "all":
+                            # samples
+                            pep_acc_path_sample = os.path.join(
+                                self.metadata_raw,
+                                acc_GSE,
+                                acc_GSE + SAMPLE_SUPP_METADATA_FILE,
+                            )
+                            self.write_processed_annotation(
+                                meta_processed_samples, pep_acc_path_sample
+                            )
+
+                            # series
+                            pep_acc_path_exp = os.path.join(
+                                self.metadata_raw,
+                                acc_GSE,
+                                acc_GSE + EXP_SUPP_METADATA_FILE,
+                            )
+                            self.write_processed_annotation(
+                                meta_processed_series, pep_acc_path_exp
+                            )
+                        elif self.supp_by == "samples":
+                            pep_acc_path_sample = os.path.join(
+                                self.metadata_raw,
+                                acc_GSE,
+                                acc_GSE + SAMPLE_SUPP_METADATA_FILE,
+                            )
+                            self.write_processed_annotation(
+                                meta_processed_samples, pep_acc_path_sample
+                            )
+                        elif self.supp_by == "series":
+                            pep_acc_path_exp = os.path.join(
+                                self.metadata_raw,
+                                acc_GSE,
+                                acc_GSE + EXP_SUPP_METADATA_FILE,
+                            )
+                            self.write_processed_annotation(
+                                meta_processed_series, pep_acc_path_exp
+                            )
+
+                    if not self.just_metadata:
+                        data_geo_folder = os.path.join(self.geo_folder, acc_GSE)
+                        self._LOGGER.debug("Data folder: " + data_geo_folder)
+
+                        if self.supp_by == "all":
+                            processed_samples_files = [
+                                each_file["file_url"]
+                                for each_file in meta_processed_samples
+                            ]
+                            for file_url in processed_samples_files:
+                                self.download_processed_file(file_url, data_geo_folder)
+
+                            processed_series_files = [
+                                each_file["file_url"]
+                                for each_file in meta_processed_series
+                            ]
+                            for file_url in processed_series_files:
+                                self.download_processed_file(file_url, data_geo_folder)
+
+                        elif self.supp_by == "samples":
+                            processed_samples_files = [
+                                each_file["file_url"]
+                                for each_file in meta_processed_samples
+                            ]
+                            for file_url in processed_samples_files:
+                                self.download_processed_file(file_url, data_geo_folder)
+
+                        elif self.supp_by == "series":
+                            processed_series_files = [
+                                each_file["file_url"]
+                                for each_file in meta_processed_series
+                            ]
+                            for file_url in processed_series_files:
+                                self.download_processed_file(file_url, data_geo_folder)
+                except Exception as processed_exception:
+                    failed_runs.append(acc_GSE)
+                    self._LOGGER.warning(f"Error occurred: {processed_exception}")
 
-    parser.add_argument(
-            "--filter",
-            default=None,
-            help="Filter regex for processed filenames [Default: None].")
+            else:
+                # download gsm metadata
+                gsm_metadata = self.get_gsm_metadata(acc_GSE, acc_GSE_list, file_gsm)
+                metadata_dict[acc_GSE] = gsm_metadata
 
-    parser.add_argument(
-            "-g", "--geo-folder", default=safe_echo("GEODATA"),
-            help="Optional: Specify a location to store processed GEO files "
-                "[Default: $GEODATA:" + safe_echo("GEODATA") + "]")
-    
-    parser.add_argument(
-            "-b", "--bam-folder", dest="bam_folder", default=safe_echo("SRABAM"),
-            help="""Optional: Specify folder of bam files. Geofetch will not
-            download sra files when corresponding bam files already exist.
-            [Default: $SRABAM:""" + safe_echo("SRABAM") + "]")
+                # download gsm metadata
+                result = self.get_SRA_meta(file_gse, file_sra, gsm_metadata)
+                if not result:
+                    continue
+                # Parse metadata from SRA
+                # Produce an annotated output from the GSM and SRARunInfo files.
+                # This will merge the GSM and SRA sample metadata into a dict of dicts,
+                # with one entry per sample.
+                # NB: There may be multiple SRA Runs (and thus lines in the RunInfo file)
+                # Corresponding to each sample.
+                # For multi samples (samples with multiple runs), we keep track of these
+                # relations in a separate table, which is called the subannotation table.
+                gsm_multi_table = {}
+                file_read = open(file_sra, "r")
+                file_write = open(file_srafilt, "w")
+                self._LOGGER.info("Parsing SRA file to download SRR records")
+                initialized = False
+
+                input_file = csv.DictReader(file_read)
+                for line in input_file:
+                    if not initialized:
+                        initialized = True
+                        wwrite = csv.DictWriter(file_write, line.keys())
+                        wwrite.writeheader()
+
+                    # Only download if it's in the include list:
+                    experiment = line["Experiment"]
+                    run_name = line["Run"]
+                    if experiment not in gsm_metadata:
+                        # print(f"Skipping: {experiment}")
+                        continue
+
+                    # local convenience variable
+                    # possibly set in the input tsv file
+                    sample_name = None  # initialize to empty
+                    try:
+                        sample_name = acc_GSE_list[acc_GSE][
+                            gsm_metadata[experiment]["gsm_id"]
+                        ]
+                    except KeyError:
+                        _LOGGER.warning(f"Error in sample_name ...")
+                    if not sample_name or sample_name == "":
+                        temp = gsm_metadata[experiment]["Sample_title"]
+                        # Now do a series of transformations to cleanse the sample name
+                        temp = temp.replace(" ", "_")
+                        # Do people put commas in their sample names? Yes.
+                        temp = temp.replace(",", "_")
+                        temp = temp.replace("__", "_")
+                        sample_name = temp
+
+                    # Otherwise, record that there's SRA data for this run.
+                    # And set a few columns that are used as input to the Looper
+                    # print("Updating columns for looper")
+                    self.update_columns(
+                        gsm_metadata,
+                        experiment,
+                        sample_name=sample_name,
+                        read_type=line["LibraryLayout"],
+                    )
+
+                    # Some experiments are flagged in SRA as having multiple runs.
+                    if gsm_metadata[experiment].get("SRR") is not None:
+                        # This SRX number already has an entry in the table.
+                        self._LOGGER.info(
+                            f"Found additional run: {run_name} ({experiment})"
+                        )
+
+                        if (
+                            isinstance(gsm_metadata[experiment]["SRR"], _STRING_TYPES)
+                            and experiment not in gsm_multi_table
+                        ):
+                            # Only one has been stuck in so far, make a list
+                            gsm_multi_table[experiment] = []
+                            # Add first the original one, which was stored as a string
+                            # previously
+                            gsm_multi_table[experiment].append(
+                                [
+                                    sample_name,
+                                    experiment,
+                                    gsm_metadata[experiment]["SRR"],
+                                ]
+                            )
+                            # Now append the current SRR number in a list as [SRX, SRR]
+                            gsm_multi_table[experiment].append(
+                                [sample_name, experiment, run_name]
+                            )
+                        else:
+                            # this is the 3rd or later sample; the first two are done,
+                            # so just add it.
+                            gsm_multi_table[experiment].append(
+                                [sample_name, experiment, run_name]
+                            )
+
+                        if self.split_experiments:
+                            # Duplicate the gsm metadata for this experiment (copy to make sure
+                            # it's not just an alias).
+                            rep_number = len(gsm_multi_table[experiment])
+                            new_SRX = experiment + "_" + str(rep_number)
+                            gsm_metadata[new_SRX] = copy.copy(gsm_metadata[experiment])
+                            # gsm_metadata[new_SRX]["SRX"] = new_SRX
+                            gsm_metadata[new_SRX]["sample_name"] += "_" + str(
+                                rep_number
+                            )
+                            gsm_metadata[new_SRX]["SRR"] = run_name
+                        else:
+                            # Either way, set the srr code to multi in the main table.
+                            gsm_metadata[experiment]["SRR"] = "multi"
+                    else:
+                        # The first SRR for this SRX is added to GSM metadata
+                        gsm_metadata[experiment]["SRR"] = run_name
+
+                    # gsm_metadata[experiment].update(line)
+
+                    # Write to filtered SRA Runinfo file
+                    wwrite.writerow(line)
+                    self._LOGGER.info(f"Get SRR: {run_name} ({experiment})")
+                    bam_file = (
+                        ""
+                        if self.bam_folder == ""
+                        else os.path.join(self.bam_folder, run_name + ".bam")
+                    )
+                    fq_file = (
+                        ""
+                        if self.fq_folder == ""
+                        else os.path.join(self.fq_folder, run_name + "_1.fq")
+                    )
+
+                    # TODO: sam-dump has a built-in prefetch. I don't have to do
+                    # any of this stuff... This also solves the bad sam-dump issues.
+
+                    if os.path.exists(bam_file):
+                        self._LOGGER.info(f"BAM found: {bam_file} . Skipping...")
+                    elif os.path.exists(fq_file):
+                        self._LOGGER.info(f"FQ found: {fq_file} .Skipping...")
+                    else:
+                        if not self.just_metadata:
+                            try:
+                                self.download_SRA_file(run_name)
+                            except Exception as err:
+                                failed_runs.append(run_name)
+                                self._LOGGER.warning(
+                                    f"Error occurred while downloading SRA file: {err}"
+                                )
 
-    parser.add_argument(
-            "-f", "--fq-folder", dest="fq_folder", default=safe_echo("SRAFQ"),
-            help="""Optional: Specify folder of fastq files. Geofetch will not
-            download sra files when corresponding fastq files already exist.
-            [Default: $SRAFQ:""" + safe_echo("SRAFQ") + "]")
+                        else:
+                            self._LOGGER.info("Dry run (no data download)")
+
+                        if self.bam_conversion and self.bam_folder != "":
+                            try:
+                                # converting sra to bam using
+                                self.sra_bam_conversion(bam_file, run_name)
+
+                                # checking if bam_file converted correctly, if not --> use fastq-dump
+                                st = os.stat(bam_file)
+                                if st.st_size < 100:
+                                    self._LOGGER.warning(
+                                        "Bam conversion failed with sam-dump. Trying fastq-dump..."
+                                    )
+                                    self.sra_bam_conversion2(
+                                        bam_file, run_name, self.picard_path
+                                    )
+
+                            except FileNotFoundError as err:
+                                self._LOGGER.info(
+                                    f"SRA file doesn't exist, please download it first: {err}"
+                                )
+
+                file_read.close()
+                file_write.close()
+
+                # accumulate subannotations
+                subannotation_dict[acc_GSE] = gsm_multi_table
+
+        # Logging additional information about processing
+        self._LOGGER.info(f"Finished processing {len(acc_GSE_list)} accession(s)")
+
+        if len(failed_runs) > 0:
+            self._LOGGER.warn(
+                f"The following samples could not be downloaded: {failed_runs}"
+            )
 
-    parser.add_argument(
-            "-P", "--pipeline_interfaces", default=None,
-            help="Optional: Specify one or more filepaths to pipeline interface yaml files. "
-                 "These will be added to the project config file to make it immediately "
-                 "compatible with looper. [Default: null]")
-    
-    # Deprecated; these are for bam conversion which now happens in sra_convert
-    # it still works here but I hide it so people don't use it, because it's confusing.
-    parser.add_argument(
-            "-s", "--sra-folder", dest="sra_folder", default=safe_echo("SRARAW"),
-            help=argparse.SUPPRESS,
-            # help="Optional: Specify a location to store sra files "
-            #   "[Default: $SRARAW:" + safe_echo("SRARAW") + "]"
+        #######################################################################################
+
+        # saving PEPs for processed data
+        if self.processed:
+            if self.supp_by == "all":
+                supp_sample_path_meta = os.path.join(
+                    self.metadata_raw, self.project_name + SAMPLE_SUPP_METADATA_FILE
+                )
+                self.write_processed_annotation(
+                    processed_metadata_samples, supp_sample_path_meta
+                )
+
+                supp_series_path_meta = os.path.join(
+                    self.metadata_raw, self.project_name + EXP_SUPP_METADATA_FILE
+                )
+                self.write_processed_annotation(
+                    processed_metadata_exp, supp_series_path_meta
+                )
+
+            elif self.supp_by == "samples":
+                supp_sample_path_meta = os.path.join(
+                    self.metadata_raw, self.project_name + SAMPLE_SUPP_METADATA_FILE
+                )
+                self.write_processed_annotation(
+                    processed_metadata_samples, supp_sample_path_meta
+                )
+
+            elif self.supp_by == "series":
+                supp_series_path_meta = os.path.join(
+                    self.metadata_raw, self.project_name + EXP_SUPP_METADATA_FILE
+                )
+                self.write_processed_annotation(
+                    processed_metadata_exp, supp_series_path_meta
+                )
+        else:
+            # Combine individual accessions into project-level annotations, and write
+            # individual accession files (if requested)
+            metadata_dict_combined = {}
+            for acc_GSE, gsm_metadata in metadata_dict.items():
+                file_annotation = os.path.join(
+                    self.metadata_expanded, acc_GSE + "_annotation.csv"
+                )
+                if self.acc_anno:
+                    self.write_gsm_annotation(
+                        gsm_metadata,
+                        file_annotation,
+                        use_key_subset=self.use_key_subset,
+                    )
+                metadata_dict_combined.update(gsm_metadata)
+
+            subannotation_dict_combined = {}
+            for acc_GSE, gsm_multi_table in subannotation_dict.items():
+                file_subannotation = os.path.join(
+                    self.metadata_expanded, acc_GSE + "_subannotation.csv"
+                )
+                if self.acc_anno:
+                    self.write_subannotation(gsm_multi_table, file_subannotation)
+                subannotation_dict_combined.update(gsm_multi_table)
+            self._LOGGER.info(
+                "Creating complete project annotation sheets and config file..."
             )
-    parser.add_argument(
-            "--bam-conversion", action="store_true",
-            # help="Turn on sequential bam conversion. Default: No conversion.",
-            help=argparse.SUPPRESS)
-    
-    parser.add_argument(
-            "--picard-path", dest="picard_path", default=safe_echo("PICARD"),
-            # help="Specify a path to the picard jar, if you want to convert "
-            # "fastq to bam [Default: $PICARD:" + safe_echo("PICARD") + "]",
-            help=argparse.SUPPRESS)
+            # If the project included more than one GSE, we can now output combined
+            # annotation tables for the entire project.
 
-    parser = add_logging_options(parser)
-    return parser.parse_args(cmdl)
+            # Write combined annotation sheet
+            file_annotation = os.path.join(
+                self.metadata_raw, self.project_name + "_annotation.csv"
+            )
+            self.write_gsm_annotation(
+                metadata_dict_combined,
+                file_annotation,
+                use_key_subset=self.use_key_subset,
+            )
 
+            # Write combined subannotation table
+            if len(subannotation_dict_combined) > 0:
+                file_subannotation = os.path.join(
+                    self.metadata_raw, self.project_name + "_subannotation.csv"
+                )
+                self.write_subannotation(
+                    subannotation_dict_combined, file_subannotation
+                )
+            else:
+                file_subannotation = "null"
 
-def write_annotation(gsm_metadata, file_annotation, use_key_subset=False):
-    """
-    Write metadata sheet out as an annotation file.
-
-    :param Mapping gsm_metadata: the data to write, parsed from a file
-        with metadata/annotation information
-    :param str file_annotation: the path to the file to write
-    :param bool use_key_subset: whether to use the keys present in the
-        metadata object given (False), or instead use a fixed set of keys
-        defined within this module (True)
-    :return str: path to file written
-    """
-    keys = ANNOTATION_SHEET_KEYS if use_key_subset else \
-        gsm_metadata[gsm_metadata.iterkeys().next()].keys()
-    _LOGGER.info("Sample annotation sheet: {}".format(file_annotation))
-    fp = expandpath(file_annotation)
-    _LOGGER.info("Writing: {}".format(fp))
-    with open(fp, 'w') as of:
-        w = csv.DictWriter(of, keys, extrasaction='ignore')
-        w.writeheader()
-        for item in gsm_metadata:
-            w.writerow(gsm_metadata[item])
-    return fp
-
-
-def write_subannotation(tabular_data, filepath, column_names=None):
-    """
-    Writes one or more tables to a given CSV filepath.
-
-    :param Mapping | Iterable[Mapping]: single KV pair collection, or collection
-        of such collections, to write to disk as tabular data
-    :param str filepath: path to file to write, possibly with environment
-        variables included, e.g. from a config file
-    :param Iterable[str] column_names: collection of names for columns to
-        write
-    :return str: path to file written
-    """
-    _LOGGER.info("Sample subannotation sheet: {}".format(filepath))
-    fp = expandpath(filepath)
-    _LOGGER.info("Writing: {}".format(fp))
-    with open(fp, 'w') as openfile:
-        writer = csv.writer(openfile, delimiter=",")
-        # write header
-        writer.writerow(column_names or ["sample_name", "SRX", "SRR"])
-        if not isinstance(tabular_data, list):
-            tabular_data = [tabular_data]
-        for table in tabular_data:
-            for key, values in table.items():
-                _LOGGER.debug("{}: {}".format(key, values))
-                writer.writerows(values)
-    return fp
+            # Write project config file
+            if not self.config_template:
+                geofetchdir = os.path.dirname(__file__)
+                self.config_template = os.path.join(geofetchdir, "config_template.yaml")
 
+            with open(self.config_template, "r") as template_file:
+                template = template_file.read()
 
-class InvalidSoftLineException(Exception):
-    """ Exception related to parsing SOFT line. """
-    def __init__(self, l):
+            template_values = {
+                "project_name": self.project_name,
+                "annotation": os.path.basename(file_annotation),
+                "subannotation": os.path.basename(file_subannotation),
+                "pipeline_interfaces": self.file_pipeline_interfaces,
+            }
+
+            for k, v in template_values.items():
+                placeholder = "{" + str(k) + "}"
+                template = template.replace(placeholder, str(v))
+
+            # save .yaml file
+            config = os.path.join(self.metadata_raw, self.project_name + "_config.yaml")
+            self._write(config, template, msg_pre="  Config file: ")
+
+    def expand_metadata_list(self, metadata_list, dict_key):
         """
-        Create the exception by providing the problematic line.
+        Expanding list items in the list by creating new items or joining them
 
-        :param str l: the problematic SOFT line
+        :param list metadata_list: list of dicts that store metadata
+        :param str dict_key: key in the dictionaries that have to be expanded
+
+        :return str: path to file written
         """
-        super(self, "{}".format(l))
+        try:
+            element_is_list = any(
+                type(list_item[dict_key]) is list for list_item in metadata_list
+            )
+            if element_is_list:
+                for n_elem in range(len(metadata_list)):
+                    if type(metadata_list[n_elem][dict_key]) is not list:
+                        metadata_list[n_elem][dict_key] = [
+                            metadata_list[n_elem][dict_key]
+                        ]
+
+                    just_string = False
+                    this_string = ""
+                    for elem in metadata_list[n_elem][dict_key]:
+                        separated_elements = elem.split(": ")
+                        if len(separated_elements) >= 2:
+                            list_of_elem = [
+                                separated_elements[0],
+                                ": ".join(separated_elements[1:]),
+                            ]
+                            sample_char = dict([list_of_elem])
+                            metadata_list[n_elem].update(sample_char)
+                        else:
+                            just_string = True
+                            if this_string != "":
+                                this_string = ", ".join([this_string, elem])
+                            else:
+                                this_string = elem
 
+                    if just_string:
+                        metadata_list[n_elem][dict_key] = this_string
+                    else:
+                        del metadata_list[n_elem][dict_key]
 
-# From Jay@Stackoverflow
-def which(program):
-    """Returns the path to a program to make sure it exists"""
-    import os
+                return metadata_list
+            else:
+                self._LOGGER.debug(
+                    "metadata with %s was not expanded, as item is not list" % dict_key
+                )
+                return metadata_list
+        except KeyError as err:
+            self._LOGGER.warning("Key Error: %s" % err)
+            return metadata_list
+        except ValueError as err1:
+            self._LOGGER.warning("Value Error: %s" % err1)
+            return metadata_list
+
+    def filter_gsm(self, meta_processed_samples: list, gsm_list: dict) -> list:
+        """
+        Getting metadata list of all samples of one experiment and filtering it
+        by the list of GSM that was specified in the input files.
+        And then changing names of the sample names.
 
-    def is_exe(fp):
-        return os.path.isfile(fp) and os.access(fp, os.X_OK)
+        :param meta_processed_samples: list of metadata dicts of samples
+        :param gsm_list: list of dicts where GSM (samples) are keys and
+            sample names are values. Where values can be empty string
+        """
 
-    fpath, fname = os.path.split(program)
-    if fpath:
-        if is_exe(program):
-            return program
-    else:
-        for path in os.environ["PATH"].split(os.pathsep):
-            path = path.strip('"')
-            exe_file = os.path.join(path, program)
-            if is_exe(exe_file):
-                return exe_file
+        if gsm_list.keys():
+            new_gsm_list = []
+            for gsm_sample in meta_processed_samples:
+                if gsm_sample["Sample_geo_accession"] in gsm_list.keys():
+                    gsm_sample_new = gsm_sample
+                    if gsm_list[gsm_sample["Sample_geo_accession"]] != "":
+                        gsm_sample_new["sample_name"] = gsm_list[
+                            gsm_sample["Sample_geo_accession"]
+                        ]
+                    new_gsm_list.append(gsm_sample_new)
+            return new_gsm_list
+        return meta_processed_samples
+
+    @staticmethod
+    def get_list_of_keys(list_of_dict):
+        """
+        Getting list of all keys that are in the dictionaries in the list
 
+        :param list list_of_dict: list of dicts with metadata
 
-def safe_echo(var):
-    """ Returns an environment variable if it exists, or an empty string if not"""
-    return os.getenv(var, "")
+        :return list: list of dictionary keys
+        """
 
+        list_of_keys = []
+        for element in list_of_dict:
+            list_of_keys.extend(list(element.keys()))
+        return list(set(list_of_keys))
 
-def update_columns(metadata, experiment_name, sample_name, read_type):
-    """
-    Update the metadata associated with a particular experiment.
-    
-    For the experiment indicated, this function updates the value (mapping), 
-    including new data and populating columns used by looper based on 
-    existing values in the mapping.
-    
-    :param Mapping metadata: the key-value mapping to update
-    :param str experiment_name: name of the experiment from which these
-        data came and are associated; the key in the metadata mapping
-        for which the value is to be updated
-    :param str sample_name: name of the sample with which these data are
-        associated
-    :param str read_type: usually "single" or "paired," an indication of the
-        type of sequencing reads for this experiment
-    :return Mapping: 
-    """
-    
-    exp = metadata[experiment_name]
-
-    # Protocol-agnostic
-    exp["sample_name"] = sample_name
-    exp["protocol"] = exp["Sample_library_selection"]
-    exp["read_type"] = read_type
-    exp["organism"] = exp['Sample_organism_ch1']
-    exp["data_source"] = "SRA"
-    exp["SRX"] = experiment_name
-
-    # Protocol specified is lowercased prior to checking here to alleviate
-    # dependence on case for the value in the annotations file.
-    bisulfite_protocols = {"reduced representation": "RRBS", "random": "WGBS"}
-
-    # Conditional on bisulfite sequencing
-    # print(":" + exp["Sample_library_strategy"] + ":")
-    # Try to be smart about some library methods, refining protocol if possible.
-    if exp["Sample_library_strategy"] == "Bisulfite-Seq":
-        # print("Parsing protocol")
-        proto = exp["Sample_library_selection"].lower()
-        if proto in bisulfite_protocols:
-            exp["protocol"] = bisulfite_protocols[proto]
-
-    return exp
-
-
-def download_processed_files(file_url, data_folder, tar_re, filter_re=None):
-    """
-    Given a url for a file, download it, and extract anything passing the filter.
-    :param str file_url: the URL of the file to download
-    :param str data_folder: the local folder where the file should be saved
-    :param re.Pattern tar_re: a regulator expression (produced from re.compile)
-        that pulls out filenames with .tar in them
-    :param re.Pattern filter_re: a regular expression (produced from
-        re.compile) to filter filenames of interest.
-    :return bool: True if the file is downloaded successfully; false if it does
-        not pass filters and is not downloaded.
-    """
-    # download file
+    def unify_list_keys(self, processed_meta_list):
+        """
+        Unifying list of dicts with metadata, so every dict will have
+            same keys
+
+        :param list processed_meta_list: list of dicts with metadata
+
+        :return str: list of unified dicts with metadata
+        """
+        list_of_keys = self.get_list_of_keys(processed_meta_list)
+        for k in list_of_keys:
+            for list_elem in range(len(processed_meta_list)):
+                if k not in processed_meta_list[list_elem]:
+                    processed_meta_list[list_elem][k] = ""
+        return processed_meta_list
+
+    def write_gsm_annotation(self, gsm_metadata, file_annotation, use_key_subset=False):
+        """
+        Write metadata sheet out as an annotation file.
+
+        :param Mapping gsm_metadata: the data to write, parsed from a file
+            with metadata/annotation information
+        :param str file_annotation: the path to the file to write
+        :param bool use_key_subset: whether to use the keys present in the
+            metadata object given (False), or instead use a fixed set of keys
+            defined within this module (True)
+        :return str: path to file written
+        """
+        if use_key_subset:
+            keys = ANNOTATION_SHEET_KEYS
+        else:
+            # keys = gsm_metadata[gsm_metadata.keys().next()].keys()
+            keys = list(list(gsm_metadata.values())[0].keys())
+
+        self._LOGGER.info(f"Sample annotation sheet: {file_annotation}")
+        fp = expandpath(file_annotation)
+        self._LOGGER.info(f"Writing: {fp}")
+        with open(fp, "w") as of:
+            w = csv.DictWriter(of, keys, extrasaction="ignore")
+            w.writeheader()
+            for item in gsm_metadata:
+                w.writerow(gsm_metadata[item])
+        return fp
+
+    def write_processed_annotation(self, processed_metadata, file_annotation_path):
+        """
+        Saving annotation file by providing list of dictionaries with files metadata
+        :param list processed_metadata: list of dictionaries with files metadata
+        :param str file_annotation_path: the path to the metadata file that has to be saved
+        """
+        if len(processed_metadata) == 0:
+            self._LOGGER.info(
+                "No files found. No data to save. File %s won't be created"
+                % file_annotation_path
+            )
+            return False
+
+        # create folder if does not exist
+        pep_file_folder = os.path.split(file_annotation_path)[0]
+        if not os.path.exists(pep_file_folder):
+            os.makedirs(pep_file_folder)
+
+        self._LOGGER.info("Unifying and saving of metadata... ")
+        processed_metadata = self.unify_list_keys(processed_metadata)
+
+        with open(file_annotation_path, "w") as m_file:
+            dict_writer = csv.DictWriter(m_file, processed_metadata[0].keys())
+            dict_writer.writeheader()
+            dict_writer.writerows(processed_metadata)
+        self._LOGGER.info(
+            "\033[92mFile %s has been saved successfully\033[0m" % file_annotation_path
+        )
+
+        geofetchdir = os.path.dirname(__file__)
+        config_template = os.path.join(geofetchdir, "config_processed_template.yaml")
+
+        with open(config_template, "r") as template_file:
+            template = template_file.read()
+
+        template_values = {
+            "project_name": self.project_name,
+            "sample_table": os.path.basename(file_annotation_path),
+            "geo_folder": self.geo_folder,
+            "pipeline_interfaces": self.file_pipeline_interfaces,
+        }
+
+        for k, v in template_values.items():
+            placeholder = "{" + str(k) + "}"
+            template = template.replace(placeholder, str(v))
+
+        # save .yaml file
+        yaml_name = os.path.split(file_annotation_path)[1][:-4] + ".yaml"
+        config = os.path.join(pep_file_folder, yaml_name)
+        self._write(config, template, msg_pre="  Config file: ")
+        return True
+
+    def download_SRA_file(self, run_name):
+        """
+        Downloading SRA file by ising 'prefetch' utility from the SRA Toolkit
+        more info: (http://www.ncbi.nlm.nih.gov/books/NBK242621/)
+        :param str run_name: SRR number of the SRA file
+        """
+
+        # Set up a simple loop to try a few times in case of failure
+        t = 0
+        while True:
+            t = t + 1
+            subprocess_return = subprocess.call(
+                ["prefetch", run_name, "--max-size", "50000000"]
+            )
 
-    def download_file(file_url, data_folder, sleep_after=0.5):
+            if subprocess_return == 0:
+                break
+
+            if t >= NUM_RETRIES:
+                raise RuntimeError(
+                    f"Prefetch retries of {run_name} failed. Try this sample later"
+                )
+
+            self._LOGGER.info(
+                "Prefetch attempt failed, wait a few seconds to try again"
+            )
+            time.sleep(t * 2)
+
+    @staticmethod
+    def which(program):
+        """
+        return str:  the path to a program to make sure it exists
+        """
+        import os
+
+        def is_exe(fp):
+            return os.path.isfile(fp) and os.access(fp, os.X_OK)
+
+        fpath, fname = os.path.split(program)
+        if fpath:
+            if is_exe(program):
+                return program
+        else:
+            for path in os.environ["PATH"].split(os.pathsep):
+                path = path.strip('"')
+                exe_file = os.path.join(path, program)
+                if is_exe(exe_file):
+                    return exe_file
+
+    def sra_bam_conversion(self, bam_file, run_name):
+        """
+        Converting of SRA file to BAM file by using samtools function "sam-dump"
+        :param str bam_file: path to BAM file that has to be created
+        :param str run_name: SRR number of the SRA file that has to be converted
+        """
+        self._LOGGER.info("Converting to bam: " + run_name)
+        sra_file = os.path.join(self.sra_folder, run_name + ".sra")
+        if not os.path.exists(sra_file):
+            raise FileNotFoundError(sra_file)
+
+        # The -u here allows unaligned reads, and seems to be
+        # required for some sra files regardless of aligned state
+        cmd = (
+            "sam-dump -u "
+            + os.path.join(self.sra_folder, run_name + ".sra")
+            + " | samtools view -bS - > "
+            + bam_file
+        )
+        # sam-dump -u SRR020515.sra | samtools view -bS - > test.bam
+
+        self._LOGGER.info(f"Conversion command: {cmd}")
+        subprocess.call(cmd, shell=True)
+
+    @staticmethod
+    def update_columns(metadata, experiment_name, sample_name, read_type):
+        """
+        Update the metadata associated with a particular experiment.
+
+        For the experiment indicated, this function updates the value (mapping),
+        including new data and populating columns used by looper based on
+        existing values in the mapping.
+
+        :param Mapping metadata: the key-value mapping to update
+        :param str experiment_name: name of the experiment from which these
+            data came and are associated; the key in the metadata mapping
+            for which the value is to be updated
+        :param str sample_name: name of the sample with which these data are
+            associated
+        :param str read_type: usually "single" or "paired," an indication of the
+            type of sequencing reads for this experiment
+        :return Mapping:
+        """
+
+        exp = metadata[experiment_name]
+
+        # Protocol-agnostic
+        exp["sample_name"] = sample_name
+        exp["protocol"] = exp["Sample_library_selection"]
+        exp["read_type"] = read_type
+        exp["organism"] = exp["Sample_organism_ch1"]
+        exp["data_source"] = "SRA"
+        exp["SRX"] = experiment_name
+
+        # Protocol specified is lowercased prior to checking here to alleviate
+        # dependence on case for the value in the annotations file.
+        bisulfite_protocols = {"reduced representation": "RRBS", "random": "WGBS"}
+
+        # Conditional on bisulfite sequencing
+        # print(":" + exp["Sample_library_strategy"] + ":")
+        # Try to be smart about some library methods, refining protocol if possible.
+        if exp["Sample_library_strategy"] == "Bisulfite-Seq":
+            # print("Parsing protocol")
+            proto = exp["Sample_library_selection"].lower()
+            if proto in bisulfite_protocols:
+                exp["protocol"] = bisulfite_protocols[proto]
+
+        return exp
+
+    def sra_bam_conversion2(self, bam_file, run_name, picard_path=None):
+        """
+        Converting of SRA file to BAM file by using fastq-dump
+        (is used when sam-dump fails, yielding an empty bam file. Here fastq -> bam conversion is used)
+        :param str bam_file: path to BAM file that has to be created
+        :param str run_name: SRR number of the SRA file that has to be converted
+        :param str picard_path: Path to The Picard toolkit. More info: https://broadinstitute.github.io/picard/
+        """
+
+        # check to make sure it worked
+        cmd = (
+            "fastq-dump --split-3 -O "
+            + os.path.realpath(self.sra_folder)
+            + " "
+            + os.path.join(self.sra_folder, run_name + ".sra")
+        )
+        self._LOGGER.info(f"Command: {cmd}")
+        subprocess.call(cmd, shell=True)
+        if not picard_path:
+            self._LOGGER.warning("Can't convert the fastq to bam without picard path")
+        else:
+            # was it paired data? you have to process it differently
+            # so it knows it's paired end
+            fastq0 = os.path.join(self.sra_folder, run_name + ".fastq")
+            fastq1 = os.path.join(self.sra_folder, run_name + "_1.fastq")
+            fastq2 = os.path.join(self.sra_folder, run_name + "_2.fastq")
+
+            cmd = "java -jar " + picard_path + " FastqToSam"
+            if os.path.exists(fastq1) and os.path.exists(fastq2):
+                cmd += " FASTQ=" + fastq1
+                cmd += " FASTQ2=" + fastq2
+            else:
+                cmd += " FASTQ=" + fastq0
+            cmd += " OUTPUT=" + bam_file
+            cmd += " SAMPLE_NAME=" + run_name
+            cmd += " QUIET=true"
+            self._LOGGER.info(f"Conversion command: {cmd}")
+            subprocess.call(cmd, shell=True)
+
+    def write_subannotation(self, tabular_data, filepath, column_names=None):
+        """
+        Writes one or more tables to a given CSV filepath.
+
+        :param tabular_data: Mapping | Iterable[Mapping]: single KV pair collection, or collection
+            of such collections, to write to disk as tabular data
+        :param str filepath: path to file to write, possibly with environment
+            variables included, e.g. from a config file
+        :param Iterable[str] column_names: collection of names for columns to
+            write
+        :return str: path to file written
+        """
+        self._LOGGER.info(f"Sample subannotation sheet: {filepath}")
+        fp = expandpath(filepath)
+        self._LOGGER.info(f"Writing: {fp}")
+        with open(fp, "w") as openfile:
+            writer = csv.writer(openfile, delimiter=",")
+            # write header
+            writer.writerow(column_names or ["sample_name", "SRX", "SRR"])
+            if not isinstance(tabular_data, list):
+                tabular_data = [tabular_data]
+            for table in tabular_data:
+                for key, values in table.items():
+                    self._LOGGER.debug(f"{key}: {values}")
+                    writer.writerows(values)
+        return fp
+
+    def download_file(self, file_url, data_folder, new_name=None, sleep_after=0.5):
+        """
+        Given an url for a file, downloading to specified folder
+        :param str file_url: the URL of the file to download
+        :param str data_folder: path to the folder where data should be downloaded
+        :param float sleep_after: time to sleep after downloading
+        :param str new_name: new file name in the
+        """
         filename = os.path.basename(file_url)
-        full_filepath = os.path.join(data_folder, filename)        
+        if new_name is None:
+            full_filepath = os.path.join(data_folder, filename)
+        else:
+            full_filepath = os.path.join(data_folder, new_name)
+
         if not os.path.exists(full_filepath):
-            _LOGGER.info("\033[38;5;242m")  # set color to gray
-            ret = subprocess.call(['wget', '--no-clobber', file_url, '-P', data_folder])
+            self._LOGGER.info(f"\033[38;5;242m")  # set color to gray
+            # if dir does not exist:
+            if not os.path.exists(data_folder):
+                os.makedirs(data_folder)
+            ret = subprocess.call(
+                ["wget", "--no-clobber", file_url, "-O", full_filepath]
+            )
+            self._LOGGER.info(f"\033[38;5;242m{ret}\033[0m")
             time.sleep(sleep_after)
-            _LOGGER.info("\033[0m") # Reset to default terminal color
+            self._LOGGER.info(f"\033[0m")  # Reset to default terminal color
         else:
-            _LOGGER.info("\033[38;5;242mFile {} exists.\033[0m".format(full_filepath))
+            self._LOGGER.info(f"\033[38;5;242mFile {full_filepath} exists.\033[0m")
 
+    def get_list_of_processed_files(self, file_gse, file_gsm):
+        """
+        Given a paths to GSE and GSM metafile create a list of dicts of metadata of processed files
+        :param str file_gse: the path to gse metafile
+        :param str file_gsm: the path to gse metafile
+        :return list: list of metadata of processed files
+        """
+        tar_re = re.compile(r".*\.tar$")
+        gse_numb = None
+        meta_processed_samples = []
+        meta_processed_series = {"GSE": "", "files": []}
+        for line in open(file_gse, "r"):
 
+            if re.compile(r"!Series_geo_accession").search(line):
+                gse_numb = self.get_value(line)
+                meta_processed_series["GSE"] = gse_numb
+            found = re.findall(SER_SUPP_FILE_PATTERN, line)
 
+            if found:
+                pl = parse_SOFT_line(line)
+                file_url = pl[list(pl.keys())[0]].rstrip()
+                filename = os.path.basename(file_url)
+                self._LOGGER.debug(f"Processed GSE file found: %s" % str(file_url))
+
+                # search for tar file:
+                if tar_re.search(filename):
+                    # find and download filelist - file with information about files in tar
+                    index = file_url.rfind("/")
+                    tar_files_list_url = file_url[: index + 1] + "filelist.txt"
+                    # file_list_name
+                    filelist_path = os.path.join(
+                        self.metadata_expanded, gse_numb + "_file_list.txt"
+                    )
+                    self.download_file(
+                        tar_files_list_url,
+                        self.metadata_expanded,
+                        gse_numb + "_file_list.txt",
+                    )
+
+                    nb = len(meta_processed_samples) - 1
+                    for line_gsm in open(file_gsm, "r"):
+                        if line_gsm[0] == "^":
+                            nb = len(self.check_file_existance(meta_processed_samples))
+                            meta_processed_samples.append(
+                                {"files": [], "GSE": gse_numb}
+                            )
+                        else:
+                            try:
+                                pl = parse_SOFT_line(line_gsm.strip("\n"))
+                            except IndexError:
+                                continue
+                            element_keys = list(pl.keys())[0]
+                            element_values = list(pl.values())[0]
+                            if not re.findall(SUPP_FILE_PATTERN, line_gsm):
+                                if (
+                                    element_keys
+                                    not in meta_processed_samples[nb].keys()
+                                ):
+                                    meta_processed_samples[nb].update(pl)
+                                else:
+                                    if (
+                                        type(meta_processed_samples[nb][element_keys])
+                                        is not list
+                                    ):
+                                        meta_processed_samples[nb][element_keys] = [
+                                            meta_processed_samples[nb][element_keys]
+                                        ]
+                                        meta_processed_samples[nb][element_keys].append(
+                                            element_values
+                                        )
+                                    else:
+                                        meta_processed_samples[nb][element_keys].append(
+                                            element_values
+                                        )
+
+                        found_gsm = re.findall(SUPP_FILE_PATTERN, line_gsm)
+
+                        if found_gsm:
+                            pl = parse_SOFT_line(line_gsm)
+                            file_url_gsm = pl[list(pl.keys())[0]].rstrip()
+                            self._LOGGER.debug(
+                                f"Processed GSM file found: %s" % str(file_url_gsm)
+                            )
+                            if file_url_gsm != "NONE":
+                                meta_processed_samples[nb]["files"].append(file_url_gsm)
+
+                    self.check_file_existance(meta_processed_samples)
+                    meta_processed_samples = self.separate_list_of_files(
+                        meta_processed_samples
+                    )
+                    meta_processed_samples = self.separate_file_url(
+                        meta_processed_samples
+                    )
+
+                    self._LOGGER.info(
+                        f"Total number of processed SAMPLES files found is: "
+                        f"%s" % str(len(meta_processed_samples))
+                    )
+
+                    # expand meta_processed_samples with information about type and size
+                    file_info_add = self.read_tar_filelist(filelist_path)
+                    for index_nr in range(len(meta_processed_samples)):
+                        file_name = meta_processed_samples[index_nr]["file"]
+                        meta_processed_samples[index_nr].update(
+                            file_info_add[file_name]
+                        )
+
+                    if self.filter_re:
+                        meta_processed_samples = self.run_filter(meta_processed_samples)
+                    if self.filter_size:
+                        meta_processed_samples = self.run_size_filter(
+                            meta_processed_samples
+                        )
+
+                # other files than .tar: saving them into meta_processed_series list
+                else:
+                    meta_processed_series["files"].append(file_url)
 
-    filename = os.path.basename(file_url)
-    full_filepath = os.path.join(data_folder, filename)
-    ntry = 0
-    while ntry < 10:
-        try:
-            if tar_re.search(filename):
-                _LOGGER.info("\033[92mDownloading tar archive\033[0m")
-                download_file(file_url, data_folder)
-                t = tarfile.open(full_filepath, 'r')
-                members = t.getmembers()
-                
-                if filter_re:
-                    pass_filt = [m for m in members if filter_re.search(m.name)]
+            # adding metadata to the experiment file
+            try:
+                bl = parse_SOFT_line(line.rstrip("\n"))
+                bl_key = list(bl.keys())[0]
+                bl_value = list(bl.values())[0]
+
+                if bl_key not in meta_processed_series.keys():
+                    meta_processed_series.update(bl)
+                else:
+                    if type(meta_processed_series[bl_key]) is not list:
+                        meta_processed_series[bl_key] = [meta_processed_series[bl_key]]
+                        meta_processed_series[bl_key].append(bl_value)
+                    else:
+                        meta_processed_series[bl_key].append(bl_value)
+            except IndexError as ind_err:
+                self._LOGGER.debug(
+                    f"IndexError in adding value to meta_processed_series: %s" % ind_err
+                )
+
+        meta_processed_series = self.separate_list_of_files(meta_processed_series)
+        meta_processed_series = self.separate_file_url(meta_processed_series)
+        self._LOGGER.info(
+            f"Total number of processed SERIES files found is: "
+            f"%s" % str(len(meta_processed_series))
+        )
+        if self.filter_re:
+            meta_processed_series = self.run_filter(meta_processed_series)
+
+        return meta_processed_samples, meta_processed_series
+
+    @staticmethod
+    def check_file_existance(meta_processed_sample):
+        """
+        Checking if last element of the list has files. If list of files is empty deleting it
+        """
+        nb = len(meta_processed_sample) - 1
+        if nb > -1:
+            if len(meta_processed_sample[nb]["files"]) == 0:
+                del meta_processed_sample[nb]
+                nb -= 1
+        return meta_processed_sample
+
+    @staticmethod
+    def separate_list_of_files(meta_list, col_name="files"):
+        """
+        This method is separating list of files (dict value) or just simple dict
+        into two different dicts
+        """
+        separated_list = []
+        if type(meta_list) == list:
+            for meta_elem in meta_list:
+                for file_elem in meta_elem[col_name]:
+                    new_dict = meta_elem.copy()
+                    new_dict.pop(col_name, None)
+                    new_dict["file"] = file_elem
+                    separated_list.append(new_dict)
+        elif type(meta_list) == dict:
+            for file_elem in meta_list[col_name]:
+                new_dict = meta_list.copy()
+                new_dict.pop(col_name, None)
+                new_dict["file"] = file_elem
+                separated_list.append(new_dict)
+        else:
+            return TypeError("Incorrect type")
+
+        return separated_list
+
+    @staticmethod
+    def separate_file_url(meta_list):
+        """
+        This method is adding dict key without file_name without path
+        """
+        separated_list = []
+        for meta_elem in meta_list:
+            new_dict = meta_elem.copy()
+            new_dict["file_url"] = meta_elem["file"]
+            new_dict["file"] = os.path.basename(meta_elem["file"])
+            # new_dict["sample_name"] = os.path.basename(meta_elem["file"])
+            try:
+                new_dict["sample_name"] = str(meta_elem["Sample_title"])
+            except KeyError:
+                new_dict["sample_name"] = os.path.basename(meta_elem["file"])
+            separated_list.append(new_dict)
+        return separated_list
+
+    def run_filter(self, meta_list, col_name="file"):
+        """
+        If user specified filter it will filter all this files here by col_name
+        """
+        filtered_list = []
+        for meta_elem in meta_list:
+            if self.filter_re.search(meta_elem[col_name].lower()):
+                filtered_list.append(meta_elem)
+        self._LOGGER.info(
+            "\033[32mTotal number of files after filter is: %i \033[0m"
+            % len(filtered_list)
+        )
+
+        return filtered_list
+
+    def run_size_filter(self, meta_list, col_name="file_size"):
+        """
+        function for filtering file size
+        """
+        if self.filter_size is not None:
+            filtered_list = []
+            for meta_elem in meta_list:
+                if meta_elem[col_name] <= self.filter_size:
+                    filtered_list.append(meta_elem)
+        else:
+            self._LOGGER.info(
+                "\033[32mTotal number of files after size filter NONE?? \033[0m"
+            )
+            return meta_list
+        self._LOGGER.info(
+            "\033[32mTotal number of files after size filter is: %i \033[0m"
+            % len(filtered_list)
+        )
+        return filtered_list
+
+    @staticmethod
+    def read_tar_filelist(file_path):
+        """
+        Creating list for supplementary files that are listed in "filelist.txt"
+        :param str file_path: path to the file with information about files that are zipped ("filelist.txt")
+        :return dict: dict of supplementary file names and additional information
+        """
+
+        files_info = {}
+        with open(file_path, newline="") as csvfile:
+            csv_reader = csv.reader(csvfile, delimiter="\t")
+            line_count = 0
+            for row in csv_reader:
+                if line_count == 0:
+                    name_index = row.index("Name")
+                    size_index = row.index("Size")
+                    type_index = row.index("Type")
+
+                    line_count += 1
                 else:
-                    pass_filt = t.getmembers() 
+                    files_info[row[name_index]] = {
+                        "file_size": row[size_index],
+                        "type": row[type_index],
+                    }
 
-                files_to_extract = [m for m in pass_filt if not os.path.exists(os.path.join(data_folder, m.name))]
-                
-                msg = "Files in archive: {}; passed filter: {}; not existing: {}.".format(
-                    len(members), len(pass_filt), len(files_to_extract))
+        return files_info
 
-                _LOGGER.info(msg)
-                if len(files_to_extract) > 0:
-                    t.extractall(data_folder, members=files_to_extract)
+    @staticmethod
+    def get_value(all_line):
+        line_value = all_line.split("= ")[-1]
+        return line_value.split(": ")[-1].rstrip("\n")
 
-                if False:  # Delete archive?
-                    os.unlink(full_filepath)
-                return True
-            if not filter_re:
-                _LOGGER.info("No filter regex, downloading")
-                download_file(file_url, data_folder)
-                return True
-            elif filter_re.search(filename):
-                _LOGGER.info("\033[92mMatches filter regex, downloading\033[0m")
-                download_file(file_url, data_folder)
+    def download_processed_file(self, file_url, data_folder):
+
+        """
+        Given a url for a file, download it, and extract anything passing the filter.
+        :param str file_url: the URL of the file to download
+        :param str data_folder: the local folder where the file should be saved
+        :return bool: True if the file is downloaded successfully; false if it does
+        not pass filters and is not downloaded.
+
+        # :param re.Pattern tar_re: a regulator expression (produced from re.compile)
+        #    that pulls out filenames with .tar in them --- deleted
+        # :param re.Pattern filter_re: a regular expression (produced from
+        #    re.compile) to filter filenames of interest.
+        """
+
+        if not self.geo_folder:
+            self._LOGGER.error(
+                "You must provide a geo_folder to download processed data."
+            )
+            sys.exit(1)
+
+        filename = os.path.basename(file_url)
+        ntry = 0
+
+        while ntry < 10:
+            try:
+                self.download_file(file_url, data_folder)
+                self._LOGGER.info(
+                    "\033[92mFile %s has been downloaded successfully\033[0m"
+                    % f"{data_folder}/{filename}"
+                )
                 return True
-            else:
-                _LOGGER.info("\033[91mDoesn't match filter regex\033[0m")
-                return False
-        except IOError as e:
-            _LOGGER.error(str(e))
-            # The server times out if we are hitting it too frequently,
-            # so we should sleep a bit to reduce frequency
-            sleeptime = (ntry + 1)**3 
-            _LOGGER.info("Sleeping for {} seconds".format(sleeptime))
-            time.sleep(sleeptime)
-            ntry += 1
-            if ntry > 4:
-                raise e
-
-
-def run_geofetch(cmdl):
-    """ Main script driver/workflow """
-    args = _parse_cmdl(cmdl)
-    global _LOGGER
-    _LOGGER = logger_via_cli(args, name="geofetch")
-
-    # check to make sure prefetch is callable
-    if not args.just_metadata and not args.processed:
-        if not is_command_callable("prefetch"):
-            raise SystemExit("You must first install the sratoolkit, with prefetch in your PATH.")
-
-    if args.name:
-        project_name = args.name
-    else:
-        project_name = os.path.splitext(os.path.basename(args.input))[0]
-
-
-    if args.filter:
-        filter_re = re.compile(args.filter)
-        tar_re = re.compile(r".*\.tar$")
-    else:
-        filter_re = None
-        tar_re = None        
 
-    def render_env_var(ev):
-        return "{} ({})".format(ev, expandpath(ev))
+            except IOError as e:
+                self._LOGGER.error(str(e))
+                # The server times out if we are hitting it too frequently,
+                # so we should sleep a bit to reduce frequency
+                sleeptime = (ntry + 1) ** 3
+                self._LOGGER.info(f"Sleeping for {sleeptime} seconds")
+                time.sleep(sleeptime)
+                ntry += 1
+                if ntry > 4:
+                    raise e
+
+    def get_SRA_meta(self, file_gse, file_sra, gsm_metadata):
+        """
+        Parse out the SRA project identifier from the GSE file
 
-    if args.metadata_folder:
-        metadata_expanded = expandpath(args.metadata_folder)
-        if os.path.isabs(metadata_expanded):
-            metadata_raw = args.metadata_folder
-        else:
-            metadata_expanded = os.path.abspath(metadata_expanded)
-            metadata_raw = os.path.abspath(args.metadata_root)        
-        metadata_raw = args.metadata_folder
-    else:
-        metadata_expanded = expandpath(args.metadata_root)
-        if os.path.isabs(metadata_expanded):
-            metadata_raw = args.metadata_root
+        :param str file_gse: full path to GSE.soft metafile
+        :param str file_sra: full path to SRA.csv metafile that has to be downloaded
+        :param dict gsm_metadata: dict of GSM metadata
+        """
+        #
+        acc_SRP = None
+        for line in open(file_gse, "r"):
+            found = re.findall(PROJECT_PATTERN, line)
+            if found:
+                acc_SRP = found[0]
+                self._LOGGER.info(f"Found SRA Project accession: {acc_SRP}")
+                break
+
+        if not acc_SRP:
+            # If I can't get an SRA accession, maybe raw data wasn't submitted to SRA
+            # as part of this GEO submission. Can't proceed.
+            self._LOGGER.warning(
+                "\033[91mUnable to get SRA accession (SRP#) from GEO GSE SOFT file. "
+                "No raw data?\033[0m"
+            )
+            # but wait; another possibility: there's no SRP linked to the GSE, but there
+            # could still be an SRX linked to the (each) GSM.
+            if len(gsm_metadata) == 1:
+                try:
+                    acc_SRP = gsm_metadata.keys()[0]
+                    self._LOGGER.warning(
+                        "But the GSM has an SRX number; instead of an "
+                        "SRP, using SRX identifier for this sample: " + acc_SRP
+                    )
+                except TypeError:
+                    self._LOGGER.warning("Error in gsm_metadata")
+                    return False
+
+            # else:
+            #     # More than one sample? not sure what to do here. Does this even happen?
+            #     continue
+        # Now we have an SRA number, grab the SraRunInfo Metadata sheet:
+        # The SRARunInfo sheet has additional sample metadata, which we will combine
+        # with the GSM file to produce a single sample a
+
+        if not os.path.isfile(file_sra) or self.refresh_metadata:
+            try:
+                # downloading metadata
+                Accession(acc_SRP).fetch_metadata(file_sra)
+            except Exception as err:
+                self._LOGGER.warning(
+                    f"\033[91mError occurred, while downloading SRA Info Metadata of {acc_SRP}. "
+                    f"Error: {err}  \033[0m"
+                )
+                return False
         else:
-            metadata_expanded = os.path.abspath(metadata_expanded)
-            metadata_raw = os.path.abspath(args.metadata_root)
-
-        # Postpend the project name as a subfolder (only for -m option)
-        metadata_expanded = os.path.join(metadata_expanded, project_name)
-        metadata_raw = os.path.join(metadata_raw, project_name)
-
-    _LOGGER.info("Metadata folder: {}".format(metadata_expanded))
-
-
-
-
-    # Some sanity checks before proceeding
-    if args.bam_conversion and not args.just_metadata and not which("samtools"):
-        raise SystemExit("For SAM/BAM processing, samtools should be on PATH.")
-
-    acc_GSE_list = parse_accessions(args.input, metadata_expanded, args.just_metadata)
-    
-    # Loop through each accession.
-    # This will process that accession, produce metadata and download files for
-    # the GSM #s included in the list for each GSE#.
-    # acc_GSE = "GSE61150" # example
-    
-    # This loop populates a list of metadata.
-    metadata_dict = OrderedDict()
-    subannotation_dict = OrderedDict()
-    failed_runs = []
-
-    acc_GSE_keys = acc_GSE_list.keys()
-    nkeys = len(acc_GSE_keys)
-    ncount = 0
-    for acc_GSE in acc_GSE_list.keys():
-        ncount += 1
-        if ncount <= args.skip:
-            continue
-        elif ncount == args.skip + 1:
-            _LOGGER.info("Skipped {} accessions. Starting now.".format(args.skip))
-        _LOGGER.info("\033[38;5;228mProcessing accession {} of {}: '{}'\033[0m".format(
-            ncount, nkeys, acc_GSE))
-        if len(re.findall(GSE_PATTERN, acc_GSE)) != 1:
-            print(len(re.findall(GSE_PATTERN, acc_GSE)))
-            _LOGGER.warning("This does not appear to be a correctly formatted "
-                            "GSE accession! Continue anyway...")
-    
+            self._LOGGER.info("Found previous SRA file: " + file_sra)
+
+        self._LOGGER.info(f"SRP: {acc_SRP}")
+        return True
+
+    def get_gsm_metadata(self, acc_GSE, acc_GSE_list, file_gsm):
+        """
+        A simple state machine to parse SOFT formatted files (Here, the GSM file)
+
+        :param str acc_GSE: GSE number (Series accession)
+        :param dict acc_GSE_list: list of GSE
+        :param str file_gsm: full path to GSM.soft metafile
+        :return dict: dictionary of experiment information (gsm_metadata)
+        """
+        gsm_metadata = {}
+
         # Get GSM#s (away from sample_name)
-        GSM_limit_list = list(acc_GSE_list[acc_GSE].keys())    #[x[1] for x in acc_GSE_list[acc_GSE]]
-    
-
-        if (len(acc_GSE_list[acc_GSE]) > 0):
-            _LOGGER.info("Limit to: {}".format(list(acc_GSE_list[acc_GSE])))    # a list of GSM#s
-
-        if args.refresh_metadata:
-            _LOGGER.info("Refreshing metadata...")
-        # For each GSE acc, produce a series of metadata files
-        file_gse = os.path.join(metadata_expanded, acc_GSE + '_GSE.soft')
-        file_gsm = os.path.join(metadata_expanded, acc_GSE + '_GSM.soft')
-        file_sra = os.path.join(metadata_expanded, acc_GSE + '_SRA.csv')
-        file_srafilt = os.path.join(metadata_expanded, acc_GSE + '_SRA_filt.csv')
-    
-    
-        # Grab the GSE and GSM SOFT files from GEO.
-        # The GSE file has metadata describing the experiment, which includes
-        # The SRA number we need to download the raw data from SRA
-        # The GSM file has metadata describing each sample, which we will use to
-        # produce a sample annotation sheet.
-        if not os.path.isfile(file_gse) or args.refresh_metadata:
-            Accession(acc_GSE).fetch_metadata(file_gse)
-        else:
-            _LOGGER.info("Found previous GSE file: " + file_gse)
-    
-        if not os.path.isfile(file_gsm) or args.refresh_metadata:
-            Accession(acc_GSE).fetch_metadata(file_gsm, typename="GSM")
-        else:
-            _LOGGER.info("Found previous GSM file: " + file_gsm)
-    
-        # A simple state machine to parse SOFT formatted files (Here, the GSM file)
-        gsm_metadata = OrderedDict()
-        # For multi samples (samples with multiple runs), we keep track of these
-        # relations in a separate table, which is called the subannotation table.
-        gsm_multi_table = OrderedDict()
+        GSM_limit_list = list(acc_GSE_list[acc_GSE].keys())
+
         # save the state
         current_sample_id = None
         current_sample_srx = False
         samples_list = []
-        for line in open(file_gsm, 'r'):
+        for line in open(file_gsm, "r"):
             line = line.rstrip()
             if len(line) == 0:  # Apparently SOFT files can contain blank lines
                 continue
-            if line[0] is "^":
+            if line[0] == "^":
                 pl = parse_SOFT_line(line)
-                if len(acc_GSE_list[acc_GSE]) > 0 and pl['SAMPLE'] not in GSM_limit_list:
-                    #sys.stdout.write("  Skipping " + a['SAMPLE'] + ".")
+                if (
+                    len(acc_GSE_list[acc_GSE]) > 0
+                    and pl["SAMPLE"] not in GSM_limit_list
+                ):
+                    # sys.stdout.write("  Skipping " + a['SAMPLE'] + ".")
                     current_sample_id = None
                     continue
-                current_sample_id = pl['SAMPLE']
+                current_sample_id = pl["SAMPLE"]
                 current_sample_srx = False
-                columns_init = [("sample_name", ""), ("protocol", ""),
-                                ("organism", ""), ("read_type", ""),
-                                ("data_source", None), ("SRR", None), ("SRX", None)]
-                gsm_metadata[current_sample_id] = OrderedDict(columns_init)
-
-                _LOGGER.debug("Found sample: {}".format(current_sample_id))
+                gsm_metadata[current_sample_id] = {
+                    "sample_name": "",
+                    "protocol": "",
+                    "organism": "",
+                    "read_type": "",
+                    "data_source": None,
+                    "SRR": None,
+                    "SRX": None,
+                }
+
+                self._LOGGER.debug(f"Found sample: {current_sample_id}")
                 samples_list.append(current_sample_id)
             elif current_sample_id is not None:
                 try:
                     pl = parse_SOFT_line(line)
                 except IndexError:
-                    # TODO: do we "fail the current sample" here and remove it
-                    # from gsm_metadata? Or just skip the line?
-                    _LOGGER.debug("Failed to parse alleged SOFT line for sample "
-                                  "ID {}; line: {}".format(current_sample_id, line))
+                    self._LOGGER.debug(
+                        f"Failed to parse alleged SOFT line for sample ID {current_sample_id}; "
+                        f"line: {line}"
+                    )
                     continue
                 gsm_metadata[current_sample_id].update(pl)
-    
-                # For processed data, here's where we would download it
-                if args.processed and not args.just_metadata:
-                    found = re.findall(SUPP_FILE_PATTERN, line)
-                    if found:
-                        _LOGGER.debug("Processed GSM file: " + pl[pl.keys()[0]])
-    
+
                 # Now convert the ids GEO accessions into SRX accessions
                 if not current_sample_srx:
                     found = re.findall(EXPERIMENT_PATTERN, line)
                     if found:
-                        _LOGGER.debug("(SRX accession: {})".format(found[0]))
+                        self._LOGGER.debug(f"(SRX accession: {found[0]})")
                         srx_id = found[0]
                         gsm_metadata[srx_id] = gsm_metadata.pop(current_sample_id)
-                        gsm_metadata[srx_id]["gsm_id"] = current_sample_id  # save the GSM id
+                        gsm_metadata[srx_id][
+                            "gsm_id"
+                        ] = current_sample_id  # save the GSM id
                         current_sample_id = srx_id
                         current_sample_srx = True
-    
         # GSM SOFT file parsed, save it in a list
-        _LOGGER.info("Processed {} samples.".format(len(samples_list)))
-        metadata_dict[acc_GSE] = gsm_metadata
-    
+        self._LOGGER.info(f"Processed {len(samples_list)} samples.")
+        return gsm_metadata
 
-        # Parse out the SRA project identifier from the GSE file
-        acc_SRP = None
-        for line in open(file_gse, 'r'):
-            found = re.findall(PROJECT_PATTERN, line)
-            if found:
-                acc_SRP = found[0]
-                _LOGGER.info("Found SRA Project accession: {}".format(acc_SRP))
-                break
-            # For processed data, here's where we would download it
-            if args.processed and not args.just_metadata:
-                if not args.geo_folder:
-                    _LOGGER.error("You must provide a geo_folder to download processed data.")
-                    Sys.exit
-                found = re.findall(SER_SUPP_FILE_PATTERN, line)
-                if found:
-                    pl = parse_SOFT_line(line)
-                    file_url = pl[pl.keys()[0]].rstrip()
-                    data_folder = os.path.join(args.geo_folder, acc_GSE)
-                    _LOGGER.info("\033[38;5;195mProcessed GSE file: " + str(file_url) + "\033[0m")
-                    _LOGGER.debug("Data folder: " + data_folder)
-                    download_processed_files(file_url, data_folder, tar_re, filter_re)
+    def _write(self, f_var_value, content, msg_pre=None, omit_newline=False):
+        fp = expandpath(f_var_value)
+        self._LOGGER.info((msg_pre or "") + fp)
+        with open(fp, "w") as f:
+            f.write(content)
+            if not omit_newline:
+                f.write("\n")
 
 
+def _parse_cmdl(cmdl):
+    parser = argparse.ArgumentParser(
+        description="Automatic GEO and SRA data downloader"
+    )
 
-        if not acc_SRP:
-            # If I can't get an SRA accession, maybe raw data wasn't submitted to SRA
-            # as part of this GEO submission. Can't proceed.
-            _LOGGER.warning("\033[91mUnable to get SRA accession (SRP#) from GEO GSE SOFT file. No raw data?\033[0m")
-            # but wait; another possibility: there's no SRP linked to the GSE, but there
-            # could still be an SRX linked to the (each) GSM.
-            if len(gsm_metadata) == 1:
-                acc_SRP = gsm_metadata.keys()[0]
-                _LOGGER.warning("But the GSM has an SRX number; instead of an "
-                                "SRP, using SRX identifier for this sample: " + acc_SRP)
-            else:
-                # More than one sample? not sure what to do here. Does this even happen?
-                continue
-    
-        # Now we have an SRA number, grab the SraRunInfo Metadata sheet:
-        # The SRARunInfo sheet has additional sample metadata, which we will combine
-        # with the GSM file to produce a single sample a
-        if not os.path.isfile(file_sra) or args.refresh_metadata:
-            Accession(acc_SRP).fetch_metadata(file_sra)
-        else:
-            _LOGGER.info("Found previous SRA file: " + file_sra)
-    
-        _LOGGER.info("SRP: {}".format(acc_SRP))
-
-        # Parse metadata from SRA
-        # Produce an annotated output from the GSM and SRARunInfo files.
-        # This will merge the GSM and SRA sample metadata into a dict of dicts,
-        # with one entry per sample.
-        # NB: There may be multiple SRA Runs (and thus lines in the RunInfo file)
-        # Corresponding to each sample.
-        if not args.processed:
-            file_read = open(file_sra, 'r')
-            file_write = open(file_srafilt, 'w')
-            _LOGGER.info("Parsing SRA file to download SRR records")
-            initialized = False
-            
-            input_file = csv.DictReader(file_read)
-            for line in input_file:
-                if not initialized:
-                    initialized = True
-                    w = csv.DictWriter(file_write, line.keys())
-                    w.writeheader()
-                #print(line)
-                #print(gsm_metadata[line['SampleName']])
-                # SampleName is not necessarily the GSM number, though frequently it is
-                #gsm_metadata[line['SampleName']].update(line)
-    
-                # Only download if it's in the include list:
-                experiment = line["Experiment"]
-                run_name = line["Run"]
-                if experiment not in gsm_metadata:
-                    # print("Skipping: {}".format(experiment))
-                    continue
-    
-                # local convenience variable
-                # possibly set in the input tsv file
-                sample_name = None  # initialize to empty
-                try:
-                    sample_name = acc_GSE_list[acc_GSE][gsm_metadata[experiment]["gsm_id"]]
-                except KeyError:
-                    pass
-                if not sample_name or sample_name is "":
-                    temp = gsm_metadata[experiment]['Sample_title']
-                    # Now do a series of transformations to cleanse the sample name
-                    temp = temp.replace(" ", "_")
-                    # Do people put commas in their sample names? Yes.
-                    temp = temp.replace(",", "_")
-                    temp = temp.replace("__", "_")
-                    sample_name = temp
-    
-                # Otherwise, record that there's SRA data for this run.
-                # And set a few columns that are used as input to the Looper
-                # print("Updating columns for looper")
-                update_columns(gsm_metadata, experiment, sample_name=sample_name,
-                                read_type=line['LibraryLayout'])
-
-                # Some experiments are flagged in SRA as having multiple runs.
-                if gsm_metadata[experiment].get("SRR") is not None:
-                    # This SRX number already has an entry in the table.
-                    _LOGGER.info("Found additional run: {} ({})".format(run_name, experiment))
-    
-                    if isinstance(gsm_metadata[experiment]["SRR"], _STRING_TYPES) \
-                            and experiment not in gsm_multi_table:
-                        # Only one has been stuck in so far, make a list
-                        gsm_multi_table[experiment] = []
-                        # Add first the original one, which was stored as a string
-                        # previously
-                        gsm_multi_table[experiment].append(
-                            [sample_name, experiment, gsm_metadata[experiment]["SRR"]])
-                        # Now append the current SRR number in a list as [SRX, SRR]
-                        gsm_multi_table[experiment].append([sample_name, experiment, run_name])
-                    else:
-                        # this is the 3rd or later sample; the first two are done,
-                        # so just add it.
-                        gsm_multi_table[experiment].append([sample_name, experiment, run_name])
-    
-                    if args.split_experiments:
-                        # Duplicate the gsm metadata for this experiment (copy to make sure
-                        # it's not just an alias).
-                        rep_number = len(gsm_multi_table[experiment])
-                        new_SRX = experiment + "_" + str(rep_number)
-                        gsm_metadata[new_SRX] = copy.copy(gsm_metadata[experiment])
-                        # gsm_metadata[new_SRX]["SRX"] = new_SRX
-                        gsm_metadata[new_SRX]["sample_name"] += "_" + str(rep_number)
-                        gsm_metadata[new_SRX]["SRR"] = run_name
-                    else:
-                        # Either way, set the srr code to multi in the main table.
-                        gsm_metadata[experiment]["SRR"] = "multi"
-                else:
-                    # The first SRR for this SRX is added to GSM metadata
-                    gsm_metadata[experiment]["SRR"] = run_name
-    
-                #gsm_metadata[experiment].update(line)
-    
-                # Write to filtered SRA Runinfo file
-                w.writerow(line)
-                _LOGGER.info("Get SRR: {} ({})".format(run_name, experiment))
-                bam_file = "" if args.bam_folder == "" else os.path.join(args.bam_folder, run_name + ".bam")
-                fq_file = "" if args.fq_folder == "" else os.path.join(args.fq_folder, run_name + "_1.fq")
-    
-                # TODO: sam-dump has a built-in prefetch. I don't have to do
-                # any of this stuff... This also solves the bad sam-dump issues.
-    
-                if os.path.exists(bam_file):
-                    _LOGGER.info("BAM found:" + bam_file)
-                elif os.path.exists(fq_file):
-                    _LOGGER.info("FQ found:" + fq_file)
-                else:
-                    if not args.just_metadata:
-                        # Use the 'prefetch' utility from the SRA Toolkit
-                        # to download the raw reads.
-                        # (http://www.ncbi.nlm.nih.gov/books/NBK242621/)
-
-                        # Set up a simple loop to try a few times in case of failure
-                        t = 0
-                        while True:
-                            t = t+1
-                            subprocess_return = subprocess.call(['prefetch', run_name, '--max-size', '50000000'])
-                            if subprocess_return == 0:
-                                break
-                            if t >= NUM_RETRIES:
-                                _LOGGER.info("Prefetch retries failed. Try this sample later")
-                                failed_runs.append(run_name)
-                                break
-                            _LOGGER.info("Prefetch attempt failed, wait a few seconds to try again")
-                            time.sleep(t*2)
-                    else:
-                        _LOGGER.info("Dry run (no data download)")
-
-
-    
-                    if args.bam_conversion and args.bam_folder is not '':
-                        _LOGGER.info("Converting to bam: " + run_name)
-                        sra_file = os.path.join(args.sra_folder, run_name + ".sra")
-                        if not os.path.exists(sra_file):
-                            _LOGGER.info("SRA file doesn't exist, please "
-                                         "download it first: " + sra_file)
-                            continue
-    
-                        # The -u here allows unaligned reads, and seems to be
-                        # required for some sra files regardless of aligned state
-                        cmd = "sam-dump -u " + \
-                              os.path.join(args.sra_folder, run_name + ".sra") + \
-                              " | samtools view -bS - > " + bam_file
-                        #sam-dump -u SRR020515.sra | samtools view -bS - > test.bam
-    
-                        _LOGGER.info("Conversion command: {}".format(cmd))
-                        subprocess.call(cmd, shell=True)
-    
-                # check to make sure it worked
-                # NS: Sometimes sam-dump fails, yielding an empty bam file, but
-                # a fastq-dump works. This happens on files with bad quality
-                # encodings. I contacted GEO about it in December 2015
-                # Here we check the file size and use fastq -> bam conversion
-                # if the sam-dump failed.
-                if args.bam_conversion and args.bam_folder is not '':
-                    st = os.stat(bam_file)
-                    # print("File size: " + str(st.st_size))
-                    if st.st_size < 100:
-                        _LOGGER.warning("Bam conversion failed with sam-dump. Trying fastq-dump...")
-                        # recreate?
-                        cmd = "fastq-dump --split-3 -O " + \
-                              os.path.realpath(args.sra_folder) + " " + \
-                              os.path.join(args.sra_folder, run_name + ".sra")
-                        _LOGGER.info("Command: {}".format(cmd))
-                        subprocess.call(cmd, shell=True)
-                        if not args.picard_path:
-                            _LOGGER.warning("Can't convert the fastq to bam without picard path")
-                        else:
-                            # was it paired data? you have to process it differently
-                            # so it knows it's paired end
-                            fastq0 = os.path.join(args.sra_folder, run_name + ".fastq")
-                            fastq1 = os.path.join(args.sra_folder, run_name + "_1.fastq")
-                            fastq2 = os.path.join(args.sra_folder, run_name + "_2.fastq")
-        
-                            cmd = "java -jar " + args.picard_path + " FastqToSam"
-                            if os.path.exists(fastq1) and os.path.exists(fastq2):
-                                cmd += " FASTQ=" + fastq1
-                                cmd += " FASTQ2=" +  fastq2
-                            else:
-                                cmd += " FASTQ=" +  fastq0
-                            cmd += " OUTPUT=" + bam_file
-                            cmd += " SAMPLE_NAME=" + run_name
-                            cmd += " QUIET=true"
-                            _LOGGER.info("Conversion command: {}".format(cmd))
-                            subprocess.call(cmd, shell=True)
-
-            file_read.close()
-            file_write.close()
-    
-        # accumulate subannotations
-        subannotation_dict[acc_GSE] = gsm_multi_table
-
-    # Combine individual accessions into project-level annotations, and write
-    # individual accession files (if requested)
-
-    metadata_dict_combined = OrderedDict()
-    for acc_GSE, gsm_metadata in metadata_dict.iteritems():
-        file_annotation = os.path.join(metadata_expanded, acc_GSE + '_annotation.csv')
-        if args.acc_anno:
-            write_annotation(gsm_metadata, file_annotation, use_key_subset=args.use_key_subset)
-        metadata_dict_combined.update(gsm_metadata)
-
-    subannotation_dict_combined = OrderedDict()
-    for acc_GSE, gsm_multi_table in subannotation_dict.iteritems():
-        file_subannotation = os.path.join(
-            metadata_expanded, acc_GSE + '_subannotation.csv')
-        if args.acc_anno:
-            write_subannotation(gsm_multi_table, file_subannotation)
-        subannotation_dict_combined.update(gsm_multi_table)
-
-    _LOGGER.info("Finished processing {} accession(s)".format(len(acc_GSE_list)))
-
-    if (len(failed_runs) > 0):
-        _LOGGER.warn("The following samples could not be downloaded: {}".format(failed_runs))
-
-    # if user specified a pipeline interface path, add it into the project config
-    if args.pipeline_interfaces:
-        file_pipeline_interfaces = args.pipeline_interfaces
-    else:
-        file_pipeline_interfaces = "null"
-
-    _LOGGER.info("Creating complete project annotation sheets and config file...")
-    # If the project included more than one GSE, we can now output combined
-    # annotation tables for the entire project.
-
-    # Write combined annotation sheet
-    file_annotation = os.path.join(metadata_raw, project_name + '_annotation.csv')
-    write_annotation(metadata_dict_combined, file_annotation,
-                     use_key_subset=args.use_key_subset)
-    
-    # Write combined subannotation table
-    if len(subannotation_dict_combined) > 0:
-        file_subannotation = os.path.join(
-            metadata_raw, project_name + '_subannotation.csv')
-        write_subannotation(subannotation_dict_combined, file_subannotation)
-    else:
-        file_subannotation = "null"
-    
-    # Write project config file
-
-    if not args.config_template:
-        geofetchdir = os.path.dirname(__file__)
-        args.config_template = os.path.join(geofetchdir, "config_template.yaml")
+    processed_group = parser.add_argument_group("processed")
+    raw_group = parser.add_argument_group("raw")
 
-    with open(args.config_template, 'r') as template_file:
-        template = template_file.read()
+    parser.add_argument(
+        "-V", "--version", action="version", version=f"%(prog)s {__version__}"
+    )
 
-    template_values = {
-        "project_name": project_name,
-        "annotation": os.path.basename(file_annotation),
-        "subannotation": os.path.basename(file_subannotation),
-        "pipeline_interfaces": file_pipeline_interfaces}
+    # Required
+    parser.add_argument(
+        "-i",
+        "--input",
+        dest="input",
+        required=True,
+        help="required: a GEO (GSE) accession, or a file with a list of GSE numbers",
+    )
 
-    for k, v in template_values.items():
-        placeholder = "{" + str(k) + "}"
-        template = template.replace(placeholder, str(v))
+    # Optional
+    parser.add_argument(
+        "-n", "--name", help="Specify a project name. Defaults to GSE number"
+    )
 
-    config = os.path.join(metadata_raw, project_name + "_config.yaml")
-    _write(config, template, msg_pre="  Config file: ")
+    parser.add_argument(
+        "-m",
+        "--metadata-root",
+        dest="metadata_root",
+        default=safe_echo("SRAMETA"),
+        help="Specify a parent folder location to store metadata. "
+        "The project name will be added as a subfolder "
+        "[Default: $SRAMETA:" + safe_echo("SRAMETA") + "]",
+    )
 
+    parser.add_argument(
+        "-u",
+        "--metadata-folder",
+        help="Specify an absolute folder location to store metadata. "
+        "No subfolder will be added. Overrides value of --metadata-root "
+        "[Default: Not used (--metadata-root is used by default)]",
+    )
 
-def _write(f_var_value, content, msg_pre=None, omit_newline=False):
-    fp = expandpath(f_var_value)
-    _LOGGER.info((msg_pre or "") + fp)
-    with open(fp, 'w') as f:
-        f.write(content)
-        if not omit_newline:
-            f.write("\n")
+    parser.add_argument(
+        "--just-metadata",
+        action="store_true",
+        help="If set, don't actually run downloads, just create metadata",
+    )
+
+    parser.add_argument(
+        "-r",
+        "--refresh-metadata",
+        action="store_true",
+        help="If set, re-download metadata even if it exists.",
+    )
+
+    parser.add_argument(
+        "--config-template", default=None, help="Project config yaml file template."
+    )
+
+    parser.add_argument(
+        "-P",
+        "--pipeline_interfaces",
+        default=None,
+        help="Optional: Specify one or more filepaths to pipeline interface yaml files. "
+        "These will be added to the project config file to make it immediately "
+        "compatible with looper. [Default: null]",
+    )
+
+    parser.add_argument(
+        "-k",
+        "--skip",
+        default=0,
+        type=int,
+        help="Skip some accessions. [Default: no skip].",
+    )
+
+    parser.add_argument(
+        "--acc-anno",
+        action="store_true",
+        help="Also produce annotation sheets for each accession, not just"
+        " for the whole project combined",
+    )
+
+    parser.add_argument(
+        "--use-key-subset",
+        action="store_true",
+        help="Use just the keys defined in this module when writing out metadata.",
+    )
+
+    processed_group.add_argument(
+        "-p",
+        "--processed",
+        default=False,
+        action="store_true",
+        help="Download processed data [Default: download raw data].",
+    )
+
+    processed_group.add_argument(
+        "--data-source",
+        dest="supp_by",
+        choices=["all", "samples", "series"],
+        default="samples",
+        help="Optional: Specifies the source of data on the GEO record"
+        " to retrieve processed data, which may be attached to the"
+        " collective series entity, or to individual samples. "
+        "Allowable values are: samples, series or both (all). "
+        "Ignored unless 'processed' flag is set. [Default: all]",
+    )
+
+    processed_group.add_argument(
+        "--filter",
+        default=None,
+        help="Optional: Filter regex for processed filenames [Default: None]."
+        "Ignored unless 'processed' flag is set.",
+    )
+
+    processed_group.add_argument(
+        "--filter-size",
+        dest="filter_size",
+        default=None,
+        help="""Optional: Filter size for processed files
+                that are stored as sample repository [Default: None].
+                Supported input formats : 12B, 12KB, 12MB, 12GB. 
+                Ignored unless 'processed' flag is set.""",
+    )
+
+    processed_group.add_argument(
+        "-g",
+        "--geo-folder",
+        default=safe_echo("GEODATA"),
+        help="Optional: Specify a location to store processed GEO files."
+        " Ignored unless 'processed' flag is set."
+        "[Default: $GEODATA:" + safe_echo("GEODATA") + "]",
+    )
+
+    raw_group.add_argument(
+        "-x",
+        "--split-experiments",
+        action="store_true",
+        help="""Split SRR runs into individual samples. By default, SRX
+            experiments with multiple SRR Runs will have a single entry in the
+            annotation table, with each run as a separate row in the
+            subannotation table. This setting instead treats each run as a
+            separate sample""",
+    )
+
+    raw_group.add_argument(
+        "-b",
+        "--bam-folder",
+        dest="bam_folder",
+        default=safe_echo("SRABAM"),
+        help="""Optional: Specify folder of bam files. Geofetch will not
+            download sra files when corresponding bam files already exist.
+            [Default: $SRABAM:"""
+        + safe_echo("SRABAM")
+        + "]",
+    )
+
+    raw_group.add_argument(
+        "-f",
+        "--fq-folder",
+        dest="fq_folder",
+        default=safe_echo("SRAFQ"),
+        help="""Optional: Specify folder of fastq files. Geofetch will not
+            download sra files when corresponding fastq files already exist.
+            [Default: $SRAFQ:"""
+        + safe_echo("SRAFQ")
+        + "]",
+    )
+
+    # Deprecated; these are for bam conversion which now happens in sra_convert
+    # it still works here but I hide it so people don't use it, because it's confusing.
+    raw_group.add_argument(
+        "-s",
+        "--sra-folder",
+        dest="sra_folder",
+        default=safe_echo("SRARAW"),
+        help=argparse.SUPPRESS,
+        # help="Optional: Specify a location to store sra files "
+        #   "[Default: $SRARAW:" + safe_echo("SRARAW") + "]"
+    )
+    raw_group.add_argument(
+        "--bam-conversion",
+        action="store_true",
+        # help="Turn on sequential bam conversion. Default: No conversion.",
+        help=argparse.SUPPRESS,
+    )
+
+    raw_group.add_argument(
+        "--picard-path",
+        dest="picard_path",
+        default=safe_echo("PICARD"),
+        # help="Specify a path to the picard jar, if you want to convert "
+        # "fastq to bam [Default: $PICARD:" + safe_echo("PICARD") + "]",
+        help=argparse.SUPPRESS,
+    )
+
+    parser = add_logging_options(parser)
+    return parser.parse_args(cmdl)
+
+
+def safe_echo(var):
+    """Returns an environment variable if it exists, or an empty string if not"""
+    return os.getenv(var, "")
+
+
+class InvalidSoftLineException(Exception):
+    """Exception related to parsing SOFT line."""
+
+    def __init__(self, l):
+        """
+        Create the exception by providing the problematic line.
+
+        :param str l: the problematic SOFT line
+        """
+        super(self, f"{l}")
 
 
 def main():
-    """ Run the script. """
-    run_geofetch(sys.argv[1:])
+    """Run the script."""
+    args = _parse_cmdl(sys.argv[1:])
+    args_dict = vars(args)
+    print(args_dict)
+    Geofetcher(**args_dict).fetch_all(args_dict["input"])
+    # print(ab.get_list_of_processed_files())
 
 
 if __name__ == "__main__":
-    main()
+    try:
+        sys.exit(main())
+
+    except KeyboardInterrupt:
+        print("Pipeline aborted.")
+        sys.exit(1)
diff --git a/geofetch/sraconvert.py b/geofetch/sraconvert.py
index aa45424..a320c02 100755
--- a/geofetch/sraconvert.py
+++ b/geofetch/sraconvert.py
@@ -8,6 +8,7 @@
 
 __version__ = "0.1.0"
 
+
 def _parse_cmdl(cmdl):
     description = """ The SRA data converter is a wrapper around sra-tools that
     provides convenience functions for converting or deleting sra data in
@@ -16,51 +17,72 @@ def _parse_cmdl(cmdl):
     parser = ArgumentParser(description=description)
     # parser = pypiper.add_pypiper_args(parser, args=["output-parent"])
     parser.add_argument(
-            "-m", "--mode", default="convert",
-            choices=['convert', 'delete_sra', 'delete_bam', 'delete_fq'],
-            help="What do you want to do? Default: convert")
-    
-    parser.add_argument(
-            "-f", "--format", default="fastq", choices=['fastq', 'bam'],
-            help="Convert to what format? Default: fastq")
-    
+        "-m",
+        "--mode",
+        default="convert",
+        choices=["convert", "delete_sra", "delete_bam", "delete_fq"],
+        help="What do you want to do? Default: convert",
+    )
+
     parser.add_argument(
-            "-b", "--bamfolder", 
-            default=safe_echo("SRABAM"),
-            help="Optional: Specify a location to store bam files "
-            "[Default: $SRABAM:" + safe_echo("SRABAM") + "]")
+        "-f",
+        "--format",
+        default="fastq",
+        choices=["fastq", "bam"],
+        help="Convert to what format? Default: fastq",
+    )
 
     parser.add_argument(
-            "-q", "--fqfolder",
-            default=safe_echo("SRAFQ"),
-            help="Optional: Specify a location to store fastq files "
-            "[Default: $SRAFQ:" + safe_echo("SRAFQ") + "]")
-    
+        "-b",
+        "--bamfolder",
+        default=safe_echo("SRABAM"),
+        help="Optional: Specify a location to store bam files "
+        "[Default: $SRABAM:" + safe_echo("SRABAM") + "]",
+    )
+
     parser.add_argument(
-            "-s", "--srafolder", default=safe_echo("SRARAW"),
-            help="Optional: Specify a location to store pipeline output "
-            "[Default: $SRARAW:" + safe_echo("SRARAW") + "]")
+        "-q",
+        "--fqfolder",
+        default=safe_echo("SRAFQ"),
+        help="Optional: Specify a location to store fastq files "
+        "[Default: $SRAFQ:" + safe_echo("SRAFQ") + "]",
+    )
 
     parser.add_argument(
-            "--keep-sra", action='store_true', default=False,
-            help="On convert mode, keep original sra data?")
+        "-s",
+        "--srafolder",
+        default=safe_echo("SRARAW"),
+        help="Optional: Specify a location to store pipeline output "
+        "[Default: $SRARAW:" + safe_echo("SRARAW") + "]",
+    )
 
-    parser.add_argument("-S", "--sample-name", required=False, nargs="+",
-            help="Name for sample to run",
-            metavar="SAMPLE_NAME",)
+    parser.add_argument(
+        "--keep-sra",
+        action="store_true",
+        default=False,
+        help="On convert mode, keep original sra data?",
+    )
 
     parser.add_argument(
-            "-r", "--srr", required=True, nargs="+",
-            help="SRR files")
+        "-S",
+        "--sample-name",
+        required=False,
+        nargs="+",
+        help="Name for sample to run",
+        metavar="SAMPLE_NAME",
+    )
 
-    parser = pypiper.add_pypiper_args(parser, groups=["config", "logmuse"],
-        args=["output-parent", "recover"])
+    parser.add_argument("-r", "--srr", required=True, nargs="+", help="SRR files")
 
+    parser = pypiper.add_pypiper_args(
+        parser, groups=["config", "logmuse"], args=["output-parent", "recover"]
+    )
 
     return parser.parse_args(cmdl)
 
+
 def safe_echo(var):
-    """ Returns an environment variable if it exists, or an empty string if not"""
+    """Returns an environment variable if it exists, or an empty string if not"""
     return os.getenv(var, "")
 
 
@@ -77,7 +99,7 @@ def uniqify(seq):  # Dave Kirby
 
 
 def main():
-    """ Run the script. """
+    """Run the script."""
     cmdl = sys.argv[1:]
     args = _parse_cmdl(cmdl)
     global _LOGGER
@@ -91,7 +113,7 @@ def main():
     else:
         primary_srr_acc = os.path.splitext(os.path.basename(args.srr[0]))[0]
         run_name = primary_srr_acc
-    
+
     if args.output_parent:
         outfolder = os.path.join(args.output_parent, run_name)
     else:
@@ -105,26 +127,30 @@ def main():
 
     for i in range(nfiles):
         srr_acc = os.path.splitext(os.path.basename(args.srr[i]))[0]
-        pm.info("Processing {} of {} files: {}".format(str(i+1), str(nfiles), srr_acc))
+        pm.info(
+            "Processing {} of {} files: {}".format(str(i + 1), str(nfiles), srr_acc)
+        )
 
         bamfile = os.path.join(args.bamfolder, srr_acc + ".bam")
         fq_prefix = os.path.join(args.fqfolder, srr_acc)
-        
+
         if args.mode == "convert":
             infile = args.srr[i]
             if not os.path.isfile(infile):
                 pm.warning("Couldn't find sra file at: {}.".format(infile))
                 failed_files.append(args.srr[i])
-            if args.format == 'fastq':
+            if args.format == "fastq":
                 # fastq-dump --split-files will produce *_1.fastq and *_2.fastq
                 # for paired-end data, and only *_1.fastq for single-end data.
                 outfile = "{fq_prefix}_1.fastq.gz".format(fq_prefix=fq_prefix)
                 cmd = "fastq-dump {data_source} --split-files --gzip -O {outfolder}".format(
-                    data_source=infile, outfolder=args.fqfolder, nofail=True)
-            elif args.format == 'bam':
+                    data_source=infile, outfolder=args.fqfolder, nofail=True
+                )
+            elif args.format == "bam":
                 outfile = os.path.join(args.bamfolder, args.srr[i] + ".bam")
                 cmd = "sam-dump -u {data_source} | samtools view -bS - > {outfile}".format(
-                    data_source=infile, outfile=outfile, nofail=True)
+                    data_source=infile, outfile=outfile, nofail=True
+                )
             else:
                 raise KeyError("Unknown format: {}".format(args.format))
 
@@ -145,13 +171,13 @@ def main():
             fq_prefix = os.path.join(args.fqfolder, srr_acc)
             pm.clean_add("{fq_prefix}.fastq.gz".format(fq_prefix=fq_prefix))
             pm.clean_add("{fq_prefix}_[0-9].fastq.gz".format(fq_prefix=fq_prefix))
-        elif args.mode =="delete_sra":
+        elif args.mode == "delete_sra":
             delete_sra = True
             # if specifically requested to delete sra files
 
         if not args.keep_sra and os.path.isfile(outfile):
             # Only delete if the output file was created...
-            # we can't trust the sra toolkit return codes because they 
+            # we can't trust the sra toolkit return codes because they
             # can return 0 even if the command didn't complete, causing us to
             # delete the sra file when we have no other copy of that data.
             delete_sra = True
@@ -160,13 +186,17 @@ def main():
             pm.timestamp("Cleaning sra file: {}".format(infile))
             pm.clean_add(infile)
 
-
     if len(failed_files) > 0:
-        pm.fail_pipeline(Exception("Unable to locate the following files: {}".format(",".join(failed_files))))
+        pm.fail_pipeline(
+            Exception(
+                "Unable to locate the following files: {}".format(
+                    ",".join(failed_files)
+                )
+            )
+        )
 
     pm.stop_pipeline()
 
 
-
 if __name__ == "__main__":
     main()
diff --git a/geofetch/utils.py b/geofetch/utils.py
index 9b191b4..f34ae9a 100644
--- a/geofetch/utils.py
+++ b/geofetch/utils.py
@@ -1,13 +1,17 @@
 """ Independently-importable utilities to circumvent true scripts. """
 
-from collections import OrderedDict
 import logging
 import os
 import subprocess
+import re
 
 
-__author__ = ["Vince Reuter", "Nathan Sheffield"]
-__email__ = "vreuter@virginia.edu"
+__author__ = [
+    "Oleksandr Khoroshevskyi",
+    "Vince Reuter",
+    "Nathan Sheffield",
+]
+__email__ = "bnt4me@virginia.edu"
 
 __all__ = ["parse_accessions"]
 
@@ -23,7 +27,7 @@
     "GSE": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?targ=gse&acc={ACCESSION}&form=text&view=full",
     "GSM": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?targ=gsm&acc={ACCESSION}&form=text&view=full",
     "SRP": "https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?save=efetch&db=sra&rettype=runinfo&term={ACCESSION}",
-    "SRX": "https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?save=efetch&db=sra&rettype=runinfo&term={ACCESSION}"
+    "SRX": "https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?save=efetch&db=sra&rettype=runinfo&term={ACCESSION}",
 }
 
 
@@ -63,7 +67,7 @@ def parse_accessions(input_arg, metadata_folder, just_metadata=False):
         actual data associated with the accession
     """
 
-    acc_GSE_list = OrderedDict()
+    acc_GSE_list = {}
 
     if not os.path.isfile(input_arg):
         _LOGGER.info("Trying {} (not a file) as accession...".format(input_arg))
@@ -72,32 +76,31 @@ def parse_accessions(input_arg, metadata_folder, just_metadata=False):
             base, ext = os.path.splitext(input_arg)
             if ext:
                 raise ValueError("SRP-like input must be an SRP accession")
-            file_sra = os.path.join(
-                metadata_folder, "SRA_{}.csv".format(input_arg))
+            file_sra = os.path.join(metadata_folder, "SRA_{}.csv".format(input_arg))
             # Fetch and write the metadata for this SRP accession.
             Accession(input_arg).fetch_metadata(file_sra)
             if just_metadata:
                 return
             # Read the Run identifiers to download.
             run_ids = []
-            with open(file_sra, 'r') as f:
+            with open(file_sra, "r") as f:
                 for l in f:
                     if l.startswith("SRR"):
                         r_id = l.split(",")[0]
                         run_ids.append(r_id)
             _LOGGER.info("{} run(s)".format(len(run_ids)))
             for r_id in run_ids:
-                subprocess.call(['prefetch', r_id, '--max-size', '50000000'])
+                subprocess.call(["prefetch", r_id, "--max-size", "50000000"])
             # Early return if we've just handled SRP accession directly.
             return
         else:
             acc_GSE = input_arg
-            acc_GSE_list[acc_GSE] = OrderedDict()
+            acc_GSE_list[acc_GSE] = {}
     else:
         _LOGGER.info("Accession list file found: {}".format(input_arg))
 
         # Read input file line by line.
-        for line in open(input_arg, 'r'):
+        for line in open(input_arg, "r"):
             if (not line) or (line[0] in ["#", "\n", "\t"]):
                 continue
             fields = [x.rstrip() for x in line.split("\t")]
@@ -113,14 +116,14 @@ def parse_accessions(input_arg, metadata_folder, just_metadata=False):
                 if len(fields) > 2 and gsm != "":
                     # There must have been a limit (GSM specified)
                     # include a name if it doesn't already exist
-                    sample_name = fields[2].rstrip()
+                    sample_name = fields[2].rstrip().replace(" ", "_")
                 else:
                     sample_name = gsm
 
-                if acc_GSE_list.has_key(gse):  # GSE already has a GSM; add the next one
+                if gse in acc_GSE_list:  # GSE already has a GSM; add the next one
                     acc_GSE_list[gse][gsm] = sample_name
                 else:
-                    acc_GSE_list[gse] = OrderedDict({gsm: sample_name})
+                    acc_GSE_list[gse] = {gsm: sample_name}
             else:
                 # No GSM limit; use empty dict.
                 acc_GSE_list[gse] = {}
@@ -141,7 +144,7 @@ def parse_SOFT_line(l):
 
 
 class AccessionException(Exception):
-    """ Exceptional condition(s) dealing with accession number(s). """
+    """Exceptional condition(s) dealing with accession number(s)."""
 
     def __init__(self, reason=""):
         """
@@ -154,7 +157,7 @@ def __init__(self, reason=""):
 
 
 class Accession(object):
-    """ Working with accession numbers. """
+    """Working with accession numbers."""
 
     _LOGGER = logging.getLogger("{}.{}".format(__name__, "Accession"))
 
@@ -173,9 +176,9 @@ def __init__(self, accn, strict=True):
         typename, number = self._validate(accn)
         if strict and not is_known_type(accn):
             raise AccessionException(
-                    "Unknown accession type for '{}': '{}'; "
-                    "supported types: {}".format(
-                            accn, typename, URL_BY_ACC.keys()))
+                "Unknown accession type for '{}': '{}'; "
+                "supported types: {}".format(accn, typename, URL_BY_ACC.keys())
+            )
         self.accn = accn
         self.typename = typename.upper()
 
@@ -200,8 +203,11 @@ def fetch_metadata(self, outpath=None, typename=None):
         try:
             full_url = url_base.format(**format_kwargs)
         except KeyError:
-            _LOGGER.error("Couldn't populate URL format '{}' with {}".
-                          format(url_base, format_kwargs))
+            _LOGGER.error(
+                "Couldn't populate URL format '{}' with {}".format(
+                    url_base, format_kwargs
+                )
+            )
             raise
         _LOGGER.debug("Fetching: '%s'", full_url)
 
@@ -225,21 +231,22 @@ def fetch_metadata(self, outpath=None, typename=None):
 
     @staticmethod
     def _validate(accn):
-        """ Determine if given value looks like an accession. """
+        """Determine if given value looks like an accession."""
         typename, number = split_accn(accn)
         if len(typename) != 3:
             raise AccessionException(
-                    "Not a three-character accession prefix: '{}' ('{}')".
-                    format(accn, typename))
+                "Not a three-character accession prefix: '{}' ('{}')".format(
+                    accn, typename
+                )
+            )
         try:
             number = int(number)
         except (TypeError, ValueError):
             raise AccessionException(
-                    "Not an integral accession number: '{}' ('{}')".
-                    format(accn, number))
+                "Not an integral accession number: '{}' ('{}')".format(accn, number)
+            )
         return typename, number
 
-
     @staticmethod
     def accn_type_exception(accn, typename, include_known=True):
         """
@@ -255,8 +262,7 @@ def accn_type_exception(accn, typename, include_known=True):
         """
         message = "Unknown accn type for '{}': '{}'".format(accn, typename)
         if include_known:
-            message = "{}; known types: {}".format(
-                message, URL_BY_ACC.keys())
+            message = "{}; known types: {}".format(message, URL_BY_ACC.keys())
         return AccessionException(message)
 
 
@@ -270,3 +276,20 @@ def split_accn(accn):
     """
     typename, number_text = accn[:3], accn[3:]
     return typename.upper(), number_text
+
+
+def convert_size(size_str: str) -> int:
+    abbreviation_dict = {"gb": 1073741824, "mb": 1048576, "kb": 1024, "b": 1}
+    supported_formats = r"(\dgb|\dmb|\db|\dkb)$"
+    reg_number = r"^\d+"
+    abbreviation = re.findall(supported_formats, size_str)
+    size_numb = re.findall(reg_number, size_str)
+
+    # check if list is empty
+    if len(abbreviation) == 0:
+        size_in_bytes = size_numb[0]
+    else:
+        abb = abbreviation[0][1:]
+        size_in_bytes = int(size_numb[0]) * abbreviation_dict[abb]
+
+    return size_in_bytes
diff --git a/mkdocs.yml b/mkdocs.yml
index 51b05c8..c6c6549 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -8,10 +8,12 @@ nav:
   - Getting started:
     - Introduction: README.md
     - Install and configure: install.md
-    - Tutorial: tutorial.md
     - SRA convert: sra_convert.md
+    - Tutorial for processed data: processed-data-downloading.md
+    - Tutorial for raw data: raw-data-downloading.md
   - How-to Guides:
     - Specifying samples to download: file-specification.md
+    - Set SRA data download location: howto-location.md
   - Reference:
     - Metadata output: metadata_output.md
     - Usage: usage.md
diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt
index 1cb8269..7f66d6b 100644
--- a/requirements/requirements-all.txt
+++ b/requirements/requirements-all.txt
@@ -3,3 +3,5 @@ colorama>=0.3.9
 peppy>=0.19.1
 logmuse>=0.0.2
 ubiquerg>=0.4.4
+logmuse>=0.2.7
+ubiquerg>=0.6.0
diff --git a/setup.py b/setup.py
index f88de4e..d9f60d1 100644
--- a/setup.py
+++ b/setup.py
@@ -10,47 +10,30 @@
 # Additional keyword arguments for setup().
 extra = {}
 
+# Ordinary dependencies
+
 
 def read_reqs(reqs_name):
     deps = []
-    with open(os.path.join(REQDIR, "requirements-{}.txt".format(reqs_name)), 'r') as f:
+    with open(os.path.join(REQDIR, "requirements-{}.txt".format(reqs_name)), "r") as f:
         for l in f:
             if not l.strip():
                 continue
-            #deps.append(l.split("=")[0].rstrip("<>"))
+            # deps.append(l.split("=")[0].rstrip("<>"))
             deps.append(l)
     return deps
 
 
-# 2to3
-if sys.version_info >= (3, ):
-    extra["use_2to3"] = True
-extra["install_requires"] = read_reqs("all")
-
-
-# Additional files to include with package
-def get_static(name, condition=None):
-    static = [os.path.join(name, f) for f in os.listdir(
-            os.path.join(os.path.dirname(os.path.realpath(__file__)), name))]
-    if condition is None:
-        return static
-    else:
-        return [i for i in filter(lambda x: eval(condition), static)]
-
+DEPENDENCIES = read_reqs("all")
+extra["install_requires"] = DEPENDENCIES
 
-# scripts to be added to the $PATH
-# scripts = get_static("scripts", condition="'.' in x")
 scripts = None
 
-with open("{}/_version.py".format(PACKAGE), 'r') as versionfile:
+with open("{}/_version.py".format(PACKAGE), "r") as versionfile:
     version = versionfile.readline().split()[-1].strip("\"'\n")
 
-# Handle the pypi README formatting.
-try:
-    import pypandoc
-    long_description = pypandoc.convert_file('README.md', 'rst')
-except(IOError, ImportError, OSError, RuntimeError):
-    long_description = open('README.md').read()
+with open("README.md") as f:
+    long_description = f.read()
 
 setup(
     name=PACKAGE,
@@ -61,26 +44,28 @@ def get_static(name, condition=None):
     classifiers=[
         "Development Status :: 4 - Beta",
         "License :: OSI Approved :: BSD License",
-        "Programming Language :: Python :: 2.7",
-        "Programming Language :: Python :: 3.5",
-        "Programming Language :: Python :: 3.6",
-        "Topic :: Scientific/Engineering :: Bio-Informatics"
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Topic :: Scientific/Engineering :: Bio-Informatics",
     ],
     keywords="project, bioinformatics, sequencing, ngs, workflow, GUI",
     url="https://github.com/pepkit/{}/".format(PACKAGE),
-    author=u"Nathan Sheffield, Vince Reuter",
+    author="Nathan Sheffield, Vince Reuter, Oleksandr Khoroshevskyi",
     license="BSD2",
     entry_points={
         "console_scripts": [
             "geofetch = geofetch.geofetch:main",
-            "sraconvert = geofetch.sraconvert:main"
+            "sraconvert = geofetch.sraconvert:main",
         ],
     },
-    package_data={PACKAGE: ['templates/*']},
+    package_data={PACKAGE: ["templates/*"]},
     scripts=scripts,
     include_package_data=True,
-    test_suite="tests", 
+    test_suite="tests",
     tests_require=read_reqs("dev"),
-    setup_requires=(["pytest-runner"] if {"test", "pytest", "ptr"} & set(sys.argv) else []), 
+    setup_requires=(
+        ["pytest-runner"] if {"test", "pytest", "ptr"} & set(sys.argv) else []
+    ),
     **extra
 )


Test patch:
diff --git a/.github/workflows/run-pytest.yml b/.github/workflows/run-pytest.yml
index 50f04ca..cd9bae7 100644
--- a/.github/workflows/run-pytest.yml
+++ b/.github/workflows/run-pytest.yml
@@ -11,7 +11,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        python-version: ["3.6", "3.9", "3.10"]
+        python-version: ["3.7", "3.10"]
         os: [ubuntu-latest]
 
     steps:
@@ -22,14 +22,14 @@ jobs:
       with:
         python-version: ${{ matrix.python-version }}
 
-    - name: Install dev dependancies
+    - name: Install dev dependencies
       run: if [ -f requirements/requirements-dev.txt ]; then pip install -r requirements/requirements-dev.txt; fi
 
-    - name: Install test dependancies
+    - name: Install test dependencies
       run: if [ -f requirements/requirements-test.txt ]; then pip install -r requirements/requirements-test.txt; fi
 
     - name: Install package
       run: python -m pip install .
 
     - name: Run pytest tests
-      run: pytest tests -x -vv --cov=./ --cov-report=xml --remote-data
+      run: pytest tests -x -vv
diff --git a/requirements/requirements-test.txt b/requirements/requirements-test.txt
index 606b3da..b7f7baf 100644
--- a/requirements/requirements-test.txt
+++ b/requirements/requirements-test.txt
@@ -1,2 +1,3 @@
-coveralls==1.1
-pytest-cov==2.4.0
+coveralls>=1.1
+pytest-cov>=2.4.0
+pytest>7.1
diff --git a/geofetch/example_accessions.txt b/tests/__init__.py
similarity index 100%
rename from geofetch/example_accessions.txt
rename to tests/__init__.py
diff --git a/tests/test_files/input_acc.txt b/tests/test_files/input_acc.txt
new file mode 100644
index 0000000..4e27a95
--- /dev/null
+++ b/tests/test_files/input_acc.txt
@@ -0,0 +1,6 @@
+GSE185701	GSM5621756	Huh7 siNC H3K27ac
+GSE185701	GSM5621758	
+GSE185701	GSM5621760	Huh7_DHX37
+GSE185701	GSM5621760	Huh7_DHX38
+GSE185701	GSM5621761	
+GSE201096
diff --git a/tests/test_files/soft_files/GSE138657/GSE.soft b/tests/test_files/soft_files/GSE138657/GSE.soft
new file mode 100644
index 0000000..69cf109
--- /dev/null
+++ b/tests/test_files/soft_files/GSE138657/GSE.soft
@@ -0,0 +1,46 @@
+^SERIES = GSE138657
+!Series_title = Mapping the global chromatin connectivity network in colorectal cancer cell HCT116 and Oxaliplatin-resistant cell HCT116OxR
+!Series_geo_accession = GSE138657
+!Series_status = Public on Oct 01 2021
+!Series_submission_date = Oct 09 2019
+!Series_last_update_date = Oct 02 2021
+!Series_summary = This SuperSeries is composed of the SubSeries listed below.
+!Series_overall_design = Refer to individual Series
+!Series_type = Expression profiling by high throughput sequencing
+!Series_type = Genome binding/occupancy profiling by high throughput sequencing
+!Series_type = Other
+!Series_sample_id = GSM4114982
+!Series_sample_id = GSM4114983
+!Series_sample_id = GSM4115516
+!Series_sample_id = GSM4115517
+!Series_sample_id = GSM4115598
+!Series_sample_id = GSM4115599
+!Series_sample_id = GSM4115600
+!Series_sample_id = GSM4115601
+!Series_sample_id = GSM4115602
+!Series_sample_id = GSM4115603
+!Series_sample_id = GSM4115604
+!Series_sample_id = GSM4115605
+!Series_sample_id = GSM4115606
+!Series_sample_id = GSM4115607
+!Series_sample_id = GSM4115628
+!Series_sample_id = GSM4115629
+!Series_contact_name = lian,qing,jiao
+!Series_contact_email = wangyunshan135@126.com
+!Series_contact_institute = the Second Hospital of Shandong University
+!Series_contact_address = 247 Beiyuan Road
+!Series_contact_city = Ji'nan
+!Series_contact_state = Shandong Province
+!Series_contact_zip/postal_code = 250033
+!Series_contact_country = China
+!Series_supplementary_file = ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE138nnn/GSE138657/suppl/GSE138657_RAW.tar
+!Series_platform_id = GPL11154
+!Series_platform_organism = Homo sapiens
+!Series_platform_taxid = 9606
+!Series_sample_organism = Homo sapiens
+!Series_sample_taxid = 9606
+!Series_relation = SuperSeries of: GSE138647
+!Series_relation = SuperSeries of: GSE138652
+!Series_relation = SuperSeries of: GSE138654
+!Series_relation = SuperSeries of: GSE138656
+!Series_relation = BioProject: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA576681
diff --git a/tests/test_files/soft_files/GSE138657/GSM.soft b/tests/test_files/soft_files/GSE138657/GSM.soft
new file mode 100644
index 0000000..22d67e2
--- /dev/null
+++ b/tests/test_files/soft_files/GSE138657/GSM.soft
@@ -0,0 +1,660 @@
+^SAMPLE = GSM4114982
+!Sample_title = RNA-seq_HCT116
+!Sample_geo_accession = GSM4114982
+!Sample_status = Public on Oct 01 2021
+!Sample_submission_date = Oct 09 2019
+!Sample_last_update_date = Oct 01 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = HCT116 cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = tissue: Colorectal cancer
+!Sample_characteristics_ch1 = cell type: HCT116 cells
+!Sample_characteristics_ch1 = genotype: Oxaliplatin-unresistant
+!Sample_molecule_ch1 = polyA RNA
+!Sample_extract_protocol_ch1 = HCT116 cells were further purified from Pancreatic cancer tissue  .mRNAs were extracted from samples using TRIzol (Invitrogen) following the methods by Chomczynski
+!Sample_extract_protocol_ch1 = RNA libraries were prepared for sequencing using standard Illumina protocols
+!Sample_data_processing = Raw sequencing data was first filtered by Trimmomatic (version: 0.36), low-quality reads were discarded and adaptor sequences were trimmed
+!Sample_data_processing = Reads mapped to the exon regions of each gene were counted by featurecouts (Subread-1.5.1; Bioconductor) and FPKMs were calculated. Genes differential expressed between groups were identified using the edgeR package
+!Sample_data_processing = A corrected P-value cutoff of 0.05 and Fold-change cutoff of 2 were used to judge the statistical significance of gene expression differences.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: tab-delimited text files include FPKM values for each Sample
+!Sample_platform_id = GPL11154
+!Sample_contact_name = lian,qing,jiao
+!Sample_contact_email = wangyunshan135@126.com
+!Sample_contact_institute = the Second Hospital of Shandong University
+!Sample_contact_address = 247 Beiyuan Road
+!Sample_contact_city = Ji'nan
+!Sample_contact_state = Shandong Province
+!Sample_contact_zip/postal_code = 250033
+!Sample_contact_country = China
+!Sample_instrument_model = Illumina HiSeq 2000
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN13001027
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX6970343
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4114nnn/GSM4114982/suppl/GSM4114982_HCT116-FPKM.txt.gz
+!Sample_series_id = GSE138647
+!Sample_series_id = GSE138657
+!Sample_data_row_count = 0
+^SAMPLE = GSM4114983
+!Sample_title = RNA-seq_HCT116OxR
+!Sample_geo_accession = GSM4114983
+!Sample_status = Public on Oct 01 2021
+!Sample_submission_date = Oct 09 2019
+!Sample_last_update_date = Oct 01 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = HCT116 cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = tissue: Colorectal cancer
+!Sample_characteristics_ch1 = cell type: HCT116 cells
+!Sample_characteristics_ch1 = genotype: Oxaliplatin-resistant
+!Sample_molecule_ch1 = polyA RNA
+!Sample_extract_protocol_ch1 = HCT116 cells were further purified from Pancreatic cancer tissue  .mRNAs were extracted from samples using TRIzol (Invitrogen) following the methods by Chomczynski
+!Sample_extract_protocol_ch1 = RNA libraries were prepared for sequencing using standard Illumina protocols
+!Sample_data_processing = Raw sequencing data was first filtered by Trimmomatic (version: 0.36), low-quality reads were discarded and adaptor sequences were trimmed
+!Sample_data_processing = Reads mapped to the exon regions of each gene were counted by featurecouts (Subread-1.5.1; Bioconductor) and FPKMs were calculated. Genes differential expressed between groups were identified using the edgeR package
+!Sample_data_processing = A corrected P-value cutoff of 0.05 and Fold-change cutoff of 2 were used to judge the statistical significance of gene expression differences.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: tab-delimited text files include FPKM values for each Sample
+!Sample_platform_id = GPL11154
+!Sample_contact_name = lian,qing,jiao
+!Sample_contact_email = wangyunshan135@126.com
+!Sample_contact_institute = the Second Hospital of Shandong University
+!Sample_contact_address = 247 Beiyuan Road
+!Sample_contact_city = Ji'nan
+!Sample_contact_state = Shandong Province
+!Sample_contact_zip/postal_code = 250033
+!Sample_contact_country = China
+!Sample_instrument_model = Illumina HiSeq 2000
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN13001026
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX6970344
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4114nnn/GSM4114983/suppl/GSM4114983_HCT116OxR-FPKM.txt.gz
+!Sample_series_id = GSE138647
+!Sample_series_id = GSE138657
+!Sample_data_row_count = 0
+^SAMPLE = GSM4115516
+!Sample_title = Hi-C_HCT116
+!Sample_geo_accession = GSM4115516
+!Sample_status = Public on Oct 01 2021
+!Sample_submission_date = Oct 09 2019
+!Sample_last_update_date = Oct 01 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = HCT116 cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = tissue: Colorectal cancer
+!Sample_characteristics_ch1 = cell type: HCT116 cells
+!Sample_characteristics_ch1 = genotype: Oxaliplatin-unresistant
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = The cross-linked cells were lysed in lysis buffer,fragmentation of the genome using restriction enzymes
+!Sample_extract_protocol_ch1 = Hi-C libraries were prepared for sequencing using standard Illumina protocols
+!Sample_data_processing = Raw sequencing data was first filtered by Trimmomatic (version: 0.36), low-quality reads were discarded and adaptor sequences were trimmed
+!Sample_data_processing = The BWA package was used to map the  sequences against the human reference genome with the parameter -n 0. Mapped  reads with MAPQ quality scores ≥20 were chosen for further analysis.
+!Sample_data_processing = We applied the ICE  method to normalize the interaction matrix for different resolutions
+!Sample_data_processing = If a region between two adjacent boundaries was less than 400 kb, the region was  marked as a TAD boundary
+!Sample_data_processing = The Juicer pipeline’s HiCCUPS was applied for discovery of locally enriched  peaks
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: all the locally  enriched peaks were determined by HiCCUPS
+!Sample_platform_id = GPL11154
+!Sample_contact_name = lian,qing,jiao
+!Sample_contact_email = wangyunshan135@126.com
+!Sample_contact_institute = the Second Hospital of Shandong University
+!Sample_contact_address = 247 Beiyuan Road
+!Sample_contact_city = Ji'nan
+!Sample_contact_state = Shandong Province
+!Sample_contact_zip/postal_code = 250033
+!Sample_contact_country = China
+!Sample_instrument_model = Illumina HiSeq 2000
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = Hi-C
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN13001580
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX6970812
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4115nnn/GSM4115516/suppl/GSM4115516_diffloopgene_HCT116.txt.gz
+!Sample_series_id = GSE138652
+!Sample_series_id = GSE138657
+!Sample_data_row_count = 0
+^SAMPLE = GSM4115517
+!Sample_title = Hi-C_HCT116OxR
+!Sample_geo_accession = GSM4115517
+!Sample_status = Public on Oct 01 2021
+!Sample_submission_date = Oct 09 2019
+!Sample_last_update_date = Oct 01 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = HCT116 cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = tissue: Colorectal cancer
+!Sample_characteristics_ch1 = cell type: HCT116 cells
+!Sample_characteristics_ch1 = genotype: Oxaliplatin-resistant
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = The cross-linked cells were lysed in lysis buffer,fragmentation of the genome using restriction enzymes
+!Sample_extract_protocol_ch1 = Hi-C libraries were prepared for sequencing using standard Illumina protocols
+!Sample_data_processing = Raw sequencing data was first filtered by Trimmomatic (version: 0.36), low-quality reads were discarded and adaptor sequences were trimmed
+!Sample_data_processing = The BWA package was used to map the  sequences against the human reference genome with the parameter -n 0. Mapped  reads with MAPQ quality scores ≥20 were chosen for further analysis.
+!Sample_data_processing = We applied the ICE  method to normalize the interaction matrix for different resolutions
+!Sample_data_processing = If a region between two adjacent boundaries was less than 400 kb, the region was  marked as a TAD boundary
+!Sample_data_processing = The Juicer pipeline’s HiCCUPS was applied for discovery of locally enriched  peaks
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: all the locally  enriched peaks were determined by HiCCUPS
+!Sample_platform_id = GPL11154
+!Sample_contact_name = lian,qing,jiao
+!Sample_contact_email = wangyunshan135@126.com
+!Sample_contact_institute = the Second Hospital of Shandong University
+!Sample_contact_address = 247 Beiyuan Road
+!Sample_contact_city = Ji'nan
+!Sample_contact_state = Shandong Province
+!Sample_contact_zip/postal_code = 250033
+!Sample_contact_country = China
+!Sample_instrument_model = Illumina HiSeq 2000
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = Hi-C
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+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = tissue: Colorectal cancer
+!Sample_characteristics_ch1 = cell type: HCT116 cells
+!Sample_characteristics_ch1 = genotype: Oxaliplatin-unresistant
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Around 50,000 living cells were taken for each library preparation. The cells were  lysed in 1 × Lysis Buffer to get the nuclei
+!Sample_extract_protocol_ch1 = TruePrep DNA Library Prep Kit for Illumina was used to construct the transposase-treated libraries
+!Sample_data_processing = Raw data were stored in FASTQ format, including the base sequence and  corresponding quality information. Adaptor-polluted or low-quality reads were then  filtered out to get the clean data
+!Sample_data_processing = Clean data were mapped to reference genome by Bowtie2, and visualized by IGV
+!Sample_data_processing = The enrichment analysis of  GO term (http://geneontology.org/) or KEGG pathway (http://www.kegg.jp/) was  based on hypergeometric test with the threshold q value < 0.05, to find the significant  enrichment of detected genes.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Peaks corresponding  to the open region in genome were detected by MACS2
+!Sample_platform_id = GPL11154
+!Sample_contact_name = lian,qing,jiao
+!Sample_contact_email = wangyunshan135@126.com
+!Sample_contact_institute = the Second Hospital of Shandong University
+!Sample_contact_address = 247 Beiyuan Road
+!Sample_contact_city = Ji'nan
+!Sample_contact_state = Shandong Province
+!Sample_contact_zip/postal_code = 250033
+!Sample_contact_country = China
+!Sample_instrument_model = Illumina HiSeq 2000
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN13001581
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX6971480
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4115nnn/GSM4115628/suppl/GSM4115628_HCT116_peaks.txt.gz
+!Sample_series_id = GSE138656
+!Sample_series_id = GSE138657
+!Sample_data_row_count = 0
+^SAMPLE = GSM4115629
+!Sample_title = ATAC-seq_HCT116OxR
+!Sample_geo_accession = GSM4115629
+!Sample_status = Public on Oct 01 2021
+!Sample_submission_date = Oct 09 2019
+!Sample_last_update_date = Oct 01 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = HCT116 cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = tissue: Colorectal cancer
+!Sample_characteristics_ch1 = cell type: HCT116 cells
+!Sample_characteristics_ch1 = genotype: Oxaliplatin-resistant
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Around 50,000 living cells were taken for each library preparation. The cells were  lysed in 1 × Lysis Buffer to get the nuclei
+!Sample_extract_protocol_ch1 = TruePrep DNA Library Prep Kit for Illumina was used to construct the transposase-treated libraries
+!Sample_data_processing = Raw data were stored in FASTQ format, including the base sequence and  corresponding quality information. Adaptor-polluted or low-quality reads were then  filtered out to get the clean data
+!Sample_data_processing = Clean data were mapped to reference genome by Bowtie2, and visualized by IGV
+!Sample_data_processing = The enrichment analysis of  GO term (http://geneontology.org/) or KEGG pathway (http://www.kegg.jp/) was  based on hypergeometric test with the threshold q value < 0.05, to find the significant  enrichment of detected genes.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Peaks corresponding  to the open region in genome were detected by MACS2
+!Sample_platform_id = GPL11154
+!Sample_contact_name = lian,qing,jiao
+!Sample_contact_email = wangyunshan135@126.com
+!Sample_contact_institute = the Second Hospital of Shandong University
+!Sample_contact_address = 247 Beiyuan Road
+!Sample_contact_city = Ji'nan
+!Sample_contact_state = Shandong Province
+!Sample_contact_zip/postal_code = 250033
+!Sample_contact_country = China
+!Sample_instrument_model = Illumina HiSeq 2000
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN13001585
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX6971481
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4115nnn/GSM4115629/suppl/GSM4115629_HCT116OxR_peaks.txt.gz
+!Sample_series_id = GSE138656
+!Sample_series_id = GSE138657
+!Sample_data_row_count = 0
diff --git a/tests/test_files/soft_files/GSE146537/GSE.soft b/tests/test_files/soft_files/GSE146537/GSE.soft
new file mode 100644
index 0000000..b77ee9b
--- /dev/null
+++ b/tests/test_files/soft_files/GSE146537/GSE.soft
@@ -0,0 +1,36 @@
+^SERIES = GSE146537
+!Series_title = Chromatin landscape analysis of ccRCC cell line upon SETD2 rescue [ATACseq_JHRCC12]
+!Series_geo_accession = GSE146537
+!Series_status = Public on Nov 15 2021
+!Series_submission_date = Mar 06 2020
+!Series_last_update_date = Mar 25 2022
+!Series_pubmed_id = 35115713
+!Series_summary = SETD2, a H3K36 trimethyltransferase, is frequently mutated in human cancers with the highest prevalence (13%) in clear cell renal cell carcinoma (ccRCC). Genomic profiling of primary ccRCC tumors reveals a positive correlation between SETD2 mutations and metastasis. However, whether and how SETD2-loss promotes metastasis remains unclear. Here, we detected SETD2 mutations in 24 of 51 (47%) metastatic ccRCC tumors. Using SETD2-mutant metastatic ccRCC patient-derived cell line and xenograft models, we showed that H3K36me3 restoration greatly reduced distant metastases of ccRCC in mice. An integrated ATAC-seq, ChIP-seq, and transcriptome analysis concluded a tumor suppressor model in which loss of SETD2-mediated H3K36me3 activates enhancers to drive oncogenic transcription through dysregulating histone chaperone recruitment, enhancing histone exchange, and expanding chromatin accessibility. Furthermore, we uncovered mechanism-based therapeutic strategies for SETD2-deficient cancer through inhibition of histone chaperones. Overall, SETD2-loss creates a permissive epigenetic landscape for cooperating oncogenic drivers to amplify transcriptional output, providing unique therapeutic opportunities.
+!Series_overall_design = ATAC-Seq was performed in SETD2-proficient and SETD2-deficient JHRCC12 cells in 3 replicates.
+!Series_type = Genome binding/occupancy profiling by high throughput sequencing
+!Series_contributor = Yuchen,,Xie
+!Series_contributor = Merve,,Sahin
+!Series_contributor = Emily,H,Cheng
+!Series_sample_id = GSM4391896
+!Series_sample_id = GSM4391897
+!Series_sample_id = GSM4391898
+!Series_sample_id = GSM4391899
+!Series_sample_id = GSM4391900
+!Series_sample_id = GSM4391901
+!Series_contact_name = Christina,S,Leslie
+!Series_contact_department = Computational Biology Program
+!Series_contact_institute = Memorial Sloan Kettering Cancer Center
+!Series_contact_address = 417 E 68th St
+!Series_contact_city = New York
+!Series_contact_state = NY
+!Series_contact_zip/postal_code = 10065
+!Series_contact_country = USA
+!Series_supplementary_file = ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE146nnn/GSE146537/suppl/GSE146537_RAW.tar
+!Series_platform_id = GPL16791
+!Series_platform_organism = Homo sapiens
+!Series_platform_taxid = 9606
+!Series_sample_organism = Homo sapiens
+!Series_sample_taxid = 9606
+!Series_relation = SubSeries of: GSE146583
+!Series_relation = BioProject: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA610881
+!Series_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRP251915
diff --git a/tests/test_files/soft_files/GSE146537/GSM.soft b/tests/test_files/soft_files/GSE146537/GSM.soft
new file mode 100644
index 0000000..3769f84
--- /dev/null
+++ b/tests/test_files/soft_files/GSE146537/GSM.soft
@@ -0,0 +1,234 @@
+^SAMPLE = GSM4391896
+!Sample_title = ATAC-Seq JHRCC12_SETD2_mut_rep1
+!Sample_geo_accession = GSM4391896
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 JHRCC12 cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the hg19 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322350
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866141
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391896/suppl/GSM4391896_JHRCC12_SETD2_mut_rep1.bw
+!Sample_series_id = GSE146537
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391897
+!Sample_title = ATAC-Seq JHRCC12_SETD2_mut_rep2
+!Sample_geo_accession = GSM4391897
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 JHRCC12 cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the hg19 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322349
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866142
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391897/suppl/GSM4391897_JHRCC12_SETD2_mut_rep2.bw
+!Sample_series_id = GSE146537
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391898
+!Sample_title = ATAC-Seq JHRCC12_SETD2_mut_rep3
+!Sample_geo_accession = GSM4391898
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 JHRCC12 cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the hg19 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322348
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866143
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391898/suppl/GSM4391898_JHRCC12_SETD2_mut_rep3.bw
+!Sample_series_id = GSE146537
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391899
+!Sample_title = ATAC-Seq JHRCC12_SETD2_rescue_rep1
+!Sample_geo_accession = GSM4391899
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 JHRCC12 cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the hg19 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322347
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866144
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391899/suppl/GSM4391899_JHRCC12_SETD2_rescue_rep1.bw
+!Sample_series_id = GSE146537
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391900
+!Sample_title = ATAC-Seq JHRCC12_SETD2_rescue_rep2
+!Sample_geo_accession = GSM4391900
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 JHRCC12 cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the hg19 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322346
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866145
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391900/suppl/GSM4391900_JHRCC12_SETD2_rescue_rep2.bw
+!Sample_series_id = GSE146537
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391901
+!Sample_title = ATAC-Seq JHRCC12_SETD2_rescue_rep3
+!Sample_geo_accession = GSM4391901
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 JHRCC12 cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the hg19 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322345
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866146
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391901/suppl/GSM4391901_JHRCC12_SETD2_rescue_rep3.bw
+!Sample_series_id = GSE146537
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
diff --git a/tests/test_files/soft_files/GSE146539/GSE.soft b/tests/test_files/soft_files/GSE146539/GSE.soft
new file mode 100644
index 0000000..edfd5e9
--- /dev/null
+++ b/tests/test_files/soft_files/GSE146539/GSE.soft
@@ -0,0 +1,56 @@
+^SERIES = GSE146539
+!Series_title = Genome binding/occupancy profiling of ccRCC cell line upon SETD2 rescue [ChIPseq_JHRCC12]
+!Series_geo_accession = GSE146539
+!Series_status = Public on Nov 15 2021
+!Series_submission_date = Mar 06 2020
+!Series_last_update_date = Mar 25 2022
+!Series_pubmed_id = 35115713
+!Series_summary = SETD2, a H3K36 trimethyltransferase, is frequently mutated in human cancers with the highest prevalence (13%) in clear cell renal cell carcinoma (ccRCC). Genomic profiling of primary ccRCC tumors reveals a positive correlation between SETD2 mutations and metastasis. However, whether and how SETD2-loss promotes metastasis remains unclear. Here, we detected SETD2 mutations in 24 of 51 (47%) metastatic ccRCC tumors. Using SETD2-mutant metastatic ccRCC patient-derived cell line and xenograft models, we showed that H3K36me3 restoration greatly reduced distant metastases of ccRCC in mice. An integrated ATAC-seq, ChIP-seq, and transcriptome analysis concluded a tumor suppressor model in which loss of SETD2-mediated H3K36me3 activates enhancers to drive oncogenic transcription through dysregulating histone chaperone recruitment, enhancing histone exchange, and expanding chromatin accessibility. Furthermore, we uncovered mechanism-based therapeutic strategies for SETD2-deficient cancer through inhibition of histone chaperones. Overall, SETD2-loss creates a permissive epigenetic landscape for cooperating oncogenic drivers to amplify transcriptional output, providing unique therapeutic opportunities.
+!Series_overall_design = ChIP-Seq was performed in SETD2-proficient and SETD2-deficient JHRCC12 cells in 2 replicates.
+!Series_type = Genome binding/occupancy profiling by high throughput sequencing
+!Series_contributor = Yuchen,,Xie
+!Series_contributor = Merve,,Sahin
+!Series_contributor = Emily,H,Cheng
+!Series_sample_id = GSM4391908
+!Series_sample_id = GSM4391909
+!Series_sample_id = GSM4391910
+!Series_sample_id = GSM4391911
+!Series_sample_id = GSM4391912
+!Series_sample_id = GSM4391913
+!Series_sample_id = GSM4391914
+!Series_sample_id = GSM4391915
+!Series_sample_id = GSM4391916
+!Series_sample_id = GSM4391917
+!Series_sample_id = GSM4391918
+!Series_sample_id = GSM4391919
+!Series_sample_id = GSM4391920
+!Series_sample_id = GSM4391921
+!Series_sample_id = GSM4391922
+!Series_sample_id = GSM4391923
+!Series_sample_id = GSM4391924
+!Series_sample_id = GSM4391925
+!Series_sample_id = GSM4391926
+!Series_sample_id = GSM4391927
+!Series_sample_id = GSM4391928
+!Series_sample_id = GSM4391929
+!Series_sample_id = GSM4391930
+!Series_sample_id = GSM4391931
+!Series_sample_id = GSM4391932
+!Series_sample_id = GSM4391933
+!Series_contact_name = Christina,S,Leslie
+!Series_contact_department = Computational Biology Program
+!Series_contact_institute = Memorial Sloan Kettering Cancer Center
+!Series_contact_address = 417 E 68th St
+!Series_contact_city = New York
+!Series_contact_state = NY
+!Series_contact_zip/postal_code = 10065
+!Series_contact_country = USA
+!Series_supplementary_file = ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE146nnn/GSE146539/suppl/GSE146539_RAW.tar
+!Series_platform_id = GPL16791
+!Series_platform_organism = Homo sapiens
+!Series_platform_taxid = 9606
+!Series_sample_organism = Homo sapiens
+!Series_sample_taxid = 9606
+!Series_relation = SubSeries of: GSE146583
+!Series_relation = BioProject: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA610879
+!Series_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRP251918
diff --git a/tests/test_files/soft_files/GSE146539/GSM.soft b/tests/test_files/soft_files/GSE146539/GSM.soft
new file mode 100644
index 0000000..9426624
--- /dev/null
+++ b/tests/test_files/soft_files/GSE146539/GSM.soft
@@ -0,0 +1,1066 @@
+^SAMPLE = GSM4391908
+!Sample_title = ChIP-Seq SETD2_mut_H3K4me1_rep1
+!Sample_geo_accession = GSM4391908
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = chip antibody: H3K4me1 (Abcam, ab8895)
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = ChIP
+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322338
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866160
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391908/suppl/GSM4391908_JHRCC12_SETD2_mut_H3K4me1_rep1.bw
+!Sample_series_id = GSE146539
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391909
+!Sample_title = ChIP-Seq SETD2_mut_H3K4me1_rep2
+!Sample_geo_accession = GSM4391909
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = chip antibody: H3K4me1 (Abcam, ab8895)
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = ChIP
+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322237
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866161
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391909/suppl/GSM4391909_JHRCC12_SETD2_mut_H3K4me1_rep2.bw
+!Sample_series_id = GSE146539
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391910
+!Sample_title = ChIP-Seq SETD2_rescue_H3K4me1_rep1
+!Sample_geo_accession = GSM4391910
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = chip antibody: H3K4me1 (Abcam, ab8895)
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = ChIP
+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322333
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866162
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391910/suppl/GSM4391910_JHRCC12_SETD2_rescue_H3K4me1_rep1.bw
+!Sample_series_id = GSE146539
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391911
+!Sample_title = ChIP-Seq SETD2_rescue_H3K4me1_rep2
+!Sample_geo_accession = GSM4391911
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = chip antibody: H3K4me1 (Abcam, ab8895)
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = ChIP
+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322332
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866163
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391911/suppl/GSM4391911_JHRCC12_SETD2_rescue_H3K4me1_rep2.bw
+!Sample_series_id = GSE146539
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391912
+!Sample_title = ChIP-Seq SETD2_mut_H3K4me3_rep1
+!Sample_geo_accession = GSM4391912
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = chip antibody: H3K4me3 (Abcam, ab8580)
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = ChIP
+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322331
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866164
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391912/suppl/GSM4391912_JHRCC12_SETD2_mut_H3K4me3_rep1.bw
+!Sample_series_id = GSE146539
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391913
+!Sample_title = ChIP-Seq SETD2_mut_H3K4me3_rep2
+!Sample_geo_accession = GSM4391913
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = chip antibody: H3K4me3 (Abcam, ab8580)
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = ChIP
+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322330
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866165
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391913/suppl/GSM4391913_JHRCC12_SETD2_mut_H3K4me3_rep2.bw
+!Sample_series_id = GSE146539
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391914
+!Sample_title = ChIP-Seq SETD2_rescue_H3K4me3_rep1
+!Sample_geo_accession = GSM4391914
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = chip antibody: H3K4me3 (Abcam, ab8580)
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
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+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322329
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866166
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391914/suppl/GSM4391914_JHRCC12_SETD2_rescue_H3K4me3_rep1.bw
+!Sample_series_id = GSE146539
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+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
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+!Sample_characteristics_ch1 = chip antibody: H3K4me3 (Abcam, ab8580)
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
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+!Sample_library_strategy = ChIP-Seq
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+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866167
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+!Sample_status = Public on Nov 15 2021
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+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
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+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
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+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
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+!Sample_contact_name = Christina,S,Leslie
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+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
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+!Sample_contact_country = USA
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+!Sample_library_strategy = ChIP-Seq
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+!Sample_status = Public on Nov 15 2021
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
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+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
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+!Sample_contact_name = Christina,S,Leslie
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+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
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+!Sample_contact_city = New York
+!Sample_contact_state = NY
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+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
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+!Sample_contact_state = NY
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+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = ChIP
+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322321
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866174
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391922/suppl/GSM4391922_JHRCC12_SETD2_rescue_H3K56AC_rep1.bw
+!Sample_series_id = GSE146539
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391923
+!Sample_title = ChIP-Seq SETD2_rescue_H3K56ac_rep2
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+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = chip antibody: H3K56ac (Millipore, 07-677)
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
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+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322320
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866175
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+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
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+!Sample_characteristics_ch1 = chip antibody: H3K27me3 (Cell Signaling Technology, 9733)
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
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+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
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+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
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+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
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+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866176
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+!Sample_series_id = GSE146539
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+!Sample_status = Public on Nov 15 2021
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+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
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+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
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+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
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+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
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+!Sample_contact_city = New York
+!Sample_contact_state = NY
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+!Sample_contact_country = USA
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
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+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
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+!Sample_contact_name = Christina,S,Leslie
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+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
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+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
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+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
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+!Sample_contact_city = New York
+!Sample_contact_state = NY
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+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
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+!Sample_contact_city = New York
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
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+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = ChIP
+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322313
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866182
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391930/suppl/GSM4391930_JHRCC12_SETD2_rescue_H3K36me3_rep1.bw
+!Sample_series_id = GSE146539
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391931
+!Sample_title = ChIP-Seq SETD2_rescue_H3K36me3_rep2
+!Sample_geo_accession = GSM4391931
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = chip antibody: H3K36me3 (Abcam, ab9050)
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = ChIP
+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322312
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866183
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391931/suppl/GSM4391931_JHRCC12_SETD2_rescue_H3K36me3_rep2.bw
+!Sample_series_id = GSE146539
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391932
+!Sample_title = ChIP-Seq SETD2_mut_input
+!Sample_geo_accession = GSM4391932
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = chip antibody: none
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = ChIP
+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322311
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866184
+!Sample_supplementary_file_1 = NONE
+!Sample_series_id = GSE146539
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391933
+!Sample_title = ChIP-Seq SETD2_rescue_input
+!Sample_geo_accession = GSM4391933
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = chip antibody: none
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = ChIP
+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322310
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866185
+!Sample_supplementary_file_1 = NONE
+!Sample_series_id = GSE146539
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
diff --git a/tests/test_files/soft_files/GSE146540/GSE.soft b/tests/test_files/soft_files/GSE146540/GSE.soft
new file mode 100644
index 0000000..0b2829d
--- /dev/null
+++ b/tests/test_files/soft_files/GSE146540/GSE.soft
@@ -0,0 +1,36 @@
+^SERIES = GSE146540
+!Series_title = Gene expression analysis of ccRCC cell line upon SETD2 rescue [RNAseq_JHRCC12]
+!Series_geo_accession = GSE146540
+!Series_status = Public on Nov 15 2021
+!Series_submission_date = Mar 06 2020
+!Series_last_update_date = Mar 25 2022
+!Series_pubmed_id = 35115713
+!Series_summary = SETD2, a H3K36 trimethyltransferase, is frequently mutated in human cancers with the highest prevalence (13%) in clear cell renal cell carcinoma (ccRCC). Genomic profiling of primary ccRCC tumors reveals a positive correlation between SETD2 mutations and metastasis. However, whether and how SETD2-loss promotes metastasis remains unclear. Here, we detected SETD2 mutations in 24 of 51 (47%) metastatic ccRCC tumors. Using SETD2-mutant metastatic ccRCC patient-derived cell line and xenograft models, we showed that H3K36me3 restoration greatly reduced distant metastases of ccRCC in mice. An integrated ATAC-seq, ChIP-seq, and transcriptome analysis concluded a tumor suppressor model in which loss of SETD2-mediated H3K36me3 activates enhancers to drive oncogenic transcription through dysregulating histone chaperone recruitment, enhancing histone exchange, and expanding chromatin accessibility. Furthermore, we uncovered mechanism-based therapeutic strategies for SETD2-deficient cancer through inhibition of histone chaperones. Overall, SETD2-loss creates a permissive epigenetic landscape for cooperating oncogenic drivers to amplify transcriptional output, providing unique therapeutic opportunities.
+!Series_overall_design = RNA-Seq assay was perfomed in SETD2-proficient and SETD2-deficient JHRCC12 cells in 3 replicates
+!Series_type = Expression profiling by high throughput sequencing
+!Series_contributor = Yuchen,,Xie
+!Series_contributor = Merve,,Sahin
+!Series_contributor = Emily,H,Cheng
+!Series_sample_id = GSM4391934
+!Series_sample_id = GSM4391935
+!Series_sample_id = GSM4391936
+!Series_sample_id = GSM4391937
+!Series_sample_id = GSM4391938
+!Series_sample_id = GSM4391939
+!Series_contact_name = Christina,S,Leslie
+!Series_contact_department = Computational Biology Program
+!Series_contact_institute = Memorial Sloan Kettering Cancer Center
+!Series_contact_address = 417 E 68th St
+!Series_contact_city = New York
+!Series_contact_state = NY
+!Series_contact_zip/postal_code = 10065
+!Series_contact_country = USA
+!Series_supplementary_file = ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE146nnn/GSE146540/suppl/GSE146540_All_gene_counts.txt.gz
+!Series_platform_id = GPL20301
+!Series_platform_organism = Homo sapiens
+!Series_platform_taxid = 9606
+!Series_sample_organism = Homo sapiens
+!Series_sample_taxid = 9606
+!Series_relation = SubSeries of: GSE146583
+!Series_relation = BioProject: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA610878
+!Series_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRP251919
diff --git a/tests/test_files/soft_files/GSE146540/GSM.soft b/tests/test_files/soft_files/GSE146540/GSM.soft
new file mode 100644
index 0000000..31ee5bc
--- /dev/null
+++ b/tests/test_files/soft_files/GSE146540/GSM.soft
@@ -0,0 +1,252 @@
+^SAMPLE = GSM4391934
+!Sample_title = RNA-Seq JHRCC12_SETD2_mut_rep1
+!Sample_geo_accession = GSM4391934
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = After RiboGreen quantification and quality control of Agilent BioAnalyzer, 6-15 ng of total RNA underwent amplification (12 cycles) using the SMART-seq V4 (Clontech) Ultra Low Input RNA kit for sequencing. 10 ng of amplified cDNA was used to prepare Illumina HiSeq libraries with the Kapa DNA library preparation chemistry (Kapa Biosystems) using 8 cycles of PCR.
+!Sample_description = All_gene_counts.txt.gz
+!Sample_description = 1633_1
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the hg19 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL20301
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 4000
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322309
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866186
+!Sample_supplementary_file_1 = NONE
+!Sample_series_id = GSE146540
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391935
+!Sample_title = RNA-Seq JHRCC12_SETD2_mut_rep2
+!Sample_geo_accession = GSM4391935
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = After RiboGreen quantification and quality control of Agilent BioAnalyzer, 6-15 ng of total RNA underwent amplification (12 cycles) using the SMART-seq V4 (Clontech) Ultra Low Input RNA kit for sequencing. 10 ng of amplified cDNA was used to prepare Illumina HiSeq libraries with the Kapa DNA library preparation chemistry (Kapa Biosystems) using 8 cycles of PCR.
+!Sample_description = All_gene_counts.txt.gz
+!Sample_description = 1633_2
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the hg19 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL20301
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 4000
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322308
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866187
+!Sample_supplementary_file_1 = NONE
+!Sample_series_id = GSE146540
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391936
+!Sample_title = RNA-Seq JHRCC12_SETD2_mut_rep3
+!Sample_geo_accession = GSM4391936
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = After RiboGreen quantification and quality control of Agilent BioAnalyzer, 6-15 ng of total RNA underwent amplification (12 cycles) using the SMART-seq V4 (Clontech) Ultra Low Input RNA kit for sequencing. 10 ng of amplified cDNA was used to prepare Illumina HiSeq libraries with the Kapa DNA library preparation chemistry (Kapa Biosystems) using 8 cycles of PCR.
+!Sample_description = All_gene_counts.txt.gz
+!Sample_description = 1633_3
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the hg19 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL20301
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 4000
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322307
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866188
+!Sample_supplementary_file_1 = NONE
+!Sample_series_id = GSE146540
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391937
+!Sample_title = RNA-Seq JHRCC12_SETD2_rescue_rep1
+!Sample_geo_accession = GSM4391937
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = After RiboGreen quantification and quality control of Agilent BioAnalyzer, 6-15 ng of total RNA underwent amplification (12 cycles) using the SMART-seq V4 (Clontech) Ultra Low Input RNA kit for sequencing. 10 ng of amplified cDNA was used to prepare Illumina HiSeq libraries with the Kapa DNA library preparation chemistry (Kapa Biosystems) using 8 cycles of PCR.
+!Sample_description = All_gene_counts.txt.gz
+!Sample_description = 2210_1
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the hg19 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL20301
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 4000
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322306
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866189
+!Sample_supplementary_file_1 = NONE
+!Sample_series_id = GSE146540
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391938
+!Sample_title = RNA-Seq JHRCC12_SETD2_rescue_rep2
+!Sample_geo_accession = GSM4391938
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = After RiboGreen quantification and quality control of Agilent BioAnalyzer, 6-15 ng of total RNA underwent amplification (12 cycles) using the SMART-seq V4 (Clontech) Ultra Low Input RNA kit for sequencing. 10 ng of amplified cDNA was used to prepare Illumina HiSeq libraries with the Kapa DNA library preparation chemistry (Kapa Biosystems) using 8 cycles of PCR.
+!Sample_description = All_gene_counts.txt.gz
+!Sample_description = 2210_2
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the hg19 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL20301
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 4000
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322305
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866190
+!Sample_supplementary_file_1 = NONE
+!Sample_series_id = GSE146540
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391939
+!Sample_title = RNA-Seq JHRCC12_SETD2_rescue_rep3
+!Sample_geo_accession = GSM4391939
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = After RiboGreen quantification and quality control of Agilent BioAnalyzer, 6-15 ng of total RNA underwent amplification (12 cycles) using the SMART-seq V4 (Clontech) Ultra Low Input RNA kit for sequencing. 10 ng of amplified cDNA was used to prepare Illumina HiSeq libraries with the Kapa DNA library preparation chemistry (Kapa Biosystems) using 8 cycles of PCR.
+!Sample_description = All_gene_counts.txt.gz
+!Sample_description = 2210_3
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the hg19 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL20301
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 4000
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322304
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866191
+!Sample_supplementary_file_1 = NONE
+!Sample_series_id = GSE146540
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
diff --git a/tests/test_files/soft_files/GSE146583/GSE.soft b/tests/test_files/soft_files/GSE146583/GSE.soft
new file mode 100644
index 0000000..cb28988
--- /dev/null
+++ b/tests/test_files/soft_files/GSE146583/GSE.soft
@@ -0,0 +1,87 @@
+^SERIES = GSE146583
+!Series_title = SETD2 Loss Creates A Permissive Epigenetic Landscape that Promotes Kidney Cancer Metastasis and Engenders Therapeutic Vulnerabilities
+!Series_geo_accession = GSE146583
+!Series_status = Public on Nov 15 2021
+!Series_submission_date = Mar 06 2020
+!Series_last_update_date = Mar 25 2022
+!Series_pubmed_id = 35115713
+!Series_summary = This SuperSeries is composed of the SubSeries listed below.
+!Series_overall_design = Refer to individual Series
+!Series_type = Genome binding/occupancy profiling by high throughput sequencing
+!Series_type = Expression profiling by high throughput sequencing
+!Series_sample_id = GSM4391896
+!Series_sample_id = GSM4391897
+!Series_sample_id = GSM4391898
+!Series_sample_id = GSM4391899
+!Series_sample_id = GSM4391900
+!Series_sample_id = GSM4391901
+!Series_sample_id = GSM4391902
+!Series_sample_id = GSM4391903
+!Series_sample_id = GSM4391904
+!Series_sample_id = GSM4391905
+!Series_sample_id = GSM4391906
+!Series_sample_id = GSM4391907
+!Series_sample_id = GSM4391908
+!Series_sample_id = GSM4391909
+!Series_sample_id = GSM4391910
+!Series_sample_id = GSM4391911
+!Series_sample_id = GSM4391912
+!Series_sample_id = GSM4391913
+!Series_sample_id = GSM4391914
+!Series_sample_id = GSM4391915
+!Series_sample_id = GSM4391916
+!Series_sample_id = GSM4391917
+!Series_sample_id = GSM4391918
+!Series_sample_id = GSM4391919
+!Series_sample_id = GSM4391920
+!Series_sample_id = GSM4391921
+!Series_sample_id = GSM4391922
+!Series_sample_id = GSM4391923
+!Series_sample_id = GSM4391924
+!Series_sample_id = GSM4391925
+!Series_sample_id = GSM4391926
+!Series_sample_id = GSM4391927
+!Series_sample_id = GSM4391928
+!Series_sample_id = GSM4391929
+!Series_sample_id = GSM4391930
+!Series_sample_id = GSM4391931
+!Series_sample_id = GSM4391932
+!Series_sample_id = GSM4391933
+!Series_sample_id = GSM4391934
+!Series_sample_id = GSM4391935
+!Series_sample_id = GSM4391936
+!Series_sample_id = GSM4391937
+!Series_sample_id = GSM4391938
+!Series_sample_id = GSM4391939
+!Series_sample_id = GSM4391940
+!Series_sample_id = GSM4391941
+!Series_sample_id = GSM4391942
+!Series_sample_id = GSM4391943
+!Series_sample_id = GSM4391944
+!Series_sample_id = GSM4391945
+!Series_contact_name = Christina,S,Leslie
+!Series_contact_department = Computational Biology Program
+!Series_contact_institute = Memorial Sloan Kettering Cancer Center
+!Series_contact_address = 417 E 68th St
+!Series_contact_city = New York
+!Series_contact_state = NY
+!Series_contact_zip/postal_code = 10065
+!Series_contact_country = USA
+!Series_supplementary_file = ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE146nnn/GSE146583/suppl/GSE146583_RAW.tar
+!Series_platform_id = GPL16791
+!Series_platform_id = GPL17021
+!Series_platform_id = GPL20301
+!Series_platform_organism = Homo sapiens
+!Series_platform_organism = Mus musculus
+!Series_platform_taxid = 9606
+!Series_platform_taxid = 10090
+!Series_sample_organism = Homo sapiens
+!Series_sample_organism = Mus musculus
+!Series_sample_taxid = 9606
+!Series_sample_taxid = 10090
+!Series_relation = SuperSeries of: GSE146537
+!Series_relation = SuperSeries of: GSE146538
+!Series_relation = SuperSeries of: GSE146539
+!Series_relation = SuperSeries of: GSE146540
+!Series_relation = SuperSeries of: GSE146541
+!Series_relation = BioProject: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA610874
diff --git a/tests/test_files/soft_files/GSE146583/GSM.soft b/tests/test_files/soft_files/GSE146583/GSM.soft
new file mode 100644
index 0000000..cbc0332
--- /dev/null
+++ b/tests/test_files/soft_files/GSE146583/GSM.soft
@@ -0,0 +1,2026 @@
+^SAMPLE = GSM4391896
+!Sample_title = ATAC-Seq JHRCC12_SETD2_mut_rep1
+!Sample_geo_accession = GSM4391896
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 JHRCC12 cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the hg19 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322350
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866141
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391896/suppl/GSM4391896_JHRCC12_SETD2_mut_rep1.bw
+!Sample_series_id = GSE146537
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391897
+!Sample_title = ATAC-Seq JHRCC12_SETD2_mut_rep2
+!Sample_geo_accession = GSM4391897
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 JHRCC12 cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the hg19 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322349
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866142
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391897/suppl/GSM4391897_JHRCC12_SETD2_mut_rep2.bw
+!Sample_series_id = GSE146537
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391898
+!Sample_title = ATAC-Seq JHRCC12_SETD2_mut_rep3
+!Sample_geo_accession = GSM4391898
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 JHRCC12 cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the hg19 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322348
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866143
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391898/suppl/GSM4391898_JHRCC12_SETD2_mut_rep3.bw
+!Sample_series_id = GSE146537
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391899
+!Sample_title = ATAC-Seq JHRCC12_SETD2_rescue_rep1
+!Sample_geo_accession = GSM4391899
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 JHRCC12 cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the hg19 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322347
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866144
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391899/suppl/GSM4391899_JHRCC12_SETD2_rescue_rep1.bw
+!Sample_series_id = GSE146537
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391900
+!Sample_title = ATAC-Seq JHRCC12_SETD2_rescue_rep2
+!Sample_geo_accession = GSM4391900
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 JHRCC12 cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the hg19 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322346
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866145
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391900/suppl/GSM4391900_JHRCC12_SETD2_rescue_rep2.bw
+!Sample_series_id = GSE146537
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391901
+!Sample_title = ATAC-Seq JHRCC12_SETD2_rescue_rep3
+!Sample_geo_accession = GSM4391901
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 JHRCC12 cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the hg19 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322345
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866146
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391901/suppl/GSM4391901_JHRCC12_SETD2_rescue_rep3.bw
+!Sample_series_id = GSE146537
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391902
+!Sample_title = ATAC-Seq RTE_SETD2_wt_rep1
+!Sample_geo_accession = GSM4391902
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Murine renal tubular epithelial cells
+!Sample_organism_ch1 = Mus musculus
+!Sample_taxid_ch1 = 10090
+!Sample_characteristics_ch1 = cell type: primary renal tubular epithelial (RTE) cells
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Kidneys were dissected from Setd2(F/F)Ksp-Cre+ and Setd2(F/F) mice at 4-5 weeks of age. Kidney cortices were minced and digested in advanced DMEM/F12 (Thermo Fisher Scientific) containing liberase (Sigma) for 1h at 37 °C. Digested samples were filtered through 40 μm strainers (BD Biosciences) and washed with cold Hank's Balanced Salt Solution (HBSS, Thermo Fisher Scientific) twice. Finally, samples were resuspended in advanced DMEM/F12 supplemented with penicillin/streptomycin (Thermo Fisher Scientific), non-essential amino acids (NEAA, Thermo Fisher Scientific), glutamine (Thermo Fisher Scientific), 10 mM HEPES (Thermo Fisher Scientific), Insulin-transferrin-selenium (Thermo Fisher Scientific), 20 ng/ml EGF (R & D Systems), and 100 nM hydrocortisone (Sigma), and plated in dishes.
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 murine renal tubular epithelial cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the mm10 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: mm10
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL17021
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322344
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866154
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+^SAMPLE = GSM4391903
+!Sample_title = ATAC-Seq RTE_SETD2_wt_rep2
+!Sample_geo_accession = GSM4391903
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Murine renal tubular epithelial cells
+!Sample_organism_ch1 = Mus musculus
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+!Sample_characteristics_ch1 = cell type: primary renal tubular epithelial (RTE) cells
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
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+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 murine renal tubular epithelial cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the mm10 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: mm10
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL17021
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322343
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866155
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+!Sample_series_id = GSE146538
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+^SAMPLE = GSM4391904
+!Sample_title = ATAC-Seq RTE_SETD2_wt_rep3
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+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Murine renal tubular epithelial cells
+!Sample_organism_ch1 = Mus musculus
+!Sample_taxid_ch1 = 10090
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+!Sample_characteristics_ch1 = genotype: SETD2-proficient
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+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 murine renal tubular epithelial cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the mm10 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: mm10
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL17021
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322342
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866156
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+^SAMPLE = GSM4391905
+!Sample_title = ATAC-Seq RTE_SETD2_KO_rep1
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+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
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+!Sample_organism_ch1 = Mus musculus
+!Sample_taxid_ch1 = 10090
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+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 murine renal tubular epithelial cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the mm10 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: mm10
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL17021
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322341
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866157
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+!Sample_data_row_count = 0
+^SAMPLE = GSM4391906
+!Sample_title = ATAC-Seq RTE_SETD2_KO_rep2
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+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
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+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 murine renal tubular epithelial cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the mm10 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: mm10
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL17021
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322340
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866158
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391906/suppl/GSM4391906_ko_Rep2_Norm.bw
+!Sample_series_id = GSE146538
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+!Sample_data_row_count = 0
+^SAMPLE = GSM4391907
+!Sample_title = ATAC-Seq RTE_SETD2_KO_rep3
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+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
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+!Sample_organism_ch1 = Mus musculus
+!Sample_taxid_ch1 = 10090
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+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 50,000 murine renal tubular epithelial cells were used for the transposition reaction at 37 °C for 30 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles as described previously (Buenrostro et al., 2013). Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by AMPure Beads at a 1.5x ratio.
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were mapped to the mm10 genome assembly using Bowtie2 (v2.3.4.3), and non-uniquely mapping reads were removed.
+!Sample_data_processing = Peak calling was performed on each replicate using MACS2 (v2.1.2) with ‘-p 1e-2 --extsize 200 --shift -100 --nomodel’ parameters.
+!Sample_data_processing = Genome_build: mm10
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL17021
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
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+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866159
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391907/suppl/GSM4391907_ko_Rep3_Norm.bw
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+^SAMPLE = GSM4391908
+!Sample_title = ChIP-Seq SETD2_mut_H3K4me1_rep1
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+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = chip antibody: H3K4me1 (Abcam, ab8895)
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
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+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866160
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+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391909
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+!Sample_geo_accession = GSM4391909
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
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+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
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+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
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+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322237
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866161
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391909/suppl/GSM4391909_JHRCC12_SETD2_mut_H3K4me1_rep2.bw
+!Sample_series_id = GSE146539
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+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
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+!Sample_characteristics_ch1 = chip antibody: H3K4me1 (Abcam, ab8895)
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
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+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866162
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+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
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+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
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+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
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+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866163
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+!Sample_type = SRA
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+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
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+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
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+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
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+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
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+!Sample_contact_name = Christina,S,Leslie
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+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
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+!Sample_contact_city = New York
+!Sample_contact_state = NY
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+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
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+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
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+!Sample_contact_city = New York
+!Sample_contact_state = NY
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+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
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+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = ChIP
+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322326
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866169
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+!Sample_series_id = GSE146539
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391918
+!Sample_title = ChIP-Seq SETD2_rescue_H3K27ac_rep1
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+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
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+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
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+!Sample_characteristics_ch1 = chip antibody: H3K27ac (Abcam, ab4729)
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
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+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322325
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866170
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+!Sample_status = Public on Nov 15 2021
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+!Sample_type = SRA
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
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+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
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+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
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+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
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+!Sample_contact_name = Christina,S,Leslie
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+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
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+!Sample_contact_city = New York
+!Sample_contact_state = NY
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+!Sample_contact_name = Christina,S,Leslie
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+!Sample_contact_state = NY
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+!Sample_contact_state = NY
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = ChIP
+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322318
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866177
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+!Sample_series_id = GSE146539
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+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = chip antibody: H3K27me3 (Cell Signaling Technology, 9733)
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
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+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322317
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866178
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+!Sample_series_id = GSE146539
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+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
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+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
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+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866179
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+^SAMPLE = GSM4391928
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
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+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
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+!Sample_contact_name = Christina,S,Leslie
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+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
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+!Sample_contact_city = New York
+!Sample_contact_state = NY
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
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+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
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+!Sample_contact_city = New York
+!Sample_contact_state = NY
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+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
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+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
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+!Sample_contact_city = New York
+!Sample_contact_state = NY
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+!Sample_extract_protocol_ch1 = 2 x 10^6 JHRCC12 cells were cross-linked with 1% paraformaldehyde for 10 min at room temperature and quenched by glycine. Cells were washed with cold PBS, pelleted by centrifugation, and lysed. After sonication, samples were spun down and incubated with 1 μg primary antibody for each ChIP experiment at 4 °C overnight. Magnetic beads (Thermo Fisher Scientific) were added the next day and incubated at 4 °C for 2h. Samples were then washed and histone complexes were eluted. The eluted samples were treated with RNase A, proteinase K, reversed crosslink, and purified with Qiagen PCR purification kit.
+!Sample_extract_protocol_ch1 = Library preparation were performed by the Integrated Genomics Operation Core Facility at MSKCC.
+!Sample_data_processing = Reads were aligned to hg19 using BWA (v0.7.17-r1188) with parameters ‘-q 5 -l 32 -k 2’. Uniquely aligned paired reads were extracted using SAMtools. PCR duplicates were removed using Picard tools (v2.18.16).
+!Sample_data_processing = Peak calling was performed for each individual and pooled replicates of each cell type using MACS2 v2.1.2 with parameters ‘-g hs -p 0.01 --keep-dup all --no-model -c $input_dna_control’
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Normalized coverage track in bigWig format
+!Sample_platform_id = GPL16791
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = ChIP
+!Sample_library_source = genomic
+!Sample_library_strategy = ChIP-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322310
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866185
+!Sample_supplementary_file_1 = NONE
+!Sample_series_id = GSE146539
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391934
+!Sample_title = RNA-Seq JHRCC12_SETD2_mut_rep1
+!Sample_geo_accession = GSM4391934
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = After RiboGreen quantification and quality control of Agilent BioAnalyzer, 6-15 ng of total RNA underwent amplification (12 cycles) using the SMART-seq V4 (Clontech) Ultra Low Input RNA kit for sequencing. 10 ng of amplified cDNA was used to prepare Illumina HiSeq libraries with the Kapa DNA library preparation chemistry (Kapa Biosystems) using 8 cycles of PCR.
+!Sample_description = All_gene_counts.txt.gz
+!Sample_description = 1633_1
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the hg19 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL20301
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 4000
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322309
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866186
+!Sample_supplementary_file_1 = NONE
+!Sample_series_id = GSE146540
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391935
+!Sample_title = RNA-Seq JHRCC12_SETD2_mut_rep2
+!Sample_geo_accession = GSM4391935
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = After RiboGreen quantification and quality control of Agilent BioAnalyzer, 6-15 ng of total RNA underwent amplification (12 cycles) using the SMART-seq V4 (Clontech) Ultra Low Input RNA kit for sequencing. 10 ng of amplified cDNA was used to prepare Illumina HiSeq libraries with the Kapa DNA library preparation chemistry (Kapa Biosystems) using 8 cycles of PCR.
+!Sample_description = All_gene_counts.txt.gz
+!Sample_description = 1633_2
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the hg19 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL20301
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 4000
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322308
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866187
+!Sample_supplementary_file_1 = NONE
+!Sample_series_id = GSE146540
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391936
+!Sample_title = RNA-Seq JHRCC12_SETD2_mut_rep3
+!Sample_geo_accession = GSM4391936
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = After RiboGreen quantification and quality control of Agilent BioAnalyzer, 6-15 ng of total RNA underwent amplification (12 cycles) using the SMART-seq V4 (Clontech) Ultra Low Input RNA kit for sequencing. 10 ng of amplified cDNA was used to prepare Illumina HiSeq libraries with the Kapa DNA library preparation chemistry (Kapa Biosystems) using 8 cycles of PCR.
+!Sample_description = All_gene_counts.txt.gz
+!Sample_description = 1633_3
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the hg19 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL20301
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 4000
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322307
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866188
+!Sample_supplementary_file_1 = NONE
+!Sample_series_id = GSE146540
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391937
+!Sample_title = RNA-Seq JHRCC12_SETD2_rescue_rep1
+!Sample_geo_accession = GSM4391937
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = After RiboGreen quantification and quality control of Agilent BioAnalyzer, 6-15 ng of total RNA underwent amplification (12 cycles) using the SMART-seq V4 (Clontech) Ultra Low Input RNA kit for sequencing. 10 ng of amplified cDNA was used to prepare Illumina HiSeq libraries with the Kapa DNA library preparation chemistry (Kapa Biosystems) using 8 cycles of PCR.
+!Sample_description = All_gene_counts.txt.gz
+!Sample_description = 2210_1
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the hg19 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL20301
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 4000
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322306
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866189
+!Sample_supplementary_file_1 = NONE
+!Sample_series_id = GSE146540
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391938
+!Sample_title = RNA-Seq JHRCC12_SETD2_rescue_rep2
+!Sample_geo_accession = GSM4391938
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = After RiboGreen quantification and quality control of Agilent BioAnalyzer, 6-15 ng of total RNA underwent amplification (12 cycles) using the SMART-seq V4 (Clontech) Ultra Low Input RNA kit for sequencing. 10 ng of amplified cDNA was used to prepare Illumina HiSeq libraries with the Kapa DNA library preparation chemistry (Kapa Biosystems) using 8 cycles of PCR.
+!Sample_description = All_gene_counts.txt.gz
+!Sample_description = 2210_2
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the hg19 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL20301
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 4000
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322305
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866190
+!Sample_supplementary_file_1 = NONE
+!Sample_series_id = GSE146540
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391939
+!Sample_title = RNA-Seq JHRCC12_SETD2_rescue_rep3
+!Sample_geo_accession = GSM4391939
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Clear cell renal cell carcinoma cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell line: JHRCC12
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Cells were cultured in F10 medium (advanced DMEM/F12) with 10% FBS, 1× L-glutamine, 1× sodium pyruvate, 1× nonessential amino acid, and 1× penicillin/streptomycin).
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = After RiboGreen quantification and quality control of Agilent BioAnalyzer, 6-15 ng of total RNA underwent amplification (12 cycles) using the SMART-seq V4 (Clontech) Ultra Low Input RNA kit for sequencing. 10 ng of amplified cDNA was used to prepare Illumina HiSeq libraries with the Kapa DNA library preparation chemistry (Kapa Biosystems) using 8 cycles of PCR.
+!Sample_description = All_gene_counts.txt.gz
+!Sample_description = 2210_3
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the hg19 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL20301
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 4000
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322304
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866191
+!Sample_supplementary_file_1 = NONE
+!Sample_series_id = GSE146540
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391940
+!Sample_title = RNA-Seq RTE_SETD2_wt_rep1
+!Sample_geo_accession = GSM4391940
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Murine renal tubular epithelial cells
+!Sample_organism_ch1 = Mus musculus
+!Sample_taxid_ch1 = 10090
+!Sample_characteristics_ch1 = cell type: primary renal tubular epithelial (RTE) cells
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Kidneys were dissected from Setd2(F/F)Ksp-Cre+ and Setd2(F/F) mice at 4-5 weeks of age. Kidney cortices were minced and digested in advanced DMEM/F12 (Thermo Fisher Scientific) containing liberase (Sigma) for 1h at 37 °C. Digested samples were filtered through 40 μm strainers (BD Biosciences) and washed with cold Hank's Balanced Salt Solution (HBSS, Thermo Fisher Scientific) twice. Finally, samples were resuspended in advanced DMEM/F12 supplemented with penicillin/streptomycin (Thermo Fisher Scientific), non-essential amino acids (NEAA, Thermo Fisher Scientific), glutamine (Thermo Fisher Scientific), 10 mM HEPES (Thermo Fisher Scientific), Insulin-transferrin-selenium (Thermo Fisher Scientific), 20 ng/ml EGF (R & D Systems), and 100 nM hydrocortisone (Sigma), and plated in dishes.
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = The isolated RNA samples were then processed using the TruSeq RNA Sample Prep kit (Illumina).
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the mm10 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: mm10
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL17021
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322303
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866192
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391940/suppl/GSM4391940_wt_1.gene.count.txt.gz
+!Sample_series_id = GSE146541
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391941
+!Sample_title = RNA-Seq RTE_SETD2_wt_rep2
+!Sample_geo_accession = GSM4391941
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Murine renal tubular epithelial cells
+!Sample_organism_ch1 = Mus musculus
+!Sample_taxid_ch1 = 10090
+!Sample_characteristics_ch1 = cell type: primary renal tubular epithelial (RTE) cells
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Kidneys were dissected from Setd2(F/F)Ksp-Cre+ and Setd2(F/F) mice at 4-5 weeks of age. Kidney cortices were minced and digested in advanced DMEM/F12 (Thermo Fisher Scientific) containing liberase (Sigma) for 1h at 37 °C. Digested samples were filtered through 40 μm strainers (BD Biosciences) and washed with cold Hank's Balanced Salt Solution (HBSS, Thermo Fisher Scientific) twice. Finally, samples were resuspended in advanced DMEM/F12 supplemented with penicillin/streptomycin (Thermo Fisher Scientific), non-essential amino acids (NEAA, Thermo Fisher Scientific), glutamine (Thermo Fisher Scientific), 10 mM HEPES (Thermo Fisher Scientific), Insulin-transferrin-selenium (Thermo Fisher Scientific), 20 ng/ml EGF (R & D Systems), and 100 nM hydrocortisone (Sigma), and plated in dishes.
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = The isolated RNA samples were then processed using the TruSeq RNA Sample Prep kit (Illumina).
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the mm10 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: mm10
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL17021
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322302
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866193
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391941/suppl/GSM4391941_wt_2.gene.count.txt.gz
+!Sample_series_id = GSE146541
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391942
+!Sample_title = RNA-Seq RTE_SETD2_wt_rep3
+!Sample_geo_accession = GSM4391942
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Murine renal tubular epithelial cells
+!Sample_organism_ch1 = Mus musculus
+!Sample_taxid_ch1 = 10090
+!Sample_characteristics_ch1 = cell type: primary renal tubular epithelial (RTE) cells
+!Sample_characteristics_ch1 = genotype: SETD2-proficient
+!Sample_growth_protocol_ch1 = Kidneys were dissected from Setd2(F/F)Ksp-Cre+ and Setd2(F/F) mice at 4-5 weeks of age. Kidney cortices were minced and digested in advanced DMEM/F12 (Thermo Fisher Scientific) containing liberase (Sigma) for 1h at 37 °C. Digested samples were filtered through 40 μm strainers (BD Biosciences) and washed with cold Hank's Balanced Salt Solution (HBSS, Thermo Fisher Scientific) twice. Finally, samples were resuspended in advanced DMEM/F12 supplemented with penicillin/streptomycin (Thermo Fisher Scientific), non-essential amino acids (NEAA, Thermo Fisher Scientific), glutamine (Thermo Fisher Scientific), 10 mM HEPES (Thermo Fisher Scientific), Insulin-transferrin-selenium (Thermo Fisher Scientific), 20 ng/ml EGF (R & D Systems), and 100 nM hydrocortisone (Sigma), and plated in dishes.
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = The isolated RNA samples were then processed using the TruSeq RNA Sample Prep kit (Illumina).
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the mm10 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: mm10
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL17021
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322301
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866194
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391942/suppl/GSM4391942_wt_3.gene.count.txt.gz
+!Sample_series_id = GSE146541
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391943
+!Sample_title = RNA-Seq RTE_SETD2_KO_rep1
+!Sample_geo_accession = GSM4391943
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Murine renal tubular epithelial cells
+!Sample_organism_ch1 = Mus musculus
+!Sample_taxid_ch1 = 10090
+!Sample_characteristics_ch1 = cell type: primary renal tubular epithelial (RTE) cells
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Kidneys were dissected from Setd2(F/F)Ksp-Cre+ and Setd2(F/F) mice at 4-5 weeks of age. Kidney cortices were minced and digested in advanced DMEM/F12 (Thermo Fisher Scientific) containing liberase (Sigma) for 1h at 37 °C. Digested samples were filtered through 40 μm strainers (BD Biosciences) and washed with cold Hank's Balanced Salt Solution (HBSS, Thermo Fisher Scientific) twice. Finally, samples were resuspended in advanced DMEM/F12 supplemented with penicillin/streptomycin (Thermo Fisher Scientific), non-essential amino acids (NEAA, Thermo Fisher Scientific), glutamine (Thermo Fisher Scientific), 10 mM HEPES (Thermo Fisher Scientific), Insulin-transferrin-selenium (Thermo Fisher Scientific), 20 ng/ml EGF (R & D Systems), and 100 nM hydrocortisone (Sigma), and plated in dishes.
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = The isolated RNA samples were then processed using the TruSeq RNA Sample Prep kit (Illumina).
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the mm10 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: mm10
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL17021
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322300
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866195
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391943/suppl/GSM4391943_ko_1.gene.count.txt.gz
+!Sample_series_id = GSE146541
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391944
+!Sample_title = RNA-Seq RTE_SETD2_KO_rep2
+!Sample_geo_accession = GSM4391944
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Murine renal tubular epithelial cells
+!Sample_organism_ch1 = Mus musculus
+!Sample_taxid_ch1 = 10090
+!Sample_characteristics_ch1 = cell type: primary renal tubular epithelial (RTE) cells
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Kidneys were dissected from Setd2(F/F)Ksp-Cre+ and Setd2(F/F) mice at 4-5 weeks of age. Kidney cortices were minced and digested in advanced DMEM/F12 (Thermo Fisher Scientific) containing liberase (Sigma) for 1h at 37 °C. Digested samples were filtered through 40 μm strainers (BD Biosciences) and washed with cold Hank's Balanced Salt Solution (HBSS, Thermo Fisher Scientific) twice. Finally, samples were resuspended in advanced DMEM/F12 supplemented with penicillin/streptomycin (Thermo Fisher Scientific), non-essential amino acids (NEAA, Thermo Fisher Scientific), glutamine (Thermo Fisher Scientific), 10 mM HEPES (Thermo Fisher Scientific), Insulin-transferrin-selenium (Thermo Fisher Scientific), 20 ng/ml EGF (R & D Systems), and 100 nM hydrocortisone (Sigma), and plated in dishes.
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = The isolated RNA samples were then processed using the TruSeq RNA Sample Prep kit (Illumina).
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the mm10 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: mm10
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL17021
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322299
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866196
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391944/suppl/GSM4391944_ko_2.gene.count.txt.gz
+!Sample_series_id = GSE146541
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
+^SAMPLE = GSM4391945
+!Sample_title = RNA-Seq RTE_SETD2_KO_rep3
+!Sample_geo_accession = GSM4391945
+!Sample_status = Public on Nov 15 2021
+!Sample_submission_date = Mar 06 2020
+!Sample_last_update_date = Nov 15 2021
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = Murine renal tubular epithelial cells
+!Sample_organism_ch1 = Mus musculus
+!Sample_taxid_ch1 = 10090
+!Sample_characteristics_ch1 = cell type: primary renal tubular epithelial (RTE) cells
+!Sample_characteristics_ch1 = genotype: SETD2-deficient
+!Sample_growth_protocol_ch1 = Kidneys were dissected from Setd2(F/F)Ksp-Cre+ and Setd2(F/F) mice at 4-5 weeks of age. Kidney cortices were minced and digested in advanced DMEM/F12 (Thermo Fisher Scientific) containing liberase (Sigma) for 1h at 37 °C. Digested samples were filtered through 40 μm strainers (BD Biosciences) and washed with cold Hank's Balanced Salt Solution (HBSS, Thermo Fisher Scientific) twice. Finally, samples were resuspended in advanced DMEM/F12 supplemented with penicillin/streptomycin (Thermo Fisher Scientific), non-essential amino acids (NEAA, Thermo Fisher Scientific), glutamine (Thermo Fisher Scientific), 10 mM HEPES (Thermo Fisher Scientific), Insulin-transferrin-selenium (Thermo Fisher Scientific), 20 ng/ml EGF (R & D Systems), and 100 nM hydrocortisone (Sigma), and plated in dishes.
+!Sample_molecule_ch1 = total RNA
+!Sample_extract_protocol_ch1 = Total RNA was extracted and cleaned up using RNeasy Mini Kit (Qiagen).
+!Sample_extract_protocol_ch1 = The isolated RNA samples were then processed using the TruSeq RNA Sample Prep kit (Illumina).
+!Sample_data_processing = Raw reads were trimmed and filtered for quality using Trimmomatic (v0.38). Trimmed reads were then aligned against the mm10 genome using STAR (v2.7.1a).
+!Sample_data_processing = For each RefSeq annotated gene, reads overlapping with exon regions were counted using HTSeq (v0.11.2).
+!Sample_data_processing = Genome_build: mm10
+!Sample_data_processing = Supplementary_files_format_and_content: Gene quantification count in txt format
+!Sample_platform_id = GPL17021
+!Sample_contact_name = Christina,S,Leslie
+!Sample_contact_department = Computational Biology Program
+!Sample_contact_institute = Memorial Sloan Kettering Cancer Center
+!Sample_contact_address = 417 E 68th St
+!Sample_contact_city = New York
+!Sample_contact_state = NY
+!Sample_contact_zip/postal_code = 10065
+!Sample_contact_country = USA
+!Sample_instrument_model = Illumina HiSeq 2500
+!Sample_library_selection = cDNA
+!Sample_library_source = transcriptomic
+!Sample_library_strategy = RNA-Seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14322298
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX7866197
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4391nnn/GSM4391945/suppl/GSM4391945_ko_3.gene.count.txt.gz
+!Sample_series_id = GSE146541
+!Sample_series_id = GSE146583
+!Sample_data_row_count = 0
diff --git a/tests/test_files/soft_files/GSE150868/GSE.soft b/tests/test_files/soft_files/GSE150868/GSE.soft
new file mode 100644
index 0000000..d09f51c
--- /dev/null
+++ b/tests/test_files/soft_files/GSE150868/GSE.soft
@@ -0,0 +1,66 @@
+^SERIES = GSE150868
+!Series_title = Determine differences in chromatin accessibility in primary human AML samples by ATAC-seq.
+!Series_geo_accession = GSE150868
+!Series_status = Public on Feb 06 2022
+!Series_submission_date = May 19 2020
+!Series_last_update_date = Feb 08 2022
+!Series_summary = ATAC-Seq was performed for 35 AML primary specimens from primary AML cells, followed by a detailed ATAC-Seq pipeline for data processing. We provide both raw files as well as various processed files such as individual and consensus peaks.
+!Series_overall_design = Chromatin accessiblity was analyzed on 35 AML primary specimens using Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) in order to analyze transcription factor activity in combination of mRNA seq data in GPR56 KD scenario.
+!Series_type = Genome binding/occupancy profiling by high throughput sequencing
+!Series_contributor = Lixiazi,,He
+!Series_contributor = Caroline,,Pabst
+!Series_contributor = Christian,,Arnold
+!Series_contributor = Judith,,Zaugg
+!Series_sample_id = GSM4559921
+!Series_sample_id = GSM4559922
+!Series_sample_id = GSM4559923
+!Series_sample_id = GSM4559924
+!Series_sample_id = GSM4559925
+!Series_sample_id = GSM4559926
+!Series_sample_id = GSM4559927
+!Series_sample_id = GSM4559928
+!Series_sample_id = GSM4559929
+!Series_sample_id = GSM4559930
+!Series_sample_id = GSM4559931
+!Series_sample_id = GSM4559932
+!Series_sample_id = GSM4559933
+!Series_sample_id = GSM4559934
+!Series_sample_id = GSM4559935
+!Series_sample_id = GSM4559936
+!Series_sample_id = GSM4559937
+!Series_sample_id = GSM4559938
+!Series_sample_id = GSM4559939
+!Series_sample_id = GSM4559940
+!Series_sample_id = GSM4559941
+!Series_sample_id = GSM4559942
+!Series_sample_id = GSM4559943
+!Series_sample_id = GSM4559944
+!Series_sample_id = GSM4559945
+!Series_sample_id = GSM4559946
+!Series_sample_id = GSM4559947
+!Series_sample_id = GSM4559948
+!Series_sample_id = GSM4559949
+!Series_sample_id = GSM4559950
+!Series_sample_id = GSM4559951
+!Series_sample_id = GSM4559952
+!Series_sample_id = GSM4559953
+!Series_sample_id = GSM4559954
+!Series_sample_id = GSM4559955
+!Series_contact_name = Christian,,Arnold
+!Series_contact_email = christian.arnold@embl.de
+!Series_contact_institute = EMBL
+!Series_contact_address = Meyerhofstraße 1
+!Series_contact_city = Heidelberg
+!Series_contact_state = Baden-Württemberg
+!Series_contact_zip/postal_code = 69117
+!Series_contact_country = Germany
+!Series_supplementary_file = ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE150nnn/GSE150868/suppl/GSE150868_RAW.tar
+!Series_supplementary_file = ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE150nnn/GSE150868/suppl/GSE150868_consensusPeaks_all_minOverlap10.bed.gz
+!Series_supplementary_file = ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE150nnn/GSE150868/suppl/GSE150868_consensusPeaks_all_minOverlap10.bed.rawCounts.txt.gz
+!Series_platform_id = GPL18573
+!Series_platform_organism = Homo sapiens
+!Series_platform_taxid = 9606
+!Series_sample_organism = Homo sapiens
+!Series_sample_taxid = 9606
+!Series_relation = BioProject: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA633890
+!Series_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRP262351
diff --git a/tests/test_files/soft_files/GSE150868/GSM.soft b/tests/test_files/soft_files/GSE150868/GSM.soft
new file mode 100644
index 0000000..d9e2c7b
--- /dev/null
+++ b/tests/test_files/soft_files/GSE150868/GSM.soft
@@ -0,0 +1,1365 @@
+^SAMPLE = GSM4559921
+!Sample_title = 11244
+!Sample_geo_accession = GSM4559921
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973883
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364446
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559921/suppl/GSM4559921_11244.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559922
+!Sample_title = 22412
+!Sample_geo_accession = GSM4559922
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973882
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364447
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559922/suppl/GSM4559922_22412.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559923
+!Sample_title = 23207
+!Sample_geo_accession = GSM4559923
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973881
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364448
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559923/suppl/GSM4559923_23207.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559924
+!Sample_title = 25963
+!Sample_geo_accession = GSM4559924
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973880
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364449
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+!Sample_series_id = GSE150868
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+!Sample_geo_accession = GSM4559925
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973879
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364450
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+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
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+!Sample_organism_ch1 = Homo sapiens
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
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+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
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+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364451
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+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
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+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973877
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364452
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+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
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+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973876
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364453
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+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
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+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973875
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364454
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+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
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+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
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+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973898
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364455
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559930/suppl/GSM4559930_06H133.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559931
+!Sample_title = 06H143
+!Sample_geo_accession = GSM4559931
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973897
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364456
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559931/suppl/GSM4559931_06H143.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559932
+!Sample_title = 07H005
+!Sample_geo_accession = GSM4559932
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973896
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364457
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559932/suppl/GSM4559932_07H005.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559933
+!Sample_title = 07H009
+!Sample_geo_accession = GSM4559933
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973895
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364458
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559933/suppl/GSM4559933_07H009.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559934
+!Sample_title = 07H062
+!Sample_geo_accession = GSM4559934
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973894
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364459
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559934/suppl/GSM4559934_07H062.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559935
+!Sample_title = 07H158
+!Sample_geo_accession = GSM4559935
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973893
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364460
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559935/suppl/GSM4559935_07H158.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559936
+!Sample_title = 08H082
+!Sample_geo_accession = GSM4559936
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973892
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364461
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559936/suppl/GSM4559936_08H082.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559937
+!Sample_title = 08H138
+!Sample_geo_accession = GSM4559937
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973864
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364462
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559937/suppl/GSM4559937_08H138.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559938
+!Sample_title = 09H083
+!Sample_geo_accession = GSM4559938
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973891
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364463
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559938/suppl/GSM4559938_09H083.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559939
+!Sample_title = 09H115
+!Sample_geo_accession = GSM4559939
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973890
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364464
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559939/suppl/GSM4559939_09H115.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559940
+!Sample_title = 10H166
+!Sample_geo_accession = GSM4559940
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973889
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364465
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559940/suppl/GSM4559940_10H166.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559941
+!Sample_title = 11H008
+!Sample_geo_accession = GSM4559941
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973888
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364466
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559941/suppl/GSM4559941_11H008.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559942
+!Sample_title = 11H027
+!Sample_geo_accession = GSM4559942
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973887
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364467
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559942/suppl/GSM4559942_11H027.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559943
+!Sample_title = 11H142
+!Sample_geo_accession = GSM4559943
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973886
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364468
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559943/suppl/GSM4559943_11H142.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559944
+!Sample_title = 11H160
+!Sample_geo_accession = GSM4559944
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973885
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364469
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559944/suppl/GSM4559944_11H160.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559945
+!Sample_title = 12H010
+!Sample_geo_accession = GSM4559945
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973884
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364470
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559945/suppl/GSM4559945_12H010.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559946
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+!Sample_geo_accession = GSM4559946
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973853
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364471
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559946/suppl/GSM4559946_13H073.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559947
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+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973870
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364472
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+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559948
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+!Sample_geo_accession = GSM4559948
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973869
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364473
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559948/suppl/GSM4559948_14H001.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559949
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+!Sample_geo_accession = GSM4559949
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973868
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364474
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559949/suppl/GSM4559949_14H007.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559950
+!Sample_title = E170001
+!Sample_geo_accession = GSM4559950
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
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+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973852
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364475
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559950/suppl/GSM4559950_E170001.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559951
+!Sample_title = E180007
+!Sample_geo_accession = GSM4559951
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973874
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364476
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559951/suppl/GSM4559951_E180007.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559952
+!Sample_title = E180013
+!Sample_geo_accession = GSM4559952
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973873
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364477
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559952/suppl/GSM4559952_E180013.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559953
+!Sample_title = E180015
+!Sample_geo_accession = GSM4559953
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973872
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364478
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559953/suppl/GSM4559953_E180015.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559954
+!Sample_title = E180029
+!Sample_geo_accession = GSM4559954
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973871
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364479
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559954/suppl/GSM4559954_E180029.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
+^SAMPLE = GSM4559955
+!Sample_title = E180117
+!Sample_geo_accession = GSM4559955
+!Sample_status = Public on Feb 06 2022
+!Sample_submission_date = May 19 2020
+!Sample_last_update_date = Feb 07 2022
+!Sample_type = SRA
+!Sample_channel_count = 1
+!Sample_source_name_ch1 = AML cells
+!Sample_organism_ch1 = Homo sapiens
+!Sample_taxid_ch1 = 9606
+!Sample_characteristics_ch1 = cell type: primary AML cells
+!Sample_molecule_ch1 = genomic DNA
+!Sample_extract_protocol_ch1 = Briefly 50,000 cells from frozen samples were collected, then placed into lysis buffer (10 mM Tris·Cl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween 20), followed by Tn5 transposase-mediated tagmentation and adaptor incorporation at sites of accessible chromatin carried out using the Nextera DNA Library Prep kit (FC-121-1030, Illumina) at 37°C for 30 min.
+!Sample_extract_protocol_ch1 = Library amplification was performed using NEB Nex High-Fidelity 2x PCR Master Mix (M0541L, NEB) and primer combinations provided in Nextera DNA Library Prep kit.  The library was optimized for enrichment for 100-1000 bps fragments using SPRI beads based size-selection and the quality of the purified DNA library was analyzed on a Bioanalyzer (2100 Expert software, Agilent Technologies) using High Sensitivity DNA Chips (5067-4626; Agilent Technologies Inc.). The appropriate concentration of sample was determined using the Qubit Fluorometer (Molecular Probes). Ten 40nM samples were pooled and run on a NextSeq 500/550 High Output Kit (20024907; Illumina, Inc. San Diego, CA) and the NextSeq 500 Illumina Sequencer to obtain paired end reads of 75 bp.
+!Sample_data_processing = Adaptor trimming with Trimmomatic v0.38 (parameters: ILLUMINACLIP::1:30:4:1:true TRAILING:3 MINLEN:20)
+!Sample_data_processing = Alignment with Bowtie2 v2.3.4.3 (parameters: -X2000 --very-sensitive -t)
+!Sample_data_processing = Post-alignment processing (using samtools v1.9, Picard tools v2.18.16-SNAPSHOT): (0) cleaning the BAM file with Picard CleanSam, FixMateInformation, AddOrReplaceReadGroups and ReorderSam,  (1) removing mitochondrial reads and reads from non-assembled contigs or alternative haplotypes, (2) marking and removing duplicate reads with MarkDuplicates, (3) filtering reads with a mapping quality below 10 (-F 4 -q 10), (4) adjusting read start sites as described previously by 4 and -5 base pairs, and (5) removing reads with insertions or deletions using samtools, followed by final cleaning and sorting using Picard SortSam, CleanSam, and FixMateInformation
+!Sample_data_processing = Peak calling with MACS2 (parameters: -q 0.01 -g hg19 --nomodel --keep-dup all) followed by filtering using bedtools subtract v2.27.1 against the publicly available blacklist regions
+!Sample_data_processing = Genome_build: hg19
+!Sample_data_processing = Supplementary_files_format_and_content: We provide the individual peak files as well as the main consensus peak set along with raw read counts for each sample
+!Sample_platform_id = GPL18573
+!Sample_contact_name = Christian,,Arnold
+!Sample_contact_email = christian.arnold@embl.de
+!Sample_contact_institute = EMBL
+!Sample_contact_address = Meyerhofstraße 1
+!Sample_contact_city = Heidelberg
+!Sample_contact_state = Baden-Württemberg
+!Sample_contact_zip/postal_code = 69117
+!Sample_contact_country = Germany
+!Sample_instrument_model = Illumina NextSeq 500
+!Sample_library_selection = other
+!Sample_library_source = genomic
+!Sample_library_strategy = ATAC-seq
+!Sample_relation = BioSample: https://www.ncbi.nlm.nih.gov/biosample/SAMN14973867
+!Sample_relation = SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX8364480
+!Sample_supplementary_file_1 = ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4559nnn/GSM4559955/suppl/GSM4559955_E180117.narrowPeak.gz
+!Sample_series_id = GSE150868
+!Sample_data_row_count = 0
diff --git a/tests/test_geofetch.py b/tests/test_geofetch.py
index 4875976..a64b4c0 100644
--- a/tests/test_geofetch.py
+++ b/tests/test_geofetch.py
@@ -1,9 +1,189 @@
-from geofetch import parse_accessions
+from geofetch import parse_accessions, Geofetcher
+import os
 import pytest
 
+INPUT_ACC_FILE = "tests/test_files/input_acc.txt"
+GSE_FILES = "tests/test_files/soft_files"
+GSE_SOFT_NAME = "GSE.soft"
+GSM_SOFT_NAME = "GSM.soft"
 
-@pytest.mark.skip("Not implemented")
-def test_accessions():
-	parse_accessions("GSE12345", None)
-	assert True
 
+def get_soft_path(gse_numb, sample_len, series_len):
+    """
+    Function that creates parameters to
+    run  test test_file_list
+    """
+    return (
+        os.path.join(GSE_FILES, gse_numb, GSE_SOFT_NAME),
+        os.path.join(GSE_FILES, gse_numb, GSM_SOFT_NAME),
+        sample_len,
+        series_len,
+    )
+
+
+# Soft files that are in the folder
+processed_meta_file_test = [
+    get_soft_path("GSE138657", 14, 0),
+    get_soft_path("GSE146537", 6, 0),
+    get_soft_path("GSE146539", 24, 0),
+    get_soft_path("GSE146540", 0, 1),
+    get_soft_path("GSE146583", 42, 0),
+    get_soft_path("GSE150868", 35, 2),
+]
+
+
+class TestAccParser:
+    """
+    Testing input parser
+    """
+
+    def test_accessions(self):
+        parser_data = parse_accessions("GSE12345", "")
+        assert parser_data == {"GSE12345": {}}
+
+    def test_accessions_file(self):
+        actuall_data = {
+            "GSE185701": {
+                "GSM5621756": "Huh7_siNC_H3K27ac",
+                "GSM5621758": "",
+                "GSM5621760": "Huh7_DHX38",
+                "GSM5621761": "",
+            },
+            "GSE201096": {},
+        }
+
+        parser_data = parse_accessions(INPUT_ACC_FILE, "")
+
+        assert parser_data == actuall_data
+
+
+class TestListProcessedMetaFiles:
+    """
+    Testing
+    """
+
+    @pytest.fixture(scope="function")
+    def initiate_geofetcher(self, tmpdir):
+        instance = Geofetcher(
+            just_metadata=True, processed=True, name="test", metadata_folder=tmpdir
+        )
+        yield instance
+
+    @pytest.mark.parametrize(
+        "soft_gse, soft_gsm, sample_len, series_len", processed_meta_file_test
+    )
+    def test_file_list(
+        self, soft_gse, soft_gsm, sample_len, series_len, initiate_geofetcher
+    ):
+        (
+            meta_processed_samples,
+            meta_processed_series,
+        ) = initiate_geofetcher.get_list_of_processed_files(soft_gse, soft_gsm)
+        assert len(meta_processed_samples) == sample_len
+        assert len(meta_processed_series) == series_len
+
+    def test_downloading_soft_files(self, initiate_geofetcher):
+        initiate_geofetcher.fetch_all("GSE138657")
+        downloaded_meta_files = list(os.walk(initiate_geofetcher.metadata_expanded))[0][
+            2
+        ]
+
+        assert "GSE138657_GSM.soft" in downloaded_meta_files
+        assert "GSE138657_GSE.soft" in downloaded_meta_files
+
+    def test_creating_pep_files(self, initiate_geofetcher):
+        initiate_geofetcher.fetch_all("GSE138657")
+        downloaded_meta_files = list(os.walk(initiate_geofetcher.metadata_expanded))[0][
+            2
+        ]
+
+        assert "GSE138657_samples.csv" in downloaded_meta_files
+        assert "GSE138657_samples.yaml" in downloaded_meta_files
+
+
+class TestDownloadingProcFiles:
+    """
+    Testing downloading processed files
+    """
+
+    @pytest.fixture(scope="function")
+    def initiate_geofetcher(self, tmpdir):
+        instance = Geofetcher(
+            just_metadata=True, processed=True, name="test", metadata_folder=tmpdir
+        )
+        yield instance
+
+    @pytest.mark.parametrize(
+        "file_url, file_name",
+        [
+            (
+                "ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5025nnn/GSM5025270/suppl/GSM5025270_CBS-u2-KO-H3K4me3-ChIP-seq_peaks.bed.gz",
+                "GSM5025270_CBS-u2-KO-H3K4me3-ChIP-seq_peaks.bed.gz",
+            )
+        ],
+    )
+    def test_downloading_files(self, file_url, file_name, tmpdir, initiate_geofetcher):
+        initiate_geofetcher.download_processed_file(file_url, tmpdir)
+
+        assert len(tmpdir.listdir()) == 1
+        assert os.path.basename(tmpdir.listdir()[0]) == file_name
+
+
+class TestFilters:
+    """
+    Testing file filters
+    """
+
+    @pytest.fixture(scope="function")
+    def initiate_geofetcher(self, tmpdir):
+        instance = Geofetcher(
+            just_metadata=True,
+            processed=True,
+            name="test",
+            metadata_folder=tmpdir,
+            filter="\.Bed.gz$",
+            filter_size="2MB",
+        )
+        yield instance
+
+    @pytest.mark.parametrize(
+        "meta_list, output",
+        [
+            (
+                [
+                    {"name": "dupa", "file": "dupa.bed.gz", "file_size": "3MB"},
+                    {"name": "cycki", "file": "cycki.BED.gz", "file_size": "13KB"},
+                    {"name": "ogon", "file": "ogon.xlx.gz", "file_size": "4B"},
+                    {"name": "KROwa", "file": "muuu.BigBed.gz", "file_size": "2000KB"},
+                ],
+                [
+                    {"name": "dupa", "file": "dupa.bed.gz", "file_size": "3MB"},
+                    {"name": "cycki", "file": "cycki.BED.gz", "file_size": "13KB"},
+                ],
+            )
+        ],
+    )
+    def test_filter(self, meta_list, output, initiate_geofetcher):
+        result = initiate_geofetcher.run_filter(meta_list)
+        assert result == output
+
+    @pytest.mark.parametrize(
+        "meta_list, output",
+        [
+            (
+                [
+                    {"name": "dupa", "file": "dupa.bed.gz", "file_size": 3000000},
+                    {"name": "cycki", "file": "cycki.BED.gz", "file_size": 2203000},
+                    {"name": "ogon", "file": "ogon.xlx.gz", "file_size": 1103000},
+                    {"name": "KROwa", "file": "muuu.BigBed.gz", "file_size": 53000},
+                ],
+                [
+                    {"name": "ogon", "file": "ogon.xlx.gz", "file_size": 1103000},
+                    {"name": "KROwa", "file": "muuu.BigBed.gz", "file_size": 53000},
+                ],
+            )
+        ],
+    )
+    def test_size_filter(self, meta_list, output, initiate_geofetcher):
+        result = initiate_geofetcher.run_size_filter(meta_list)
+        assert result == output