semvec/pypi-packages huggingface dataset:
omics_pipe 1.1.0
Extensible computational pipeline for running next generation sequencing analyses
License: MIT
Keywords: biology omics transcriptomics pipeline RNA-seq ChIP-seq WGS WES genome exome HPC

Omics pipe is an open-source, modular computational platform that automates best practice multi-omics data analysis pipelines published in Nature Protocols and other commonly used pipelines, such as GATK.  It currently automates and provides summary reports for two RNAseq and miRNAseq pipelines, variant calling from whole exome sequencing (WES), variant calling and copy  number variation analysis from whole genome sequencing (WGS), two ChIPseq pipelines and a custom RNAseq pipeline for personalized genomic medicine reporting.  It also provides automated support for interacting with the The Cancer Genome Atlas (TCGA) datasets, including automatic download and processing of the samples in this database.
encode-dataframe 0.1
Convert UCSC's ENCODE metadata into pandas DataFrames
License: MIT

ENCODE dataframe
================

I wanted a better way of exploring and downloading raw data from the ENCODE
project.

For example, I'd like to get the BAM files for all ChIP-seq experiments done in
uninduced MEL cells (from the mm9 assembly).

One strategy would be to individually go through each track hub (e.g., histone
mods from LICR, http://genome.cit.nih.gov/cgi-bin/hgFileUi?db=mm9&g=wgEncodeLicrHistone), filter data, and download files individually.

Another strategy would be to go directly to the download page
(http://hgdownload.cse.ucsc.edu/goldenPath/mm9/encodeDCC/wgEncodeLicrHistone/)
and extract the files that end in `.bam`.

This small package takes advantage of the `files.txt` files (here's an `example
<http://hgdownload.cse.ucsc.edu/goldenPath/mm9/encodeDCC/wgEncodeLicrHistone/files.txt>`_)
that describe all the metadata on the download page.

The `files.txt` files are downloaded from each ENCODE track hub in the assembly
of interest.  Then these files are parsed and concatenated together into one
big `pandas.DataFrame` that can be used to find the data you care about.

Installation
------------

::

    pip install encode-dataframe


Usage
-----
Mirror the files.  This may take a minute or so.  If you've cloned the git
repo, you already have a copy of the mm9 files.

>>> import encode_dataframe as edf
>>> edf.mirror_metadata_files('mm9')

Create a large DataFrame:

>>> df = edf.encode_dataframe('mm9')

>>> len(df)
5865

Armed with the dataframe, we can now slice and dice to get the data we care
about.  Eventually I'd like to run a ChromHMM segmentation on MEL cells, but
I need to get the data first . . .

Choose a cell type

>>> interesting = df.cell == 'MEL'

And only BAM files

>>> interesting &= df.type == 'bam'

And only ChIP- or DNase-seq

>>> interesting &= df.dataType.isin(['ChipSeq', 'DnaseSeq'])

And only untreated (in this case, uninduced) cells:

>>> interesting &= df.treatment != 'DMSO_2.0pct'

And only one replicate (some have 2 or 3)

>>> interesting &= df.replicate == '1'

And only those that don't have some issue with them (looks like older versions
have some text in the objStatus field):

>>> interesting &= df.objStatus.isnull()

How many do we have to work with?

>>> m = df[interesting]
>>> len(m)
60

Some of these are controls (input or IgG), and there are some duplicates (looks
like H3K4me3 ChIP-seq uses 2 different controls; CTCF was done by different
groups).  How many unique antibodies?

>>> len(m.antibody.unique())
46

So here are the files I should download:

>>> urls = m.url.values