Repository: NICHD-BSPC/termseq-peaks (language: python)

Issue:
ModuleNotFoundError: No module named 'idr'
Hi,
Got an error while ran the script for calling the peaks.

$ termseq_peaks /media/majorram/Analysis_Data/singhrn/transcriptome_atlas/tss/trimmed/output/barcode07.plus.bedgraph /media/majorram/Analysis_Data/singhrn/transcriptome_atlas/tss/trimmed/output/barcode08.plus.bedgraph /media/majorram/Analysis_Data/singhrn/transcriptome_atlas/tss/trimmed/output/barcode09.plus.bedgraph --peaks /media/majorram/Analysis_Data/singhrn/transcriptome_atlas/tss/trimmed/output/barcode789.plus.peaks --strand +
Traceback (most recent call last):
  File "/home/majorram/anaconda3/bin/termseq_peaks", line 11, in <module>
    from peaklib.peaklib import idr_peak_calls
  File "/home/majorram/anaconda3/lib/python3.9/site-packages/peaklib/peaklib.py", line 6, in <module>
    from idr import idr
ModuleNotFoundError: No module named 'idr'



Discussion / hints:

Fix patch:
diff --git a/.circleci/config.yml b/.circleci/config.yml
deleted file mode 100644
index ba91a90..0000000
--- a/.circleci/config.yml
+++ /dev/null
@@ -1,29 +0,0 @@
-version: 2
-
-jobs:
-  test:
-    docker:
-      - image: continuumio/miniconda3
-
-    steps:
-      - checkout
-      - run:
-          name: install requirements
-          command: conda install -y pytest --file requirements.txt --channel conda-forge --channel bioconda
-      - run:
-          name: build tarball package and pip install it
-          command: |
-            python setup.py sdist && \
-            pip install dist/*tar.gz
-      - run:
-          name: confirm script installed correctly
-          command: termseq_peaks -h
-
-      - run:
-          name: run example data
-          command: (cd peaklib/test && bash test.sh)
-workflows:
-  version: 2
-  test:
-    jobs:
-      - test
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
new file mode 100644
index 0000000..d2a1b95
--- /dev/null
+++ b/.github/workflows/main.yml
@@ -0,0 +1,34 @@
+name: tests
+on: push
+jobs:
+  test:
+    name: testing
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: true
+    steps:
+    - uses: actions/checkout@v1
+    - name: build env
+      run: |
+        eval "$(conda shell.bash hook)"
+        conda create -p ./env \
+          --file requirements.txt \
+          --file optional-requirements.txt \
+          --file test-requirements.txt \
+          --channel conda-forge \
+          --channel bioconda \
+          --strict-channel-priority
+
+        conda activate ./env
+        python setup.py sdist && pip install dist/*.tar.gz
+    - name: confirm script installed correctly
+      run: |
+        eval "$(conda shell.bash hook)" && conda activate ./env
+        termseq_peaks -h
+        class_annot -h
+        table_output -h
+
+    - name: run example date
+      run: |
+        eval "$(conda shell.bash hook)" && conda activate ./env
+        (cd peaklib/test && bash test.sh)
diff --git a/.gitignore b/.gitignore
index 4e2f4e8..5f42b9a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,3 +9,7 @@ wheels/
 *.egg-info/
 .installed.cfg
 MANIFEST
+*.gzi
+*.fai
+env/
+*.gz
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 0000000..24f9beb
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,12 @@
+# 0.2
+
+- Fixed dependencies
+- Added new class annotation and output-reformatting scripts (`class_annot` and
+  `table_output`)
+- added tests running GitHub Actions
+- new scripts, `class_annot` and `table_output`, which can be used for downstream annotation
+
+
+# 0.1
+
+Initial version
diff --git a/README.rst b/README.rst
index d9f10f9..e50dd23 100644
--- a/README.rst
+++ b/README.rst
@@ -50,13 +50,17 @@ One general solution to this is to apply the Irreproducible Discovery Rate
 method (IDR), originally developed for the ENCODE project. By design, the IDR
 method only takes two replicates at a time.
 
+Functions for post-processing are available.
+The functions ``assign`` and ``table_output`` are add-on functions to peaklib
+designed to filter and map the 3'-end peaks relative to annotated ORFs.
+
 
 Usage
 -----
 
 Prepare input
 +++++++++++++
-Required input is normalized signal bedGraphs for each replicate. If data are
+``termseq-peaks`` required input is normalized signal bedGraphs for each replicate. If data are
 stranded, there should be separate files for each strand. Gzipped bedGraphs are
 supported automatically if the filename ends in ``.gz``.
 
@@ -165,6 +169,101 @@ genomes, but has worked quite well for term-seq in several bacterial genomes.
 Furthermore, since we need to perform IDR between all pairwise combinations of
 replicates, the running time scales as O(nreplicates^2).
 
+Post-processing
+---------------
+
+Usage
++++++
+
+**Prepare input**
+
+Required ``assign`` input are:
+
+- strand-specific narrowPeak file, where each interval represents the full size of
+  the detected peak. This can be the output of peaklib function.
+
+- strand-specific bigWigs corresponding to the narrowPeak files. These files might
+  be generated with bamCoverage. I.e. for negative strand bigwig:
+
+::
+
+    bamCoverage \
+        --bam rep1.bam \
+        -o rep1_minus.bw \
+        --binSize 1 \
+        --Offset 1 \
+        --minMappingQuality 20 \
+        --samFlagInclude 16 \
+        --normalizeUsing RPKM 
+
+For positive strand bigwig, swap ``--samFlagInclude 16``  for ``--samFlagExclude 16`` 
+
+- annotation gtf file. The function assumes it contains the mRNAs, sRNAs, tRNAs and rRNAs.
+
+- genome fasta file
+
+- file containing the list of tRNAs and rRNAs names in the 1st column of a tab-separated file. Names can be exact or regex.
+
+Required ``table_output`` input are:
+
+- strand-specific curated peaks assigned to ORFs in a tab-separated file. Typically this is
+  the output of the ``assign`` function.
+
+- optional: opposite strand-specific curated peaks assigned to ORFs in a tab-separated file. Typically
+  this is the output of the ``assign`` function.
+
+- optional: Kinefold output file corresponding to the one or both strand(s) curated peaks.
+
+
+**Run**
+
+The ``assign`` function can be run with:
+
+::
+
+    termseq-peaks assign \
+        --sample sample1_minus \
+        --narrowPeak sample1_minus.narrowPeak \
+        --bw sample1_minus-rep1.bw sample1_minus-rep2.bw \
+        --fasta genome.fa \
+        --gtf annotation.gtf \
+        --trRNA trRNAs.tsv
+
+The curated peaks assigned to an ORF can be found in the output file ``all.sample1_minus.tsv``
+
+The ``table_output`` function can be run with:
+
+::
+
+    termseq-peaks table_output \
+        --sample sample1 \
+        --assigned all.sample1_minus.tsv \
+        --assigned2 all.sample1_plus.tsv \
+        --kinefold_scores kinefold_output.tsv \
+
+The summary file is saved as ``sample1_TableS1.tsv``.
+
+Algorithm
++++++++++
+
+Function ``assign``:
+
+- Return a 1bp-coordinate narrowPeak file corresponding to the highest score coordinate within cluster distance
+- Assign peaks to particular classes:
+    - primary: within 3'end of any ORF (mRNA, tRNA, rRNA, sRNA) included and param_down-bp downstream on the same
+      strand AND has the highest readcount of all such peaks associated within the same region.
+    - secondary: fulfills the above criteria with respect to location BUT is NOT the peak with the highest readcount.
+    - antisense: located within param_antisense-bp upstream, downstream or in an ORF of the opposite strand.
+    - internal: within an any ORF (mRNA, tRNA, rRNA, sRNA) coordinates, excluding the 3'end coordinate on the same strand.
+    - orphan: not associated with any of the above categories. Peaks can have multiple classifications.
+- Also returns lists of peaks within param_upstart-bp upstream of start codon to param_downstart-bp downstream of start
+  codon, and within param_upstop-bp downstream of start codon to the stop codon.
+
+Function ``table_output``:
+
+- Concatenate results from the function ``assign`` and optionally adds the Kinefold scores if provided.
+
+
 References
 ----------
 
diff --git a/bin/class_annot b/bin/class_annot
new file mode 100755
index 0000000..ccdb1f0
--- /dev/null
+++ b/bin/class_annot
@@ -0,0 +1,168 @@
+#!/usr/bin/env python
+
+"""
+Annotates termination peaks with the following classes:
+
+"Primary":
+    - within 3'end included of any ORF (mRNA, tRNA, rRNA, sRNA) and
+      a downstream window (--param_down) on the same strand
+    - must be on same strand as ORF
+    - AND has the highest readcount of all such peaks associated within the
+      region defined by --cluster_length
+
+"Secondary":
+    - Fulfills the Primary criteria with respect to location BUT is NOT the
+      peak with the highest readcount.
+
+"Antisense":
+    - located within upstream window, downstream or within an ORF on the
+      opposite strand.
+
+"Internal":
+    - within any ORF (mRNA, tRNA, rRNA, sRNA) on the same strand, excluding the
+      3'end coordinate
+
+"Orphan":
+    - not associated with any of the categories above
+
+
+Requires:
+
+- bigWig files representing 3' ends of reads
+- narrowPeak output file output from termseq-peaks
+- genome fasta
+- GTF annotation
+"""
+
+from peaklib.assign_class import assign
+
+
+usage = __doc__
+
+import argparse
+
+ap = argparse.ArgumentParser(usage=usage)
+
+# required arguments
+ap.add_argument(
+    "--sample",
+    required=True,
+    help="""Label used to name output files.""",
+)
+
+ap.add_argument(
+    "--narrowPeak",
+    required=True,
+    help="""Path to narrowPeak output file created by termseq-peaks.""",
+)
+
+ap.add_argument(
+    "--bw",
+    nargs="+",
+    required=True,
+    help="""List of paths to bigWig files representing the 3' ends of reads. See
+    docs for details; often this is from unique reads taking the first bp
+    of R1 using deeptools. These should be normalized already (see docs).""",
+)
+
+ap.add_argument(
+    "--fasta",
+    required=True,
+    help="""Path to reference genome fasta, used to extract sequence around peaks.""",
+)
+
+ap.add_argument(
+    "--gtf",
+    required=True,
+    help="""Path to gtf annotations.""",
+)
+
+ap.add_argument(
+    "--output",
+    default='data',
+    help="""Path to the output directory. Default is '%(default)s'""",
+)
+
+ap.add_argument(
+    "--trrna",
+    default=[],
+    help="""Path to file containing the names of tRNAs and rRNAs. Can be exact gene
+    names or regex. Default is not to use any.""",
+)
+
+ap.add_argument(
+    "--cluster_length",
+    default=100,
+    help="""If multiple peaks are found within this distance of each other,
+    then select the highest peak among them and only report that one. Default
+    is %(default)s.""",
+)
+
+ap.add_argument(
+    "--param_antisense",
+    default=50,
+    help="""Threshold distance to ORF to assign to class antisense. Default is
+    %(default)s.""",
+)
+
+ap.add_argument(
+    "--param_down",
+    default=50,
+    help="""Threshold distance downstream of 3'end of ORF to assign to class
+    Primary or Secondary. Default is %(default)s.""",
+)
+
+ap.add_argument(
+    "--param_upstart",
+    default=200,
+    help="""Threshold distance upstream of 5'end of an mRNA ORF to include when
+    returning the list of peaks in promoters. Default is %(default)s.""",
+)
+
+ap.add_argument(
+    "--param_downstart",
+    default=50,
+    help="""Threshold distance downstream of 5'end of an mRNA ORF to include when
+    returning the list of peaks in promoters. Default is %(default)s.""",
+)
+
+ap.add_argument(
+    "--param_upstop",
+    default=50,
+    help="""Threshold distance downstream of 5' end of an mRNA ORF to exclude when
+    return the list of peaks upstream of 3' of mRNA ORF. Default is %(default)s.""",
+)
+
+ap.add_argument(
+    "--param_fasta",
+    default=50,
+    help="""Length upstream of the 3'end of ORF for which the fasta sequence will be returned.
+    Default is %(default)s.""",
+)
+
+ap.add_argument(
+    "--param_downfasta",
+    default=10,
+    help="""Length downstream of the 3'end of ORF for which the fasta sequence will be returned.
+    Default is %(default)s.""",
+)
+args = ap.parse_args()
+
+
+assign(
+    sample=args.sample,
+    narrowPeak=args.narrowPeak,
+    bw=args.bw,
+    fasta=args.fasta,
+    gtf=args.gtf,
+    trRNA=args.trRNA,
+    output=args.output,
+    cluster_length=args.cluster_length,
+    param_antisense=args.param_antisense,
+    param_down=args.param_down,
+    param_upstart=args.param_upstart,
+    param_downstart=args.param_downstart,
+    param_upstop=args.param_upstop,
+    param_fasta=args.param_fasta,
+    param_downfasta=args.param_downfasta
+)
diff --git a/bin/table_output b/bin/table_output
new file mode 100755
index 0000000..3ea72c4
--- /dev/null
+++ b/bin/table_output
@@ -0,0 +1,58 @@
+#!/usr/bin/env python
+
+"""
+Outputs a table from provided assigned peaks.
+"""
+
+from peaklib.assign_class import table_output
+
+usage = __doc__
+
+import argparse
+
+ap = argparse.ArgumentParser(usage=usage)
+
+# required arguments
+ap.add_argument(
+    "--sample",
+    required=True,
+    help="""Label used to compose output files.""",
+)
+
+ap.add_argument(
+    "--assigned",
+    required=True,
+    help="""Path to file with curated peaks. Typically this is the output of
+    the class_annot tool. Can be for a single strand or both.""",
+)
+
+# arguments with default values
+ap.add_argument(
+    "--assigned2",
+    default=None,
+    help="""Optional. Path to file with curated peaks of opposite strand if
+    parameter assigned was for a single strand. Typically this is the output of
+    the assign function. Default is None.""",
+)
+
+ap.add_argument(
+    "--output",
+    default='data',
+    help="""Path to output directory. Default is `data`.""",
+)
+
+ap.add_argument(
+    "--kinefold_scores",
+    default=None,
+    help="""Optional. Path to Kinefold output file corresponding to the curated
+    peaks. Default is None.""",
+)
+args = ap.parse_args()
+
+table_output(
+    sample=args.sample,
+    assigned=args.assigned,
+    assigned2=args.assigned2,
+    output=args.output,
+    kinefold_scores=args.kinefold_scores,
+)
diff --git a/optional-requirements.txt b/optional-requirements.txt
new file mode 100644
index 0000000..9ea90b4
--- /dev/null
+++ b/optional-requirements.txt
@@ -0,0 +1,1 @@
+bedtools
diff --git a/peaklib/assign_class.py b/peaklib/assign_class.py
new file mode 100644
index 0000000..9fd9a0c
--- /dev/null
+++ b/peaklib/assign_class.py
@@ -0,0 +1,1119 @@
+#!/usr/bin/env python
+
+import os
+import pandas as pd
+import numpy as np
+import pybedtools
+from pybedtools import featurefuncs
+import csv
+import subprocess as sp
+import gzip
+import pysam
+
+
+
+
+class CuratePeaks:
+    def __init__(self, sample, narrowPeak, bw, cluster_length, output, region=None):
+        """
+        Return a 1bp-coordinate narrowPeak file corresponding to the highest score coordinate
+        within cluster distance
+
+        Parameters
+        ----------
+
+        sample : str
+            Label for output
+
+        narrowPeak : str
+            Path to strand-specific narrowPeak file, where each interval represents the full
+            size of the detected peak
+
+        bw : list
+            List of strand-specific bigWigs corresponding to the narrowPeak argument
+
+        cluster_length : int
+            If multiple peaks are found within this distance of each other,
+            then select the highest peak among them and only report that one.
+
+        output : str
+            Path to output directory. Will be created if it does not already
+            exist.
+
+        region : str
+            Subset region to use for this object. Useful for spot-checking and
+            testing.
+        """
+
+        self.region = region
+        self.output = output
+
+        self.narrowpeak_df = pybedtools.BedTool(narrowPeak).to_dataframe()
+
+        # incoming narrowPeak intervals may be large; select the single-bp
+        # highest point within each interval
+        self.onebp = self.precise_peak(sample, self.narrowpeak_df, bw)
+
+        # there may be several 1-bp peaks in the above output that are within
+        # `cluster_length`. Select only the highest within that range.
+        self.curated = self.max_in_cluster(self.onebp, cluster_length)
+
+    def precise_peak(self, sample, narrowpeak_df, bw):
+        """
+        Return a 1bp-coordinate dataframe corresponding to the highest score
+        within narrowPeak intervals.
+
+        If this object was provided a `region`, only that subsetted region will
+        be used.
+        """
+        if not os.path.exists(self.output):
+            os.makedirs(self.output)
+
+        npzfn = os.path.join(self.output, sample + ".npz")
+        rawfn = os.path.join(self.output, sample + ".raw.tsv")
+
+        # Use multiBigwigSummary to average the bigwigs, and select the highest
+        # single-bp position within the interval to be reported
+
+        if self.region:
+            region_arg = ['--region', self.region]
+        else:
+            region_arg = []
+        sp.run(
+            [
+                'multiBigwigSummary', 'bins', '-bs', '1', '-b'
+            ] + bw + region_arg + [
+                '-o', npzfn, '--outRawCounts', rawfn
+            ],
+            check=True)
+
+        raw = pd.read_csv(rawfn, sep="\t")
+        # raw is a dataframe like:
+        #
+        #  chr start end  bw1 bw2 bw3
+        #  chr 0     1    5   6   10
+        #  chr 1     2    8   3   4
+
+        raw["sum"] = raw.iloc[:, -len(bw) :].sum(axis=1)
+        #  chr start end  bw1 bw2 bw3 sum
+        #  chr 0     1    5   6   10  21
+        #  chr 1     2    8   3   14  25
+
+        raw["'end'"] = pd.to_numeric(raw["'end'"])
+
+        def highest(interval, raw):
+            subraw = raw[
+                (raw["#'chr'"] == interval['chrom']) & \
+                (raw["'end'"] >= interval['start']) & \
+                (raw["'end'"] <= interval['end'])
+            ]
+            subraw = subraw.loc[subraw['sum'].idxmax(),:]
+            return subraw[["'end'", 'sum']]
+
+        # narrowpeak_df is a dataframe like:
+        #
+        # chrom  start  end  name  score  strand  ...
+        # chr    1      3    .     1000   +       ...
+        narrowpeak_df[['highest_coord', 'sum']] = narrowpeak_df.apply(
+            lambda x: highest(x, raw), axis=1)
+        # chrom  start  end  name  score  strand  ...  highest_coord  sum
+        # chr    1      3    .     1000   +       ...  2              25
+
+        narrowpeak_df['start'] = narrowpeak_df['highest_coord']
+        narrowpeak_df['end'] = narrowpeak_df['highest_coord']
+        # chrom  start  end  name  score  strand  ...  highest_coord  sum
+        # chr    2      2    .     1000   +       ...  2              25
+
+        # Replace the name '.' by a combination of chr_start_end
+        narrowpeak_df.iloc[:, 3] = narrowpeak_df.iloc[:, 0:3].apply(
+            lambda x: "_".join(x.map(str)), axis=1
+        )
+        # Sort by chromosome then by start coordinate
+        narrowpeak_df = narrowpeak_df.sort_values(["chrom", "start"])
+        return narrowpeak_df
+
+    def max_in_cluster(self, onebp, cluster_length):
+        """
+        Return the peak with highest score among peaks within cluster range
+        """
+        df = (
+            pybedtools.BedTool.from_dataframe(onebp, header=None)
+            .cluster(d=cluster_length)
+            .to_dataframe(header=None)
+        )
+        df.columns = ['chrom', 'start', 'end', 'name', 'score', 'strand',
+                      'na1', 'na2', 'na3', 'na4', 'highest_coord', 'sum', 'cluster']
+        idx = df.groupby('cluster')["score"].transform(max) == df["score"]
+        return df[idx].iloc[:, 0:10]
+
+
+class PrepGtfs:
+    def __init__(self, gtf):
+        """
+        Splits the annotation file in 2 dataframes of ORFs minus their 3'end coordinate,
+        and another of the 3'end coordinates only.
+
+        Parameters
+        ----------
+
+        gtf : str
+            Path to annotation gtf file, assumes it contains the mRNAs, sRNAs, tRNAs and rRNAs
+
+        intergenic_min_size: integer
+            Minimun intergenic region size under which the 2 same-strand adjacent ORFs
+            will be considered too close to assign the peak to a promoter
+
+        """
+        self.full = pybedtools.BedTool(gtf)
+        self.orf = self.split_3end(gtf)[0]
+        self.orf3end = self.split_3end(gtf)[1]
+
+    def split_3end(self, gtf):
+        """
+        Return 2 dataframes corresponding to the ORFs minus the 3' end coordinate (gtfintern)
+        and to the 3'end coordinate only (gtf3prime).
+        """
+        gtf = pybedtools.BedTool(gtf)
+        gtf3prime = gtf.each(
+            featurefuncs.three_prime,
+            downstream=0,
+            upstream=1).saveas()
+        gtfintern = gtf.subtract(gtf3prime, s=True)
+        return [gtfintern.to_dataframe(), gtf3prime.to_dataframe()]
+
+
+
+class ClassAssign:
+    def __init__(
+        self,
+        peakdf,
+        gtfs,
+        fasta,
+        param_antisense,
+        param_down,
+        trRNA,
+        param_upstart,
+        param_downstart,
+        param_upstop,
+        param_fasta,
+        param_downfasta,
+        output,
+        sample,
+        offset=None
+    ):
+        """
+        Assign peaks to particular classes:
+        - primary: within 3'end of any ORF (mRNA, tRNA, rRNA, sRNA) included and param_down-bp
+          downstream on the same strand AND has the highest readcount of all such peaks
+          associated within the same region.
+        - secondary: fulfills the above criteria with respect to location BUT
+          is NOT the peak with the highest readcount.
+        - antisense: located within param_antisense-bp upstream, downstream or in an
+          ORF of the opposite strand.
+        - internal: within an any ORF (mRNA, tRNA, rRNA, sRNA) coordinates, excluding the
+          3'end coordinate on the same strand.
+        - orphan: not associated with any of the above categories.
+        Peaks can have multiple classifications.
+        Also returns lists of peaks within param_upstart-bp upstream of start codon to
+        param_downstart-bp downstream of start codon, and within param_upstop-bp downstream
+        of start codon to the stop codon.
+
+        Parameters
+        ----------
+
+        peakdf: dataframe
+            List of curated 1bp-peaks. Typically this is the output `curated` of class CuratePeaks
+
+        gtfs: list of 2 dataframes
+            A dataframe of ORFs minus their 3'end coordinate, and a dataframe of the 3'end
+            coordinates only. Typically, this is the output of class PrepGtfs
+
+        fasta: str
+            Filename for genome fasta
+
+        param_antisense: int
+            Threshold distance to ORF to assign to class antisense
+
+        param_down: int
+            Threshold distance downstream of 3'end of ORF to assign to class
+            Primary or Secondary
+        trRNA: str
+            Filename to file containing the list of tRNAs and rRNAs. Names can be exact or
+            regex.
+        param_upstart: int
+            Threshold distance upstream of 5'end of an mRNA ORF to include when
+            returning the list of peaks in promoters
+
+        param_downstart: int
+            Threshold distance downstream of 5'end of an mRNA ORF to include when
+            returning the list of peaks in promoters
+
+        param_upstop: int
+            Threshold distance downstream of 5' end of an mRNA ORF to exclude when
+            return the list of peaks upstream of 3' of mRNA ORF
+
+        param_fasta: int
+            Length upstream of the 3'end of ORF for which the fasta sequence will be returned
+
+        param_downfasta: int
+            Length downstream of the 3'end of ORF for which the fasta sequence will be returned
+
+        output: str
+            Path to output directory
+
+        sample: str
+            Name of sample
+        """
+        self.peakdf = peakdf
+        self.fai = self.index_func(fasta)
+        self.prepeaks = pybedtools.BedTool.from_dataframe(
+            peakdf[["chrom", "start", "end", "name", "score", "strand"]]
+        )
+        self.prebedmRNA = pybedtools.BedTool.from_dataframe(gtfs.orf)
+        self.prebedmRNA3 = pybedtools.BedTool.from_dataframe(gtfs.orf3end)
+
+        # if the offset is set, shift all coordinates
+        self.offset = offset
+        self.bedpeaks = self.prepeaks.shift(g=self.fai[0], s=-self.offset) if \
+            self.offset else self.prepeaks
+        self.bedmRNA = self.prebedmRNA.shift(g=self.fai[0], s=-self.offset) if \
+            self.offset else self.prebedmRNA
+        self.bedmRNA3 = self.prebedmRNA3.shift(g=self.fai[0], s=-self.offset) if \
+            self.offset else self.prebedmRNA3
+        self.gtfsfull = gtfs.full.shift(g=self.fai[0], s=-self.offset) if \
+            self.offset else gtfs.full
+
+        self.antisense = self.antisense_func(
+            self.peakdf, self.bedpeaks, self.bedmRNA, param_antisense, self.fai[0]
+        )
+        self.internal = self.internal_func(
+            self.peakdf, self.bedpeaks, self.gtfsfull, self.fai[0]
+        )
+        self.primary = self.primary_func(
+            self.peakdf, self.bedpeaks, self.bedmRNA3, param_down, trRNA, self.fai, output, sample
+        )
+        self.details_class = self.details_class_func(
+            self.primary, self.antisense, self.internal
+        )
+        self.upstart_upstop = self.upstart_upstop_func(
+            self.peakdf,
+            self.bedpeaks,
+            trRNA,
+            gtfs.orf,
+            self.bedmRNA3,
+            param_upstart,
+            param_downstart,
+            param_upstop,
+        )
+        self.fasta = self.fasta_func(
+            self.peakdf, self.fai, param_fasta, param_downfasta
+        )
+
+    def index_func(self, fasta):
+        """ Return index file after creating it if did not exist"""
+        if not os.path.exists(fasta + '.fai'):
+            # checks if fasta is gzipped
+            with gzip.open(fasta, 'r') as fh:
+                try:
+                    fh.read(1)
+                    # if gzipped, unzip
+                    os.system('gunzip -c ' + fasta + ' > ' + fasta.replace('.gz', ''))
+                    fasta = fasta.replace('.gz', '')
+                finally:
+                    pysam.faidx(fasta)
+        return [fasta + '.fai', fasta]
+
+    intsct_cols = ['chrom', 'start', 'end', 'name', 'score', 'strand',
+                        'intsct_chrom', 'insct_source', 'intsct_feature',
+                        'intsct_start', 'intsct_end', 'intsct_score',
+                        'intsct_strand', 'intsct_na', 'intsct_name']
+
+    def antisense_func(self,
+                       peakdf,
+                       bedpeaks,
+                       bedmRNA,
+                       param_antisense,
+                       fai,
+                       intsct_cols=intsct_cols):
+        """ Determine peaks antisense from ORFs"""
+        temp = bedpeaks.intersect(
+                [
+                    bedmRNA.slop(g=fai, b=param_antisense),
+                ],
+                loj=True,
+                S=True,
+            ).to_dataframe(header=None)
+        temp.columns = intsct_cols
+        # temp is a dataframe like:
+        # chrom    start   end   strand   name           ...  instc_name
+        # chr      227     230   +        chr_227_230_+  ...  gene_id "ID1";
+        # chr      227     230   +        chr_227_230_+  ...  gene_id "ID2";
+
+        peakdf["antisense"] = temp \
+            .groupby("name")["intsct_name"] \
+            .apply(lambda x: ", ".join(x)) \
+            .to_frame()["intsct_name"]
+        # peakdf:
+        # name          chrom    start   end   strand   ...  antisense
+        # chr_227_230_+ chr      227     230   +        ...  gene_id "ID1"; gene_id "ID2";
+
+        peakdf["antisense"] = np.where(
+            peakdf["antisense"] == ".", np.nan, ("A:" + peakdf["antisense"])
+        )
+        # peakdf:
+        # name          chrom    start   end   strand   ...  antisense
+        # chr_227_230_+ chr      227     230   +        ...  A:gene_id "ID1"; gene_id "ID2";
+
+        return peakdf["antisense"]
+
+    def internal_func(self, peakdf, bedpeaks, gtffull, fai, intsct_cols=intsct_cols):
+        """ Determine peaks internal to ORF, in same orientation"""
+        # need to shift internal coord of minus strand ORF 3'end by -1 because of bedtools intersect nt-base
+        # use bedtools shift
+        shifted = gtffull.shift(g=fai, p=0,m=1)
+        temp = bedpeaks.intersect(
+            shifted,
+            loj=True, s=True, f=1
+        ).to_dataframe(header=None)
+        temp.columns = intsct_cols
+        # temp is a dataframe like:
+        # chrom    start   end   strand   name           ...  instc_name
+        # chr      227     230   +        chr_227_230_+  ...  gene_id "ID3";
+        # chr      227     230   +        chr_227_230_+  ...  gene_id "ID4";
+
+        peakdf["internal"] = (
+            temp
+            .groupby("name")["intsct_name"]
+            .apply(lambda x: ", ".join(x))
+            .to_frame()["intsct_name"]
+        )
+        # peakdf:
+        # name          chrom    start   end   strand   ...  internal
+        # chr_227_230_+ chr      227     230   +        ...  gene_id "ID3"; gene_id "ID4";
+
+        peakdf["internal"] = np.where(
+            peakdf["internal"] == ".", np.nan, ("I:" + peakdf["internal"])
+        )
+        # peakdf:
+        # name          chrom    start   end   strand   ...  internal
+        # chr_227_230_+ chr      227     230   +        ...  I:gene_id "ID3"; gene_id "ID4";
+
+        return peakdf["internal"]
+
+    def primary_func(self,
+                     peakdf,
+                     bedpeaks,
+                     bedmRNA3,
+                     param_down,
+                     trRNA,
+                     fai,
+                     output,
+                     sample,
+                     intsct_cols=intsct_cols):
+        """
+        Determine primary and secondary peaks:
+        - Primary: within 3'end of any ORF (mRNA, tRNA, rRNA, sRNA) included
+        and param_down-bp downstream on the same strand AND has the highest readcount of all such peaks
+        associated within the same region.
+        - Secondary: identical to Primary but not the highest readcount.
+        """
+
+        shifted = bedmRNA3.shift(g=fai[0], p=0,m=1)
+
+        peaksmrna = bedpeaks.intersect(
+                shifted.each(
+                    featurefuncs.three_prime,
+                    downstream=param_down,
+                    upstream=0),
+                loj=True,
+                s=True,
+            ).to_dataframe(header=None)
+        peaksmrna.columns = intsct_cols
+        # peaksmrna is a dataframe like:
+        # chrom    start   end   strand   name           ...  instc_name
+        # chr      227     230   +        chr_227_230_+  ...  gene_id "ID5";
+        # chr      227     230   +        chr_227_230_+  ...  gene_id "ID6";
+
+        peaksmrna["origin"] = np.where(peaksmrna["intsct_name"] != ".", "mRNA", np.nan)
+        # chrom    start   end   strand   name           ...  instc_name        origin
+        # chr      227     230   +        chr_227_230_+  ...  gene_id "ID5";    mRNA
+        # chr      227     230   +        chr_227_230_+  ...  gene_id "tRNA1";  mRNA
+
+        # remove rRNA and tRNA from mRNA origin
+        for i in pd.read_csv(trRNA, sep="\t", header=None)[0]:
+            peaksmrna["origin"] = np.where(
+                peaksmrna["intsct_name"].str.contains(i), np.nan, peaksmrna["origin"]
+            )
+        # chrom    start   end   strand   name           ...  instc_name        origin
+        # chr      227     230   +        chr_227_230_+  ...  gene_id "ID5";    mRNA
+        # chr      227     230   +        chr_227_230_+  ...  gene_id "tRNA1";  NaN
+
+        peaksmrna["origin"] = peaksmrna["origin"].replace("nan", np.NaN)
+
+        # the primary peak is defined as the highest score peak within primary range
+        # of a mRNA, tRNA, rRNA or sRNA
+        idx = (
+            peaksmrna.groupby("intsct_name")["score"].transform(max) == peaksmrna["score"]
+        )
+        peaksmrna["primary"] = np.where(
+            (idx) & (peaksmrna["intsct_name"] != "."), peaksmrna["intsct_name"], np.nan
+        )
+        # chrom    start   end   strand   name           ...  instc_name       origin  primary
+        # chr      227     230   +        chr_227_230_+  ...  gene_id "ID5";   mRNA    gene_id "ID5";
+        # chr      227     230   +        chr_227_230_+  ...  gene_id "tRNA1"; NaN
+
+        # calculate distances between gene 3'end and termination peak
+        peaksmrna["distance"] = np.where(
+            peaksmrna["strand"] == "+",
+            peaksmrna["start"] - peaksmrna["intsct_start"] +1,
+            peaksmrna["intsct_end"] - peaksmrna["start"] +1,
+        )
+        peaksmrna["distance"] = np.where(
+            peaksmrna["intsct_name"] != ".", peaksmrna["distance"], np.nan
+        )
+        # chrom    start   end   ...  instc_name       origin  primary          distance
+        # chr      227     230   ...  gene_id "ID5";   mRNA    gene_id "ID5";   5
+        # chr      227     230   ...  gene_id "tRNA1"; NaN     gene_id "tRNA1"; 12
+
+        # distance to mRNAs only (exclude tRNA and rRNA)
+        peaksmrna["distance_to_mRNA"] = np.where(
+            peaksmrna["origin"] == "mRNA", peaksmrna["distance"], np.nan
+        )
+
+        # save the distance matrix
+        fn = os.path.join(output, sample + "_primary_distances.tsv")
+        peaksmrna[
+            [
+                "chrom",
+                "start",
+                "end",
+                "name",
+                "intsct_name",
+                "distance",
+                "distance_to_mRNA",
+            ]
+        ].to_csv(fn, sep="\t", index=None, header=None, quoting=csv.QUOTE_NONE)
+
+        # copy peaksmrna results over to peakdf primary
+        peakdf["primary"] = (
+            peaksmrna.groupby("name")["primary"]
+            .apply(lambda x: ",".join(set(x.dropna())))
+            .to_frame()["primary"]
+        )
+        # peakdf is a dataframe like:
+        #               chrom   start   end  ...    primary
+        # chr_228_228   chr     228     228  ...    gene_id "ID5";
+
+        peakdf["primary"] = np.where(
+            peakdf["primary"] == "", np.nan, ("P:" + peakdf["primary"])
+        )
+        #               chrom   start   end  ...    primary
+        # chr_228_228   chr     228     228  ...    P: gene_id "ID5";
+
+        peakdf["primary_origin"] = (
+            peaksmrna.groupby("name")["origin"]
+            .apply(lambda x: ",".join(set(x.dropna())))
+            .to_frame()["origin"]
+        )
+        #               chrom   start   end  ...    primary            primary_origin
+        # chr_228_228   chr     228     228  ...    P: gene_id "ID5";  mRNA
+
+        peakdf["primary_distance_to_mRNA"] = (
+            peaksmrna.groupby("name")["distance_to_mRNA"]
+            .apply(lambda x: ",".join(set(x.dropna().astype(int).astype(str))))
+            .to_frame()["distance_to_mRNA"]
+        )
+        #               chrom   start   end  ...    primary_origin  primary_distance_to_mRNA
+        # chr_228_228   chr     228     228  ...    mRNA            5
+
+        # secondary peaks
+        peaksmrna["secondary"] = np.where(
+            (~idx) & (peaksmrna["intsct_name"] != "."), peaksmrna["intsct_name"], np.nan
+        )
+        # peaksmrna is a dataframe like:
+        # chrom start   end  ... primary        distance  distance_to_mRNA  secondary
+        # chr   227     230 ...  gene_id "ID5"; 5         5
+        # chr   227     230  ...                12                          gene_id "tRNA1";
+
+        peakdf["secondary"] = (
+            peaksmrna.groupby("name")["secondary"]
+            .apply(lambda x: ",".join(x.dropna()))
+            .to_frame()["secondary"]
+        )
+        #               chrom   start   ...    primary_origin  primary_distance_to_mRNA    secondary
+        # chr_228_228   chr     228     ...    mRNA            5                           gene_id "tRNA1";
+
+        peakdf["secondary"] = np.where(
+            peakdf["secondary"] == "", np.nan, ("S:" + peakdf["secondary"])
+        )
+        #               chrom   start   ...    primary_origin  primary_distance_to_mRNA    secondary
+        # chr_228_228   chr     228     ...    mRNA            5                           S: gene_id "tRNA1";
+
+        return peakdf[
+            ["primary", "primary_origin", "primary_distance_to_mRNA", "secondary"]
+        ]
+
+    def details_class_func(self, primary, antisense, internal):
+        """ Concatenate the results from primary, antisense and internal """
+        df = pd.concat([primary[["primary", "secondary"]], internal, antisense], axis=1)
+        df["details"] = df.apply(lambda x: ",".join(x.dropna().map(str)), axis=1)
+        # df is a dataframe like:
+        #               primary             secondary             antisense
+        # chr_228_228   P: gene_id "ID5";   S: gene_id "tRNA1";   A:gene_id "ID1"; gene_id "ID2";
+        #
+        #               internal                            details
+        #               I:gene_id "ID3"; gene_id "ID4";     P: gene_id "ID5";S: gene_id "tRNA1";A:gene_id "ID1"; gene_id "ID2";I:gene_id "ID3"; gene_id "ID4";
+
+        # add 'primary', 'secondary', ... to the classification
+        df["classification"] = np.where(~df["primary"].isna(), "primary, ", "")
+        df["classification"] = np.where(
+            ~df["secondary"].isna(),
+            (df["classification"] + "secondary, "),
+            df["classification"],
+        )
+        df["classification"] = np.where(
+            ~df["internal"].isna(),
+            (df["classification"] + "internal, "),
+            df["classification"],
+        )
+        df["classification"] = np.where(
+            ~df["antisense"].isna(),
+            (df["classification"] + "antisense, "),
+            df["classification"],
+        )
+        df["classification"] = np.where(
+            df["classification"] == "", "orphan", df["classification"]
+        )
+        #               primary             ...     details                                 classification
+        # chr_228_228   P: gene_id "ID5";   ...     P: gene_id "ID5"; ... gene_id "ID4";    primary, sedcondary, antisense, internal
+        # chr_450_450                                                                       orphan
+
+        return df[["details", "classification"]]
+
+    def upstart_upstop_func(
+        self,
+        peakdf,
+        bedpeaks,
+        trRNA,
+        orf,
+        bedmRNA3,
+        param_upstart,
+        param_downstart,
+        param_upstop,
+    ):
+        # make bed mRNA without tRNA, rRNA (nor sRNA)
+        mRNAonly = orf.copy()
+        for i in pd.read_csv(trRNA, sep="\t", header=None)[0]:
+            mRNAonly = mRNAonly[~mRNAonly["attributes"].str.contains(i)]
+        premRNAonly = pybedtools.BedTool.from_dataframe(mRNAonly)
+        bedmRNAonly = premRNAonly.shift(g=self.fai[0], s=-self.offset) if \
+            self.offset else premRNAonly
+
+        # label peaks within 10bp downstream of any ORF
+        peakstmp = bedpeaks.intersect(
+            [
+                bedmRNA3.each(featurefuncs.three_prime, downstream=10, upstream=0),
+            ],
+            loj=True,
+            s=True,
+        ).to_dataframe(header=None)
+        peakstmp.columns = [
+            "chrom", "start", "end",
+            "name", "score", "strand",
+            "chr-intersect", "source-intersect", "feature-intersect",
+            "start-intersect", "end-intersect", "score-intersect",
+            "strand-intersect", "na-intersect", "name-intersect"
+        ]
+        # peakstmp is a dataframe like:
+        # chrom     start   end     ...     name-intersect
+        # chr       228     228     ...     gene_id "ID10"
+        # chr       450     450     ...     .
+
+        # discard peaks within 10bp downstream of an ORF
+        peakstmp = peakstmp[peakstmp["chr-intersect"] != "."]
+        # chrom     start   end     ... name-intersect
+        # chr       228     228     ... gene_id "ID10"
+
+        peakdf["within_10bp-downstream"] = (
+            peakstmp.groupby("name")["name-intersect"]
+            .apply(lambda x: ", ".join(x))
+            .to_frame()["name-intersect"]
+        )
+        # peakdf is a dataframe like:
+        #                   chrom     start   end     ...     within_10bp_dowsntream
+        #  chr_228_228      chr       228     228     ...     gene_id "ID10"
+        #  chr_450_450      chr       450     450     ...     NaN
+
+        # calculate distances peaks to start of mRNA only ORF
+        peakstmp = pd.concat(
+            [
+                bedpeaks.sort()
+                .closest(
+                    bedmRNAonly.each(
+                        featurefuncs.five_prime, downstream=1, upstream=0
+                    ).sort(),
+                    D="b",
+                    k=10,
+                    s=True,
+                )
+                .to_dataframe(header=None)
+            ]
+        )
+        peakstmp.columns = [
+            "chrom", "start", "end",
+            "name", "score", "strand",
+            "chr-intersect", "source-intersect", "feature-intersect",
+            "start-intersect", "end-intersect", "score-intersect",
+            "strand-intersect", "na-intersect", "name-intersect", "distance"
+        ]
+        # peakstmp is a dataframe like:
+        # chrom     start   end     ...     name-intersect  distance
+        # chr       228     228     ...     gene_id "ID10"  19
+        # chr       630     630     ...     gene_id "ID11"  299
+
+        # add 1 to distances because closest calculate the length of interval between features
+        peakstmp["distance"] = np.where(
+            peakstmp["distance"] >= 0,
+            peakstmp["distance"] + 1,
+            peakstmp["distance"] - 1,
+        )
+        # start 200bp: subset peakstmp to peaks within param_upstart upstream and param_downstart
+        # downstream of start of ORF
+        peakstmp = peakstmp[
+            (
+                (
+                    (peakstmp["distance"].abs() <= param_upstart)
+                    & (peakstmp["distance"] < 0)
+                )
+                | (
+                    (peakstmp["distance"].abs() <= param_downstart)
+                    & (peakstmp["distance"].abs() >= 0)
+                )
+            )
+        ]
+        # chrom     start   end     ...     name-intersect  distance
+        # chr       228     228     ...     gene_id "ID10"  19
+
+        # copy results over to peakdf
+        peakdf["upstart200"] = (
+            peakstmp.groupby("name")["name-intersect"]
+            .apply(lambda x: ", ".join(x))
+            .to_frame()["name-intersect"]
+        )
+        peakdf["dist_upstart200"] = (
+            peakstmp.groupby("name")["distance"]
+            .apply(lambda x: ", ".join(map(str, x)))
+            .to_frame()["distance"]
+        )
+        # peakdf is a dataframe like:
+        #                   chrom     start   end     ...     upstart200        dist_upstart200
+        #  chr_228_228      chr       228     228     ...     gene_id "ID10"    19
+        #  chr_450_450      chr       450     450     ...     NaN               NaN
+
+        # stop: within mRNA only ORF but not in the first 50bp
+        mRNAonly51 = mRNAonly.copy()
+        # bed of param_upstop + 1bp downstream of ORF start to ORF end
+        mRNAonly51["start"] = np.where(
+            mRNAonly51["strand"] == "+",
+            mRNAonly51["start"] + param_upstop,
+            mRNAonly51["start"],
+        )
+        mRNAonly51["end"] = np.where(
+            mRNAonly51["strand"] == "+",
+            mRNAonly51["end"],
+            mRNAonly51["end"] - param_upstop,
+        )
+        bedmRNAonly51 = pybedtools.BedTool.from_dataframe(mRNAonly)
+
+        #  peaks intersecting with mRNAonly51
+        bedtmp = bedpeaks.sort().intersect(bedmRNAonly51, s=True, loj=True)
+        peakstmp = bedtmp.closest(
+            bedmRNAonly.each(  # distance to start codon
+                featurefuncs.five_prime, downstream=1, upstream=0
+            ).sort(),
+            D="b",
+            k=10,
+            s=True,
+        ).to_dataframe(header=None)
+
+        peakstmp.columns = [
+            "chrom", "start", "end",
+            "name", "score", "strand",
+            "chr-intersect", "source-intersect", "feature-intersect",
+            "start-intersect", "end-intersect", "score-intersect",
+            "strand-intersect", "na-intersect", "name-intersect",
+            "chr-closest", "source-closest", "feature-closest",
+            "start-closest", "end-closest", "score-closest",
+            "strand-closest", "na-closest", "name-closest",
+            "distance"
+        ]
+        # chrom     start ... name-intersect    ...     name-closest    distance
+        # chr       93        gene_id "ID30";   ...     gene_id "ID30"; 63
+        # chr       842       -1                ...     gene_id "ID40"; 183
+
+        # only keep distances for same gene than intersect
+        peakstmp = peakstmp[
+            (
+                (peakstmp["name-intersect"] == peakstmp["name-closest"])
+                & (peakstmp["distance"] >= param_upstop)
+            )
+        ]
+        # chrom     start ... name-intersect    ...     name-closest    distance
+        # chr       93        gene_id "ID30";   ...     gene_id "ID30"; 63
+
+        # fix distances to ORF, because closest give to outside of ORF
+        peakstmp["distance"] = np.where(
+            peakstmp["distance"] >= 0,
+            peakstmp["distance"] + 1,
+            peakstmp["distance"] - 1,
+        )
+        # copy over results to peakdf
+        peakdf["upstop"] = (
+            peakstmp.groupby("name")["name-closest"]
+            .apply(lambda x: ", ".join(x))
+            .to_frame()["name-closest"]
+        )
+        peakdf["dist_upstop"] = (
+            peakstmp.groupby("name")["distance"]
+            .apply(lambda x: ", ".join(map(str, x)))
+            .to_frame()["distance"]
+        )
+        return peakdf[
+            [
+                "within_10bp-downstream",
+                "upstart200",
+                "dist_upstart200",
+                "upstop",
+                "dist_upstop",
+            ]
+        ]
+
+    def fasta_func(self, peakdf, fai, param_fasta, param_downfasta):
+        # add fasta sequences
+        dfplus = pybedtools.BedTool.from_dataframe(
+            peakdf[peakdf["strand"] == "+"], header=None
+        )
+        dfminus = pybedtools.BedTool.from_dataframe(
+            peakdf[peakdf["strand"] == "-"], header=None
+        )
+        if self.offset:
+            dfplus = dfplus.shift(g=fai[0], s=-self.offset)
+            dfminus = dfminus.shift(g=fai[0], s=-self.offset)
+        genome = {}
+        with open(fai[0]) as f:
+            for line in f:
+                chrom = line.split()
+                genome[chrom[0]] = (0, int(chrom[1]))
+        # want params.fasta bp upstream of the 3'end, 3'end then 10 bp downstream, so need to add 1 upstream
+        dfplus2 = dfplus.each(
+            featurefuncs.three_prime,
+            downstream=param_downfasta,
+            upstream=int(param_fasta) + 1,
+            genome=genome,
+        ).saveas()
+        # same for minus strand, but was shifted by 1bp when on the minus strand
+        dfminus2 = dfminus.each(
+            featurefuncs.three_prime,
+            downstream=int(param_downfasta) + 1,
+            upstream=param_fasta,
+            genome=genome,
+        ).saveas()
+        # concatenate both strands
+        df2 = dfplus2.cat(dfminus2, postmerge=False).sort()
+        # truncate to chromosome size in case an interval would go over
+        df2 = df2.truncate_to_chrom(genome)
+        # get sequence
+        df3 = df2.sequence(fi=fai[1], s=True)
+        fatmp = pd.read_csv(df3.seqfn, header=None).iloc[1::2, :]
+        fatmp["idx"] = list(peakdf.index)
+        fatmp = fatmp.set_index("idx")
+        fatmp.columns = ["upstream_3end_seq"]
+        return fatmp
+
+
+def assign(
+    sample,
+    narrowPeak,
+    bw,
+    fasta,
+    gtf,
+    trRNA,
+    output='data',
+    cluster_length=100,
+    param_antisense=50,
+    param_down=50,
+    param_upstart=200,
+    param_downstart=50,
+    param_upstop=50,
+    param_fasta=50,
+    param_downfasta=10,
+    region=None,
+    offset=None
+):
+    """
+    Parameters
+    ----------
+
+    sample : str
+        Label used to compose output files
+
+    narrowPeak : str
+        Path to narrowPeak output file created by termseq-peaks.
+
+    bw : list
+        List of paths to bigWig files representing the 3' ends of reads. See
+        docs for details; often this is from unique reads taking the first bp
+        of R1 using deeptools. These should be normalized already (see docs)
+
+    fasta : str
+        Path to reference genome fasta, used to extract sequence around peaks
+
+    gtf : str
+        Path to gtf annotations.
+
+    trRNA: str
+        Path to file containing the names of tRNAs and rRNAs. Can be exact gene
+        names or regex.
+
+    output: str
+        Path to output directory. Default is `data`.
+
+    cluster_length: int
+        If multiple peaks are found within this distance of each other,
+        then select the highest peak among them and only report that one. Default is 100.
+
+    param_antisense: int
+        Threshold distance to ORF to assign to class antisense. Default is 50.
+
+    param_down: int
+        Threshold distance downstream of 3'end of ORF to assign to class
+        Primary or Secondary. Default is 50.
+
+    param_upstart: int
+        Threshold distance upstream of 5'end of an mRNA ORF to include when
+        returning the list of peaks in promoters. Default is 200.
+
+    param_downstart: int
+        Threshold distance downstream of 5'end of an mRNA ORF to include when
+        returning the list of peaks in promoters. Default is 50.
+
+    param_upstop: int
+        Threshold distance downstream of 5' end of an mRNA ORF to exclude when
+        return the list of peaks upstream of 3' of mRNA ORF. Default is 50.
+
+    param_fasta: int
+        Length upstream of the 3'end of ORF for which the fasta sequence will be returned.
+        Default is 50.
+
+    param_downfasta: int
+        Length downstream of the 3'end of ORF for which the fasta sequence will be returned.
+        Default is 10.
+    """
+    peaks = CuratePeaks(sample, narrowPeak, bw, cluster_length, output, region)
+    gtfs = PrepGtfs(gtf)
+    peak = peaks.curated.set_index("name", drop=False)
+
+    peakdf = peak.copy()
+    # The class ClassAssign returns each of the assignment columns as class properties
+    assigned = ClassAssign(
+        peakdf=peakdf,
+        gtfs=gtfs,
+        fasta=fasta,
+        param_antisense=param_antisense,
+        param_down=param_down,
+        trRNA=trRNA,
+        param_upstart=param_upstart,
+        param_downstart=param_downstart,
+        param_upstop=param_upstop,
+        param_fasta=param_fasta,
+        param_downfasta=param_downfasta,
+        output=output,
+        sample=sample,
+        offset=offset
+    )
+
+    # Concatenate all the AssignClass properties into one full dataframe
+    full = pd.concat(
+        [
+            peak,
+            assigned.antisense,
+            assigned.internal,
+            assigned.primary,
+            assigned.details_class,
+            assigned.upstart_upstop,
+            assigned.fasta,
+        ],
+        axis=1,
+    )
+    # save the .narrowPeak files
+    narrowPeakCols = [
+        "chrom",
+        "start",
+        "end",
+        "name",
+        "score",
+        "strand",
+        "na1",
+        "na2",
+        "na3",
+        "na4",
+    ]
+    # narrowPeak of full dataframe
+    full[narrowPeakCols].sort_values(by="score", ascending=False).to_csv(
+        os.path.join(output, "all." + sample + ".narrowPeak"),
+        sep="\t",
+        index=None,
+        header=None,
+        quoting=csv.QUOTE_NONE,
+    )
+    # narrowPeak of peaks in the promoters but not within 10bp dowsntream of start
+    full[full["upstart200"].notnull() & full["within_10bp-downstream"].isnull()][
+        narrowPeakCols
+    ].sort_values(by="score", ascending=False).to_csv(
+        os.path.join(
+            output, "all.200bp-start.minus10bpdownstream." + sample + ".narrowPeak"
+        ),
+        sep="\t",
+        index=None,
+        header=None,
+        quoting=csv.QUOTE_NONE,
+    )
+    # narrowPeak of peaks in the promoters
+    full[full["upstart200"].notnull()][narrowPeakCols].sort_values(
+        by="score", ascending=False
+    ).to_csv(
+        os.path.join(output, "all.200bp-start." + sample + ".narrowPeak"),
+        sep="\t",
+        index=None,
+        header=None,
+        quoting=csv.QUOTE_NONE,
+    )
+    # narrowPeak of peaks within ORF but not within param_upstop of start
+    full[full["upstop"].notnull()][narrowPeakCols].sort_values(
+        by="score", ascending=False
+    ).to_csv(
+        os.path.join(output, "all.51bp-stop." + sample + ".narrowPeak"),
+        sep="\t",
+        index=None,
+        header=None,
+        quoting=csv.QUOTE_NONE,
+    )
+    # save .tsv of the full dataframe
+    full[
+        [
+            "chrom",
+            "start",
+            "strand",
+            "classification",
+            "details",
+            "primary_origin",
+            "score",
+            "upstream_3end_seq",
+            "upstart200",
+            "dist_upstart200",
+            "within_10bp-downstream",
+            "upstop",
+            "dist_upstop",
+        ]
+    ].sort_values(by="score", ascending=False).to_csv(
+        os.path.join(output, "all." + sample + ".tsv"),
+        sep="\t",
+        index=None,
+        header=True,
+        quoting=csv.QUOTE_NONE,
+    )
+
+
+def table_output(sample, assigned, assigned2, kinefold_scores, output='data'):
+    """
+    Parameters
+    ----------
+
+    assigned : str
+        Path to file with curated peaks. Typically this is the output of the assign function.
+        Can be for a single strand or both.
+
+    assigned2 : str
+        Optional. Path to file with curated peaks of opposite strand if parameter assigned
+        was for a single strand. Typically this is the output of the assign function.
+
+    kinefold_scores : str
+        Optional. Path to Kinefold output file corresponding to the curated peaks.
+
+    output: str
+        Path to output directory. Default is `data`.
+    """
+
+    score_cols = [
+        "3' end",
+        "Strand",
+        "3' end_seq",
+        "Kinefold_structure",
+        "A-tract",
+        "Hairpin",
+        "Hp_structure",
+        "Loop_seq",
+        "U-Tract",
+        "dGU",
+        "dGL",
+        "dGH",
+        "dGHA",
+        "dGA",
+        "dGB",
+        "TS",
+        "NA",
+    ]
+    if assigned2:
+        df = pd.concat(
+            [pd.read_csv(assigned, sep="\t"), pd.read_csv(assigned2, sep="\t")]
+        )
+    else:
+        df = pd.read_csv(assigned, sep="\t")
+    df = df.reset_index()
+    if kinefold_scores:
+        scores = pd.read_csv(kinefold_scores, sep="\t", names=score_cols)
+        scores = scores.iloc[1:, :].reset_index()
+        tableS1 = df[
+            [
+                "chrom",
+                "start",
+                "strand",
+                "score",
+                "classification",
+                "details",
+                "upstream_3end_seq",
+            ]
+        ].join(
+            scores[
+                [
+                    "3' end",
+                    "TS",
+                    "Kinefold_structure",
+                    "A-tract",
+                    "Hairpin",
+                    "Hp_structure",
+                    "Loop_seq",
+                    "U-Tract",
+                ]
+            ]
+        )
+    else:
+        tableS1 = df[
+            [
+                "chrom",
+                "start",
+                "strand",
+                "score",
+                "classification",
+                "details",
+                "upstream_3end_seq",
+            ]
+        ]
+
+    tableS1.sort_values(["chrom", "start", "strand"]).to_csv(
+        os.path.join(output, sample + "_TableS1.tsv"),
+        sep="\t",
+        header=True,
+        index=False,
+        quoting=csv.QUOTE_NONE,
+    )
diff --git a/requirements.txt b/requirements.txt
index 432ba1b..d64e515 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,3 +3,4 @@ pandas
 numpy
 pybedtools
 idr
+deeptools
diff --git a/setup.py b/setup.py
index ce6ae9b..55a2d68 100644
--- a/setup.py
+++ b/setup.py
@@ -3,11 +3,11 @@
 requirements = [i.strip() for i in open('requirements.txt')]
 setup(
     name='termseq_peaks',
-    version='0.1',
+    version='0.2',
     author='Ryan Dale',
     author_email='dalerr@nih.gov',
     license='MIT',
-    scripts=['bin/termseq_peaks'],
+    scripts=['bin/termseq_peaks', 'bin/class_annot', 'bin/table_output'],
     url='https://github.com/nichd-bspc/termseq-peaks',
     packages=['peaklib'],
     install_requires=requirements,


Test patch:
diff --git a/peaklib/test/assigned2.tsv b/peaklib/test/assigned2.tsv
new file mode 100644
index 0000000..e79c2fc
--- /dev/null
+++ b/peaklib/test/assigned2.tsv
@@ -0,0 +1,5 @@
+chrom	start	strand	classification	details	primary_origin	score	upstream_3end_seq	upstart200	dist_upstart200	within_10bp-downstream	upstop	dist_upstop
+chr	2286287	-	primary, 	P:gene_id "EG30067"; gene_name "proL";		2986635.0	ACCGTCATGGGGTGTCGGGGGTCGGAGGTTCAAATCCTCTCGTGCCGACCAAAAATCCCAA			gene_id "EG30067"; gene_name "proL";		
+chr	2278567	-	antisense, 	A:gene_id "EG11419"; gene_name "bcr";		1085.844	GGTTGACATGCATCAACAAAGGAAGCCTTTTAGCTTCCTCGTTGTGCAATAGATCACCGTT					
+chr	2284559	-	internal, 	I:gene_id "EG12049"; gene_name "yejM";		954.333	CAACGCTTGCTGGTCGCATTTATTCCTACGTAAGCATTATCGGCCATTTCAGCTTCCTGGT					
+chr	2282535	-	internal, 	I:gene_id "EG10885"; gene_name "rplY";		865.2705	CCTGTAGCAGGATTAATTTAAGAGAGAAAGAAATGTTTACTATCAACGCAGAAGTACGTAA	gene_id "EG10885"; gene_name "rplY";	19			
diff --git a/peaklib/test/ecoli-subset.gtf b/peaklib/test/ecoli-subset.gtf
new file mode 100644
index 0000000..9a10ffc
--- /dev/null
+++ b/peaklib/test/ecoli-subset.gtf
@@ -0,0 +1,11 @@
+chr	EcoCyc	exon	2277893	2278237	.	-	.	gene_id "EG12042"; gene_name "yejG";
+chr	EcoCyc	exon	2278570	2279760	.	-	.	gene_id "EG11419"; gene_name "bcr";
+chr	EcoCyc	exon	2279788	2280483	.	-	.	gene_id "EG12044"; gene_name "rsuA";
+chr	EcoCyc	exon	2280632	2282392	.	+	.	gene_id "EG12045"; gene_name "radD";
+chr	EcoCyc	exon	2282517	2282801	.	+	.	gene_id "EG10885"; gene_name "rplY";
+chr	EcoCyc	exon	2282940	2283947	.	-	.	gene_id "EG12048"; gene_name "yejK";
+chr	EcoCyc	exon	2284129	2284356	.	+	.	gene_id "EG12043"; gene_name "yejL";
+chr	EcoCyc	exon	2284376	2286136	.	+	.	gene_id "EG12049"; gene_name "yejM";
+chr	EcoCyc	exon	2286211	2286287	.	+	.	gene_id "EG30067"; gene_name "proL";
+chr	EcoCyc	exon	2286390	2288900	.	-	.	gene_id "EG12051"; gene_name "yejO";
+chr	.	exon	2278258	2278498	.	-	.	gene_id "G0-10700"; gene_name " ";
diff --git a/peaklib/test/ecoli_subset.fasta.gz b/peaklib/test/ecoli_subset.fasta.gz
new file mode 100644
index 0000000..7b9c59e
Binary files /dev/null and b/peaklib/test/ecoli_subset.fasta.gz differ
diff --git a/peaklib/test/expected_output/all.asdf.tsv b/peaklib/test/expected_output/all.asdf.tsv
new file mode 100644
index 0000000..2f8053c
--- /dev/null
+++ b/peaklib/test/expected_output/all.asdf.tsv
@@ -0,0 +1,6 @@
+chrom	start	strand	classification	details	primary_origin	score	upstream_3end_seq	upstart200	dist_upstart200	within_10bp-downstream	upstop	dist_upstop
+chr	2286287	+	primary, 	P:gene_id "EG30067"; gene_name "proL";		2986635.0	ACCGTCATGGGGTGTCGGGGGTCGGAGGTTCAAATCCTCTCGTGCCGACCAAAAATCCCAA			gene_id "EG30067"; gene_name "proL";		
+chr	2278567	+	antisense, 	A:gene_id "EG11419"; gene_name "bcr";		1085.844	GGTTGACATGCATCAACAAAGGAAGCCTTTTAGCTTCCTCGTTGTGCAATAGATCACCGTT					
+chr	2284559	+	internal, 	I:gene_id "EG12049"; gene_name "yejM";		954.333	CAACGCTTGCTGGTCGCATTTATTCCTACGTAAGCATTATCGGCCATTTCAGCTTCCTGGT					
+chr	2282535	+	internal, 	I:gene_id "EG10885"; gene_name "rplY";		865.2705	CCTGTAGCAGGATTAATTTAAGAGAGAAAGAAATGTTTACTATCAACGCAGAAGTACGTAA	gene_id "EG10885"; gene_name "rplY";	19			
+chr	2282580	+	internal, 	I:gene_id "EG10885"; gene_name "rplY";		865.2705	ACGCAGAAGTACGTAAAGAGCAGGGTAAGGGTGCGAGCCGCCGCCTGCGTGCCGCTAACAA					
diff --git a/peaklib/test/expected_output/asdf.raw.tsv b/peaklib/test/expected_output/asdf.raw.tsv
new file mode 100644
index 0000000..72a717e
--- /dev/null
+++ b/peaklib/test/expected_output/asdf.raw.tsv
@@ -0,0 +1,10001 @@
+#'chr'	'start'	'end'	'sample1.subset.bam.forward.unique.bigwig'	'sample2.subset.bam.forward.unique.bigwig'
+chr	2277000	2277001	0.0	0.0
+chr	2277001	2277002	0.0	0.0
+chr	2277002	2277003	0.0	0.0
+chr	2277003	2277004	0.0	0.0
+chr	2277004	2277005	0.0	0.0
+chr	2277005	2277006	0.0	0.0
+chr	2277006	2277007	0.0	0.0
+chr	2277007	2277008	0.0	0.0
+chr	2277008	2277009	0.0	0.0
+chr	2277009	2277010	0.0	0.0
+chr	2277010	2277011	0.0	0.0
+chr	2277011	2277012	0.0	0.0
+chr	2277012	2277013	0.0	0.0
+chr	2277013	2277014	0.0	0.0
+chr	2277014	2277015	0.0	0.0
+chr	2277015	2277016	0.0	0.0
+chr	2277016	2277017	0.0	0.0
+chr	2277017	2277018	0.0	0.0
+chr	2277018	2277019	0.0	0.0
+chr	2277019	2277020	0.0	0.0
+chr	2277020	2277021	0.0	0.0
+chr	2277021	2277022	0.0	0.0
+chr	2277022	2277023	0.0	0.0
+chr	2277023	2277024	0.0	0.0
+chr	2277024	2277025	0.0	0.0
+chr	2277025	2277026	0.0	0.0
+chr	2277026	2277027	0.0	0.0
+chr	2277027	2277028	0.0	0.0
+chr	2277028	2277029	0.0	0.0
+chr	2277029	2277030	0.0	0.0
+chr	2277030	2277031	0.0	0.0
+chr	2277031	2277032	0.0	0.0
+chr	2277032	2277033	0.0	0.0
+chr	2277033	2277034	0.0	0.0
+chr	2277034	2277035	0.0	0.0
+chr	2277035	2277036	0.0	0.0
+chr	2277036	2277037	0.0	0.0
+chr	2277037	2277038	0.0	0.0
+chr	2277038	2277039	0.0	0.0
+chr	2277039	2277040	0.0	0.0
+chr	2277040	2277041	0.0	0.0
+chr	2277041	2277042	0.0	0.0
+chr	2277042	2277043	0.0	0.0
+chr	2277043	2277044	0.0	0.0
+chr	2277044	2277045	40201.0	0.0
+chr	2277045	2277046	0.0	0.0
+chr	2277046	2277047	0.0	0.0
+chr	2277047	2277048	0.0	0.0
+chr	2277048	2277049	0.0	0.0
+chr	2277049	2277050	0.0	0.0
+chr	2277050	2277051	0.0	0.0
+chr	2277051	2277052	0.0	0.0
+chr	2277052	2277053	0.0	0.0
+chr	2277053	2277054	0.0	0.0
+chr	2277054	2277055	0.0	0.0
+chr	2277055	2277056	0.0	0.0
+chr	2277056	2277057	0.0	0.0
+chr	2277057	2277058	0.0	0.0
+chr	2277058	2277059	0.0	0.0
+chr	2277059	2277060	0.0	0.0
+chr	2277060	2277061	0.0	0.0
+chr	2277061	2277062	0.0	0.0
+chr	2277062	2277063	0.0	0.0
+chr	2277063	2277064	0.0	0.0
+chr	2277064	2277065	0.0	0.0
+chr	2277065	2277066	0.0	0.0
+chr	2277066	2277067	0.0	0.0
+chr	2277067	2277068	0.0	0.0
+chr	2277068	2277069	0.0	0.0
+chr	2277069	2277070	0.0	0.0
+chr	2277070	2277071	0.0	0.0
+chr	2277071	2277072	0.0	0.0
+chr	2277072	2277073	0.0	0.0
+chr	2277073	2277074	0.0	0.0
+chr	2277074	2277075	0.0	0.0
+chr	2277075	2277076	0.0	0.0
+chr	2277076	2277077	0.0	0.0
+chr	2277077	2277078	0.0	0.0
+chr	2277078	2277079	0.0	0.0
+chr	2277079	2277080	0.0	0.0
+chr	2277080	2277081	0.0	0.0
+chr	2277081	2277082	0.0	0.0
+chr	2277082	2277083	0.0	0.0
+chr	2277083	2277084	0.0	0.0
+chr	2277084	2277085	0.0	0.0
+chr	2277085	2277086	0.0	0.0
+chr	2277086	2277087	0.0	0.0
+chr	2277087	2277088	0.0	0.0
+chr	2277088	2277089	0.0	0.0
+chr	2277089	2277090	0.0	0.0
+chr	2277090	2277091	0.0	0.0
+chr	2277091	2277092	0.0	0.0
+chr	2277092	2277093	0.0	0.0
+chr	2277093	2277094	0.0	0.0
+chr	2277094	2277095	0.0	0.0
+chr	2277095	2277096	0.0	0.0
+chr	2277096	2277097	0.0	0.0
+chr	2277097	2277098	0.0	0.0
+chr	2277098	2277099	0.0	0.0
+chr	2277099	2277100	0.0	0.0
+chr	2277100	2277101	0.0	0.0
+chr	2277101	2277102	0.0	0.0
+chr	2277102	2277103	0.0	0.0
+chr	2277103	2277104	0.0	0.0
+chr	2277104	2277105	0.0	0.0
+chr	2277105	2277106	0.0	0.0
+chr	2277106	2277107	0.0	0.0
+chr	2277107	2277108	0.0	0.0
+chr	2277108	2277109	0.0	0.0
+chr	2277109	2277110	0.0	0.0
+chr	2277110	2277111	0.0	0.0
+chr	2277111	2277112	0.0	0.0
+chr	2277112	2277113	0.0	0.0
+chr	2277113	2277114	0.0	0.0
+chr	2277114	2277115	0.0	0.0
+chr	2277115	2277116	0.0	0.0
+chr	2277116	2277117	0.0	0.0
+chr	2277117	2277118	0.0	0.0
+chr	2277118	2277119	0.0	0.0
+chr	2277119	2277120	0.0	0.0
+chr	2277120	2277121	0.0	0.0
+chr	2277121	2277122	0.0	0.0
+chr	2277122	2277123	0.0	0.0
+chr	2277123	2277124	0.0	0.0
+chr	2277124	2277125	0.0	0.0
+chr	2277125	2277126	0.0	0.0
+chr	2277126	2277127	0.0	0.0
+chr	2277127	2277128	0.0	0.0
+chr	2277128	2277129	0.0	0.0
+chr	2277129	2277130	0.0	0.0
+chr	2277130	2277131	0.0	0.0
+chr	2277131	2277132	0.0	0.0
+chr	2277132	2277133	0.0	0.0
+chr	2277133	2277134	0.0	0.0
+chr	2277134	2277135	0.0	0.0
+chr	2277135	2277136	0.0	0.0
+chr	2277136	2277137	0.0	0.0
+chr	2277137	2277138	0.0	0.0
+chr	2277138	2277139	0.0	0.0
+chr	2277139	2277140	0.0	0.0
+chr	2277140	2277141	0.0	0.0
+chr	2277141	2277142	0.0	0.0
+chr	2277142	2277143	0.0	0.0
+chr	2277143	2277144	0.0	0.0
+chr	2277144	2277145	0.0	0.0
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+chr	2286868	2286869	0.0	0.0
+chr	2286869	2286870	0.0	0.0
+chr	2286870	2286871	0.0	0.0
+chr	2286871	2286872	0.0	0.0
+chr	2286872	2286873	0.0	0.0
+chr	2286873	2286874	0.0	0.0
+chr	2286874	2286875	0.0	0.0
+chr	2286875	2286876	0.0	0.0
+chr	2286876	2286877	0.0	0.0
+chr	2286877	2286878	0.0	0.0
+chr	2286878	2286879	0.0	0.0
+chr	2286879	2286880	0.0	0.0
+chr	2286880	2286881	0.0	0.0
+chr	2286881	2286882	0.0	0.0
+chr	2286882	2286883	0.0	0.0
+chr	2286883	2286884	0.0	0.0
+chr	2286884	2286885	0.0	0.0
+chr	2286885	2286886	0.0	0.0
+chr	2286886	2286887	0.0	0.0
+chr	2286887	2286888	0.0	0.0
+chr	2286888	2286889	0.0	0.0
+chr	2286889	2286890	0.0	0.0
+chr	2286890	2286891	0.0	0.0
+chr	2286891	2286892	0.0	0.0
+chr	2286892	2286893	0.0	0.0
+chr	2286893	2286894	0.0	0.0
+chr	2286894	2286895	0.0	0.0
+chr	2286895	2286896	0.0	0.0
+chr	2286896	2286897	0.0	0.0
+chr	2286897	2286898	0.0	0.0
+chr	2286898	2286899	0.0	0.0
+chr	2286899	2286900	0.0	0.0
+chr	2286900	2286901	0.0	0.0
+chr	2286901	2286902	0.0	0.0
+chr	2286902	2286903	0.0	0.0
+chr	2286903	2286904	0.0	0.0
+chr	2286904	2286905	0.0	0.0
+chr	2286905	2286906	0.0	0.0
+chr	2286906	2286907	0.0	0.0
+chr	2286907	2286908	0.0	0.0
+chr	2286908	2286909	0.0	0.0
+chr	2286909	2286910	0.0	0.0
+chr	2286910	2286911	0.0	0.0
+chr	2286911	2286912	0.0	0.0
+chr	2286912	2286913	0.0	0.0
+chr	2286913	2286914	0.0	0.0
+chr	2286914	2286915	0.0	0.0
+chr	2286915	2286916	0.0	0.0
+chr	2286916	2286917	0.0	0.0
+chr	2286917	2286918	0.0	0.0
+chr	2286918	2286919	0.0	0.0
+chr	2286919	2286920	0.0	0.0
+chr	2286920	2286921	0.0	0.0
+chr	2286921	2286922	0.0	0.0
+chr	2286922	2286923	0.0	0.0
+chr	2286923	2286924	0.0	0.0
+chr	2286924	2286925	0.0	0.0
+chr	2286925	2286926	0.0	0.0
+chr	2286926	2286927	0.0	0.0
+chr	2286927	2286928	0.0	0.0
+chr	2286928	2286929	0.0	0.0
+chr	2286929	2286930	0.0	0.0
+chr	2286930	2286931	0.0	0.0
+chr	2286931	2286932	0.0	0.0
+chr	2286932	2286933	0.0	0.0
+chr	2286933	2286934	0.0	0.0
+chr	2286934	2286935	0.0	0.0
+chr	2286935	2286936	0.0	0.0
+chr	2286936	2286937	0.0	0.0
+chr	2286937	2286938	0.0	0.0
+chr	2286938	2286939	0.0	0.0
+chr	2286939	2286940	0.0	0.0
+chr	2286940	2286941	0.0	0.0
+chr	2286941	2286942	0.0	0.0
+chr	2286942	2286943	0.0	0.0
+chr	2286943	2286944	0.0	0.0
+chr	2286944	2286945	0.0	0.0
+chr	2286945	2286946	0.0	0.0
+chr	2286946	2286947	0.0	0.0
+chr	2286947	2286948	0.0	0.0
+chr	2286948	2286949	0.0	0.0
+chr	2286949	2286950	0.0	0.0
+chr	2286950	2286951	0.0	0.0
+chr	2286951	2286952	0.0	0.0
+chr	2286952	2286953	0.0	0.0
+chr	2286953	2286954	0.0	0.0
+chr	2286954	2286955	0.0	0.0
+chr	2286955	2286956	0.0	0.0
+chr	2286956	2286957	0.0	0.0
+chr	2286957	2286958	0.0	0.0
+chr	2286958	2286959	0.0	0.0
+chr	2286959	2286960	0.0	0.0
+chr	2286960	2286961	0.0	0.0
+chr	2286961	2286962	0.0	0.0
+chr	2286962	2286963	0.0	0.0
+chr	2286963	2286964	0.0	0.0
+chr	2286964	2286965	0.0	0.0
+chr	2286965	2286966	0.0	0.0
+chr	2286966	2286967	0.0	0.0
+chr	2286967	2286968	0.0	0.0
+chr	2286968	2286969	0.0	0.0
+chr	2286969	2286970	0.0	0.0
+chr	2286970	2286971	0.0	0.0
+chr	2286971	2286972	0.0	0.0
+chr	2286972	2286973	0.0	0.0
+chr	2286973	2286974	0.0	0.0
+chr	2286974	2286975	0.0	0.0
+chr	2286975	2286976	0.0	0.0
+chr	2286976	2286977	0.0	0.0
+chr	2286977	2286978	0.0	0.0
+chr	2286978	2286979	0.0	0.0
+chr	2286979	2286980	0.0	0.0
+chr	2286980	2286981	0.0	0.0
+chr	2286981	2286982	0.0	0.0
+chr	2286982	2286983	0.0	0.0
+chr	2286983	2286984	0.0	0.0
+chr	2286984	2286985	0.0	0.0
+chr	2286985	2286986	0.0	0.0
+chr	2286986	2286987	0.0	0.0
+chr	2286987	2286988	0.0	0.0
+chr	2286988	2286989	0.0	0.0
+chr	2286989	2286990	0.0	0.0
+chr	2286990	2286991	0.0	0.0
+chr	2286991	2286992	0.0	0.0
+chr	2286992	2286993	0.0	0.0
+chr	2286993	2286994	0.0	0.0
+chr	2286994	2286995	0.0	0.0
+chr	2286995	2286996	0.0	0.0
+chr	2286996	2286997	0.0	0.0
+chr	2286997	2286998	0.0	0.0
+chr	2286998	2286999	0.0	0.0
+chr	2286999	2287000	0.0	0.0
diff --git a/peaklib/test/expected_output/asdf_TableS1.tsv b/peaklib/test/expected_output/asdf_TableS1.tsv
new file mode 100644
index 0000000..b3634a2
--- /dev/null
+++ b/peaklib/test/expected_output/asdf_TableS1.tsv
@@ -0,0 +1,10 @@
+chrom	start	strand	score	classification	details	upstream_3end_seq	3' end	TS	Kinefold_structure	A-tract	Hairpin	Hp_structure	Loop_seq	U-Tract
+chr	2278567	+	1085.844	antisense, 	A:gene_id "EG11419"; gene_name "bcr";	GGTTGACATGCATCAACAAAGGAAGCCTTTTAGCTTCCTCGTTGTGCAATAGATCACCGTT	2278567	1.0101473137793338	....((((((.........(((.......((((((.....)))))).)))..))))))...	AAUCAGUA	GGUCACCAGUUCGAUUCCGGUAGUCGGCACC	(((.......((((((.....)))))).)))	CCGGU	AUCAAGUCCGG
+chr	2278567	-	1085.844	antisense, 	A:gene_id "EG11419"; gene_name "bcr";	GGTTGACATGCATCAACAAAGGAAGCCTTTTAGCTTCCTCGTTGTGCAATAGATCACCGTT	2278567	1.0111248243191644	.(((((.......)))))..((((((((.......)))))..)))................	A	ACGACCUUCUAAGUCGU	(((((.......)))))	CUUCUAA	GGGCCGCAGGUU
+chr	2282535	+	865.2705	internal, 	I:gene_id "EG10885"; gene_name "rplY";	CCTGTAGCAGGATTAATTTAAGAGAGAAAGAAATGTTTACTATCAACGCAGAAGTACGTAA	2282535	1.0391093475650013	..(((((.......)))))....(((((.......))))).....................	CC	UCAGCCUUCCAAGCUGA	(((((.......)))))	CUUCCAA	UGAUGCGGGUUC
+chr	2282535	-	865.2705	internal, 	I:gene_id "EG10885"; gene_name "rplY";	CCTGTAGCAGGATTAATTTAAGAGAGAAAGAAATGTTTACTATCAACGCAGAAGTACGTAA	2282535	1.3440789602998404	((((((...)))))).((((((....((((....))))...))))))..............		GGGAUUGAAAAUCCC	((((((...))))))	GAA	CGUGUCCUUGGU
+chr	2282580	+	865.2705	internal, 	I:gene_id "EG10885"; gene_name "rplY";	ACGCAGAAGTACGTAAAGAGCAGGGTAAGGGTGCGAGCCGCCGCCTGCGTGCCGCTAACAA	2282580	1.132800317356912	..((((((...))))))...((((((((.......)))))....)))..............	AG	CGCAUUCGUAAUGCG	((((((...))))))	CGU	AAGGUCGUAGGU
+chr	2284559	+	954.333	internal, 	I:gene_id "EG12049"; gene_name "yejM";	CAACGCTTGCTGGTCGCATTTATTCCTACGTAAGCATTATCGGCCATTTCAGCTTCCTGGT	2284559	1.333782331008599	.(((((.......))))).....(((((.......))))).....................	C	CUGGAUUGUGAUUCCAG	(((((.......)))))	UUGUGAU	UUGUCGUGGGUU
+chr	2284559	-	954.333	internal, 	I:gene_id "EG12049"; gene_name "yejM";	CAACGCTTGCTGGTCGCATTTATTCCTACGTAAGCATTATCGGCCATTTCAGCTTCCTGGT	2284559	1.5554376968063965	....(((((((.........((((((.......))))))(((......)))...)))))))	AGCUGGCA	GGGCCCCCACCC	(((......)))	CCCCCA	AUUUCUGCCU
+chr	2286287	+	2986635.0	primary, 	P:gene_id "EG30067"; gene_name "proL";	ACCGTCATGGGGTGTCGGGGGTCGGAGGTTCAAATCCTCTCGTGCCGACCAAAAATCCCAA	2286287	1.2994862087029395	..........((((....)))).(((((.......))))).....(((.........))).	AAAUACGU	GCCGGUUCGAGUCCGGC	(((((.......)))))	UUCGAGU	CUUCGGCACCAA
+chr	2286287	-	2986635.0	primary, 	P:gene_id "EG30067"; gene_name "proL";	ACCGTCATGGGGTGTCGGGGGTCGGAGGTTCAAATCCTCTCGTGCCGACCAAAAATCCCAA	2286287	1.082473752884958	.(((((.......))))).....(((((.......)))))...(((((......)))))..	UGGGGGUC	GUUGGUUCGAGUCCAAU	(((((.......)))))	UUCGAGU	UGAACGCACCAU
diff --git a/peaklib/test/forward.unique.narrowPeak b/peaklib/test/forward.unique.narrowPeak
new file mode 100644
index 0000000..ade875d
--- /dev/null
+++ b/peaklib/test/forward.unique.narrowPeak
@@ -0,0 +1,11 @@
+chr	2278533	2278567	.	1085.844	+	1085.844	1085.844	-1	17
+chr	2282533	2282536	.	865.2705	+	865.2705	865.2705	-1	1
+chr	2282568	2282608	.	865.2705	+	865.2705	865.2705	-1	20
+chr	2284556	2284562	.	954.333	+	954.333	954.333	-1	3
+chr	2286234	2286236	.	1777.229	+	1777.229	1777.229	-1	1
+chr	2286240	2286244	.	9611.3415	+	9611.3415	9611.3415	-1	2
+chr	2286247	2286249	.	19549.687	+	19549.687	19549.687	-1	1
+chr	2286251	2286261	.	1183.381	+	1183.381	1183.381	-1	5
+chr	2286273	2286278	.	2689.111	+	2689.111	2689.111	-1	2
+chr	2286285	2286287	.	2986635.0	+	2986635.0	2986635.0	-1	1
+chr	2286289	2286291	.	75872.55	+	75872.55	75872.55	-1	1
diff --git a/peaklib/test/kinefold.tsv b/peaklib/test/kinefold.tsv
new file mode 100644
index 0000000..cddfb3b
--- /dev/null
+++ b/peaklib/test/kinefold.tsv
@@ -0,0 +1,10 @@
+3' end	Strand	3' end_seq	Kinefold_structure	A-tract	Hairpin	Hp_structure	Loop_seq	U-Tract	dGU	dGL	dGH	dGHA	dGA	dGB	TS
+2286287	+	CAAAAUCAACCGUAGAAAUACGUGCCGGUUCGAGUCCGGCCUUCGGCACCAAAAGUAUGUA	..........((((....)))).(((((.......))))).....(((.........))).	AAAUACGU	GCCGGUUCGAGUCCGGC	(((((.......)))))	UUCGAGU	CUUCGGCACCAA	-7.699999999999999	4.4	-8.0	-8.1	-0.09999999999999964	-9.1	1.2994862087029395
+2278567	+	AACUGACUUGUAAUCAGUAGGUCACCAGUUCGAUUCCGGUAGUCGGCACCAUCAAGUCCGG	....((((((.........(((.......((((((.....)))))).)))..))))))...	AAUCAGUA	GGUCACCAGUUCGAUUCCGGUAGUCGGCACC	(((.......((((((.....)))))).)))	CCGGU	AUCAAGUCCGG	-5.6	5.7	-1.4	-2.3	-0.8999999999999999	1.2000000000000002	1.0101473137793338
+2284559	+	CCUGGAUUGUGAUUCCAGUUGUCGUGGGUUCGAAUCCCAUUAGCCACCCCAUUAUUAGAAG	.(((((.......))))).....(((((.......))))).....................	C	CUGGAUUGUGAUUCCAG	(((((.......)))))	UUGUGAU	UUGUCGUGGGUU	-5.700000000000001	4.8	-5.1	-5.1	0.0	-7.5	1.333782331008599
+2282535	+	CCUCAGCCUUCCAAGCUGAUGAUGCGGGUUCGAUUCCCGCUGCCCGCUCCAAGAUGUGCUG	..(((((.......)))))....(((((.......))))).....................	CC	UCAGCCUUCCAAGCUGA	(((((.......)))))	CUUCCAA	UGAUGCGGGUUC	-9.600000000000001	5.0	-4.5	-4.5	0.0	-6.6	1.0391093475650013
+2282580	+	AGCGCAUUCGUAAUGCGAAGGUCGUAGGUUCGACUCCUAUUAUCGGCACCAUUAAAAUCAA	..((((((...))))))...((((((((.......)))))....)))..............	AG	CGCAUUCGUAAUGCG	((((((...))))))	CGU	AAGGUCGUAGGU	-7.999999999999998	5.9	-4.0	-4.0	0.0	-7.9	1.132800317356912
+2286287	-	ACCACCUUGACAUGGUGGGGGUCGUUGGUUCGAGUCCAAUUGAACGCACCAUCCUGCGUCC	.(((((.......))))).....(((((.......)))))...(((((......)))))..	UGGGGGUC	GUUGGUUCGAGUCCAAU	(((((.......)))))	UUCGAGU	UGAACGCACCAU	-8.200000000000001	4.4	-2.8	-5.2	-2.4000000000000004	-4.4	1.082473752884958
+2278567	-	AACGACCUUCUAAGUCGUGGGCCGCAGGUUCGAAUCCUGCAGGGCGCGCCAUUACAAUUCA	.(((((.......)))))..((((((((.......)))))..)))................	A	ACGACCUUCUAAGUCGU	(((((.......)))))	CUUCUAA	GGGCCGCAGGUU	-12.8	5.0	-3.7	-3.7	0.0	-7.0	1.0111248243191644
+2284559	-	GCCAAGGCAGAUGACGCGUGUGCCGGGAUGUAGCUGGCAGGGCCCCCACCCAUUUCUGCCU	....(((((((.........((((((.......))))))(((......)))...)))))))	AGCUGGCA	GGGCCCCCACCC	(((......)))	CCCCCA	AUUUCUGCCU	-4.9	4.3	-2.3	-3.5	-1.2000000000000002	-6.6	1.5554376968063965
+2282535	-	GGGAUUGAAAAUCCCCGUGUCCUUGGUUCGAUUCCGAGUCCGGGCACCACUAAUUCUUAAG	((((((...)))))).((((((....((((....))))...))))))..............		GGGAUUGAAAAUCCC	((((((...))))))	GAA	CGUGUCCUUGGU	-6.9	5.9	-5.1	-5.1	0.0	-9.0	1.3440789602998404
diff --git a/peaklib/test/sample1.subset.bam.forward.unique.bigwig b/peaklib/test/sample1.subset.bam.forward.unique.bigwig
new file mode 100644
index 0000000..1e8f403
Binary files /dev/null and b/peaklib/test/sample1.subset.bam.forward.unique.bigwig differ
diff --git a/peaklib/test/sample2.subset.bam.forward.unique.bigwig b/peaklib/test/sample2.subset.bam.forward.unique.bigwig
new file mode 100644
index 0000000..63e8751
Binary files /dev/null and b/peaklib/test/sample2.subset.bam.forward.unique.bigwig differ
diff --git a/peaklib/test/test_assign_class.py b/peaklib/test/test_assign_class.py
new file mode 100644
index 0000000..2307861
--- /dev/null
+++ b/peaklib/test/test_assign_class.py
@@ -0,0 +1,99 @@
+from peaklib import assign_class
+from pathlib import Path
+import hashlib
+import numpy as np
+
+HERE = Path(__file__).resolve().parent
+
+narrowPeak = HERE / "forward.unique.narrowPeak"
+bw = [HERE / 'sample1.subset.bam.forward.unique.bigwig',
+      HERE / 'sample2.subset.bam.forward.unique.bigwig']
+cluster_length = 100
+gtf = HERE / 'ecoli-subset.gtf'
+#intergenic_min_size = 50
+fasta = HERE / 'ecoli_subset.fasta.gz'
+param_antisense = 50
+param_down = 50
+trRNA = HERE / 'trRNA.tsv'
+param_upstart = 200
+param_downstart = 50
+param_upstop = 50
+param_fasta = 50
+param_downfasta = 10
+
+
+def test_curatepeaks():
+    assign_class.CuratePeaks('asdf', narrowPeak, bw, cluster_length,
+                             output=HERE / 'testout',  region='chr:2277000:2287000')
+    # possible test is to check md5sums of expected and observed outputs
+    obs_md5 = hashlib.md5(open(HERE / 'testout' / 'asdf.raw.tsv','rb').read()).digest()
+    exp_md5 = hashlib.md5(open(HERE / 'expected_output' / 'asdf.raw.tsv','rb').read()).digest()
+    assert obs_md5 == exp_md5
+
+def test_prepgtfs():
+    proc_gtf = assign_class.PrepGtfs(gtf)
+
+    # mean and std of start and end cols, to compare with expected
+    assert proc_gtf.orf['start'].mean() == 2281973.6363636362
+    assert proc_gtf.orf['start'].std() == 3125.841431446172
+    assert proc_gtf.orf['end'].mean() == 2282890.1818181816
+    assert proc_gtf.orf['end'].std() == 3438.519123639763
+    assert proc_gtf.orf3end['start'].mean() == 2282346.4545454546
+    assert proc_gtf.orf3end['start'].std() == 3289.286924658181
+
+def test_classassign():
+    peaks = assign_class.CuratePeaks('asdf', narrowPeak, bw, cluster_length,
+                                     output=HERE / 'testout',  region='chr:2277000:2287000')
+    peak = peaks.curated.set_index("name", drop=False)
+    peakdf = peak.copy()
+    proc_gtf = assign_class.PrepGtfs(gtf)
+
+    assigned = assign_class.ClassAssign(
+        peakdf=peakdf,
+        gtfs=proc_gtf,
+        fasta=str(fasta),
+        param_antisense=param_antisense,
+        param_down=param_down,
+        trRNA=trRNA,
+        param_upstart=param_upstart,
+        param_downstart=param_downstart,
+        param_upstop=param_upstop,
+        param_fasta=param_fasta,
+        param_downfasta=param_downfasta,
+        output=HERE / 'testout',
+        sample='asdf',
+        offset=2277000)
+    assert ','.join([str(i) for i in assigned.antisense.to_list()]) == 'A:gene_id "EG11419"; gene_name "bcr";,nan,nan,nan,nan'
+    assert ','.join([str(i) for i in assigned.internal.to_list()]) == 'nan,I:gene_id "EG10885"; gene_name "rplY";,I:gene_id "EG10885"; gene_name "rplY";,I:gene_id "EG12049"; gene_name "yejM";,nan'
+    assert ','.join([str(i) for i in assigned.primary['primary'].tolist()]) == 'nan,nan,nan,nan,P:gene_id "EG30067"; gene_name "proL";'
+    assert ''.join([str(i) for i in assigned.details_class['classification'].tolist()]) == 'antisense, internal, internal, internal, primary, '
+    assert ','.join([str(i) for i in assigned.upstart_upstop['within_10bp-downstream'].tolist()]) == 'nan,nan,nan,nan,gene_id "EG30067"; gene_name "proL";'
+    assert assigned.fasta.loc['chr_2278567_2278567','upstream_3end_seq'] == 'GGTTGACATGCATCAACAAAGGAAGCCTTTTAGCTTCCTCGTTGTGCAATAGATCACCGTT'
+
+def test_assign_fun():
+    assign_class.assign(
+        sample = 'asdf',
+        narrowPeak = narrowPeak,
+        bw = bw,
+        fasta = str(fasta),
+        gtf = gtf,
+        trRNA = trRNA,
+        output= HERE / 'testout',
+        region='chr:2277000:2287000',
+        offset=2277000
+    )
+    obs_md5 = hashlib.md5(open(HERE / 'testout' / 'all.asdf.tsv','rb').read()).digest()
+    exp_md5 = hashlib.md5(open(HERE / 'expected_output' / 'all.asdf.tsv','rb').read()).digest()
+    assert obs_md5 == exp_md5
+
+def test_table_fun():
+    assign_class.table_output(
+        sample = 'asdf',
+        assigned = HERE / 'expected_output' / 'all.asdf.tsv',
+        assigned2 = HERE / 'assigned2.tsv',
+        kinefold_scores = HERE / 'kinefold.tsv',
+        output= HERE / 'testout'
+    )
+    obs_md5 = hashlib.md5(open(HERE / 'testout' / 'asdf_TableS1.tsv','rb').read()).digest()
+    exp_md5 = hashlib.md5(open(HERE / 'expected_output' / 'asdf_TableS1.tsv','rb').read()).digest()
+    assert obs_md5 == exp_md5
diff --git a/peaklib/test/trRNA.tsv b/peaklib/test/trRNA.tsv
new file mode 100644
index 0000000..c417cf0
--- /dev/null
+++ b/peaklib/test/trRNA.tsv
@@ -0,0 +1,110 @@
+alaW	RNA-Ala(GGC)
+alaX	tRNA-Ala(GGC)
+alaV	tRNA-Ala(UGC)
+alaT	tRNA-Ala(UGC)
+alaU	tRNA-Ala(UGC)
+argY	tRNA-Arg(ACG)
+argV	tRNA-Arg(ACG)
+argZ	tRNA-Arg(ACG)
+argQ	tRNA-Arg(ACG)
+argX	tRNA-Arg(CCG)
+argW	tRNA-Arg(CCU)
+argU	tRNA-Arg(UCU)
+asnU	tRNA-Asn(GUU)
+asnV	tRNA-Asn(GUU)
+asnW	tRNA-Asn(GUU)
+asnT	tRNA-Asn(GUU)
+aspU	tRNA-Asp(GUC)
+aspV	tRNA-Asp(GUC)
+aspT	tRNA-Asp(GUC)
+cysT	tRNA-Cys(GCA)
+glnX	tRNA-Gln(CUG)
+glnV	tRNA-Gln(CUG)
+glnW	tRNA-Gln(UUG)
+glnU	tRNA-Gln(UUG)
+gltU	tRNA-Glu(UUC)
+gltV	tRNA-Glu(UUC)
+gltW	tRNA-Glu(UUC)
+gltT	tRNA-Glu(UUC)
+glyU	tRNA-Gly(CCC)
+glyW	tRNA-Gly(GCC)
+glyX	tRNA-Gly(GCC)
+glyY	tRNA-Gly(GCC)
+glyV	tRNA-Gly(GCC)
+glyT	tRNA-Gly(UCC)
+hisR	tRNA-His(GUG)
+ileX	tRNA-Ile(CAU)
+ileY	tRNA-Ile(CAU)
+ileV	tRNA-Ile(GAU)
+ileU	tRNA-Ile(GAU)
+ileT	tRNA-Ile(GAU)
+metV	tRNA-initiator Met(CAU)
+metW	tRNA-initiator Met(CAU)
+metY	tRNA-initiator Met(CAU)
+metZ	tRNA-initiator Met(CAU)
+leuX	tRNA-Leu(CAA)
+leuP	tRNA-Leu(CAG)
+leuV	tRNA-Leu(CAG)
+leuQ	tRNA-Leu(CAG)
+leuT	tRNA-Leu(CAG)
+leuU	tRNA-Leu(GAG)
+leuZ	tRNA-Leu(UAA)
+leuW	tRNA-Leu(UAG)
+lysT	tRNA-Lys(UUU)
+lysY	tRNA-Lys(UUU)
+lysV	tRNA-Lys(UUU)
+lysZ	tRNA-Lys(UUU)
+lysW	tRNA-Lys(UUU)
+lysQ	tRNA-Lys(UUU)
+metT	tRNA-Met(CAU)
+metU	tRNA-Met(CAU)
+pheU	tRNA-Phe(GAA)
+pheV	tRNA-Phe(GAA)
+proK	tRNA-Pro(CGG)
+proL	tRNA-Pro(GGG)
+proM	tRNA-Pro(UGG)
+selC	tRNA-Sec(UCA)
+serU	tRNA-Ser(CGA)
+serV	tRNA-Ser(GCU)
+serW	tRNA-Ser(GGA)
+serX	tRNA-Ser(GGA)
+serT	tRNA-Ser(UGA)
+thrW	tRNA-Thr(CGU)
+thrV	tRNA-Thr(GGU)
+thrT	tRNA-Thr(GGU)
+thrU	tRNA-Thr(UGU)
+trpT	tRNA-Trp(CCA)
+tyrV	tRNA-Tyr(GUA)
+tyrU	tRNA-Tyr(GUA)
+tyrT	tRNA-Tyr(GUA)
+valW	tRNA-Val(GAC)
+valV	tRNA-Val(GAC)
+valT	tRNA-Val(UAC)
+valX	tRNA-Val(UAC)
+valU	tRNA-Val(UAC)
+valY	tRNA-Val(UAC)
+valZ	tRNA-Val(UAC)
+rrsH	16S ribosomal RNA
+rrsD	16S ribosomal RNA
+rrsA	16S ribosomal RNA
+rrsE	16S ribosomal RNA
+rrsB	16S ribosomal RNA
+rrsG	16S ribosomal RNA
+rrsC	16S ribosomal RNA
+rrlH	23S ribosomal RNA
+rrlG	23S ribosomal RNA
+rrlE	23S ribosomal RNA
+rrlD	23S ribosomal RNA
+rrlC	23S ribosomal RNA
+rrlB	23S ribosomal RNA
+rrlA	23S ribosomal RNA
+rrfB	5S ribosomal RNA
+rrfF	5S ribosomal RNA
+rrfC	5S ribosomal RNA
+rrfG	5S ribosomal RNA
+rrfD	5S ribosomal RNA
+rrfA	5S ribosomal RNA
+rrfH	5S ribosomal RNA
+rrfE	5S ribosomal RNA
+ssrS	6S RNA
+
diff --git a/test-requirements.txt b/test-requirements.txt
new file mode 100644
index 0000000..e079f8a
--- /dev/null
+++ b/test-requirements.txt
@@ -0,0 +1,1 @@
+pytest