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Copy pathAnnotationFormat.py
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executable file
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# AnnotationFormat.py
__author__ = 'Wilfried Guiblet'
# Inputs: Outputs of bedtools intersect for GenCode, RepeatMasker, and Introns (custom)
# Goals: - Aggregates multiple rows with same read
# - Combine the three annotations in a curated table
import pandas as pd
import itertools
import sys
import re
import argparse
import numpy as np
def CombineTypes(row):
# Prioritizing features in a summarized column
for strand in ['Same', 'Oppo']:
if row[strand+'_gene_type'] == '.' and row[strand+'_Repeat'] == '.' and str(row[strand+'_intron_number']) in ['.', ' ', ''] and row[strand+'_ncRNA'] == '.':
row[strand+'_Comb_type'] = 'no Feature'
if row[strand+'_gene_type'] == 'lncRNA':
if str(row[strand+'_intron_number']) not in ['.', ' ', '']:
row[strand+'_Comb_type'] = 'lncRNA: Intronic'
if row[strand+'_gene_type'] == 'protein_coding':
if str(row[strand+'_intron_number']) not in ['.', ' ', '']:
row[strand+'_Comb_type'] = 'protein_coding: Intronic'
if row[strand+'_gene_type'] == 'lncRNA':
if str(row[strand+'_exon_number']) not in ['.', ' ', '']:
row[strand+'_Comb_type'] = 'lncRNA: Exonic'
if row[strand+'_gene_type'] in ['processed_pseudogene', 'unprocessed_pseudogene']:
row[strand+'_Comb_type'] = 'pseudogene'
if row[strand+'_Repeat'] not in ['.', ' ', '', 'lncRNA']:
row[strand+'_Comb_type'] = 'Repeat Element'
if row[strand+'_gene_type'] == 'protein_coding':
if str(row[strand+'_exon_number']) not in ['.', ' ', '']:
row[strand+'_Comb_type'] = 'protein_coding: Exonic'
if row[strand+'_ncRNA'] not in ['.', '-1']:
row[strand+'_Comb_type'] = 'ncRNA'
if 'rRNA' in row[strand+'_Repeat']:
row[strand+'_Comb_type'] = 'rRNA'
if row['chrom'] == 'BK000964.3':
row[strand+'_Comb_type'] = 'rRNA'
return row
def MergeDataframes(SameStrandGen_df, SameStrandIntrons_df, SameStrandRMSK_df, SameStrandNCRNA_df, OppoStrandGen_df, OppoStrandIntrons_df, OppoStrandRMSK_df, OppoStrandNCRNA_df, SameStrandUTRs_df, OppoStrandUTRs_df):
# Create empty dataframe
Out_df = pd.DataFrame(columns = ['ID', 'chrom', 'start', 'end', 'score', 'strand', 'Length', 'Counts_Unique', 'Counts_fracMM', 'Counts_Total',\
'Same_ensembl_gene_id', 'Same_external_gene_name', 'Same_gene_type',\
'Same_transcript_type', 'Same_transcript_name', 'Same_feature',\
'Same_exon_number', 'Same_intron_number', 'Same_intron_Overlap',\
'Same_exon_Overlap', 'Same_Repeat', 'Same_ncRNA', 'Same_Comb_type',\
'Oppo_ensembl_gene_id', 'Oppo_external_gene_name', 'Oppo_gene_type',\
'Oppo_transcript_type', 'Oppo_transcript_name', 'Oppo_feature',\
'Oppo_exon_number', 'Oppo_intron_number', 'Oppo_intron_Overlap',\
'Oppo_exon_Overlap', 'Oppo_Repeat', 'Oppo_ncRNA', 'Oppo_Comb_type'])
SameStrandMerged_df = SameStrandGen_df.merge(SameStrandIntrons_df, how='outer', on=['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM','counts_total'])
SameStrandMerged_df = SameStrandMerged_df.merge(SameStrandRMSK_df, how='outer', on=['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM','counts_total'])
SameStrandMerged_df = SameStrandMerged_df.merge(SameStrandNCRNA_df, how='outer', on=['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM','counts_total'])
#print(SameStrandMerged_df.dtypes)
#print(SameStrandUTRs_df.dtypes)
SameStrandMerged_df = SameStrandMerged_df.merge(SameStrandUTRs_df, how='outer', on=['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM','counts_total'])
OppoStrandMerged_df = OppoStrandGen_df.merge(OppoStrandIntrons_df, how='outer', on=['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM','counts_total'])
OppoStrandMerged_df = OppoStrandMerged_df.merge(OppoStrandRMSK_df, how='outer', on=['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM','counts_total'])
OppoStrandMerged_df = OppoStrandMerged_df.merge(OppoStrandNCRNA_df, how='outer', on=['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM','counts_total'])
#print(OppoStrandMerged_df.dtypes)
#print(OppoStrandUTRs_df.dtypes)
OppoStrandMerged_df = OppoStrandMerged_df.merge(OppoStrandUTRs_df, how='outer', on=['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM','counts_total'])
Out_df[['ID', 'chrom', 'start', 'end', 'score', 'strand', 'Length', 'Counts_Unique', 'Counts_fracMM','Counts_Total']] = SameStrandMerged_df[['ID', 'chrom', 'start', 'end', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM','counts_total']]
Out_df['Same_ensembl_gene_id'] = SameStrandMerged_df['gene_id']
Out_df['Same_external_gene_name'] = SameStrandMerged_df['gene_name']
Out_df['Same_gene_type'] = SameStrandMerged_df['gene_type']
Out_df['Same_transcript_type'] = SameStrandMerged_df['transcript_type']
Out_df['Same_transcript_name'] = SameStrandMerged_df['transcript_name']
Out_df['Same_feature'] = SameStrandMerged_df['feature']
Out_df['Same_exon_number'] = SameStrandMerged_df['exon_number']
Out_df['Same_intron_number'] = SameStrandMerged_df['intron_number']
#Out_df['Same_intron_LargeOL'] = SameStrandMerged_df['???'] FROM BEDTOOLS INTERSECT?
#Out_df['Same_exon_LargeOL'] = SameStrandMerged_df['???'] FROM BEDTOOLS INTERSECT?
Out_df['Same_Repeat'] = SameStrandMerged_df['repClass']
Out_df['Same_ncRNA'] = SameStrandMerged_df['ncrnaName']
Out_df['Same_UTR'] = SameStrandMerged_df['UTR']
Out_df['Oppo_ensembl_gene_id'] = OppoStrandMerged_df['gene_id']
Out_df['Oppo_external_gene_name'] = OppoStrandMerged_df['gene_name']
Out_df['Oppo_gene_type'] = OppoStrandMerged_df['gene_type']
Out_df['Oppo_transcript_type'] = OppoStrandMerged_df['transcript_type']
Out_df['Oppo_transcript_name'] = OppoStrandMerged_df['transcript_name']
Out_df['Oppo_feature'] = OppoStrandMerged_df['feature']
Out_df['Oppo_exon_number'] = OppoStrandMerged_df['exon_number']
Out_df['Oppo_intron_number'] = OppoStrandMerged_df['intron_number']
#Out_df['Oppo_intron_LargeOL'] = OppoStrandMerged_df['???'] FROM BEDTOOLS INTERSECT?
#Out_df['Oppo_exon_LargeOL'] = OppoStrandMerged_df['???'] FROM BEDTOOLS INTERSECT?
Out_df['Oppo_Repeat'] = OppoStrandMerged_df['repClass']
Out_df['Oppo_ncRNA'] = OppoStrandMerged_df['ncrnaName']
Out_df['Oppo_UTR'] = OppoStrandMerged_df['UTR']
Out_df = Out_df.apply(CombineTypes, axis=1)
return(Out_df)
def ProcessGencode(InputFile):
Gen_df = pd.read_csv(InputFile, sep = '\t', header = None,\
names = ['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM','counts_total',\
'genChrom', 'genStart', 'genEnd', 'Source', 'genScore', 'genStrand', 'feature', 'frame',\
'gene_id', 'gene_type', 'gene_name', 'level', 'mgi_id', 'havana_gene', 'transcript_id',\
'transcript_type', 'transcript_name', 'transcript_support_level', 'tag', 'havana_transcript',\
'exon_number', 'exon_id', 'protein_id', 'ccdsid', 'ont', 'genOverlap'])
Gen_df.iloc[:, 10:] = Gen_df.iloc[:, 10:].astype('str') # change type necessary for aggregate
#print(Gen_df.dtypes)
# Aggregate reads found in multiple entries
# https://stackoverflow.com/questions/36271413/pandas-merge-nearly-duplicate-rows-based-on-column-value
Gen_df = Gen_df.groupby(['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM',\
'counts_total']).agg({'genChrom' : ', '.join, 'genStart' : ', '.join, 'genEnd' : ', '.join,
'Source' : ', '.join, 'genScore' : ', '.join, 'genStrand' : ', '.join,
'feature' : ', '.join, 'frame' : ', '.join, 'gene_id' : ', '.join,
'gene_type' : ', '.join, 'gene_name' : ', '.join, 'level' : ', '.join,
'mgi_id' : ', '.join, 'havana_gene' : ', '.join, 'transcript_id' : ', '.join,
'transcript_type' : ', '.join, 'transcript_name' : ', '.join, 'transcript_support_level' : ', '.join,
'tag' : ', '.join, 'havana_transcript' : ', '.join, 'exon_number' : ', '.join,
'exon_id' : ', '.join, 'protein_id' : ', '.join, 'ccdsid' : ', '.join,
'ont' : ', '.join, 'genOverlap' : '; '.join
}).reset_index()
# Remove duplicate terms in cells, remove 'nan'
Gen_df.iloc[:, 10:] = Gen_df.iloc[:, 10:].applymap(lambda x: ', '.join(set(x.strip().replace(' ', '').replace('nan,', '').replace('nan', '').split(','))))
return(Gen_df)
def ProcessIntrons(InputFile):
Introns_df = pd.read_csv(InputFile, sep = '\t', header = None,\
names = ['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM','counts_total',\
'intronChrom', 'intronStart', 'intronEnd', 'intronID', 'intronScore', 'intronStrand', 'intronOverlap', 'intron_number'])
#Introns_df = Introns_df.astype('str') # change type necessary for aggregate
Introns_df.iloc[:, 10:] = Introns_df.iloc[:, 10:].astype('str') # change type necessary for aggregate
# https://stackoverflow.com/questions/36271413/pandas-merge-nearly-duplicate-rows-based-on-column-value
Introns_df = Introns_df.groupby(['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM',\
'counts_total']).agg({'intronChrom' : ', '.join, 'intronStart' : ', '.join, 'intronEnd' : ', '.join,
'intronID' : ', '.join, 'intronScore' : ', '.join, 'intronStrand' : ', '.join,
'intronOverlap' : ', '.join, 'intron_number' : ', '.join }).reset_index()
# Remove duplicate terms in cells, remove 'nan'
Introns_df.iloc[:, 10:] = Introns_df.iloc[:, 10:].applymap(lambda x: ', '.join(set(x.strip().replace(' ', '').replace('nan,', '').replace('nan', '').split(','))))
return(Introns_df)
def ProcessRMSK(InputFile):
RMSK_df = pd.read_csv(InputFile, sep = '\t', header = None, \
names = ['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM','counts_total',\
'repChrom', 'repStart', 'repEnd', 'repName', 'repClass', 'repStrand', 'repFamily', 'repOverlap'])
#RMSK_df = RMSK_df.astype('str') # change type necessary for aggregate
RMSK_df.iloc[:, 10:] = RMSK_df.iloc[:, 10:].astype('str') # change type necessary for aggregate
# https://stackoverflow.com/questions/36271413/pandas-merge-nearly-duplicate-rows-based-on-column-value
RMSK_df = RMSK_df.groupby(['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM',\
'counts_total']).agg({'repStart' : ', '.join, 'repEnd' : ', '.join, 'repName' : ', '.join,
'repClass' : ', '.join, 'repStrand': ', '.join, 'repFamily': ', '.join,
'repOverlap': ', '.join,}).reset_index()
# Remove duplicate terms in cells, remove 'nan'
RMSK_df.iloc[:, 10:] = RMSK_df.iloc[:, 10:].applymap(lambda x: ', '.join(set(x.strip().replace(' ', '').replace('nan,', '').replace('nan', '').split(','))))
return(RMSK_df)
def ProcessNCRNA(InputFile):
NCRNA_df = pd.read_csv(InputFile, sep = '\t', header = None, \
names = ['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM','counts_total',\
'ncrnaChrom', 'ncrnaStart', 'ncrnaEnd', 'ncrnaName', 'ncrnaClass', 'ncrnaStrand', 'ncrnaOverlap'])
#RMSK_df = RMSK_df.astype('str') # change type necessary for aggregate
NCRNA_df.iloc[:, 10:] = NCRNA_df.iloc[:, 10:].astype('str') # change type necessary for aggregate
# https://stackoverflow.com/questions/36271413/pandas-merge-nearly-duplicate-rows-based-on-column-value
NCRNA_df = NCRNA_df.groupby(['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM',\
'counts_total']).agg({'ncrnaStart' : ', '.join, 'ncrnaEnd' : ', '.join, 'ncrnaName' : ', '.join,
'ncrnaClass' : ', '.join, 'ncrnaStrand': ', '.join, 'ncrnaOverlap': ', '.join,}).reset_index()
# Remove duplicate terms in cells, remove 'nan'
NCRNA_df.iloc[:, 10:] = NCRNA_df.iloc[:, 10:].applymap(lambda x: ', '.join(set(x.strip().replace(' ', '').replace('nan,', '').replace('nan', '').split(','))))
return(NCRNA_df)
def ProcessUTR(InputFile):
UTR_df = pd.read_csv(InputFile, sep = '\t', header = None, \
names = ['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM','counts_total',\
'UTR', 'chrom_dup', 'UTR_start', 'UTR_end', 'UTR_ID', 'UTR_score', 'UTR_strand', 'dummy'])
UTR_df.iloc[:, 10:] = UTR_df.iloc[:, 10:].astype('str') # change type necessary for aggregate
UTR_df = UTR_df.groupby(['chrom', 'start', 'end', 'ID', 'score', 'strand', 'length', 'counts_unique', 'counts_fracMM',\
'counts_total']).agg({'UTR' : ', '.join}).reset_index()
#print(InputFile)
#print(UTR_df.dtypes)
return(UTR_df)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--SameStrandRMSK', type=str, required=True)
parser.add_argument('--SameStrandGenCode', type=str, required=True)
parser.add_argument('--SameStrandIntrons', type=str, required=True)
parser.add_argument('--SameStrandncRNA', type=str, required=True)
parser.add_argument('--SameStrandUTRs', type=str, required=True)
parser.add_argument('--OppoStrandRMSK', type=str, required=True)
parser.add_argument('--OppoStrandGenCode', type=str, required=True)
parser.add_argument('--OppoStrandIntrons', type=str, required=True)
parser.add_argument('--OppoStrandncRNA', type=str, required=True)
parser.add_argument('--OppoStrandUTRs', type=str, required=True)
parser.add_argument('--Output', type=str, required=True)
args = parser.parse_args()
SameStrandRMSK_df = ProcessRMSK(args.SameStrandRMSK)
SameStrandGen_df = ProcessGencode(args.SameStrandGenCode)
SameStrandIntrons_df = ProcessIntrons(args.SameStrandIntrons)
SameStrandNCRNA_df = ProcessNCRNA(args.SameStrandncRNA)
SameStrandUTRs_df = ProcessUTR(args.SameStrandUTRs)
OppoStrandRMSK_df = ProcessRMSK(args.OppoStrandRMSK)
OppoStrandGen_df = ProcessGencode(args.OppoStrandGenCode)
OppoStrandIntrons_df = ProcessIntrons(args.OppoStrandIntrons)
OppoStrandNCRNA_df = ProcessNCRNA(args.OppoStrandncRNA)
OppoStrandUTRs_df = ProcessUTR(args.OppoStrandUTRs)
Out_df = MergeDataframes(SameStrandGen_df, SameStrandIntrons_df, SameStrandRMSK_df, SameStrandNCRNA_df, OppoStrandGen_df, OppoStrandIntrons_df, OppoStrandRMSK_df, OppoStrandNCRNA_df, SameStrandUTRs_df, OppoStrandUTRs_df)
Out_df.to_csv(args.Output, sep = '\t', index=False)
if __name__ == '__main__':
main()
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