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get_pwy_abundance_v1.py
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get_pwy_abundance_v1.py
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#!/usr/bin/env python
import pandas as pd
import math
from subprocess import PIPE, run
import argparse
parser = argparse.ArgumentParser(description="Run fungal functional profiling, Input is the diamond blastx output.\n")
parser.add_argument('-i', '--input',
dest = "input_path",
action = "store",
default = None,
help = "Input blastx output path.\n",
required = True)
parser.add_argument('-s', '--script',
dest = "script_path",
action = "store",
default = None,
help = "folder storing needed tables and scripts.\n",
required = True)
parser.add_argument('-o1', '--output1',
dest = "output_path1",
action = "store",
default = "",
help = "Output path of the fungal pathway profiling.\n",
required = True)
parser.add_argument('-o2', '--output2',
dest = "output_path2",
action = "store",
default = "",
help = "Output path of the fungal pathway_class profiling.\n",
required = True)
parser.add_argument('-o3', '--output3',
dest = "output_path3",
action = "store",
default = "",
help = "Output path of the fungal pathway_type profiling.\n",
required = True)
parser.add_argument('-o4', '--output4',
dest = "output_path4",
action = "store",
default = "",
help = "Output path of the fungal full annotation.\n",
required = True)
option=parser.parse_args()
sample_to_process = option.input_path
scriptdir = option.script_path
output1=option.output_path1
output2=option.output_path2
output3=option.output_path3
output4=option.output_path4
id_path = scriptdir+'/id_to_clean.txt'
ann_path = scriptdir+'/jgi_ann_05-2022_reordered.tab'
uniprot_catalog = scriptdir+'/ncbi_uniprot_species.txt'
taxa_path = scriptdir+'/taxonomy_for_function.csv'
rscript_path = scriptdir+'/keggConv.R'
id_catalog=pd.read_csv(id_path,delimiter="\t",names=["pid","rid"]) #### id to clean
column_names=["proteinId","ecNum","definition","uniprotID","KO","pathway","pathway_class","pathway_type", "speciesID","catalyticActivity","cofactors","associatedDiseases"]
jgi_ann=pd.read_csv(ann_path,delimiter='\t',names=column_names,index_col=False,dtype={'fourth_column': 'str'})
blastx_out=pd.read_csv(sample_to_process,index_col=False,delimiter='\t',names=["qid","rid","id","lenght","mismatch","gapopne","qstart","qend","sstart","send","evalue","bscore"])
rid_to_uniprot_species=pd.read_csv(uniprot_catalog,delimiter='\t',names=["rid","uniprotId","speciesID"],dtype={'first_column': 'str', 'second_column': 'str'},index_col=False)
taxa=pd.read_csv(taxa_path, delimiter="\t",names=["speciesID","taxa"],index_col=False)
if len(blastx_out) >=1:
raw_counts=blastx_out['rid'].value_counts().rename_axis("rid").reset_index(name='counts')
raw_counts_clean=pd.merge(raw_counts,id_catalog) ### map long name to short names
column_names = ["proteinId","ecNum","definition","uniprotID","KO","pathway","pathway_class","pathway_type", "speciesID","catalyticActivity","cofactors","associatedDiseases"]
pwy_counts=pd.DataFrame(columns=column_names, dtype=object)
for i in range(len(raw_counts_clean)):
inter=jgi_ann.loc[jgi_ann['proteinId']==raw_counts_clean.iloc[i]['pid']]
if len(inter) == 0:
speciesID=rid_to_uniprot_species.loc[rid_to_uniprot_species['rid']==raw_counts_clean.iloc[i]['pid']]['speciesID']
if len(speciesID) == 0:
speciesID = "unclassified"
else:
speciesID=speciesID.to_string(index=False).strip()
if raw_counts_clean.iloc[i]['pid'].startswith("jgi"):
inter=[raw_counts_clean.iloc[i]['pid'],"NA","NA","NA","unidentified","unidentified","unidentified","unidentified",speciesID,"NA","NA","NA"]
inter=pd.DataFrame([inter],columns=column_names)
else:
uptID=rid_to_uniprot_species.loc[rid_to_uniprot_species['rid']==raw_counts_clean.iloc[i]['pid']]['uniprotId']
if len(uptID) == 0:
uptID = "unidentified"
inter=[raw_counts_clean.iloc[i]['pid'],"NA","NA","NA","unidentified","unidentified","unidentified","unidentified",speciesID,"NA","NA","NA"]
inter=pd.DataFrame([inter],columns=column_names)
else:
uptID=uptID.to_string(index=False).strip()
if uptID=="NaN":# UNIPROT accession is NA
inter=[raw_counts_clean.iloc[i]['pid'],"NA","NA","NA","unidentified","unidentified","unidentified","unidentified",speciesID,"NA","NA","NA"]
inter=pd.DataFrame([inter],columns=column_names)
else: # invoke the keggConv script to get pwy info, return pwy, pwy class
command = ['Rscript', rscript_path, uptID]
result = run(command, stdout=PIPE, stderr=PIPE, universal_newlines=True)
conv=result.stdout.split('|')
inter=[]
for c in range(0, len(conv), 3):
rows=[raw_counts_clean.iloc[i]['pid'],"NA","NA",uptID,conv[c].strip('\n'),conv[c+1].strip('\n'),conv[c+2].strip('\n'),"unidentified",speciesID,"NA","NA","NA"]
inter.append(rows)
inter=pd.DataFrame(inter,columns=column_names)
inter=inter.assign(counts=raw_counts_clean.iloc[i]['counts']/len(inter))
pwy_counts=pwy_counts.append(inter,ignore_index = True,sort=False)
def get_stratified_taxa(ann_type):
annotation=pwy_counts.reset_index().groupby([ann_type,"speciesID"]).agg({'counts': 'sum'}).reset_index()
annotation.columns=[ann_type,"speciesID","counts"]
if len(annotation) >= 1:
column_names=[ann_type,'counts']
ann_abd=pd.DataFrame(columns=column_names, dtype=object)
for p in annotation[ann_type].unique():
ann_subset=annotation[annotation[ann_type]==p]
ann_taxa=pd.merge(taxa,ann_subset)
ann_taxa[ann_type]=ann_taxa[ann_type].astype(str)+'|'+ann_taxa['taxa'].astype(str)
ann_taxa = ann_taxa[[ann_type,'counts']]
all_counts=ann_taxa['counts'].sum()
ann_taxa.loc[-1] = [p, all_counts] # adding a row
ann_taxa.index = ann_taxa.index + 1 # shifting index
ann_taxa.sort_index(inplace=True)
stratified_pwy=ann_taxa.groupby([ann_type]).agg({'counts': 'sum'}).reset_index()
ann_abd=ann_abd.append(stratified_pwy,ignore_index = True)
return ann_abd
else:
print('no pathway found\n')
pass
pwy_abd=get_stratified_taxa("pathway")
pwyCls_abd=get_stratified_taxa("pathway_class")
pwyTyp_abd=get_stratified_taxa("pathway_type")
pwy_abd.to_csv(output1,index=False,sep='\t')
pwyCls_abd.to_csv(output2,index=False,sep='\t')
pwyTyp_abd.to_csv(output3,index=False,sep='\t')
pwy_counts.to_csv(output4,index=False,sep='\t')
else:
print('no protein found\n')
pass