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AnnotateMutations.py
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AnnotateMutations.py
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'''
Created on Mar 31, 2017
@author: husensofteng
'''
'''
Annotates a list of genomic coordinates using overlapping_tracks of cell lines corresponding to the tumor type given in each line
'''
import os, sys, shutil
from pybedtools import BedTool, set_tempdir
from collections import Counter
temp_dir = 'tmp_pybedtoos'
if not os.path.exists(temp_dir):
os.mkdir(temp_dir)
set_tempdir(temp_dir)
def get_muts_tracks_info(muts_input_file, tracks_dir, muts_dir_out, split_muts_file_by_chr=False):
muts_tracks_files = []
tracks_files = [x for x in os.listdir(tracks_dir) if x.endswith('.bed')]
if split_muts_file_by_chr:
muts_files = []
chr_ext = "." + tracks_files[0].split('.')[-1]
if os.path.exists(muts_dir_out):
muts_tracks_files = [muts_dir_out+'/'+x for x in os.listdir(muts_dir_out) if x.endswith('_overlapping_tracks.bed10')]
if len(muts_tracks_files)>0:
return muts_tracks_files
else:
os.mkdir(muts_dir_out)
muts_files = [muts_dir_out+'/'+x for x in os.listdir(muts_dir_out) if x.endswith(chr_ext)]
if len(muts_files)<=0:
os.system("""awk '{{print $0 >> "{muts_dir}/"$1"{chr_ext}"}}' {muts_file} """.format(muts_dir=muts_dir_out, chr_ext=chr_ext, muts_file=muts_input_file))
muts_files = [muts_dir_out+'/'+x for x in os.listdir(muts_dir_out) if x.endswith(chr_ext)]
print('muts_files: ', muts_files)
print('tracks_files: ', tracks_files)
for muts_file in muts_files:
if muts_file.split('/')[-1] in tracks_files:
muts_tracks_file = muts_file+"_overlapping_tracks.bed10"
if not os.path.exists(muts_tracks_file):
print("Intersecting and Grouping: ", muts_tracks_file)
BedTool(muts_file).intersect(BedTool(tracks_dir+'/'+tracks_files[tracks_files.index(muts_file.split('/')[-1])]), wo=True, loj=True).groupby(g=[1,2,3,4,5,6,7,8,9], c=13, o=['collapse']).saveas(muts_tracks_file)
muts_tracks_files.append(muts_tracks_file)
else:
for tracks_file in tracks_files:
if not os.path.exists(muts_dir_out):
os.mkdir(muts_dir_out)
muts_tracks_file = muts_dir_out+'/'+tracks_file+"_overlapping_tracks.bed10"
if not os.path.exists(muts_tracks_file):
print("Intersecting and Grouping: ", muts_tracks_file)
BedTool(muts_input_file).intersect(BedTool(tracks_dir+'/'+tracks_file), wo=True, loj=True).groupby(g=[1,2,3,4,5,6,7,8,9], c=13, o=['collapse']).saveas(muts_tracks_file)
muts_tracks_files.append(muts_tracks_file)
print('muts_tracks_files: ', muts_tracks_files)
return muts_tracks_files
def annotate_muts(muts_tracks_file, muts_tracks_ouput_file, tissue_cell_assays, matching_cell_name_representative_dict, cancer_type_index = 9, tracks_info_index = 3):
with open(muts_tracks_file, 'r') as mut_tracks_ifile, open(muts_tracks_ouput_file, 'w') as mut_tracks_ofile:
l = mut_tracks_ifile.readline().strip().split('\t')
while len(l)>cancer_type_index:
annotations = {}
tracks = l[tracks_info_index].split(',')
for track in tracks:
#print(track, l[cancer_type_index])
cellname = track.split('#')[0]
try:
cellname = matching_cell_name_representative_dict[cellname][0]
except KeyError:
continue
try:
if cellname in tissue_cell_assays[l[cancer_type_index]]:
try:
annotations[track.split('#')[1]].append(float(track.split('#')[2]))#annotations[track.split('#')[1]].append(cellname+"#"+'#'.join(track.split('#')[1:]))
except ValueError:
annotations[track.split('#')[1]].append(track.split('#')[2])
except KeyError:
try:
annotations[track.split('#')[1]] = [float(track.split('#')[2])]
except ValueError:
annotations[track.split('#')[1]] = [track.split('#')[2]]
except IndexError:
annotations[track.split('#')[1]] = [1.0]
except IndexError:
annotations[track.split('#')[1]] = [1.0]
except KeyError:
print("Cancer type not found: ", l[cancer_type_index])
print(track)
print(cellname)
print(l[cancer_type_index])
print(tissue_cell_assays[l[cancer_type_index]])
break
for anno in sorted(annotations.keys()):
if anno=='TFBinding':
annotations[anno] = ';'.join(set(annotations[anno]))
continue
try:
annotations[anno] = str(sum(annotations[anno])/(len(annotations[anno])*1.0))
except (ValueError, TypeError):
annotations[anno] = Counter(annotations[anno]).most_common(1)[0][0]
annotations_combined = [x+":"+annotations[x] for x in sorted(annotations.keys())]
if len(annotations_combined)==0:
annotations_combined = ['NaN']
mut_tracks_ofile.write('\t'.join(l[0:9]) + '\t' + '|'.join(annotations_combined) + '\n')
l = mut_tracks_ifile.readline().strip().split('\t')
return muts_tracks_ouput_file
def retreive_key_values_from_dict_file(dict_input_file, key_value_sep='=', values_sep=','):#TFFamilyName TF_name
"Retrieves the key and its values"
key_values_dict = {}
value_key_dict = {}
with open(dict_input_file, 'r') as dict_input_file_infile:
lines = dict_input_file_infile.readlines()
for line in lines:
if line.startswith('//') or line.startswith('#'):# or '=' not in line:
continue
sl = line.strip().split(key_value_sep)
key_value = sl[0].strip()
if key_value not in key_values_dict.keys():
key_values_dict[key_value] = []
if key_value not in value_key_dict:
value_key_dict[key_value]=[key_value]
if len(sl)>1:
for s in sl[1].split(values_sep):
if s.strip()!="" and s.strip() not in key_values_dict[key_value]:
key_values_dict[key_value].append(s.strip())
if s.strip()!="":
if s.strip() not in value_key_dict:
value_key_dict[s.strip()]=[]
value_key_dict[s.strip()].append(key_value)
return key_values_dict, value_key_dict
def get_tissue_cell_mappings(tissue_cell_mappings_file, matching_cell_name_representative_dict, key_value_sep='=', values_sep=',', cell_assay_sepe=':'):
tissue_cell_assays = {}
with open(tissue_cell_mappings_file, 'r') as ifile:
lines = ifile.readlines()
for line in lines:
if line.startswith('//') or line.startswith('#') or '=' not in line:
continue
sl = line.strip().split(key_value_sep)
key_value = sl[0]
if key_value not in tissue_cell_assays.keys():
tissue_cell_assays[key_value] = []
for s in sl[1].split(values_sep):
try:
cell = matching_cell_name_representative_dict[s.split(':')[0]][0]#get the rep name of the cell
if cell not in tissue_cell_assays[key_value]:
tissue_cell_assays[key_value].append(cell)
except KeyError:
continue
return tissue_cell_assays
def get_annotated_muts(muts_input_file, tracks_dir, muts_out):
cell_names_to_use = 'CellNamesDict'
tissue_cell_mappings_file='TissueCellMatches'
representative_cell_name_matchings_dict, matching_cell_name_representative_dict = retreive_key_values_from_dict_file(cell_names_to_use)
tissue_cell_assays = get_tissue_cell_mappings(tissue_cell_mappings_file=tissue_cell_mappings_file,
matching_cell_name_representative_dict=matching_cell_name_representative_dict,
key_value_sep='=', values_sep=',', cell_assay_sepe=':')
if not os.path.exists(muts_out):
muts_tracks_files = get_muts_tracks_info(muts_input_file=muts_input_file, tracks_dir=tracks_dir, muts_dir_out=muts_out+'_tmp')
with open(muts_out, 'w') as muts_ofile:
for muts_tracks_file in muts_tracks_files:
muts_tracks_ouput_file = muts_tracks_file+"_annotated"
print("Annotating: ", muts_tracks_ouput_file)
annotate_muts(muts_tracks_file, muts_tracks_ouput_file, tissue_cell_assays, matching_cell_name_representative_dict, cancer_type_index = 5, tracks_info_index = 9)
with open(muts_tracks_ouput_file, 'r') as muts_tracks_ouput_ifile:
muts_ofile.write(muts_tracks_ouput_ifile.read())
#if os.path.exists(muts_out+'_tmp'):
# shutil.rmtree(muts_out+'_tmp')
return muts_out
if __name__ == '__main__':
muts_input_file = sys.argv[1]
tracks_dir = sys.argv[2]
muts_out = sys.argv[3]#muts_input_file+'_annotations'
get_annotated_muts(muts_input_file, tracks_dir, muts_out)