Skip to content
Go to file
Cannot retrieve contributors at this time
188 lines (168 sloc) 8.33 KB
RADAR version that looks up main scores from precomputed tables
import argparse
import ntpath
import os
import pybedtools
from multiprocessing import Process, Manager, Array, Pool
# files and directories we need
RESOURCES_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "resources/")
MAIN_SCORES_DIR = os.path.join(RESOURCES_DIR, "main_scores/{}/")
ALL_RBP_SITES = os.path.join(RESOURCES_DIR, "all_RBP_peaks_unmerged_labeled_sorted.bed")
SIGNIFICANT_PEAKS = os.path.join(RESOURCES_DIR, "significant_peaks")
MUTATIONAL_BURDEN_MAT = os.path.join(RESOURCES_DIR, "rbp_peak_significance")
REG_POWER_MAT = os.path.join(RESOURCES_DIR, "regulator_pval.txt")
ASSEMBLIES = ['hg19', 'hg38']
# class that represents a variant and stores its scores
class Variant:
def __init__(self, key):
self.key = key
self.gene_target, self.reg_power, self.mut_burden, self.main = None, None, None, None
def score_string(self): # returns tab-separated string of scores in correct order, ends with newline
tissue_specific = [score for score in (self.gene_target, self.reg_power, self.mut_burden) if score != None]
if self.main == None: # if not in regulome
return "{}\n".format("\t".join(list(self.key) + ["0"] * (6 + len(tissue_specific))))
return "{}\n".format("\t".join(list(self.key) + self.main + tissue_specific))
def score_string(key, var): # returns tab-separated string of scores in correct order, ends with newline
tissue_specific = [score for score in var[1] if score != None]
if var[0] == None: # if not in regulome
return "{}\n".format("\t".join(list(key) + ["0"] * (9 + len(tissue_specific))))
main_total, ts_total = float(var[0][-1]), sum(int(val) for val in tissue_specific)
return "{}\n".format("\t".join(list(key) + var[0] + tissue_specific + [str(ts_total), str(main_total + ts_total)]))
# helper function to search main score file for relevant variants
def search_score_files(tup):
ch, cnt = tup
curr_found = 0
variants = dict()
with open(os.path.join(MAIN_SCORES_DIR, "{}_scored".format(ch))) as file:
for line in file:
line = line.split()
key = (line[0], line[1], line[2], line[3].upper(), line[4])
# load main scores for each variant we're considering
if key in var_set:
variants[key] = variants[key] = line[5:12]
curr_found += 1
if curr_found == cnt: # break early if we've found all requested variants on this chromosome
return ch, variants
parser = argparse.ArgumentParser(description='RADAR')
parser.add_argument('-b', '--bed', help="Variant BED file", required=True)
parser.add_argument('-a', '--assembly', help="Genome assembly", default="hg19")
parser.add_argument('-o', '--outdir', help="Output directory", required=True)
parser.add_argument('-c', '--cancer', help="Cancer type for tissue-specific scoring", required=False)
parser.add_argument('-kg', '--keygenes', action="store_true", default=False, help="Compute key genes score")
parser.add_argument('-mr', '--mutrec', action="store_true", default=False, help="Compute mutation recurrence score")
parser.add_argument('-rp', '--regpower', action="store_true", default=False, help="Compute RBP regulation power score")
args = parser.parse_args()
assembly = args.assembly
if assembly not in ASSEMBLIES:
print("Invalid assembly")
# cancer type used for tissue-specific scores
if assembly == 'hg19' and (args.keygenes or args.mutrec or args.regpower):
cancer_type = args.cancer
cancer_index = CANCERS.index(cancer_type)
print("Invalid cancer type provided")
# first preprocess for any tissue-specific scores, which require intersections
variant_string_list = []
with open(args.bed) as file:
for line in file:
variants_bedtool = pybedtools.BedTool("\n".join(variant_string_list), from_string=True)
if args.keygenes and assembly == 'hg19':
# find locations for each requested significant gene in this particular cancer
significant_peaks_string_list = []
with open(SIGNIFICANT_PEAKS) as file:
next(file) # skip header
for line in file:
line = line.split()
sig = line[4 + cancer_index]
if sig == "1": # we only care if this is a significant peak
# intersect variants with significant peaks
significant_peaks_bedtool = pybedtools.BedTool("\n".join(significant_peaks_string_list), from_string=True)
variants_bedtool = variants_bedtool.intersect(significant_peaks_bedtool, c=True)
# include mutational burden
if args.mutrec and assembly == 'hg19':
# string list of burdened RBP peaks
burdened_peaks_string_list = []
with open(MUTATIONAL_BURDEN_MAT) as file:
for line in file:
line = line.split()
peak, burdens = line[:3], line[4:]
if burdens[cancer_index] == "1": # burdened
# check how many burdened RBP peaks intersect with each variant
burdened_peaks_bedtool = pybedtools.BedTool("\n".join(burdened_peaks_string_list), from_string=True)
variants_bedtool = variants_bedtool.intersect(burdened_peaks_bedtool, c=True)
# include regulatory power
if args.regpower and assembly == 'hg19':
# load regulatory power matrix
with open(REG_POWER_MAT, 'r') as file:
reg_power = set() # set of RBPs with high regulatory power in requested cancer
for line in file:
line = line.split()
rbp, sig = line[0], line[1 + cancer_index]
if sig == "1":
# check which RBPs each variant intersects
rbps_bedtool = pybedtools.BedTool(ALL_RBP_SITES)
variants_bedtool = variants_bedtool.intersect(rbps_bedtool, loj=True)
# list of variants we need to score
variant_list = [] # holds a list of variant keys, same order as given
chromosomes = dict() # maps chromosomes to number of input variants on that chromosome
variants_ts_scores = dict()
# load in variants to score
for line in variants_bedtool:
line = list(line)
key = tuple(line[:5]) # ch, start, stop, ref, alt
# add any tissue-specific scores requested
if key not in variants_ts_scores:
chromosomes[key[0]] = chromosomes[key[0]] + 1 if key[0] in chromosomes else 1
# variants[key] = Variant(key)
variants_ts_scores[key] = [None, None, None]
# only need to load these once
if args.keygenes and assembly == 'hg19': # gene-target scores are in the 6th column
val = "1" if line[5] != "0" else "0" # score is 1 if intersects at least one gene
variants_ts_scores[key][0] = val
if args.mutrec and assembly == 'hg19':
val = "1" if line[5 + (args.keygenes)] != "0" else "0" # score is 0 if intersects at least one burdened peak
variants_ts_scores[key][1] = val
if args.regpower and assembly == 'hg19':
# rbp in 9th, 10th, or 11th column depending on whether gene-target and mutational burden scores requested
rbp = line[8 + args.keygenes + args.mutrec]
# score 1 if intersects at least one powerful rbp in this cancer, else 0
val = "1" if rbp in reg_power else "0"
variants_ts_scores[key][2] = val
# search relevant main score files in parallel
var_set = set(variant_list) # make set for easy lookup
pool = Pool()
inputs = [(ch, chromosomes[ch]) for ch in chromosomes]
variants = dict(, inputs))
# write scores to output file
head, tail = ntpath.split(args.bed)
var_file = tail or ntpath.basename(head)
bed_name = var_file[:var_file.index(".bed")] if len(var_file) > 4 and var_file[-4:] == ".bed" else var_file
output_file = os.path.join(args.outdir, "{}.radar_out.bed".format(bed_name))
header = ['chr', 'start', 'stop', 'ref', 'alt', 'cross_species_conservation', 'RBP_binding_hub', 'GERP', 'Evofold', 'motif_disruption', 'RBP_gene_association', 'total_universal']
with open(output_file, 'w') as outfile:
if args.keygenes and assembly == 'hg19': outfile.write("\tkey_genes")
if args.mutrec and assembly == 'hg19': outfile.write("\tmutation_recurrence")
if args.regpower and assembly == 'hg19': outfile.write("\tRBP_regulation_power")
for var in variant_list:
ch = var[0]
outfile.write(score_string(var, [variants[ch][var] if var in variants[ch] else None, variants_ts_scores[var]]))
You can’t perform that action at this time.