def get_loci(transcripts_genepred): loci = Loci() loci.verbose = True with open(transcripts_genepred) as inf: for line in inf: if line[0] == '#': continue gpd = GenePredEntry(line.rstrip()) rng = Bed(gpd.value('chrom'), gpd.value('txStart'), gpd.value('txEnd')) rng.set_payload(gpd.value('name')) loc1 = Locus() loc1.add_member(rng) loci.add_locus(loc1) sys.stderr.write("Organizing genepred data into overlapping loci\n") sys.stderr.write("Started with " + str(len(loci.loci)) + " loci\n") loci.update_loci() sys.stderr.write("Ended with " + str(len(loci.loci)) + " loci\n") m = 0 locus2name = {} name2locus = {} for locus in loci.loci: m += 1 for member in locus.members: name = member.get_payload() if m not in locus2name: locus2name[m] = set() locus2name[m].add(name) name2locus[name] = m return [locus2name, name2locus]
def process_locus(locus, args): depth = {} s2psl = SAMtoPSLconversionFactory() unique = {} chr = locus[0].value('rname') for sam in locus: p = PSL(s2psl.convert_line(sam.get_line())) g = GenePredEntry(p.get_genepred_line()) g = g.get_smoothed(args.min_intron) for i in range(0,g.get_exon_count()): rng = str(g.value('exonStarts')[i])+"\t"+str(g.value('exonEnds')[i]) if rng not in unique: unique[rng] = 0 unique[rng]+=1 for bstr in unique: [start,end] = bstr.split("\t") for i in range(int(start),int(end)): if i not in depth: depth[i] = 0 depth[i] += unique[bstr] # add the number of these to the depth #now we can print the depth prevdepth = 0 prevstart = None lasti = None for i in sorted(depth.keys()): if depth[i] < args.min_depth: continue if depth[i] != prevdepth: #output what we have so far if we have something if prevstart: output_depth(chr+"\t"+str(prevstart)+"\t"+str(lasti+1)+"\t"+str(prevdepth),args) prevstart = i prevdepth = depth[i] lasti = i if prevstart: output_depth(chr+"\t"+str(prevstart)+"\t"+str(lasti+1)+"\t"+str(prevdepth),args)
def main(): parser = argparse.ArgumentParser() parser.add_argument('input_gpd', help="GENEPRED input or - for STDIN") args = parser.parse_args() inf = sys.stdin if args.input_gpd != '-': inf = open(args.input_gpd) seen = set() ls = RangeBasics.Loci() ls.verbose = True ls.use_direction = False for line in inf: if line[0] == '#': continue gpd = GenePredEntry(line) if gpd.value('name') in seen: sys.stderr.write( "ERROR: need uniquely named genepred entry names\n" + name + "\n") sys.exit() seen.add(gpd.value('name')) r = gpd.locus_range.copy() r.direction = None r.set_payload(gpd.value('name')) l = RangeBasics.Locus() l.add_member(r) ls.add_locus(l) ls.update_loci() z = 0 for locus in ls.loci: z += 1 for member in locus.members: print str(z) + "\t" + member.get_payload()
def copy(self): g = FuzzyGenePred() # start with a blank one why not # get the settings for pname in self.params: g.params[pname] = self.params[pname] # copy the genepreds for orig in self.gpds: g.gpds.append(GenePredEntry(orig.get_line())) #store direction g.dir = self.dir # copy the fuzzy junctions for orig in self.fuzzy_junctions: g.fuzzy_junctions.append(orig.copy()) # copy the simple junction set for orig in self.simple_junction_set: g.simple_junction_set.add(orig) # copy the start if self.start: g.start = Bed(self.start.chr,\ self.start.start-1,\ self.start.end,\ self.start.direction) g.start.set_payload([]) for v in self.start.get_payload(): g.start.get_payload().append(v) # copy the end if self.end: g.end = Bed(self.end.chr, self.end.start - 1, self.end.end, self.end.direction) g.end.set_payload([]) for v in self.end.get_payload(): g.end.get_payload().append(v) return g
def do_fuzzy(fz, sr, args): outputs = [] cnt = 0 for i in range(0, len(fz.gpds)): cnt += 1 #fz.gpds[0].entry['name'] = 'LR_'+str(cnt) g = GenePredEntry(fz.get_genepred_line()) #print g.get_bed().get_range_string() + "\t" + str(g.get_exon_count())+" exons" parts = evaluate_junctions(fz, sr, args) for part in parts: #full = "LR_"+str(outind)+"\t"+"LR_"+str(outind)+"\t"+part outputs.append(part) return outputs
def do_reduction(subset, args, nrfuzzykey, location): seen = set() for i in subset: seen.add(i) for j in subset[i]: seen.add(j) singles = [] for num in nrfuzzykey: if num not in seen: singles.append(num) #if len(subset.keys()) == 0 and len(compatible.keys()) == 0: return families = get_subset_evidence(subset, nrfuzzykey, args) gpdlines = "" tablelines = "" for num in singles: families.append(nrfuzzykey[num]) # find gpds not in the graph... for fz in families: info = fz.get_info_string() gpdline = fz.get_genepred_line() #print '&&&&&&&&&&&&&&&&' #print gpdline #print fz.get_info_string() #print '&&&&&&&&&&&&&&&&' gpd = GenePredEntry(gpdline) if not gpd.is_valid(): sys.stderr.write("WARNING: invalid genepred entry generated\n" + gpdline + "\n" + fz.get_info_string() + "\n") gpd = sorted( fz.gpds, key=lambda x: x.get_exon_count(), reverse=True)[0] #just grab one that has all the exons fz = FuzzyGenePred(gpd, juntol=args.junction_tolerance * 2) gpdline = fz.get_genepred_line() if not gpd.is_valid(): sys.stderr.write("WARNING: still problem skilling\n") continue gpdlines += gpdline + "\n" if args.output_original_table: name = gpd.entry['name'] for g in fz.gpds: tablelines += name + "\t" + g.entry['name'] + "\n" grng = gpd.get_bed() grng.direction = None if not location: location = grng location = location.merge(grng) locstring = '' if location: locstring = location.get_range_string() return [gpdlines, tablelines, locstring]
def load_from_inputs(args): #Read in the VCF file sys.stderr.write("Reading in the VCF file\n") alleles = {} #with open(args.phased_VCF) as inf: with open(args.inputs[1]) as inf: for line in inf: vcf = VCF(line) if not vcf.is_snp(): continue g = vcf.get_phased_genotype() if not g: continue if vcf.value('chrom') not in alleles: alleles[vcf.value('chrom')] = {} if vcf.value('pos') in alleles[vcf.value('chrom')]: sys.stderr.write("WARNING: seeing the same position twice.\n" + line.rstrip() + "\n") alleles[vcf.value('chrom')][vcf.value( 'pos')] = g # set our left and right sys.stderr.write("Reading in the reference genome\n") #ref = read_fasta_into_hash(args.reference_genome) ref = read_fasta_into_hash(args.inputs[0]) res1 = [] res2 = [] p = None sys.stderr.write("Introducing VCF changes to reference sequences\n") # Pretty memory intesnive to so don't go with all possible threads if args.threads > 1: p = Pool(processes=max(1, int(args.threads / 4))) for chrom in ref: # handle the case where there is no allele information if chrom not in alleles: r1q = Queue() r1q.put([0, chrom, ref[chrom]]) res1.append(r1q) r2q = Queue() r2q.put([0, chrom, ref[chrom]]) res2.append(r2q) elif args.threads > 1: res1.append( p.apply_async(adjust_reference_genome, args=(alleles[chrom], ref[chrom], 0, chrom))) res2.append( p.apply_async(adjust_reference_genome, args=(alleles[chrom], ref[chrom], 1, chrom))) else: r1q = Queue() r1q.put( adjust_reference_genome(alleles[chrom], ref[chrom], 0, chrom)) res1.append(r1q) r2q = Queue() r2q.put( adjust_reference_genome(alleles[chrom], ref[chrom], 1, chrom)) res2.append(r2q) if args.threads > 1: p.close() p.join() # now we can fill reference 1 with all our new sequences ref1 = {} c1 = 0 for i in range(0, len(res1)): res = res1[i].get() c1 += res[0] ref1[res[1]] = res[2] # now we can fill reference 2 with all our new sequences ref2 = {} c2 = 0 for i in range(0, len(res2)): res = res2[i].get() c2 += res[0] ref2[res[1]] = res[2] sys.stderr.write("Made " + str(c1) + "|" + str(c2) + " changes to the reference\n") # Now ref1 and ref2 have are the diploid sources of the transcriptome gpdnames = {} txn1 = Transcriptome() txn2 = Transcriptome() txn1.set_reference_genome_dictionary(ref1) txn2.set_reference_genome_dictionary(ref2) #with open(args.transcripts_genepred) as inf: with open(args.inputs[2]) as inf: for line in inf: if line[0] == '#': continue txn1.add_genepred_line(line.rstrip()) txn2.add_genepred_line(line.rstrip()) gpd = GenePredEntry(line.rstrip()) gpdnames[gpd.value('name')] = gpd.value('gene_name') # The transcriptomes are set but we dont' really need the references anymore # Empty our big memory things txn1.ref_hash = None txn2.ref_hash = None for chrom in ref1.keys(): del ref1[chrom] for chrom in ref2.keys(): del ref2[chrom] for chrom in ref.keys(): del ref[chrom] if not args.locus_by_gene_name: #[locus2name,name2locus] = get_loci(args.transcripts_genepred) [locus2name, name2locus] = get_loci(args.inputs[2]) else: # set locus by gene name sys.stderr.write("Organizing loci by gene name\n") locus2name = {} name2locus = {} numname = {} m = 0 for name in sorted(gpdnames): gene = gpdnames[name] if gene not in numname: m += 1 numname[gene] = m num = numname[gene] if num not in locus2name: locus2name[num] = set() locus2name[num].add(name) name2locus[name] = num sys.stderr.write("Ended with " + str(len(locus2name.keys())) + " loci\n") if args.isoform_expression: sys.stderr.write("Reading expression from a TSV\n") with open(args.isoform_expression) as inf: line1 = inf.readline() for line in inf: f = line.rstrip().split("\t") txn1.add_expression(f[0], float(f[1])) txn2.add_expression(f[0], float(f[1])) elif args.cufflinks_isoform_expression: sys.stderr.write("Using cufflinks expression\n") cuffz = 0 with open(args.cufflinks_isoform_expression) as inf: line1 = inf.readline() for line in inf: cuffz += 1 sys.stderr.write(str(cuffz) + " cufflinks entries processed\r") f = line.rstrip().split("\t") txn1.add_expression_no_update(f[0], float(f[9])) txn2.add_expression_no_update(f[0], float(f[9])) txn1.update_expression() txn2.update_expression() sys.stderr.write("\n") elif args.uniform_expression: sys.stderr.write("Using uniform expression model\n") else: sys.stderr.write( "Warning isoform expression not sepcified, using uniform expression model.\n" ) # Now we have the transcriptomes set rhos = {} # The ASE of allele 1 (the left side) randos = {} if args.seed: random.seed(args.seed) for z in locus2name: randos[z] = random.random() sys.stderr.write("Setting rho for each transcript\n") # Lets set rho for ASE for each transcript for tname in sorted(txn1.transcripts): if args.ASE_identical or args.ASE_identical == 0: rhos[tname] = float(args.ASE_identical) elif args.ASE_isoform_random: rhos[tname] = random.random() else: # we must be on locus random rhos[tname] = randos[name2locus[tname]] #Now our dataset is set up rbe = SimulationBasics.RandomBiallelicTranscriptomeEmitter(txn1, txn2) rbe.gene_names = gpdnames rbe.name2locus = name2locus rbe.set_transcriptome1_rho(rhos) return rbe
def evaluate_junctions(fz, sr, args): cnt = 0 source_names = [x.entry['name'] for x in fz.gpds] working = fz.copy() if len(working.fuzzy_junctions) == 0: return [] for i in range(0, len(working.fuzzy_junctions)): newjun = working.fuzzy_junctions[i] newjun.left.get_payload()['junc'] = [] newjun.right.get_payload()['junc'] = [] oldjun = fz.fuzzy_junctions[i] for srjun in sr: sjun = sr[srjun]['fzjun'] if oldjun.overlaps(sjun, args.junction_tolerance): for i in range(0, min(sr[srjun]['cnt'], args.downsample)): newjun.left.get_payload()['junc'].append( sjun.left.get_payload()['junc'][0]) newjun.right.get_payload()['junc'].append( sjun.right.get_payload()['junc'][0]) cnt += 1 juncs = [] starts = [] ends = [] evidences = [] for i in range(0, len(fz.fuzzy_junctions)): evidence = len(working.fuzzy_junctions[i].left.get_payload()['junc']) if evidence >= args.required_evidence: if i == 0: starts.append(working.start.start) elif working.fuzzy_junctions[i].left.get_payload()['start']: starts.append(working.fuzzy_junctions[i].left.get_payload() ['start'].start) else: starts.append(working.fuzzy_junctions[i - 1].right.start) #now ends if i == len(fz.fuzzy_junctions) - 1: ends.append(working.end.end) elif working.fuzzy_junctions[i].right.get_payload()['end']: ends.append( working.fuzzy_junctions[i].right.get_payload()['end'].end) else: ends.append(working.fuzzy_junctions[i + 1].left.end) bestleft = GenePredFuzzyBasics.mode( working.fuzzy_junctions[i].left.get_payload()['junc']) bestright = GenePredFuzzyBasics.mode( working.fuzzy_junctions[i].right.get_payload()['junc']) juncs.append([bestleft, bestright]) #print 'jun '+str(i)+' evid: '+str(evidence)+" "+str(bestleft)+" "+str(bestright) else: starts.append([]) ends.append([]) juncs.append([]) evidences.append(evidence) #print juncs #print starts #print ends #print evidences # now we can put together the runs runs = [] current_run = [] for i in range(0, len(evidences)): if evidences[i] < args.required_evidence: if len(current_run) > 0: runs.append(current_run) current_run = [] continue current_run.append(i) if len(current_run) > 0: runs.append(current_run) # now the runs are in runs #print 'runs:' parts = [] for run in runs: sarr = [] sarr.append(starts[run[0]] - 1) #put back to zero index earr = [] for i in range(0, len(run)): sarr.append(juncs[run[i]][1] - 1) earr.append(juncs[run[i]][0]) earr.append(ends[run[-1]]) # ready to build a genepred! part = '' part += str(working.start.chr) + "\t" part += '+' + "\t" part += str(sarr[0]) + "\t" part += str(earr[-1]) + "\t" part += str(sarr[0]) + "\t" part += str(earr[-1]) + "\t" part += str(len(sarr)) + "\t" part += ','.join([str(x) for x in sarr]) + ',' + "\t" part += ','.join([str(x) for x in earr]) + ',' # Final quality check here gpd = GenePredEntry("test1\ttest1\t" + part) if not gpd.is_valid(): sys.stderr.write("\nWARNING skipping invalid GPD\n" + gpd.get_line() + "\n") continue parts.append([part, source_names]) #print parts return parts
def main(): parser = argparse.ArgumentParser( description= "Rename gene and transcript elements of GenePred file that are redundant. Please specify an output if you would like report files generated for the filters." ) parser.add_argument('input', help="GENEPREDFILE or '-' for STDIN") parser.add_argument( '-o', '--output', help= "OUTPUT FILE default is STDOUT, but you need to specify an output file to get report files generated" ) parser.add_argument( '--minimum_locus_distance', type=int, default=500000, help="Genes with the same name will be renamed if this far apart") parser.add_argument( '--keep_positional_duplicates', action='store_true', help="By default we remove one of the duplicate entries") parser.add_argument( '--keep_transcript_names', action='store_true', help="By default we rename duplicated transcript names") parser.add_argument( '--keep_gene_names', action='store_true', help="By default we rename genes located at different loci.") args = parser.parse_args() inf = sys.stdin if args.input != '-': inf = open(args.input) of = sys.stdout if args.output: of = open(args.output, 'w') txdef = {} gfams = {} for line in inf: if line[0] == '#': continue g = GenePredEntry(line) loc = g.value('chrom') + ':' + ','.join( [str(x) for x in g.value('exonStarts')]) + '-' + ','.join( [str(x) for x in g.value('exonEnds')]) + '/' + g.value('strand') if loc not in txdef: txdef[loc] = [] txdef[loc].append(g) if g.value('gene_name') not in gfams: gfams[g.value('gene_name')] = [] gfams[g.value('gene_name')].append(g.value('name')) # now we have cataloged all transcripts by unique locations omissions = [] keepers = [] for loc in sorted(txdef.keys()): if args.keep_positional_duplicates: # We don't want to ommit anything here for g in txdef[loc]: keepers.append(g) continue #basically skipping this part by populating keepers num = len(txdef[loc]) if num > 1: sys.stderr.write("Found " + str(num) + " entries at location\n") sys.stderr.write(loc + "\n") sys.stderr.write("They are:\n") largest = 0 keepgene = None keepindex = -1 i = 0 for e in txdef[loc]: famsize = len(gfams[e.value('gene_name')]) sys.stderr.write(" " + e.value('gene_name') + "\t" + e.value('name') + "\t" + str(famsize) + "\n") if famsize > largest: keepgene = e largest = famsize keepindex = i i += 1 for j in range(0, len(txdef[loc])): if j != keepindex: omissions.append(txdef[loc][j]) else: keepers.append(txdef[loc][j]) sys.stderr.write(" Biggest gene family is " + keepgene.value('gene_name') + " with " + str(largest) + " transcripts\n") sys.stderr.write(" so keep that one.\n") else: keepers.append(txdef[loc][0]) sys.stderr.write("Omitting " + str(len(omissions)) + " entries for redundant positions\n") if args.output and not args.keep_positional_duplicates: of1 = open(args.output + '.positional_duplicate_omissions', 'w') for g in omissions: of1.write(g.get_line() + "\n") of1.close() # Now the keepers contains transcripts with unique locations # Lets provide unique names to remaining transcripts tnames = {} renametx = {} for g in keepers: tx = g.value('name') if tx not in tnames: tnames[tx] = [] tnames[tx].append(g) for name in tnames: if args.keep_transcript_names: continue # We don't want to rename them nsize = len(tnames[name]) if nsize > 1: sys.stderr.write("Name: " + name + " has a family of size " + str(nsize) + "\n") for i in range(0, len(tnames[name])): newname = name + '[' + str(i + 1) + '/' + str(nsize) + ']' renametx[newname] = name tnames[name][i].entry['name'] = newname sys.stderr.write("Renamed: " + str(len(renametx)) + " transcripts\n") if args.output and not args.keep_transcript_names: of1 = open(args.output + '.renamed_transcripts', 'w') for name in sorted(renametx.keys()): of1.write(name + "\t" + renametx[name] + "\n") of1.close() #now we need to arrange into gene families gnames = {} for name in tnames: for g in tnames[name]: gene = g.value('gene_name') if gene not in gnames: gnames[gene] = [] gnames[gene].append(g) renamegene = {} finished = [] for gene in gnames: if args.keep_gene_names: for g in gnames[gene]: finished.append(g) continue # We don't want to rename genes if len(gnames[gene]) == 1: finished.append(gnames[gene][0]) continue # Now we need to make sure these genes are really on the same locus. loci = Loci() loci.set_minimum_distance(args.minimum_locus_distance) for g in gnames[gene]: r = g.locus_range.copy() r.set_payload(g) loc = Locus() loc.add_member(r) loci.add_locus(loc) loci.update_loci() lcount = len(loci.loci) if lcount == 1: for g in gnames[gene]: finished.append(g) continue # need to rename some genes for i in range(0, lcount): newname = gene + '[' + str(i + 1) + '/' + str(lcount) + ']' rstr = loci.loci[i].range.get_range_string() renamegene[newname] = gene sys.stderr.write(newname + "\t" + rstr + "\n") for m in loci.loci[i].members: m.get_payload().entry['gene_name'] = newname finished.append(m.get_payload()) sys.stderr.write("Renamed: " + str(len(renamegene)) + " genes\n") if args.output and not args.keep_transcript_names: of1 = open(args.output + '.renamed_genes', 'w') for name in sorted(renamegene.keys()): of1.write(name + "\t" + renamegene[name] + "\n") of1.close() #Now lets resort by genes bygene = {} for g in finished: gene = g.value('gene_name') if gene not in bygene: bygene[gene] = [] bygene[gene].append(g) for gene in sorted(bygene.keys()): for g in bygene[gene]: of.write(g.get_line() + "\n") of.close() inf.close()
def do_prediction(compatible, args, nrfuzzykey, location): #if len(compatible.keys()) == 0: return None #all reads could be standing alone version families = [] for num in nrfuzzykey: families.append(nrfuzzykey[num]) nrfuzzykey[num].params[ 'proper_set'] = False #partial overlap is enough #get_compatible_evidence(compatible,nrfuzzykey,args) for i in compatible: for j in compatible[i]: #see if its already in there g1lines = set() for g1 in nrfuzzykey[i].gpds: g1lines.add(g1.get_line()) repeat = False for g2 in nrfuzzykey[j].gpds: if g2.get_line() in g1lines: repeat = True break if not repeat: continue together = nrfuzzykey[i].concat_fuzzy_gpd(nrfuzzykey[j]) if together: families.append(together) # now we need to find any duplicate entries and combine them newfam = [] beforefam = len(families) while len(families) > 0: fam = families.pop(0) remaining = [] for i in range(0, len(families)): if fam.is_equal_fuzzy(families[i]): added = fam.add_fuzzy_gpd(families[i]) if not added: sys.stderr.write("WARNING NOT SURE WHY NOT ADDED EQUAL\n") fam = added else: remaining.append(families[i]) families = remaining newfam.append(fam) families = newfam afterfam = len(families) # Replace the family with a set where we haven't used the same gpd line twice # This may damage the fuzzy object for i in range(0, len(families)): gset = set() for g in families[i].gpds: gset.add(g.get_line()) families[i].gpds = [GenePredEntry(x) for x in gset] # sys.stderr.write("\n\ncahnged from "+str(beforefam)+"\t"+str(afterfam)+"\n\n") gpdlines = "" tablelines = "" # find gpds not in the graph... for fz in families: info = fz.get_info_string() gpdline = fz.get_genepred_line() #print '&&&&&&&&&&&&&&&&' #print gpdline #print fz.get_info_string() #print '&&&&&&&&&&&&&&&&' gpd = GenePredEntry(gpdline) if not gpd.is_valid(): sys.stderr.write("WARNING: invalid genepred entry generated\n" + gpdline + "\n" + fz.get_info_string() + "\n") gpd = sorted( fz.gpds, key=lambda x: x.get_exon_count(), reverse=True)[0] #just grab one that has all the exons fz = FuzzyGenePred(gpd, juntol=args.junction_tolerance * 2) gpdline = fz.get_genepred_line() if not gpd.is_valid(): sys.stderr.write("WARNING: still problem skilling\n") continue gpdlines += gpdline + "\n" if args.output_original_table: name = gpd.entry['name'] for g in fz.gpds: tablelines += name + "\t" + g.entry['name'] + "\n" grng = gpd.get_bed() grng.direction = None if not location: location = grng location = location.merge(grng) locstring = '' if location: locstring = location.get_range_string() return [gpdlines, tablelines, locstring]
def main(): parser = argparse.ArgumentParser() parser.add_argument('gpd_input') parser.add_argument('bam_input') parser.add_argument('--intergenic_buffer', default=10000, type=int) parser.add_argument('--window_size', default=10000, type=int) parser.add_argument('--bin_size', default=1000, type=int) parser.add_argument( '--use_off_regions', action='store_true', help="Use a region even if there is no reads mapped to it.") parser.add_argument('--get_exons', action='store_true') args = parser.parse_args() chr_beds = {} gene_beds = [] exon_beds = [] sys.stderr.write("Reading genepred file\n") asum = 0 atot = 0 with open(args.gpd_input) as inf: for line in inf: g = GenePredEntry(line) asum += g.length() atot += 1 grng = g.get_bed() grng.direction = None if grng.chr not in chr_beds: chr_beds[grng.chr] = grng.copy() chr_beds[grng.chr] = chr_beds[grng.chr].merge(grng) gene_beds.append(grng) for i in range(0, g.get_exon_count()): erng = Bed(g.value('chrom'), g.value('exonStarts')[i], g.value('exonEnds')[i]) exon_beds.append(erng) avglen = float(asum) / float(atot) sys.stderr.write("Sorting gene bed\n") gene_beds = sort_ranges(gene_beds) gene_beds = merge_ranges(gene_beds, already_sorted=True) sys.stderr.write("Sorting chromosome beds\n") chr_beds = sort_ranges([chr_beds[x] for x in chr_beds.keys()]) sys.stderr.write("Sorting exon beds\n") exon_beds = sort_ranges(exon_beds) sys.stderr.write("Get padded genes\n") padded_gene_beds = pad_ranges(gene_beds, args.intergenic_buffer, chr_beds) padded_gene_beds = merge_ranges(padded_gene_beds, already_sorted=True) sys.stderr.write("Get intergenic regions\n") intergenic_beds = subtract_ranges(chr_beds, padded_gene_beds, already_sorted=True) intergenic_beds = merge_ranges(intergenic_beds, already_sorted=True) intergenic_beds = window_break(intergenic_beds, args.window_size) #for i in intergenic_beds: print i.get_range_string() sys.stderr.write("Get merged exons\n") exon_beds = merge_ranges(exon_beds) sys.stderr.write("Get introns\n") intron_beds = subtract_ranges(gene_beds, exon_beds, already_sorted=True) intron_beds = merge_ranges(intron_beds, already_sorted=True) intron_beds = window_break(intron_beds, args.window_size) sys.stderr.write("Going through short reads\n") cmd = "sam_to_bed_depth.py " + args.bam_input p = Popen(cmd.split(), stdout=PIPE) for x in intron_beds: x.set_payload([]) # payloads are read depths for x in intergenic_beds: x.set_payload([]) # payloads are read depths for x in exon_beds: x.set_payload([]) # payloads are read depths introndepth = [] intergenicdepth = [] exondepth = [] pseudoreadcount = 0 if not args.get_exons: exon_beds = [] section_count = 0 while True: section_count += 1 line = p.stdout.readline() if not line: break f = line.split("\t") depth = int(f[3]) curr = Bed(f[0], int(f[1]), int(f[2])) if section_count % 100 == 0: sys.stderr.write(curr.get_range_string() + " \r") pseudoreadcount += depth if len(exon_beds) > 0: while curr.cmp(exon_beds[0]) > 0 and len( exon_beds) > 0: # we've passed the region v = exon_beds.pop(0) if len(v.get_payload()) == 0 and not args.use_off_regions: continue av = average(v) exondepth.append(av) #print str(av)+" exonic "+v.get_range_string() c = curr.cmp(exon_beds[0]) if c == 0: # overlaps with intron size = curr.overlap_size(exon_beds[0]) for i in range(0, size): exon_beds[0].get_payload().append(depth) if len(intron_beds) > 0: while curr.cmp(intron_beds[0]) > 0 and len( intron_beds) > 0: # we've passed the region v = intron_beds.pop(0) if len(v.get_payload()) == 0 and not args.use_off_regions: continue av = average(v) introndepth.append(av) #print str(av)+" intronic "+v.get_range_string() c = curr.cmp(intron_beds[0]) if c == 0: # overlaps with intron size = curr.overlap_size(intron_beds[0]) for i in range(0, size): intron_beds[0].get_payload().append(depth) if len(intergenic_beds) > 0: while curr.cmp(intergenic_beds[0]) > 0 and len( intergenic_beds) > 0: # we've passed the region v = intergenic_beds.pop(0) if len(v.get_payload()) == 0 and not args.use_off_regions: continue av = average(v) intergenicdepth.append(av) display(curr, introndepth, intergenicdepth, pseudoreadcount, avglen) #print str(av)+" intergenic "+v.get_range_string() c = curr.cmp(intergenic_beds[0]) if c == 0: # overlaps with intron size = curr.overlap_size(intergenic_beds[0]) for i in range(0, size): intergenic_beds[0].get_payload().append(depth) #if c > 0: # we passed the intron # v = intergenic_beds.pop(0) # av = average(v) # intergenicdepth.append(av) # print str(av)+" intergenic "+v.get_range_string() if args.use_off_regions: for x in exon_beds: introndepth.append(average(x.get_payload())) for x in intron_beds: introndepth.append(average(x.get_payload())) for x in intergenic_beds: intergenicdepth.append(average(x.get_payload())) p.communicate()