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 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 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()
