def filter_by_count(input_prefix, output_prefix, min_count):
group_filename = input_prefix + '.group.txt'
count_filename = input_prefix + '.abundance.txt'
gff_filename = input_prefix + '.gff'
rep_filename = input_prefix + '.rep.fq'
# read group
group_max_count_fl = {}
group_max_count_p = {}
f = open(group_filename)
for line in f:
#ex: PB.1.1 i0HQ_54b0ca|c58773/f30p16/700
pbid, members = line.strip().split('\t')
group_max_count_fl[pbid] = 0
group_max_count_p[pbid] = 0
members = members.split(',')
for m in members:
tmp = m.split('|')[1].split('/')[1] #ex: tmp = f30p16
fl_count, p_count = tmp.split('p')
fl_count = int(fl_count[1:])
p_count = int(p_count)
group_max_count_fl[pbid] = max(group_max_count_fl[pbid], fl_count)
group_max_count_p[pbid] = max(group_max_count_p[pbid], p_count)
f.close()
# read abundance first
f = open(count_filename)
count_header = ''
while True:
cur_pos = f.tell()
line = f.readline()
if not line.startswith('#'):
f.seek(cur_pos)
break
else:
count_header += line
d = dict((r['pbid'], r) for r in DictReader(f, delimiter='\t'))
for k,v in d.iteritems():
print k,v
f.close()
# group_max_count_p NOT used for now
good = filter(lambda x: int(d[x]['count_fl']) >= min_count and group_max_count_fl[x] >= min_count and group_max_count_p >= 0, d)
# write output GFF
f = open(output_prefix + '.gff', 'w')
for r in GFF.collapseGFFReader(gff_filename):
if r.seqid in good: GFF.write_collapseGFF_format(f, r)
f.close()
# write output rep.fq
f = FastqWriter(output_prefix + '.rep.fq')
for r in FastqReader(rep_filename):
if r.name.split('|')[0] in good:
f.writeRecord(r)
f.close()
# write output to .abundance.txt
f = open(output_prefix + '.abundance.txt', 'w')
f.write(count_header)
writer = DictWriter(f, fieldnames=['pbid','count_fl','count_nfl','count_nfl_amb','norm_fl','norm_nfl','norm_nfl_amb'], \
delimiter='\t', lineterminator='\n')
writer.writeheader()
for k in good:
r = d[k]
writer.writerow(r)
f.close()
def main():
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument("input_prefix", help="Input prefix")
parser.add_argument("output_prefix", help="Output prefix")
parser.add_argument("--fuzzy_junction", type=int, default=5, help="Fuzzy junction max dist (default: 5bp)")
args = parser.parse_args()
#group_filename = args.input_prefix + '.group.txt'
count_filename = args.input_prefix + '.abundance.txt'
gff_filename = args.input_prefix + '.gff'
rep_filename = args.input_prefix + '.rep.fq'
recs = defaultdict(lambda: [])
reader = GFF.collapseGFFReader(gff_filename)
for r in reader:
assert r.seqid.startswith('PB.')
recs[int(r.seqid.split('.')[1])].append(r)
good = []
f = open(args.output_prefix + '.gff', 'w')
keys = recs.keys()
keys.sort()
for k in recs:
xxx = recs[k]
filter_out_subsets(xxx, args.fuzzy_junction)
for r in xxx:
GFF.write_collapseGFF_format(f, r)
good.append(r.seqid)
f.close()
# read abundance first
f = open(count_filename)
count_header = ''
while True:
cur_pos = f.tell()
line = f.readline()
if not line.startswith('#'):
f.seek(cur_pos)
break
else:
count_header += line
d = dict((r['pbid'], r) for r in DictReader(f, delimiter='\t'))
for k,v in d.iteritems():
print k,v
f.close()
# write output rep.fq
f = FastqWriter(args.output_prefix + '.rep.fq')
for r in FastqReader(rep_filename):
if r.name.split('|')[0] in good:
f.writeRecord(r)
f.close()
# write output to .abundance.txt
f = open(args.output_prefix + '.abundance.txt', 'w')
f.write(count_header)
writer = DictWriter(f, fieldnames=['pbid','count_fl','count_nfl','count_nfl_amb','norm_fl','norm_nfl','norm_nfl_amb'], \
delimiter='\t', lineterminator='\n')
writer.writeheader()
for k in good:
r = d[k]
writer.writerow(r)
f.close()
def collapse_fuzzy_junctions(gff_filename, group_filename, allow_extra_5exon, internal_fuzzy_max_dist):
def get_fl_from_id(members):
# ex: 13cycle_1Mag1Diff|i0HQ_SIRV_1d1m|c139597/f1p0/178
return sum(int(_id.split('/')[1].split('p')[0][1:]) for _id in members)
def can_merge(m, r1, r2):
if m == 'exact':
return True
else:
if not allow_extra_5exon:
return False
# below is continued only if (a) is 'subset' or 'super' AND (b) allow_extra_5exon is True
if m == 'subset':
r1, r2 = r2, r1 # rotate so r1 is always the longer one
if m == 'super' or m == 'subset':
n2 = len(r2.ref_exons)
# check that (a) r1 and r2 end on same 3' exon, that is the last acceptor site agrees
# AND (b) the 5' start of r2 is sandwiched between the matching r1 exon coordinates
if r1.strand == '+':
return abs(r1.ref_exons[-1].start - r2.ref_exons[-1].start) <= internal_fuzzy_max_dist and \
r1.ref_exons[-n2].start <= r2.ref_exons[0].start < r1.ref_exons[-n2].end
else:
return abs(r1.ref_exons[0].end - r2.ref_exons[0].end) <= internal_fuzzy_max_dist and \
r1.ref_exons[n2-1].start <= r2.ref_exons[-1].end < r1.ref_exons[n2].end
return False
d = {}
recs = defaultdict(lambda: {'+':IntervalTree(), '-':IntervalTree()}) # chr --> strand --> tree
fuzzy_match = defaultdict(lambda: [])
for r in GFF.collapseGFFReader(gff_filename):
d[r.seqid] = r
has_match = False
r.segments = r.ref_exons
for r2 in recs[r.chr][r.strand].find(r.start, r.end):
r2.segments = r2.ref_exons
m = compare_junctions.compare_junctions(r, r2, internal_fuzzy_max_dist=internal_fuzzy_max_dist)
if can_merge(m, r, r2):
fuzzy_match[r2.seqid].append(r.seqid)
has_match = True
break
if not has_match:
recs[r.chr][r.strand].insert(r.start, r.end, r)
fuzzy_match[r.seqid] = [r.seqid]
group_info = {}
with open(group_filename) as f:
for line in f:
pbid, members = line.strip().split('\t')
group_info[pbid] = [x for x in members.split(',')]
# pick for each fuzzy group the one that has the most exons (if tie, then most FL)
keys = fuzzy_match.keys()
keys.sort(key=lambda x: map(int, x.split('.')[1:]))
f_gff = open(gff_filename+'.fuzzy', 'w')
f_group = open(group_filename+'.fuzzy', 'w')
for k in keys:
all_members = []
best_pbid, best_size, best_num_exons = fuzzy_match[k][0], len(group_info[fuzzy_match[k][0]]), len(d[fuzzy_match[k][0]].ref_exons)
all_members += group_info[fuzzy_match[k][0]]
for pbid in fuzzy_match[k][1:]:
_size = get_fl_from_id(group_info[pbid])
_num_exons = len(d[pbid].ref_exons)
all_members += group_info[pbid]
if _num_exons > best_num_exons or (_num_exons == best_num_exons and _size > best_size):
best_pbid, best_size, best_num_exons = pbid, _size, _num_exons
GFF.write_collapseGFF_format(f_gff, d[best_pbid])
f_group.write("{0}\t{1}\n".format(best_pbid, ",".join(all_members)))
f_gff.close()
f_group.close()
return fuzzy_match
def collapse_fuzzy_junctions(gff_filename, group_filename, allow_extra_5exon,
internal_fuzzy_max_dist):
def get_fl_from_id(members):
# ex: 13cycle_1Mag1Diff|i0HQ_SIRV_1d1m|c139597/f1p0/178
return sum(int(_id.split('/')[1].split('p')[0][1:]) for _id in members)
def can_merge(m, r1, r2):
if m == 'exact':
return True
else:
if not allow_extra_5exon:
return False
# below is continued only if (a) is 'subset' or 'super' AND (b) allow_extra_5exon is True
if m == 'subset':
r1, r2 = r2, r1 # rotate so r1 is always the longer one
if m == 'super' or m == 'subset':
n2 = len(r2.ref_exons)
# check that (a) r1 and r2 end on same 3' exon, that is the last acceptor site agrees
# AND (b) the 5' start of r2 is sandwiched between the matching r1 exon coordinates
if r1.strand == '+':
return abs(r1.ref_exons[-1].start - r2.ref_exons[-1].start) <= internal_fuzzy_max_dist and \
r1.ref_exons[-n2].start <= r2.ref_exons[0].start < r1.ref_exons[-n2].end
else:
return abs(r1.ref_exons[0].end - r2.ref_exons[0].end) <= internal_fuzzy_max_dist and \
r1.ref_exons[n2-1].start <= r2.ref_exons[-1].end < r1.ref_exons[n2].end
return False
d = {}
recs = defaultdict(lambda: {
'+': IntervalTree(),
'-': IntervalTree()
}) # chr --> strand --> tree
fuzzy_match = defaultdict(lambda: [])
for r in GFF.collapseGFFReader(gff_filename):
d[r.seqid] = r
has_match = False
r.segments = r.ref_exons
for r2 in recs[r.chr][r.strand].find(r.start, r.end):
r2.segments = r2.ref_exons
m = compare_junctions.compare_junctions(
r, r2, internal_fuzzy_max_dist=internal_fuzzy_max_dist)
if can_merge(m, r, r2):
fuzzy_match[r2.seqid].append(r.seqid)
has_match = True
break
if not has_match:
recs[r.chr][r.strand].insert(r.start, r.end, r)
fuzzy_match[r.seqid] = [r.seqid]
group_info = {}
with open(group_filename) as f:
for line in f:
pbid, members = line.strip().split('\t')
group_info[pbid] = [x for x in members.split(',')]
# pick for each fuzzy group the one that has the most exons (if tie, then most FL)
keys = fuzzy_match.keys()
keys.sort(key=lambda x: map(int, x.split('.')[1:]))
f_gff = open(gff_filename + '.fuzzy', 'w')
f_group = open(group_filename + '.fuzzy', 'w')
for k in keys:
all_members = []
best_pbid, best_size, best_num_exons = fuzzy_match[k][0], len(
group_info[fuzzy_match[k][0]]), len(d[fuzzy_match[k][0]].ref_exons)
all_members += group_info[fuzzy_match[k][0]]
for pbid in fuzzy_match[k][1:]:
_size = get_fl_from_id(group_info[pbid])
_num_exons = len(d[pbid].ref_exons)
all_members += group_info[pbid]
if _num_exons > best_num_exons or (_num_exons == best_num_exons
and _size > best_size):
best_pbid, best_size, best_num_exons = pbid, _size, _num_exons
GFF.write_collapseGFF_format(f_gff, d[best_pbid])
f_group.write("{0}\t{1}\n".format(best_pbid, ",".join(all_members)))
f_gff.close()
f_group.close()
return fuzzy_match
def filter_by_count(input_prefix, output_prefix, min_count):
group_filename = input_prefix + ".group.txt"
count_filename = input_prefix + ".abundance.txt"
gff_filename = input_prefix + ".gff"
rep_filename = input_prefix + ".rep.fq"
# read group
group_max_count_fl = {}
group_max_count_p = {}
f = open(group_filename)
for line in f:
# ex: PB.1.1 i0HQ_54b0ca|c58773/f30p16/700
pbid, members = line.strip().split("\t")
group_max_count_fl[pbid] = 0
group_max_count_p[pbid] = 0
members = members.split(",")
for m in members:
tmp = m.split("|")[1].split("/")[1] # ex: tmp = f30p16
fl_count, p_count = tmp.split("p")
fl_count = int(fl_count[1:])
p_count = int(p_count)
group_max_count_fl[pbid] = max(group_max_count_fl[pbid], fl_count)
group_max_count_p[pbid] = max(group_max_count_p[pbid], p_count)
f.close()
# read abundance first
f = open(count_filename)
count_header = ""
while True:
cur_pos = f.tell()
line = f.readline()
if not line.startswith("#"):
f.seek(cur_pos)
break
else:
count_header += line
d = dict((r["pbid"], r) for r in DictReader(f, delimiter="\t"))
for k, v in d.iteritems():
print k, v
f.close()
# group_max_count_p NOT used for now
good = filter(
lambda x: int(d[x]["count_fl"]) >= min_count and group_max_count_fl[x] >= min_count and group_max_count_p >= 0,
d,
)
# write output GFF
f = open(output_prefix + ".gff", "w")
for r in GFF.collapseGFFReader(gff_filename):
if r.seqid in good:
GFF.write_collapseGFF_format(f, r)
f.close()
# write output rep.fq
f = FastqWriter(output_prefix + ".rep.fq")
for r in FastqReader(rep_filename):
if r.name.split("|")[0] in good:
f.writeRecord(r)
f.close()
# write output to .abundance.txt
f = open(output_prefix + ".abundance.txt", "w")
f.write(count_header)
writer = DictWriter(
f,
fieldnames=["pbid", "count_fl", "count_nfl", "count_nfl_amb", "norm_fl", "norm_nfl", "norm_nfl_amb"],
delimiter="\t",
lineterminator="\n",
)
writer.writeheader()
for k in good:
r = d[k]
writer.writerow(r)
f.close()