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'''
Calculate fragment sizes for mapped bam file.
Author: liqiming@whu.edu.cn
'''
import os
import re
import sys
import gzip
import pysam
import argparse
import pandas as pd
from itertools import groupby
from collections import Counter
class GTF:
__slots__ = ['seqname', 'source', 'feature', 'start', 'end', 'score',
'strand', 'frame', 'attributes', 'chrom', 'size']
def __init__(self, args):
for s, v in zip(self.__slots__[:9], args):
setattr(self, s, v)
self.start = int(self.start)
self.end = int(self.end)
self.chrom = self.seqname
self.size = abs(self.end - self.start)
def __repr__(self):
return "GTF({seqname}:{start}-{end})".format(
seqname=self.seqname, start=self.start, end=self.end)
def __str__(self):
return "\t".join(str(getattr(self, s)) for s in self.__slots__[:9])
@property
def gene_id(self):
return re.compile('gene_id "([^"]+)"').findall(self.attributes)[0]
@property
def transcript_id(self):
results = re.compile(
'transcript_id "([^"]+)"').findall(self.attributes)
if results:
return results[0]
def reader(fname, header=None, sep="\t", skip_while=None):
sep = re.compile(sep)
with open(fname) as f:
for line in f:
toks = sep.split(line.rstrip("\r\n"))
if skip_while:
if skip_while(toks):
continue
if header:
yield header(toks)
else:
yield toks
def filter_transcript(gtffile, method, filtered_file=os.devnull):
assert method in (
"first", "last", "max_len", "min_len"), f"{method} not support"
transcript_idlist = []
for _, group in groupby(
reader(
gtffile,
header=GTF,
skip_while=lambda toks: toks[0].startswith("#") or not (
toks[2] == "transcript")
),
lambda x: x.gene_id
):
transcript = None
if method == 'first':
transcript = list(group)[0]
elif method == 'last':
transcript = list(group)[-1]
elif method == 'max_len':
transcript = max(group, key=lambda x: x.size)
else:
transcript = min(group, key=lambda x: x.size)
transcript_idlist.append(transcript.transcript_id)
filtered_file.write(f"{str(transcript)}\n".encode())
return transcript_idlist
def overlap_length(lst1, lst2):
l = 0
for x in lst1:
for y in lst2:
l += len(range(max(x[0], y[0]), min(x[-1], y[-1]) + 1))
return l
def fragment_size(bedfile, samfile, transcript_idlist, qcut=30, ncut=1, filtered_bed=os.devnull, temp=os.devnull):
'''calculate the fragment size for each gene'''
for line in open(bedfile, 'r'):
exon_range = []
if line.startswith(('#', 'track', 'browser')):
continue
fields = line.split()
chrom = fields[0]
tx_start = int(fields[1])
tx_end = int(fields[2])
geneName = fields[3]
if geneName not in transcript_idlist:
continue
filtered_bed.write(line)
trand = fields[5].replace(" ", "_")
exon_starts = map(int, fields[11].rstrip(',\n').split(','))
exon_starts = map((lambda x: x + tx_start), exon_starts)
exon_ends = map(int, fields[10].rstrip(',\n').split(','))
exon_ends = map((lambda x, y: x + y), exon_starts, exon_ends)
for st, end in zip(exon_starts, exon_ends):
exon_range.append([st + 1, end + 1])
try:
alignedReads = samfile.fetch(chrom, tx_start, tx_end)
except:
continue
frag_sizes = []
for aligned_read in alignedReads:
if not aligned_read.is_paired:
continue
if aligned_read.is_read2:
continue
if aligned_read.mate_is_unmapped:
continue
if aligned_read.is_qcfail:
continue
if aligned_read.is_duplicate:
continue
if aligned_read.is_secondary:
continue
if aligned_read.mapq < qcut:
continue
read_st = aligned_read.pos
mate_st = aligned_read.pnext
if read_st > mate_st:
(read_st, mate_st) = (mate_st, read_st)
if read_st < tx_start or mate_st > tx_end:
continue
read_len = aligned_read.qlen
map_range = [[read_st+1, mate_st]]
frag_len = overlap_length(exon_range, map_range) + read_len
frag_sizes.append(frag_len)
if len(frag_sizes) < ncut:
continue
else:
for i in frag_sizes:
temp.write(f"{i}\n".encode())
yield i
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-i", "--input", dest="input_file",
type=str, required=True, help="Input BAM file")
parser.add_argument("-o", "--output", dest="output_file",
type=str, help="Output file")
parser.add_argument("-r", "--refgene", dest="refgene_bed", type=str, required=True,
help="Reference gene model in BED format. Must be strandard 12-column BED file. [required]")
parser.add_argument("-g", "--gtf", dest="gtf_file", type=str, required=True,
help="Reference gene model in GTF format. Used to filter 12-column transcipt BED. [required]")
parser.add_argument("-m", "--method", dest="filter_method", type=str, default="first",
help="Method to maintain transcipt when filtering 12-column transcipt BED. [first|last|max_len|min_len]")
parser.add_argument("-q", "--mapq", dest="map_qual", type=int, default=30,
help="Minimum mapping quality (phred scaled) for an alignment to be called \"uniquely mapped\"")
parser.add_argument("-n", "--frag-num", dest="fragment_num", type=int, default=1,
help="Minimum number of fragment")
parser.add_argument("-b", "--bed_filtered", dest="filtered_bed", type=str, default=os.devnull,
help="Output file to store filtered 12-column transcipt BED")
parser.add_argument("-f", "--filtered_gtf", dest="filtered_gtf", type=str, default=os.devnull,
help="Output file to store filtered GTF file, support *.gz")
parser.add_argument("-t", "--temp", dest="temp_file", type=str, default=os.devnull,
help="Output file to store processing data, support *.gz")
args = parser.parse_args()
if not os.path.exists(args.input_file + '.bai'):
print >>sys.stderr, "cannot find index file of input BAM file"
print >>sys.stderr, args.input_file + '.bai' + " does not exists"
sys.exit(0)
bed_writer = open(args.filtered_bed, "w")
gtf_writer = gzip.open(args.filtered_gtf, "wb") if args.filtered_gtf.endswith(
".gz") else open(args.filtered_gtf, "wb")
temp_writer = gzip.open(args.temp_file, "wb") if args.temp_file.endswith(
".gz") else open(args.temp_file, "wb")
transcript_idlist = filter_transcript(
args.gtf_file, args.filter_method, gtf_writer)
gtf_writer.close()
fragment_sizes = Counter(i for i in fragment_size(
args.refgene_bed,
pysam.Samfile(args.input_file),
transcript_idlist,
args.map_qual,
args.fragment_num,
bed_writer,
temp_writer
))
bed_writer.close()
temp_writer.close()
frag_sizes_df = pd.DataFrame(
{"Length": fragment_sizes.keys(), "Count": fragment_sizes.values()})
frag_sizes_df.sort_values(by="Length", ascending=True, inplace=True)
if args.output_file:
frag_sizes_df.to_csv(args.output_file, sep="\t",
header=True, index=False)
else:
print("Length\tCount")
for _, i in frag_sizes_df.iterrows():
print(i["Length"], i["Count"], sep="\t")
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