def get_highest_coverage_isoforms(input_file, gene_file):
# place overlapping chimeras into clusters
logging.debug("Building isoform cluster lookup table")
transcript_cluster_map = build_transcript_cluster_map(open(gene_file))
# build a lookup table to get genome coordinates from transcript
# coordinates
transcript_genome_map = build_transcript_genome_map(open(gene_file))
cluster_chimera_dict = collections.defaultdict(lambda: [])
for c in Chimera.parse(open(input_file)):
# TODO: adjust this to score chimeras differently!
key = (c.name, c.get_num_frags())
# get cluster of overlapping genes
cluster5p = transcript_cluster_map[c.tx_name_5p]
cluster3p = transcript_cluster_map[c.tx_name_3p]
# get genomic positions of breakpoints
coord5p = transcript_to_genome_pos(c.tx_name_5p, c.tx_end_5p-1, transcript_genome_map)
coord3p = transcript_to_genome_pos(c.tx_name_3p, c.tx_start_3p, transcript_genome_map)
# add to dictionary
cluster_chimera_dict[(cluster5p,cluster3p,coord5p,coord3p)].append(key)
# choose highest coverage chimeras within each pair of clusters
logging.debug("Finding highest coverage isoforms")
kept_chimeras = set()
for stats_list in cluster_chimera_dict.itervalues():
stats_dict = collections.defaultdict(lambda: set())
for stats_info in stats_list:
# index chimera names
stats_dict[stats_info[1:]].add(stats_info[0])
# find highest scoring key
sorted_keys = sorted(stats_dict.keys(), reverse=True)
kept_chimeras.update(stats_dict[sorted_keys[0]])
return kept_chimeras
def annotate_multihits(bamfh, reads, transcript_tid_genome_map):
hits = set()
any_unmapped = False
for r in reads:
if r.is_unmapped:
any_unmapped = True
continue
if r.rname not in transcript_tid_genome_map:
tid = r.rname
pos = r.pos
else:
# use the position that is most 5' relative to genome
left_tid, left_strand, left_pos = transcript_to_genome_pos(r.rname, r.pos, transcript_tid_genome_map)
right_tid, right_strand, right_pos = transcript_to_genome_pos(r.rname, r.aend-1, transcript_tid_genome_map)
tid = left_tid
pos = imin2(left_pos, right_pos)
hits.add((tid, pos))
#print r.qname, bamfh.getrname(r.rname), r.pos, bamfh.getrname(tid), pos
for i,r in enumerate(reads):
# annotate reads with 'HI', and 'IH' tags
r.tags = r.tags + [("HI",i), ("IH",len(reads)), ("NH", len(hits))]
return any_unmapped
def annotate_multihits(bamfh, reads, transcript_tid_genome_map):
hits = set()
any_unmapped = False
for r in reads:
if r.is_unmapped:
any_unmapped = True
continue
if r.rname not in transcript_tid_genome_map:
tid = r.rname
pos = r.pos
else:
# use the position that is most 5' relative to genome
left_tid, left_strand, left_pos = transcript_to_genome_pos(
r.rname, r.pos, transcript_tid_genome_map)
right_tid, right_strand, right_pos = transcript_to_genome_pos(
r.rname, r.aend - 1, transcript_tid_genome_map)
tid = left_tid
pos = imin2(left_pos, right_pos)
hits.add((tid, pos))
#print r.qname, bamfh.getrname(r.rname), r.pos, bamfh.getrname(tid), pos
for i, r in enumerate(reads):
# annotate reads with 'HI', and 'IH' tags
r.tags = r.tags + [("HI", i), ("IH", len(reads)), ("NH", len(hits))]
return any_unmapped
def write_output(input_file, bam_file, output_file, index_dir):
gene_file = os.path.join(index_dir, config.GENE_FEATURE_FILE)
# build a lookup table to get genome coordinates from transcript
# coordinates
transcript_genome_map = build_transcript_genome_map(open(gene_file))
tx_name_gene_map = build_tx_name_gene_map(gene_file)
genome_tx_trees = build_genome_tx_trees(gene_file)
# open BAM file for checking wild-type isoform
bamfh = pysam.Samfile(bam_file, "rb")
# group chimera isoforms together
lines = []
chimera_clusters = 0
for key,chimeras in get_chimera_groups(input_file, gene_file):
txs5p = set()
txs3p = set()
genes5p = set()
genes3p = set()
names = set()
for c in chimeras:
txs5p.add("%s:%d-%d" % (c.tx_name_5p, c.tx_start_5p, c.tx_end_5p-1))
txs3p.add("%s:%d-%d" % (c.tx_name_3p, c.tx_start_3p, c.tx_end_3p-1))
genes5p.add(c.gene_name_5p)
genes3p.add(c.gene_name_3p)
names.add(c.name)
c = get_best_coverage_chimera(chimeras)
# get chimera type and distance between genes
chimera_type, distance = get_chimera_type(tx_name_gene_map[c.tx_name_5p],
tx_name_gene_map[c.tx_name_3p],
genome_tx_trees)
# get genomic positions of chimera
chrom5p,strand5p,start5p = transcript_to_genome_pos(c.tx_name_5p, c.tx_start_5p, transcript_genome_map)
chrom5p,strand5p,end5p = transcript_to_genome_pos(c.tx_name_5p, c.tx_end_5p-1, transcript_genome_map)
if strand5p == 1:
start5p,end5p = end5p,start5p
chrom3p,strand3p,start3p = transcript_to_genome_pos(c.tx_name_3p, c.tx_start_3p, transcript_genome_map)
chrom3p,strand3p,end3p = transcript_to_genome_pos(c.tx_name_3p, c.tx_end_3p-1, transcript_genome_map)
if strand3p == 1:
start3p,end3p = end3p,start3p
# get breakpoint spanning sequences
spanning_seqs = set()
spanning_fasta_lines = []
for dr in c.get_spanning_reads():
if dr.seq in spanning_seqs:
continue
spanning_seqs.add(dr.seq)
spanning_fasta_lines.extend([">%s/%d;pos=%d;strand=%s" %
(dr.qname, dr.readnum+1, dr.pos,
"-" if dr.is_reverse else "+"),
dr.seq])
# get isoform fraction
num_wt_frags_5p, num_wt_frags_3p = get_wildtype_frags(c, bamfh)
num_chimeric_frags = c.get_num_frags()
frac5p = float(num_chimeric_frags) / (num_chimeric_frags + num_wt_frags_5p)
frac3p = float(num_chimeric_frags) / (num_chimeric_frags + num_wt_frags_3p)
# setup fields of BEDPE file
fields = [chrom5p, start5p, end5p,
chrom3p, start3p, end3p,
"CLUSTER%d" % (chimera_clusters),
c.get_num_frags(),
"+" if (strand5p == 0) else "-",
"+" if (strand3p == 0) else "-",
','.join(txs5p),
','.join(txs3p),
','.join(genes5p),
','.join(genes3p),
chimera_type, distance,
c.get_num_frags(),
c.get_num_spanning_frags(),
c.get_num_unique_positions(),
frac5p, frac3p,
','.join(spanning_fasta_lines),
','.join(names)]
lines.append(fields)
chimera_clusters += 1
bamfh.close()
logging.debug("Clustered chimeras: %d" % (chimera_clusters))
# sort
lines = sorted(lines, key=operator.itemgetter(18, 17, 16), reverse=True)
f = open(output_file, "w")
print >>f, '\t'.join(['#chrom5p', 'start5p', 'end5p',
'chrom3p', 'start3p', 'end3p',
'chimera_cluster_id', 'score',
'strand5p', 'strand3p',
'transcript_ids_5p', 'transcript_ids_3p',
'genes5p', 'genes3p',
'type', 'distance',
'total_frags',
'spanning_frags',
'unique_alignment_positions',
'isoform_fraction_5p',
'isoform_fraction_3p',
'breakpoint_spanning_reads',
'chimera_ids'])
for fields in lines:
print >>f, '\t'.join(map(str, fields))
f.close()
return config.JOB_SUCCESS