def add(self,read,A,B,dist,ov,strandmatch,signal,n_hits):
self.signal = signal
self.strandmatch = strandmatch
self.edits.append(dist)
self.overlaps.append(ov)
self.n_hits.append(n_hits)
# by convention have A precede B in the genome.
if A.pos > B.pos:
A,B = B,A
# Alignment Score - Secondbest hit score
aopt = dict(A.tags)
bopt = dict(B.tags)
qA = aopt.get('AS') - aopt.get('XS',minmapscore)
qB = bopt.get('AS') - bopt.get('XS',minmapscore)
if qA and qB:
# both anchors from the *same read* align uniquely
self.uniq_bridges += 1
self.mapquals_A.append(qA)
self.mapquals_B.append(qB)
# recover the original readname
# ('__' is forbidden in input read names!)
if '__' in A.qname:
qname = A.qname.split('__')[0][:-2]
else: # reads have been swapped at some point
qname = B.qname.split('__')[0][:-2]
self.readnames.append(qname)
# record the spliced read sequence as it was before mapping
if A.is_reverse:
self.strand_minus += 1
self.reads.append(rev_comp(read))
else:
self.strand_plus += 1
self.reads.append(read)
# identify the tissue/sample it came from
sample_name = options.name
for (prefix,tiss) in samples:
if qname.startswith(prefix):
sample_name = tiss
break
self.tissues[sample_name] += 1
self.uniq.add((read,sample_name))
self.uniq.add((rev_comp(read),sample_name))
def add(self, read, A, B, dist, ov, strandmatch, signal, n_hits):
self.signal = signal
self.strandmatch = strandmatch
self.edits.append(dist)
self.overlaps.append(ov)
self.n_hits.append(n_hits)
# by convention have A precede B in the genome.
if A.pos > B.pos:
A, B = B, A
# Alignment Score - Secondbest hit score
aopt = dict(A.tags)
bopt = dict(B.tags)
qA = aopt.get('AS') - aopt.get('XS', minmapscore)
qB = bopt.get('AS') - bopt.get('XS', minmapscore)
if qA and qB:
# both anchors from the *same read* align uniquely
self.uniq_bridges += 1
self.mapquals_A.append(qA)
self.mapquals_B.append(qB)
# recover the original readname
# ('__' is forbidden in input read names!)
if '__' in A.qname:
qname = A.qname.split('__')[0][:-2]
else: # reads have been swapped at some point
qname = B.qname.split('__')[0][:-2]
self.readnames.append(qname)
# record the spliced read sequence as it was before mapping
if A.is_reverse:
self.strand_minus += 1
self.reads.append(rev_comp(read))
else:
self.strand_plus += 1
self.reads.append(read)
# identify the tissue/sample it came from
sample_name = options.name
for (prefix, tiss) in samples:
if qname.startswith(prefix):
sample_name = tiss
break
self.tissues[sample_name] += 1
self.uniq.add((read, sample_name))
self.uniq.add((rev_comp(read), sample_name))
def get_data(self, chrom, start, end, sense):
if not self.chrom_stats:
self.index()
ofs, ldata, skip, skip_char, size = self.chrom_stats[chrom]
pad_start = 0
pad_end = 0
if start < 0:
pad_start = -start
start = 0
if end > size:
pad_end = end - size
end = end
l_start = start / ldata
l_end = end / ldata
ofs_start = l_start * skip + start + ofs
ofs_end = l_end * skip + end + ofs
s = self.mmap[ofs_start:ofs_end].replace(skip_char, "")
if pad_start or pad_end:
s = "N" * pad_start + s + "N" * pad_end
if sense == "-":
s = rev_comp(s)
return s
def get_data(self,chrom,start,end,sense):
if not self.chrom_stats:
self.index()
ofs,ldata,skip,skip_char,size = self.chrom_stats[chrom]
pad_start = 0
pad_end = 0
if start < 0:
pad_start = -start
start = 0
if end > size:
pad_end = end - size
end = end
l_start = start / ldata
l_end = end / ldata
ofs_start = l_start * skip + start + ofs
ofs_end = l_end * skip + end + ofs
s = self.mmap[ofs_start:ofs_end].replace(skip_char,"")
if pad_start or pad_end:
s = "N"*pad_start + s + "N"*pad_end
if sense == "-":
s = rev_comp(s)
return s
def find_breakpoints(A,
B,
read,
chrom,
margin=options.margin,
maxdist=options.maxdist):
def mismatches(a, b):
a, b = fromstring(a, dtype=byte), fromstring(b, dtype=byte)
return (a != b).sum()
def rnd():
return numpy.random.random()
if not options.randomize:
rnd = lambda: 0
def strandmatch(ann, sense):
if ann == sense:
return "MATCH"
if ann == "*" or len(ann) > 1:
return "NA"
return "MISMATCH"
L = len(read)
hits = []
#print "readlen",L
#print " "*2+read
eff_a = options.asize - margin
internal = read[eff_a:-eff_a].upper()
flank = L - 2 * eff_a + 2
A_flank = genome.get(chrom, A.aend - margin, A.aend - margin + flank,
'+').upper()
B_flank = genome.get(chrom, B.pos - flank + margin, B.pos + margin,
'+').upper()
l = L - 2 * eff_a
for x in range(l + 1):
spliced = A_flank[:x] + B_flank[x + 2:]
dist = mismatches(spliced, internal)
#bla = A_flank[:x].lower() + B_flank[x+2:]
#print " "*(eff_a+2)+bla,dist
ov = 0
if x < margin:
ov = margin - x
if l - x < margin:
ov = margin - (l - x)
if dist <= maxdist:
gt = A_flank[x:x + 2]
ag = B_flank[x:x + 2]
gtag = gt + ag
rc_gtag = rev_comp(gtag)
start, end = B.pos + margin - l + x, A.aend - margin + x + 1
start, end = min(start, end), max(start, end)
strand = "*"
# get strand cues from read if strand-specific sequencing was used.
if options.stranded:
if A.is_reverse:
strand = '-'
else:
strand = '+'
if options.noncanonical:
hits.append((dist, ov, strandmatch(strand, '+'), rnd(), chrom,
start, end, gtag, '+'))
hits.append((dist, ov, strandmatch(strand, '-'), rnd(), chrom,
start, end, rc_gtag, '-'))
else:
if gtag == 'GTAG':
hits.append((dist, ov, strandmatch(strand, '+'), rnd(),
chrom, start, end, 'GTAG', '+'))
elif gtag == 'CTAC':
hits.append((dist, ov, strandmatch(strand, '-'), rnd(),
chrom, start, end, 'GTAG', '-'))
if len(hits) < 2:
# unambiguous, return right away
return hits
# Hits are sorted, with low edit distance beating low anchor overlap
hits = sorted(hits)
# return only hits that are tied with the best candidate by edit distance, anchor overlap
# and strand preference (if requested). Ties are either broken by random draw or by selecting
# the first hit later, but they are kept here such that they are counted properly.
best = hits[0]
if options.strandpref:
# exploit strand information to break ties only if requested. Otherwise it's only used as control.
ties = [
h for h in hits
if (h[0] == best[0]) and (h[1] == best[1]) and (h[2] == best[2])
]
else:
ties = [h for h in hits if (h[0] == best[0]) and (h[1] == best[1])]
return ties
### anchor pairs that make it up to here are interesting
if bam_out:
bam_out.write(A)
bam_out.write(B)
#debug("A='%s' B='%s' dist=%d A.is_reverse=%s" % (A,B,dist,A.is_reverse))
if (A.is_reverse and dist > 0) or (not A.is_reverse and dist < 0):
# the anchors align in reversed orientation -> circRNA?
read = A.qname.split('__')[1]
chrom = sam.getrname(A.tid)
if A.is_reverse:
#print "ISREVERSE"
A, B = B, A
read = rev_comp(read)
bp = find_breakpoints(A, B, read, chrom)
if not bp:
N['circ_no_bp'] += 1
else:
N['circ_reads'] += 1
n_hits = len(bp)
if bp and not options.allhits:
bp = [
bp[0],
]
for h in bp:
# for some weird reason for circ we need a correction here
def find_breakpoints(A,B,read,chrom,margin=options.margin,maxdist=options.maxdist):
def mismatches(a,b):
a,b = fromstring(a,dtype=byte), fromstring(b,dtype=byte)
return (a != b).sum()
def rnd():
return numpy.random.random()
if not options.randomize:
rnd = lambda : 0
def strandmatch(ann,sense):
if ann == sense:
return "MATCH"
if ann == "*" or len(ann) > 1:
return "NA"
return "MISMATCH"
L = len(read)
hits = []
#print "readlen",L
#print " "*2+read
eff_a = options.asize-margin
internal = read[eff_a:-eff_a].upper()
flank = L - 2*eff_a + 2
A_flank = genome.get(chrom,A.aend-margin,A.aend-margin + flank,'+').upper()
B_flank = genome.get(chrom,B.pos - flank+margin,B.pos+margin,'+').upper()
l = L - 2*eff_a
for x in range(l+1):
spliced = A_flank[:x] + B_flank[x+2:]
dist = mismatches(spliced,internal)
#bla = A_flank[:x].lower() + B_flank[x+2:]
#print " "*(eff_a+2)+bla,dist
ov = 0
if x < margin:
ov = margin-x
if l-x < margin:
ov = margin-(l-x)
if dist <= maxdist:
gt = A_flank[x:x+2]
ag = B_flank[x:x+2]
gtag = gt+ag
rc_gtag = rev_comp(gtag)
start,end = B.pos+margin-l+x,A.aend-margin+x+1
start,end = min(start,end),max(start,end)
strand = "*"
# get strand cues from read if strand-specific sequencing was used.
if options.stranded:
if A.is_reverse:
strand = '-'
else:
strand = '+'
if options.noncanonical:
hits.append((dist,ov,strandmatch(strand,'+'),rnd(),chrom,start,end,gtag,'+'))
hits.append((dist,ov,strandmatch(strand,'-'),rnd(),chrom,start,end,rc_gtag,'-'))
else:
if gtag == 'GTAG':
hits.append((dist,ov,strandmatch(strand,'+'),rnd(),chrom,start,end,'GTAG','+'))
elif gtag == 'CTAC':
hits.append((dist,ov,strandmatch(strand,'-'),rnd(),chrom,start,end,'GTAG','-'))
if len(hits) < 2:
# unambiguous, return right away
return hits
# Hits are sorted, with low edit distance beating low anchor overlap
hits = sorted(hits)
# return only hits that are tied with the best candidate by edit distance, anchor overlap
# and strand preference (if requested). Ties are either broken by random draw or by selecting
# the first hit later, but they are kept here such that they are counted properly.
best = hits[0]
if options.strandpref:
# exploit strand information to break ties only if requested. Otherwise it's only used as control.
ties = [h for h in hits if (h[0] == best[0]) and (h[1] == best[1]) and (h[2] == best[2])]
else:
ties = [h for h in hits if (h[0] == best[0]) and (h[1] == best[1])]
return ties
### anchor pairs that make it up to here are interesting
if bam_out:
bam_out.write(A)
bam_out.write(B)
#debug("A='%s' B='%s' dist=%d A.is_reverse=%s" % (A,B,dist,A.is_reverse))
if (A.is_reverse and dist > 0) or (not A.is_reverse and dist < 0):
# the anchors align in reversed orientation -> circRNA?
read = A.qname.split('__')[1]
chrom = sam.getrname(A.tid)
if A.is_reverse:
#print "ISREVERSE"
A,B = B,A
read = rev_comp(read)
bp = find_breakpoints(A,B,read,chrom)
if not bp:
N['circ_no_bp'] += 1
else:
N['circ_reads'] += 1
n_hits = len(bp)
if bp and not options.allhits:
bp = [bp[0],]
for h in bp:
# for some weird reason for circ we need a correction here
dist,ov,strandmatch,rnd,chrom,start,end,signal,sense = h
h = (chrom,start+1,end-1,sense)
