def __init__(self, input_bowtie, otu_txt, primer, Rdata,
max_allowed_remain_mismatch, mm_threshold):
"""
input_bowtie --- in BowTie format (has seq, qual, and primer offset), gzipped
otu_txt --- otu file from Qiime (90%)
"""
self.predictor = BaseErrPredict(Rdata)
self.input_bowtie = input_bowtie
self.otu_txt = otu_txt
self.primer = primer
self.max_allowed_remain_mismatch = max_allowed_remain_mismatch
self.mm_threshold = mm_threshold
self.otu_info = {} # cid (after trie) --> 90% OTU id
self.cluster_by_otu = {} # OTU id --> cid --> qual,len,seq...
self.create_clusters_from_bowtie()
def __init__(self, input_bowtie, otu_txt, primer, Rdata, max_allowed_remain_mismatch, mm_threshold):
"""
input_bowtie --- in BowTie format (has seq, qual, and primer offset), gzipped
otu_txt --- otu file from Qiime (90%)
"""
self.predictor = BaseErrPredict(Rdata)
self.input_bowtie = input_bowtie
self.otu_txt = otu_txt
self.primer = primer
self.max_allowed_remain_mismatch = max_allowed_remain_mismatch
self.mm_threshold = mm_threshold
self.otu_info = {} # cid (after trie) --> 90% OTU id
self.cluster_by_otu = {} # OTU id --> cid --> qual,len,seq...
self.create_clusters_from_bowtie()
class ClusterCorrect:
def __init__(self, input_bowtie, otu_txt, primer, Rdata,
max_allowed_remain_mismatch, mm_threshold):
"""
input_bowtie --- in BowTie format (has seq, qual, and primer offset), gzipped
otu_txt --- otu file from Qiime (90%)
"""
self.predictor = BaseErrPredict(Rdata)
self.input_bowtie = input_bowtie
self.otu_txt = otu_txt
self.primer = primer
self.max_allowed_remain_mismatch = max_allowed_remain_mismatch
self.mm_threshold = mm_threshold
self.otu_info = {} # cid (after trie) --> 90% OTU id
self.cluster_by_otu = {} # OTU id --> cid --> qual,len,seq...
self.create_clusters_from_bowtie()
def create_clusters_from_bowtie(self):
"""
The 'offset' field is actually 'abundance'
The 'ref' field is actually 'cycle' offset
"""
with open(self.otu_txt) as f:
for line in f:
otuid, rest = line.strip().split(None, 1)
for x in rest.split():
self.otu_info[x] = otuid
self.cluster_by_otu[otuid] = {}
for r in BowTieReader(self.input_bowtie, False):
cid = r['ID']
otuid = self.otu_info[r['ID']]
self.cluster_by_otu[otuid][cid] = {'dirty':True, 'cids':[cid], 'len':len(r['seq']), 'seq': MutableSeq(r['seq']), 'size':int(r['offset']), \
'qual': [ord(x)-33 for x in r['qual']], 'cycle': range(int(r['ref']), int(r['ref'])+len(r['seq']))}
def iter_merge_otu(self, otuid, logf):
cluster = self.cluster_by_otu[otuid]
ckeys = cluster.keys()
ckeys.sort(key=lambda x: cluster[x]['size'], reverse=True)
print >> sys.stderr, "cluster sizes", [
cluster[x]['size'] for x in ckeys
]
i = 0
while i < len(ckeys):
r1 = cluster[ckeys[i]]
j = i + 1
changed = False
while j < len(ckeys):
r2 = cluster[ckeys[j]]
# we must check if one or both of r1, r2 has been modified (dirty=True)
# otherwise, we have checked this combo before and no need to do again
if not r1['dirty'] and not r2['dirty']:
j += 1
continue
justmm = r1['size'] >= self.mm_threshold and r1[
'size'] * 1. / r2['size'] >= self.mm_threshold
#justmm = r1['size'] >= self.mm_threshold and r2['size'] < self.mm_threshold # TODO: hard code 10 or decide to param!
flag, diff_poses, correct = self.predictor.compare_seq(\
r1['seq'], r2['seq'], r1['qual'], r2['qual'],\
r1['cycle'], r2['cycle'], self.primer, self.max_allowed_remain_mismatch, justmm)
if flag: # can correct!
changed = True
#print "Correcting!", justmm, diff_poses, correct
#print r1
#print r2
#pdb.set_trace()
logf.write("{cids}\t{pos}\n".format(
cids=",".join(r2['cids']),
pos=",".join(map(str, diff_poses))))
for pos, base, q, c in correct:
r1['seq'][pos] = base
r1['qual'][pos] = q
r1['cycle'][pos] = c
r1['cids'] += r2['cids']
r1['size'] += r2['size']
# now we avg. the qual & cycles of non-corrected poses
if r1['len'] >= r2['len']:
for pos in xrange(r2['len']):
if pos not in diff_poses:
r1['qual'][pos] = rint(
(r1['qual'][pos] * r1['size'] +
r2['qual'][pos] * r2['size']) * 1. /
(r1['size'] + r2['size']))
r1['cycle'][pos] = rint(
(r1['cycle'][pos] * r1['size'] +
r2['cycle'][pos] * r2['size']) * 1. /
(r1['size'] + r2['size']))
else: # r2 is longer
for pos in xrange(r1['len']):
if pos not in diff_poses:
r1['qual'][pos] = rint(
(r1['qual'][pos] * r1['size'] +
r2['qual'][pos] * r2['size']) * 1. /
(r1['size'] + r2['size']))
r1['cycle'][pos] = rint(
(r1['cycle'][pos] * r1['size'] +
r2['cycle'][pos] * r2['size']) * 1. /
(r1['size'] + r2['size']))
# append end of r2 to r1
for pos in xrange(r1['len'], r2['len']):
r1['seq'].append(r2['seq'][pos])
r1['qual'].append(r2['qual'][pos])
r1['cycle'].append(r2['cycle'][pos])
r1['len'] = r2['len']
assert r1['len'] == len(r1['qual']) == len(
r1['seq']) == len(r1['cycle'])
#print("after merging")
#print r1
#print r2
#pdb.set_trace()
#raw_input('wait')
# carefully remove j now that we merged it to i
del cluster[ckeys[j]]
ckeys.pop(j)
#pdb.set_trace()
else:
#print "Cannot correct!", diff_poses, correct
#print r1
#print r2
#raw_input('wait')
j += 1
#pdb.set_trace()
r1['dirty'] = changed
#pdb.set_trace()
i += 1
# re-sort in case sizes have changed the order
ckeys = cluster.keys()
ckeys.sort(key=lambda x: cluster[x]['size'], reverse=True)
def iter_merge(self):
numclust = sum(len(x) for x in self.cluster_by_otu.itervalues())
print >> sys.stderr, "initial # of clusters: ", numclust
while True:
kkeys = self.cluster_by_otu.keys()
kkeys.sort(key=lambda x: max(
p['size'] for p in self.cluster_by_otu[x].itervalues()),
reverse=True)
for k in kkeys: #self.cluster_by_otu:
self.iter_merge_otu(k, sys.stderr)
tmp = sum(len(x) for x in self.cluster_by_otu.itervalues())
print >> sys.stderr, "current # of clusters: ", tmp
if tmp == numclust:
break
numclust = tmp
def write(self, output_prefix):
"""
Output to clusters to a fasta file <output_prefix>.fasta
>{cluster_index}
{sequence here}
And to a otu-style file <output_prefix>.otu.txt
<cluster_index> \t <tab delimited seq IDs>
"""
w = FastqWriter(output_prefix + '.fq')
f = open(output_prefix + '.fasta', 'w')
h = open(output_prefix + '.otu.txt', 'w')
a = open(output_prefix + '.abundance.txt', 'w')
for cluster in self.cluster_by_otu.itervalues():
for o in cluster.itervalues():
# need to massage qual for fq writing
o['qual'] = "".join(
chr(o['qual'][i] + 33) for i in xrange(o['len']))
o['ID'] = o['cids'][0]
w.write(o)
f.write(">{0}\n{1}\n".format(o['ID'], o['seq']))
h.write("{0}\t{1}\n".format(o['ID'], "\t".join(o['cids'])))
a.write("{0}\t{1}\n".format(o['ID'], o['size']))
w.close()
f.close()
h.close()
a.close()
os.system("gzip " + w.f.name)
class ClusterCorrect:
def __init__(self, input_bowtie, otu_txt, primer, Rdata, max_allowed_remain_mismatch, mm_threshold):
"""
input_bowtie --- in BowTie format (has seq, qual, and primer offset), gzipped
otu_txt --- otu file from Qiime (90%)
"""
self.predictor = BaseErrPredict(Rdata)
self.input_bowtie = input_bowtie
self.otu_txt = otu_txt
self.primer = primer
self.max_allowed_remain_mismatch = max_allowed_remain_mismatch
self.mm_threshold = mm_threshold
self.otu_info = {} # cid (after trie) --> 90% OTU id
self.cluster_by_otu = {} # OTU id --> cid --> qual,len,seq...
self.create_clusters_from_bowtie()
def create_clusters_from_bowtie(self):
"""
The 'offset' field is actually 'abundance'
The 'ref' field is actually 'cycle' offset
"""
with open(self.otu_txt) as f:
for line in f:
otuid, rest = line.strip().split(None, 1)
for x in rest.split():
self.otu_info[x] = otuid
self.cluster_by_otu[otuid] = {}
for r in BowTieReader(self.input_bowtie, False):
cid = r['ID']
otuid = self.otu_info[r['ID']]
self.cluster_by_otu[otuid][cid] = {'dirty':True, 'cids':[cid], 'len':len(r['seq']), 'seq': MutableSeq(r['seq']), 'size':int(r['offset']), \
'qual': [ord(x)-33 for x in r['qual']], 'cycle': range(int(r['ref']), int(r['ref'])+len(r['seq']))}
def iter_merge_otu(self, otuid, logf):
cluster = self.cluster_by_otu[otuid]
ckeys = cluster.keys()
ckeys.sort(key=lambda x: cluster[x]['size'], reverse=True)
print >> sys.stderr, "cluster sizes", [cluster[x]['size'] for x in ckeys]
i = 0
while i < len(ckeys):
r1 = cluster[ckeys[i]]
j = i + 1
changed = False
while j < len(ckeys):
r2 = cluster[ckeys[j]]
# we must check if one or both of r1, r2 has been modified (dirty=True)
# otherwise, we have checked this combo before and no need to do again
if not r1['dirty'] and not r2['dirty']:
j += 1
continue
justmm = r1['size'] >= self.mm_threshold and r1['size']*1./r2['size'] >= self.mm_threshold
#justmm = r1['size'] >= self.mm_threshold and r2['size'] < self.mm_threshold # TODO: hard code 10 or decide to param!
flag, diff_poses, correct = self.predictor.compare_seq(\
r1['seq'], r2['seq'], r1['qual'], r2['qual'],\
r1['cycle'], r2['cycle'], self.primer, self.max_allowed_remain_mismatch, justmm)
if flag: # can correct!
changed = True
#print "Correcting!", justmm, diff_poses, correct
#print r1
#print r2
#pdb.set_trace()
logf.write("{cids}\t{pos}\n".format(cids=",".join(r2['cids']),pos=",".join(map(str,diff_poses))))
for pos, base, q, c in correct:
r1['seq'][pos] = base
r1['qual'][pos] = q
r1['cycle'][pos] = c
r1['cids'] += r2['cids']
r1['size'] += r2['size']
# now we avg. the qual & cycles of non-corrected poses
if r1['len'] >= r2['len']:
for pos in xrange(r2['len']):
if pos not in diff_poses:
r1['qual'][pos] = rint((r1['qual'][pos]*r1['size']+r2['qual'][pos]*r2['size'])*1./(r1['size']+r2['size']))
r1['cycle'][pos] = rint((r1['cycle'][pos]*r1['size']+r2['cycle'][pos]*r2['size'])*1./(r1['size']+r2['size']))
else: # r2 is longer
for pos in xrange(r1['len']):
if pos not in diff_poses:
r1['qual'][pos] = rint((r1['qual'][pos]*r1['size']+r2['qual'][pos]*r2['size'])*1./(r1['size']+r2['size']))
r1['cycle'][pos] = rint((r1['cycle'][pos]*r1['size']+r2['cycle'][pos]*r2['size'])*1./(r1['size']+r2['size']))
# append end of r2 to r1
for pos in xrange(r1['len'],r2['len']):
r1['seq'].append(r2['seq'][pos])
r1['qual'].append(r2['qual'][pos])
r1['cycle'].append(r2['cycle'][pos])
r1['len'] = r2['len']
assert r1['len']==len(r1['qual'])==len(r1['seq'])==len(r1['cycle'])
#print("after merging")
#print r1
#print r2
#pdb.set_trace()
#raw_input('wait')
# carefully remove j now that we merged it to i
del cluster[ckeys[j]]
ckeys.pop(j)
#pdb.set_trace()
else:
#print "Cannot correct!", diff_poses, correct
#print r1
#print r2
#raw_input('wait')
j += 1
#pdb.set_trace()
r1['dirty'] = changed
#pdb.set_trace()
i += 1
# re-sort in case sizes have changed the order
ckeys = cluster.keys()
ckeys.sort(key=lambda x: cluster[x]['size'], reverse=True)
def iter_merge(self):
numclust = sum(len(x) for x in self.cluster_by_otu.itervalues())
print >> sys.stderr, "initial # of clusters: ", numclust
while True:
kkeys = self.cluster_by_otu.keys()
kkeys.sort(key=lambda x: max(p['size'] for p in self.cluster_by_otu[x].itervalues()),reverse=True)
for k in kkeys:#self.cluster_by_otu:
self.iter_merge_otu(k, sys.stderr)
tmp = sum(len(x) for x in self.cluster_by_otu.itervalues())
print >> sys.stderr, "current # of clusters: ", tmp
if tmp == numclust:
break
numclust = tmp
def write(self, output_prefix):
"""
Output to clusters to a fasta file <output_prefix>.fasta
>{cluster_index}
{sequence here}
And to a otu-style file <output_prefix>.otu.txt
<cluster_index> \t <tab delimited seq IDs>
"""
w = FastqWriter(output_prefix + '.fq')
f = open(output_prefix + '.fasta', 'w')
h = open(output_prefix + '.otu.txt', 'w')
a = open(output_prefix + '.abundance.txt', 'w')
for cluster in self.cluster_by_otu.itervalues():
for o in cluster.itervalues():
# need to massage qual for fq writing
o['qual'] = "".join(chr(o['qual'][i]+33) for i in xrange(o['len']))
o['ID'] = o['cids'][0]
w.write(o)
f.write(">{0}\n{1}\n".format(o['ID'], o['seq']))
h.write("{0}\t{1}\n".format(o['ID'], "\t".join(o['cids'])))
a.write("{0}\t{1}\n".format(o['ID'], o['size']))
w.close()
f.close()
h.close()
a.close()
os.system("gzip " + w.f.name)