def parseResults(blastResultsPath, limitHits=MAX_HITS):
'''
returns: a map from query seq id to a list of tuples of (subject seq id, evalue) for the top hits of the query sequence in the subject genome
'''
# parse tabular results into hits. thank you, ncbi, for creating results this easy to parse.
hitsMap = {}
hitsCountMap = {}
prevSeqId = None
prevHitId = None
fh = open(blastResultsPath)
for line in fh:
splits = line.split()
try:
seqId = fasta.idFromName(splits[0]) # remove namespace prefix, e.g. 'gi|'
hitId = fasta.idFromName(splits[1])
hitEvalue = float(splits[10])
except Exception as e:
logging.exception('parseResults(): prevSeqId: {}, prevHitId: {}, line: {}'.format(prevSeqId, prevHitId, line))
# results table reports multiple "alignments" per "hit" in ascending order by evalue
# we only store the top hits.
if prevSeqId != seqId or prevHitId != hitId:
prevSeqId = seqId
prevHitId = hitId
if seqId not in hitsCountMap:
hitsCountMap[seqId] = 0
hitsMap[seqId] = []
if not limitHits or hitsCountMap[seqId] < limitHits:
hitsCountMap[seqId] += 1
hitsMap[seqId].append((hitId, hitEvalue))
fh.close()
return hitsMap
def parseResults(blastResultsPath, limitHits=MAX_HITS):
'''
returns: a map from query seq id to a list of tuples of (subject seq id, evalue) for the top hits of the query sequence in the subject genome
'''
# parse tabular results into hits. thank you, ncbi, for creating results this easy to parse.
hitsMap = {}
hitsCountMap = {}
prevSeqId = None
prevHitId = None
fh = open(blastResultsPath)
for line in fh:
splits = line.split()
try:
seqId = fasta.idFromName(
splits[0]) # remove namespace prefix, e.g. 'gi|'
hitId = fasta.idFromName(splits[1])
hitEvalue = float(splits[10])
except Exception as e:
logging.exception(
'parseResults(): prevSeqId: {}, prevHitId: {}, line: {}'.
format(prevSeqId, prevHitId, line))
# results table reports multiple "alignments" per "hit" in ascending order by evalue
# we only store the top hits.
if prevSeqId != seqId or prevHitId != hitId:
prevSeqId = seqId
prevHitId = hitId
if seqId not in hitsCountMap:
hitsCountMap[seqId] = 0
hitsMap[seqId] = []
if not limitHits or hitsCountMap[seqId] < limitHits:
hitsCountMap[seqId] += 1
hitsMap[seqId].append((hitId, hitEvalue))
fh.close()
return hitsMap
def getGoodDivergenceAlignedTrimmedSeqPair(seqId, seq, hitSeqId, hitSeq, workPath):
'''
aligns seq to hit. trims aligned seq and hit seq.
returns: pairs of pairs of id and aligned trimmed sequences for sequences in hits,
and a predicate function that, given a divergence threshold, says if the divergence of the sequences exceeds the threshold.
e.g. ((seqId, alignedTrimmedSeq), (hitSeqId, alignedTrimmedHitSeq), divergencePredicateFunc)
'''
# ALIGN SEQ and HIT
# need to align the sequences so we'z can study the rate of evolution per site
inputFasta = '>%s\n%s\n>%s\n%s\n'%(seqId, seq, hitSeqId, hitSeq)
if USE_CLUSTALW:
alignedFasta = alignFastaClustalw(inputFasta, workPath)
else:
alignedFasta = alignFastaKalign(inputFasta)
# try to recover from rare, intermittent failure of fasta alignment
if not alignedFasta:
logging.error('fasta alignment failed.\ninputFasta=%s\n' +
'alignedFasta=%s\nSleep and retry alignment.',
inputFasta, alignedFasta)
time.sleep(0.1)
alignedFasta = alignFastaKalign(inputFasta)
try:
# parse the aligned fasta into sequence ids and sequences
namelinesAndSeqs = list(fasta.readFasta(cStringIO.StringIO(alignedFasta)))
idAndSeqs = [(fasta.idFromName(seqNameline), seq) for seqNameline, seq in namelinesAndSeqs]
alignedIdAndSeq, alignedHitIdAndSeq = idAndSeqs
except Exception as e:
e.args += (inputFasta, alignedFasta)
raise
# CHECK FOR EXCESSIVE DIVERGENCE AND TRIMMING
# find most diverged sequence
# sort sequences by dash count. why?
divIdSeqs = []
for id, seq in (alignedIdAndSeq, alignedHitIdAndSeq):
dashCount = seq.count('-')
div = dashCount / float(len(seq))
g = (dashCount, div, id, seq)
divIdSeqs.append(g)
divIdSeqs.sort()
# check for excessive divergence
leastDivergedDashCount, leastDivergedDiv, leastDivergedId, leastDivergedSeq = divIdSeqs[0]
# check for excessive divergence and generate dashtrim.
mostDivergedDashCount, mostDivergedDiv, mostDivergedId, mostDivergedSeq = divIdSeqs[1]
# dashtrim = dashlen_check(mostDivergedSeq, divergence)
startTrim, endTrim, trimDivergence = dashlen_check(mostDivergedSeq)
# logging.debug('dashtrim='+str(dashtrim))
# trim and add seqs to output
def divergencePredicate(divergenceThreshold):
'''Why this logic? Ask Dennis. Function closed over local variables that returns whether or not the alignment of the sequences is too diverged.'''
if leastDivergedSeq and leastDivergedDiv > divergenceThreshold:
return True
if (startTrim or endTrim) and trimDivergence >= divergenceThreshold:
return True
return False
alignedTrimmedIdAndSeq, alignedTrimmedHitIdAndSeq = [(id, seq[startTrim:(len(seq)-endTrim)]) for id, seq in (alignedIdAndSeq, alignedHitIdAndSeq)]
return alignedTrimmedIdAndSeq, alignedTrimmedHitIdAndSeq, divergencePredicate
def makeGetSeqForId(genomeFastaPath):
'''
genomeFastaPath: location of fasta file. also location/name of blast formatted indexes of the fasta file.
'''
# suck fasta file into memory, converting it into a map from id to sequence
# in memory dict performs much better than on-disk retrieval with xdget or fastacmd.
# and genome fasta files do not take much space (on a modern computer).
fastaMap = {}
for (seqNameline, seq) in fasta.readFasta(genomeFastaPath):
seqId = fasta.idFromName(seqNameline)
fastaMap[seqId] = seq
def getSeqForIdInMemory(seqId):
return fastaMap[seqId]
return getSeqForIdInMemory
def findSeqIdWithFasta(fastaSeq, subjectIndexPath):
''' return first hit '''
try:
path = nested.makeTempPath()
util.writeToFile(fastaSeq, path)
cmd = 'blastp -outfmt 6 -query %s -db %s'%(path, subjectIndexPath)
results = util.run(cmd, shell=True)
finally:
os.remove(path)
hitId = None
for line in results.splitlines():
# example line: foo sp|P39709|SEO1_YEAST 100.00 40 0 0 1 40 1 40 3e-1884.7
# the second field is from the hit nameline.
hitId = fasta.idFromName(line.split()[1])
break # grab the first hit
return hitId
def makeGetSeqForId(genomeFastaPath):
'''
genomeFastaPath: location of fasta file. also location/name of blast formatted indexes of the fasta file.
'''
# suck fasta file into memory, converting it into a map from id to sequence
# in memory dict performs much better than on-disk retrieval with xdget or fastacmd.
# and genome fasta files do not take much space (on a modern computer).
fastaMap = {}
for (seqNameline, seq) in fasta.readFasta(genomeFastaPath):
seqId = fasta.idFromName(seqNameline)
fastaMap[seqId] = seq
def getSeqForIdInMemory(seqId):
return fastaMap[seqId]
return getSeqForIdInMemory
def getGoodDivergenceAlignedTrimmedSeqPair(seqId, seq, hitSeqId, hitSeq,
workPath):
'''
aligns seq to hit. trims aligned seq and hit seq.
returns: pairs of pairs of id and aligned trimmed sequences for sequences in hits,
and a predicate function that, given a divergence threshold, says if the divergence of the sequences exceeds the threshold.
e.g. ((seqId, alignedTrimmedSeq), (hitSeqId, alignedTrimmedHitSeq), divergencePredicateFunc)
'''
# ALIGN SEQ and HIT
# need to align the sequences so we'z can study the rate of evolution per site
inputFasta = '>%s\n%s\n>%s\n%s\n' % (seqId, seq, hitSeqId, hitSeq)
if USE_CLUSTALW:
alignedFasta = alignFastaClustalw(inputFasta, workPath)
else:
alignedFasta = alignFastaKalign(inputFasta)
# try to recover from rare, intermittent failure of fasta alignment
if not alignedFasta:
logging.error(
'fasta alignment failed.\ninputFasta=%s\n' +
'alignedFasta=%s\nSleep and retry alignment.', inputFasta,
alignedFasta)
time.sleep(0.1)
alignedFasta = alignFastaKalign(inputFasta)
try:
# parse the aligned fasta into sequence ids and sequences
namelinesAndSeqs = list(
fasta.readFasta(cStringIO.StringIO(alignedFasta)))
idAndSeqs = [(fasta.idFromName(seqNameline), seq)
for seqNameline, seq in namelinesAndSeqs]
alignedIdAndSeq, alignedHitIdAndSeq = idAndSeqs
except Exception as e:
e.args += (inputFasta, alignedFasta)
raise
# CHECK FOR EXCESSIVE DIVERGENCE AND TRIMMING
# find most diverged sequence
# sort sequences by dash count. why?
divIdSeqs = []
for id, seq in (alignedIdAndSeq, alignedHitIdAndSeq):
dashCount = seq.count('-')
div = dashCount / float(len(seq))
g = (dashCount, div, id, seq)
divIdSeqs.append(g)
divIdSeqs.sort()
# check for excessive divergence
leastDivergedDashCount, leastDivergedDiv, leastDivergedId, leastDivergedSeq = divIdSeqs[
0]
# check for excessive divergence and generate dashtrim.
mostDivergedDashCount, mostDivergedDiv, mostDivergedId, mostDivergedSeq = divIdSeqs[
1]
# dashtrim = dashlen_check(mostDivergedSeq, divergence)
startTrim, endTrim, trimDivergence = dashlen_check(mostDivergedSeq)
# logging.debug('dashtrim='+str(dashtrim))
# trim and add seqs to output
def divergencePredicate(divergenceThreshold):
'''Why this logic? Ask Dennis. Function closed over local variables that returns whether or not the alignment of the sequences is too diverged.'''
if leastDivergedSeq and leastDivergedDiv > divergenceThreshold:
return True
if (startTrim or endTrim) and trimDivergence >= divergenceThreshold:
return True
return False
alignedTrimmedIdAndSeq, alignedTrimmedHitIdAndSeq = [
(id, seq[startTrim:(len(seq) - endTrim)])
for id, seq in (alignedIdAndSeq, alignedHitIdAndSeq)
]
return alignedTrimmedIdAndSeq, alignedTrimmedHitIdAndSeq, divergencePredicate
