def spliceFromCoords( genomeFile, coordFile, obsFile ):
genomeFileHandler = open( genomeFile, "r" )
obsFileHandler = open( obsFile, "w" )
dChr2Maps = MapUtils.getDictPerSeqNameFromMapFile( coordFile )
while True:
bs = Bioseq()
bs.read( genomeFileHandler )
if bs.sequence == None:
break
if dChr2Maps.has_key( bs.header ):
lCoords = MapUtils.getMapListSortedByIncreasingMinThenMax( dChr2Maps[ bs.header ] )
splicedSeq = ""
currentSite = 0
for iMap in lCoords:
minSplice = iMap.getMin() - 1
if minSplice > currentSite:
splicedSeq += bs.sequence[ currentSite : minSplice ]
currentSite = iMap.getMax()
splicedSeq += bs.sequence[ currentSite : ]
bs.sequence = splicedSeq
bs.write( obsFileHandler )
genomeFileHandler.close()
obsFileHandler.close()
def sortSequencesByIncreasingLength(inFileName, outFileName, verbose=0):
if verbose > 0:
print "sort sequences by increasing length"
sys.stdout.flush()
if not os.path.exists( inFileName ):
print "ERROR: file '%s' doesn't exist" % ( inFileName )
sys.exit(1)
# read each seq one by one
# save them in distinct temporary files
# with their length in the name
inFileHandler = open( inFileName, "r" )
bs = Bioseq()
countSeq = 0
while True:
bs.read( inFileHandler )
if bs.header == None:
break
countSeq += 1
tmpFile = "%ibp_%inb" % ( bs.getLength(), countSeq )
bs.appendBioseqInFile( tmpFile )
if verbose > 1:
print "%s (%i bp) saved in '%s'" % ( bs.header, bs.getLength(), tmpFile )
bs.header = ""
bs.sequence = ""
inFileHandler.close()
# sort temporary file names
# concatenate them into the output file
if os.path.exists( outFileName ):
os.remove( outFileName )
lFiles = glob.glob( "*bp_*nb" )
lFiles.sort( key=lambda s:int(s.split("bp_")[0]) )
for fileName in lFiles:
cmd = "cat %s >> %s" % ( fileName, outFileName )
returnValue = os.system( cmd )
if returnValue != 0:
print "ERROR while concatenating '%s' with '%s'" % ( fileName, outFileName )
sys.exit(1)
os.remove( fileName )
return 0
def getConsensus( self, minNbNt, minPropNt=0.0, verbose=0 ):
maxPropN = 0.40 # discard consensus if more than 40% of N's
nbInSeq = self.getSize()
if verbose > 0:
print "nb of aligned sequences: %i" % ( nbInSeq ); sys.stdout.flush()
if nbInSeq < 2:
print "ERROR: can't make a consensus with less than 2 sequences"
sys.exit(1)
if minNbNt >= nbInSeq:
minNbNt = nbInSeq - 1
print "minNbNt=%i" % ( minNbNt )
if minPropNt >= 1.0:
print "ERROR: minPropNt=%.2f should be a proportion (below 1.0)" % ( minPropNt )
sys.exit(1)
lOccPerSite = self.getListOccPerSite()
nbSites = len(lOccPerSite)
if verbose > 0:
print "nb of sites: %i" % ( nbSites ); sys.stdout.flush()
seqConsensus = ""
# for each site (i.e. each column of the MSA)
nbRmvColumns = 0
countSites = 0
for dNt2Occ in lOccPerSite:
countSites += 1
if verbose > 1:
print "site %s / %i" % ( str(countSites).zfill( len(str(nbSites)) ),
nbSites )
sys.stdout.flush()
occMaxNt = 0 # occurrences of the predominant nucleotide at this site
lBestNt = []
nbNt = 0 # total nb of A, T, G and C (no gap)
# for each distinct symbol at this site (A, T, G, C, N, -,...)
for j in dNt2Occ.keys():
if j != "-":
nbNt += dNt2Occ[j]
if verbose > 1:
print "%s: %i" % ( j, dNt2Occ[j] )
if dNt2Occ[j] > occMaxNt:
occMaxNt = dNt2Occ[j]
lBestNt = [ j ]
elif dNt2Occ[j] == occMaxNt:
lBestNt.append( j )
if nbNt == 0: # some MSA programs can remove some sequences (e.g. Muscle after Recon) or when using Refalign (non-alignable TE fragments put together via a refseq)
nbRmvColumns += 1
if len( lBestNt ) >= 1:
bestNt = lBestNt[0]
# if the predominant nucleotide occurs in less than x% of the sequences, put a "N"
if minPropNt > 0.0 and nbNt != 0 and float(occMaxNt)/float(nbNt) < minPropNt:
bestNt = "N"
if int(nbNt) >= int(minNbNt):
seqConsensus += bestNt
if verbose > 1:
print "-> %s" % ( bestNt )
if nbRmvColumns:
print "WARNING: %i sites were removed (%.2f%%)" % ( nbRmvColumns, nbRmvColumns / float(nbSites) * 100 )
sys.stdout.flush()
if seqConsensus == "":
print "WARNING: no consensus can be built (no sequence left)"
return
propN = seqConsensus.count("N") / float(len(seqConsensus))
if propN >= maxPropN:
print "WARNING: no consensus can be built (%i%% of N's >= %i%%)" % ( propN * 100, maxPropN * 100 )
return
elif propN >= maxPropN * 0.5:
print "WARNING: %i%% of N's" % ( propN * 100 )
consensus = Bioseq()
consensus.sequence = seqConsensus
consensus.header = "consensus=%s length=%i nbAlign=%i" % ( self.name, len(seqConsensus), self.getSize() )
if verbose > 0:
statEntropy = self.getEntropy( verbose - 1 )
print "entropy: %s" % ( statEntropy.stringQuantiles() )
sys.stdout.flush()
return consensus
