def ppOut2PPSout():
inFile = '/Users/ivan/Documents/work/binning/data/HumanGut/PP/TS29_scaff.file.0.5.txt'
outFile = '/Users/ivan/Documents/work/binning/data/HumanGut/PP/TS29_scaff.file.0.5.PPS.txt'
dbFile = '/Users/ivan/Documents/work/binning/taxonomy/20120828/ncbitax_sqlite.db' #DB
taxonomy = taxonomy_ncbi.TaxonomyNcbi(dbFile)
out = csv.OutFileBuffer(outFile)
csv.forEachLine(inFile, PP2PPSoutParser(taxonomy, out))
out.close()
def ppOut2PPSout():
inFile = '/Users/ivan/Documents/work/binning/data/HumanGut/PP/TS29_scaff.file.0.5.txt'
outFile = '/Users/ivan/Documents/work/binning/data/HumanGut/PP/TS29_scaff.file.0.5.PPS.txt'
dbFile = '/Users/ivan/Documents/work/binning/taxonomy/20120828/ncbitax_sqlite.db' #DB
taxonomy = taxonomy_ncbi.TaxonomyNcbi(dbFile)
out = csv.OutFileBuffer(outFile)
csv.forEachLine(inFile, PP2PPSoutParser(taxonomy, out))
out.close()
def setCandidatePlacement(self, sequences, taxonomy, fastaFileDNA):
"""
Set candidate placement according to the marker gene analysis !!!
"""
outPredAllFileName = os.path.join(self.markerGeneWorkingDir,
str(os.path.basename(fastaFileDNA) + '_all.mP'))
return forEachLine(outPredAllFileName, _SetCandidatePlacement(sequences, taxonomy)).getAssignedSeqCount()
def setCandidatePlacement(self, sequences, taxonomy, fastaFileDNA):
"""
Set candidate placement according to the marker gene analysis !!!
"""
outPredAllFileName = os.path.join(self.markerGeneWorkingDir,
str(os.path.basename(fastaFileDNA) + '_all.mP'))
return forEachLine(outPredAllFileName, _SetCandidatePlacement(sequences, taxonomy)).getAssignedSeqCount()
def runMarkerGeneAnalysis(self, fastaFileDNA, outLog=None):
"""
Run hmmer HMM and mothur classify (bayesian), same param as for the 16S analysis.
"""
#read list of marker genes
mgFiles = forEachLine(self.markerGeneListFile, _MgFiles(self.markerGeneListFileDir))
#translate DNA to protein sequences
fastaFileProt = os.path.join(self.markerGeneWorkingDir, str(os.path.basename(fastaFileDNA) + '.PROT'))
dnaToProt(fastaFileDNA, fastaFileProt)
#read DNA fasta file
try:
handle = open(fastaFileDNA, "rU")
dnaSeqDict = SeqIO.to_dict(SeqIO.parse(handle, "fasta"))
handle.close()
except Exception:
sys.stderr.write(str('Cannot read file: ' + str(fastaFileDNA)))
raise
#to output all predictions in one file
outPredAllFileName = os.path.join(self.markerGeneWorkingDir,
str(os.path.basename(fastaFileDNA) + '_all.mP'))
outAllBuffer = OutFileBuffer(outPredAllFileName)
#run HMM search
mgList = mgFiles.getGeneNameList()
if outLog is not None:
stdoutLog = open(outLog, 'w')
else:
stdoutLog = subprocess.STDOUT
#for each gene perform the analysis separately
for geneName in mgList:
domFileArray = [os.path.join(self.markerGeneWorkingDir, str(geneName + '_1.dom'))] #,
# os.path.join(self.markerGeneWorkingDir, str(geneName + '_2.dom'))]
outFileArray = [os.path.join(self.markerGeneWorkingDir, str(geneName + '_1.out'))] #,
# os.path.join(self.markerGeneWorkingDir, str(geneName + '_2.out'))]
hmmFileArray = [mgFiles.getFilePath(geneName, 'hmmPROTPrim')] #,
# mgFiles.getFilePath(geneName, 'hmmPROTSec')]
cmdArray = list([])
#define cmd
for i in range(1):
if hmmFileArray[i] is not None:
cmdArray.append(str(os.path.join(self.hmmerBinDir, 'hmmsearch') + ' --domtblout ' + domFileArray[i] + ' -E 0.01' + self.processorsHmm
+ ' -o ' + outFileArray[i] + ' ' + hmmFileArray[i] + ' ' + fastaFileProt))
else:
cmdArray.append(None)
#run cmd
for cmd in cmdArray:
if cmd is not None and os.name == 'posix':
cwd = self.hmmInstallDir
if parallel.reportFailedCmd(parallel.runCmdSerial([parallel.TaskCmd(cmd, cwd)])) is not None:
sys.exit(-1)
# hmmProc = subprocess.Popen(cmd, shell=True, bufsize=-1, cwd=self.hmmInstallDir, stdout=stdoutLog)
# print 'run cmd:', cmd
# hmmProc.wait()
# print 'HMM return code:', hmmProc.returncode
# if hmmProc.returncode != 0:
# raise Exception("Command returned with non-zero %s status: %s" % (hmmProc.returncode, cmd))
else:
print 'Marker genes analysis, doesn`t run (no posix): ', cmd
#get regions that match to the HMM profile ()
entryDictList = []
for i in range(1):
if cmdArray[i] is not None:
entryDictList.append(forEachLine(domFileArray[i], _MgRegions()).getEntryDict())
else:
entryDictList.append(None)
entryDict1 = entryDictList[0]
# entryDict2 = entryDictList[1]
#extract regions found in the protein sequences that were found by the HMM and generate corresponding DNA sequences
regionDnaFasta = os.path.join(self.markerGeneWorkingDir, str(geneName + '_dna.gff'))
outFileBuffer = OutFileBuffer(regionDnaFasta)
for seqName in entryDict1:
i = -1
for e in entryDict1[seqName]:
i += 1
from1 = entryDict1[seqName][i][0]
to1 = entryDict1[seqName][i][1]
assert ((from1 != None) and (to1 != None))
#compare the results found by the primary and secondary HMM profiles
# if (entryDict2 != None) and (seqName in entryDict2):
# if len(entryDict2[seqName]) >= (i+1):
# from2 = entryDict2[seqName][i][0]
# to2 = entryDict2[seqName][i][1]
#if from1 != from2 or to1 != to2:
# print str('Different positions in' + seqName + ' from1:' + str(from1) + ' from2:' + str(from2)
# + ' to1:' + str(to1) + ' to2:' + str(to2))
#extract regions from the DNA sequences (consider 3 ORF and reverse complements)
#name of the whole sequence
dnaSeqName = re.sub(r'([0-9]+_[0-9]+)_[pr]+[012]', r'\1', seqName)
#whole DNA sequence
dnaSeq = dnaSeqDict[dnaSeqName].seq
#reverse complement (contains "pr")
tagRev = 'p'
if re.match(r'[0-9]+_[0-9]+_pr[012]', seqName):
dnaSeq = dnaSeq.reverse_complement()
tagRev = 'pr'
#shift "0"
if re.match(r'[0-9]+_[0-9]+_[pr]+0', seqName):
tagFrom = ((from1 - 1)*3)
tagTo = (to1*3)
tagRev += '0'
dnaSeq = dnaSeq[tagFrom:tagTo]
#shift "1"
elif re.match(r'[0-9]+_[0-9]+_[pr]+1', seqName):
tagFrom = (((from1 - 1)*3) + 1)
tagTo = ((to1*3) + 1)
tagRev += '1'
dnaSeq = dnaSeq[tagFrom:tagTo]
#shift "2"
elif re.match(r'[0-9]+_[0-9]+_[pr]+2', seqName):
tagFrom = (((from1 - 1)*3) + 2)
tagTo = ((to1*3) + 2)
tagRev += '2'
dnaSeq = dnaSeq[tagFrom:tagTo]
#error
else:
sys.stderr.write('Wrong seq name: ' + seqName + ' \n')
dnaSeq = None
tag = str(str(tagFrom) + '_' + str(tagTo) + '_' + tagRev)
outFileBuffer.writeText(str('>' + dnaSeqName + '_' + tag + '\n' + dnaSeq + '\n'))
outFileBuffer.close()
#if no marker gene found
if outFileBuffer.isEmpty():
continue
#run mothur classify (bayesian? the same as for the 16S analysis)
templateFile = mgFiles.getFilePath(geneName, 'templateDNA')
taxonomyFile = mgFiles.getFilePath(geneName, 'taxonomyDNA')
assert ((templateFile is not None) and (taxonomyFile is not None))
cmd = str('' + self.mothur + ' "#classify.seqs(fasta=' + regionDnaFasta + ', template=' + templateFile
+ ', taxonomy=' + taxonomyFile + ', ' + self.mothurParam + ')"')
if os.name == 'posix':
print('Mothur processing: %s' % os.path.basename(templateFile).split('_', 1)[0])
cwd = self.markerGeneWorkingDir
if parallel.reportFailedCmd(parallel.runCmdSerial([parallel.TaskCmd(cmd, cwd, stdout=stdoutLog)])) is not None:
sys.exit(-1)
# mothurProc = subprocess.Popen(cmd, shell=True, bufsize=-1, cwd=self.markerGeneWorkingDir, stdout=stdoutLog)
# print 'run cmd:', cmd
# mothurProc.wait()
# print 'mothur return code:', mothurProc.returncode
# if mothurProc.returncode != 0:
# raise Exception("Command returned with non-zero %s status: %s" % (mothurProc.returncode, cmd))
else:
print 'Cannot run mothur since your system is not "posix" but', str('"' + os.name + '"'), '\n', cmd
#transform the mothur output to a simple output (name, ncbid, weight)
#mothurPredFileName = os.path.join(self.markerGeneWorkingDir,
# str(geneName + '_dna.' + os.path.basename(taxonomyFile) + 'onomy')) # taxonomy
#!!!!!!!!!!!!!
mothurPredFileName = common.getMothurOutputFilePath(regionDnaFasta, taxonomyFile)
if not os.path.isfile(mothurPredFileName):
mothurPredFileName = common.getMothurOutputFilePath(regionDnaFasta, taxonomyFile, suffix='.bayesian.taxonomy')
if not os.path.isfile(mothurPredFileName):
print("Can't open file: %s" % mothurPredFileName)
outPredFileName = os.path.join(self.markerGeneWorkingDir,
str(os.path.basename(fastaFileDNA) + '_' + geneName + '.mP'))
outBuffer = OutFileBuffer(outPredFileName, bufferText=True)
forEachLine(mothurPredFileName, _MothurOutFileParser(outBuffer, geneName))
if not outAllBuffer.isEmpty():
outAllBuffer.writeText('\n')
outAllBuffer.writeText(outBuffer.getTextBuffer())
if outLog is not None:
stdoutLog.close()
outAllBuffer.close()
def _init(self, align=True, dm=True, cluster=True):
"""
Init data, compute: alignment, distance matrix, clusters.
"""
if self._initDone:
return
self._initDone = True
fastaPathList = [] # fasta files containing regions that correspond to particular marker genes
self._mgList = [] # list of names of marker genes
mgToFastaPath = dict([]) # marker gene name -> fasta file path
#collect regions from Amphora mg
for fastaFile in glob.glob(os.path.join(os.path.normpath(self._mgWorkingDir),'*.gff')):
fastaPathList.append(fastaFile)
for path in fastaPathList:
name = re.sub('([^\.]+)\..*$', r'\1' , os.path.basename(path))
mg = re.sub(r'([^_]+)_dna', r'\1',name)
dir = os.path.dirname(path)
self._mgList.append(mg)
mgToFastaPath[mg] = path
#add 16S
s16List = ['5S_rRNA', '16S_rRNA', '23S_rRNA']
for mg in s16List:
mgToFastaPath[mg] = str(self._s16Prefix + '.' + mg + '.fna')
self._mgList.append(mg)
#For each marker gene create filtered fasta file that contains for each mg and sequence at most one region.
mgToFilteredFastaPath = dict([])
mgToSeqNameToTaxPathDict = dict([]) #mg -> seqName (~region name) -> pred
for mg in self._mgList:
mgToSeqNameToTaxPathDict[mg] = dict([])
for seq in self._sequences.sequences:
id = str(str(seq.scaffold.id) + '_' + str(seq.id))
for mg,tag,pred in zip(seq.getCandidateTaxPathSourceList(), seq.getCandidateTaxPathTagList(),
seq.getCandidateTaxPathDictList()):
mgToSeqNameToTaxPathDict[mg][str(id + '_' + tag)] = pred
#for each marker gene: choose only one sequence region for each mg and sequence
#all sequences are predicted at least at superkingdom
for mg in self._mgList:
seqNameToPred = mgToSeqNameToTaxPathDict[mg] #sequence region predictions for this mg
seqNameToSeq = fastaFileToDict(mgToFastaPath[mg]) #read the fasta file
outPath = os.path.normpath(os.path.join(self._clustDir, str(mg + '.filter.fna')))
mgToFilteredFastaPath[mg] = outPath
out = OutFileBuffer(outPath)
seqBaseToSeqName = dict([]) # sequence base (scaffId_seqId) -> region name
for seqName in seqNameToSeq:
seqBase = re.sub(r'^([0-9]+_[0-9]+)[^0-9].*',r'\1', seqName)
if seqBase not in seqBaseToSeqName:
seqBaseToSeqName[seqBase] = []
seqBaseToSeqName[seqBase].append(seqName)
for seqBase in seqBaseToSeqName:
seqId = int(re.sub(r'^[0-9]+_([0-9]+)',r'\1', seqBase))
seqBaseTaxPathDict = self._sequences.getSequence(seqId).getTaxonomyPath()
list = seqBaseToSeqName[seqBase]
candidateSeq = [] # sequence region is predicted at least at rank superkingdom
for seqName in list:
if seqName not in seqNameToPred:
taxPathDict = None
else:
taxPathDict = seqNameToPred[seqName]
if taxPathDict != None:
candidateSeq.append(seqName)
if len(candidateSeq) == 0:
continue
candidateSeq2 = [] # sequence regions predicted at least at the same rank as the whole sequence
for seqName in candidateSeq:
taxPathDict = seqNameToPred[seqName]
if ((seqBaseTaxPathDict == None)
or (len(taxPathDict) >= len(seqBaseTaxPathDict))): #predict at least at the same level
candidateSeq2.append(seqName)
if len(candidateSeq2) > 0: #take the longest sequence
sMax = candidateSeq2[0]
for s in candidateSeq2[1:]:
if len(seqNameToSeq[s]) > len(seqNameToSeq[sMax]):
sMax = s
else: #all sequence regions are predicted higher than the sequence
sMax = candidateSeq[0] #sequence region with the most specific prediction
for s in candidateSeq[1:]:
taxPathDictMax = seqNameToPred[sMax]
taxPathDictS = seqNameToPred[s]
if taxPathDictS == None:
continue
if taxPathDictMax == None:
sMax = s
continue
if len(taxPathDictMax) < len(taxPathDictS):
sMax = s
candidateSeq3 = [] #get all sequence regions with the most specific prediction
taxPathDictMax = seqNameToPred[sMax]
for s in candidateSeq:
taxPathDictS = seqNameToPred[s]
if taxPathDictMax == None:
candidateSeq3.append(s)
elif len(taxPathDictS) == len(taxPathDictMax):
candidateSeq3.append(s)
sMax = candidateSeq3[0]
for s in candidateSeq3[1:]: #take the longest sequence
if len(seqNameToSeq[sMax]) < len(seqNameToSeq[s]):
sMax = s
out.writeText(str('>' + str(sMax) + '\n' + str(seqNameToSeq[sMax]) + '\n'))
out.close()
mgToAlignPath = dict([])
for mg in self._mgList:
mgToAlignPath[mg] = os.path.normpath(os.path.join(self._clustDir, str(mg + '.align.fna')))
#build alignment
if align:
for mg in self._mgList:
alignCmd = str(self._config.get('aligner') + ' -in ' + mgToFilteredFastaPath[mg]
+ ' -out ' + mgToAlignPath[mg] + ' -quiet')
assert os.name == 'posix'
predictProc = subprocess.Popen(alignCmd, cwd=self._mgWorkingDir, shell=True, bufsize=-1) #stdout=subprocess.STDOUT, stderr=subprocess.STDOUT)
predictProc.wait()
print 'Muscle return code for', mg, ':', predictProc.returncode
if predictProc.returncode != 0:
sys.stderr.write(str(alignCmd + ' \n'))
#compute DM
if dm:
for mg in self._mgList:
mothur = os.path.join(os.path.normpath(self._configRRNA16S.get('mothurInstallDir')), 'mothur')
mothurCmd = str('time ' + mothur + ' "#dist.seqs(fasta=' + mgToAlignPath[mg]
+ ', processors=2, countends=F, calc=nogaps, cutoff=0.3, output=lt)"')
assert os.name == 'posix'
mothurProc = subprocess.Popen(mothurCmd, shell=True, bufsize=-1, cwd=self._mgWorkingDir)
mothurProc.wait()
print 'Mothur return code dist:', mg, mothurProc.returncode
#distFilePath = os.path.join(os.path.dirname(mgToAlignPath[mg]), str(mg + '.align.phylip.dist'))
#self._mgToDM[mg] = forEachLine(distFilePath, DM())
#self._mgToDM[mg].printDM()
#cluster
if cluster:
for mg in self._mgList:
distFilePath = os.path.join(os.path.dirname(mgToAlignPath[mg]), str(mg + '.align.phylip.dist'))
mothur = os.path.join(os.path.normpath(self._configRRNA16S.get('mothurInstallDir')), 'mothur')
mothurCmd = str('time ' + mothur + ' "#cluster(phylip=' + distFilePath
+ ', method=furthest, hard=t, precision=1000)"')
assert os.name == 'posix'
mothurProc = subprocess.Popen(mothurCmd, shell=True, bufsize=-1, cwd=self._mgWorkingDir)
mothurProc.wait()
print 'Mothur return code cluster:', mg, mothurProc.returncode
#read DM and clusters
#sequence predictions
self._seqIdToTaxPathDict = dict([])
self._seqIdToWeight = dict([])
for seq in self._sequences.sequences:
id = int(seq.id)
self._seqIdToTaxPathDict[id] = seq.getTaxonomyPath()
self._seqIdToWeight[id] = seq.getTaxonomyPathWeight()
#similarity thresholds
thresholds = self._configMG.get('mgSimilarityThresholds')
self._mgToMaxThreshold = dict([])
tmpDict = getMapping(self._configMG.get('mgSimilarityThresholds'), 0, 1, sep='\t', comment = '#')
for k in tmpDict:
self._mgToMaxThreshold[k] = float(tmpDict[k][0])
self._mgToDM = dict([])
self._mgToCluster = dict([])
for mg in self._mgList:
file = os.path.join(os.path.dirname(mgToAlignPath[mg]), str(mg + '.align.phylip.dist'))
self._mgToDM[mg] = forEachLine(file, DM())
file = os.path.join(os.path.dirname(mgToAlignPath[mg]), str(mg + '.align.phylip.fn.list'))
self._mgToCluster[mg] = forEachLine(file, MCluster(self._seqIdToTaxPathDict, self._mgToMaxThreshold[mg]))
def _init(self, align=True, dm=True, cluster=True):
"""
Init data, compute: alignment, distance matrix, clusters.
"""
if self._initDone:
return
self._initDone = True
fastaPathList = [
] # fasta files containing regions that correspond to particular marker genes
self._mgList = [] # list of names of marker genes
mgToFastaPath = dict([]) # marker gene name -> fasta file path
#collect regions from Amphora mg
for fastaFile in glob.glob(
os.path.join(os.path.normpath(self._mgWorkingDir), '*.gff')):
fastaPathList.append(fastaFile)
for path in fastaPathList:
name = re.sub('([^\.]+)\..*$', r'\1', os.path.basename(path))
mg = re.sub(r'([^_]+)_dna', r'\1', name)
dir = os.path.dirname(path)
self._mgList.append(mg)
mgToFastaPath[mg] = path
#add 16S
s16List = ['5S_rRNA', '16S_rRNA', '23S_rRNA']
for mg in s16List:
mgToFastaPath[mg] = str(self._s16Prefix + '.' + mg + '.fna')
self._mgList.append(mg)
#For each marker gene create filtered fasta file that contains for each mg and sequence at most one region.
mgToFilteredFastaPath = dict([])
mgToSeqNameToTaxPathDict = dict(
[]) #mg -> seqName (~region name) -> pred
for mg in self._mgList:
mgToSeqNameToTaxPathDict[mg] = dict([])
for seq in self._sequences.sequences:
id = str(str(seq.scaffold.id) + '_' + str(seq.id))
for mg, tag, pred in zip(seq.getCandidateTaxPathSourceList(),
seq.getCandidateTaxPathTagList(),
seq.getCandidateTaxPathDictList()):
mgToSeqNameToTaxPathDict[mg][str(id + '_' + tag)] = pred
#for each marker gene: choose only one sequence region for each mg and sequence
#all sequences are predicted at least at superkingdom
for mg in self._mgList:
seqNameToPred = mgToSeqNameToTaxPathDict[
mg] #sequence region predictions for this mg
seqNameToSeq = fastaFileToDict(
mgToFastaPath[mg]) #read the fasta file
outPath = os.path.normpath(
os.path.join(self._clustDir, str(mg + '.filter.fna')))
mgToFilteredFastaPath[mg] = outPath
out = OutFileBuffer(outPath)
seqBaseToSeqName = dict(
[]) # sequence base (scaffId_seqId) -> region name
for seqName in seqNameToSeq:
seqBase = re.sub(r'^([0-9]+_[0-9]+)[^0-9].*', r'\1', seqName)
if seqBase not in seqBaseToSeqName:
seqBaseToSeqName[seqBase] = []
seqBaseToSeqName[seqBase].append(seqName)
for seqBase in seqBaseToSeqName:
seqId = int(re.sub(r'^[0-9]+_([0-9]+)', r'\1', seqBase))
seqBaseTaxPathDict = self._sequences.getSequence(
seqId).getTaxonomyPath()
list = seqBaseToSeqName[seqBase]
candidateSeq = [
] # sequence region is predicted at least at rank superkingdom
for seqName in list:
if seqName not in seqNameToPred:
taxPathDict = None
else:
taxPathDict = seqNameToPred[seqName]
if taxPathDict != None:
candidateSeq.append(seqName)
if len(candidateSeq) == 0:
continue
candidateSeq2 = [
] # sequence regions predicted at least at the same rank as the whole sequence
for seqName in candidateSeq:
taxPathDict = seqNameToPred[seqName]
if ((seqBaseTaxPathDict == None)
or (len(taxPathDict) >= len(seqBaseTaxPathDict))
): #predict at least at the same level
candidateSeq2.append(seqName)
if len(candidateSeq2) > 0: #take the longest sequence
sMax = candidateSeq2[0]
for s in candidateSeq2[1:]:
if len(seqNameToSeq[s]) > len(seqNameToSeq[sMax]):
sMax = s
else: #all sequence regions are predicted higher than the sequence
sMax = candidateSeq[
0] #sequence region with the most specific prediction
for s in candidateSeq[1:]:
taxPathDictMax = seqNameToPred[sMax]
taxPathDictS = seqNameToPred[s]
if taxPathDictS == None:
continue
if taxPathDictMax == None:
sMax = s
continue
if len(taxPathDictMax) < len(taxPathDictS):
sMax = s
candidateSeq3 = [
] #get all sequence regions with the most specific prediction
taxPathDictMax = seqNameToPred[sMax]
for s in candidateSeq:
taxPathDictS = seqNameToPred[s]
if taxPathDictMax == None:
candidateSeq3.append(s)
elif len(taxPathDictS) == len(taxPathDictMax):
candidateSeq3.append(s)
sMax = candidateSeq3[0]
for s in candidateSeq3[1:]: #take the longest sequence
if len(seqNameToSeq[sMax]) < len(seqNameToSeq[s]):
sMax = s
out.writeText(
str('>' + str(sMax) + '\n' + str(seqNameToSeq[sMax]) +
'\n'))
out.close()
mgToAlignPath = dict([])
for mg in self._mgList:
mgToAlignPath[mg] = os.path.normpath(
os.path.join(self._clustDir, str(mg + '.align.fna')))
#build alignment
if align:
for mg in self._mgList:
alignCmd = str(
self._config.get('aligner') + ' -in ' +
mgToFilteredFastaPath[mg] + ' -out ' + mgToAlignPath[mg] +
' -quiet')
assert os.name == 'posix'
predictProc = subprocess.Popen(
alignCmd, cwd=self._mgWorkingDir, shell=True, bufsize=-1
) #stdout=subprocess.STDOUT, stderr=subprocess.STDOUT)
predictProc.wait()
print 'Muscle return code for', mg, ':', predictProc.returncode
if predictProc.returncode != 0:
sys.stderr.write(str(alignCmd + ' \n'))
#compute DM
if dm:
for mg in self._mgList:
mothur = os.path.join(
os.path.normpath(
self._configRRNA16S.get('mothurInstallDir')), 'mothur')
mothurCmd = str(
'time ' + mothur + ' "#dist.seqs(fasta=' +
mgToAlignPath[mg] +
', processors=2, countends=F, calc=nogaps, cutoff=0.3, output=lt)"'
)
assert os.name == 'posix'
mothurProc = subprocess.Popen(mothurCmd,
shell=True,
bufsize=-1,
cwd=self._mgWorkingDir)
mothurProc.wait()
print 'Mothur return code dist:', mg, mothurProc.returncode
#distFilePath = os.path.join(os.path.dirname(mgToAlignPath[mg]), str(mg + '.align.phylip.dist'))
#self._mgToDM[mg] = forEachLine(distFilePath, DM())
#self._mgToDM[mg].printDM()
#cluster
if cluster:
for mg in self._mgList:
distFilePath = os.path.join(os.path.dirname(mgToAlignPath[mg]),
str(mg + '.align.phylip.dist'))
mothur = os.path.join(
os.path.normpath(
self._configRRNA16S.get('mothurInstallDir')), 'mothur')
mothurCmd = str('time ' + mothur + ' "#cluster(phylip=' +
distFilePath +
', method=furthest, hard=t, precision=1000)"')
assert os.name == 'posix'
mothurProc = subprocess.Popen(mothurCmd,
shell=True,
bufsize=-1,
cwd=self._mgWorkingDir)
mothurProc.wait()
print 'Mothur return code cluster:', mg, mothurProc.returncode
#read DM and clusters
#sequence predictions
self._seqIdToTaxPathDict = dict([])
self._seqIdToWeight = dict([])
for seq in self._sequences.sequences:
id = int(seq.id)
self._seqIdToTaxPathDict[id] = seq.getTaxonomyPath()
self._seqIdToWeight[id] = seq.getTaxonomyPathWeight()
#similarity thresholds
thresholds = self._configMG.get('mgSimilarityThresholds')
self._mgToMaxThreshold = dict([])
tmpDict = getMapping(self._configMG.get('mgSimilarityThresholds'),
0,
1,
sep='\t',
comment='#')
for k in tmpDict:
self._mgToMaxThreshold[k] = float(tmpDict[k][0])
self._mgToDM = dict([])
self._mgToCluster = dict([])
for mg in self._mgList:
file = os.path.join(os.path.dirname(mgToAlignPath[mg]),
str(mg + '.align.phylip.dist'))
self._mgToDM[mg] = forEachLine(file, DM())
file = os.path.join(os.path.dirname(mgToAlignPath[mg]),
str(mg + '.align.phylip.fn.list'))
self._mgToCluster[mg] = forEachLine(
file,
MCluster(self._seqIdToTaxPathDict, self._mgToMaxThreshold[mg]))
def runMarkerGeneAnalysis(self, fastaFileDNA, outLog=None):
"""
Run hmmer HMM and mothur classify (bayesian), same param as for the 16S analysis.
"""
#read list of marker genes
mgFiles = forEachLine(self.markerGeneListFile, _MgFiles(self.markerGeneListFileDir))
#translate DNA to protein sequences
fastaFileProt = os.path.join(self.markerGeneWorkingDir, str(os.path.basename(fastaFileDNA) + '.PROT'))
dnaToProt(fastaFileDNA, fastaFileProt)
#read DNA fasta file
try:
handle = open(fastaFileDNA, "rU")
dnaSeqDict = SeqIO.to_dict(SeqIO.parse(handle, "fasta"))
handle.close()
except Exception:
sys.stderr.write(str('Cannot read file: ' + str(fastaFileDNA)))
raise
#to output all predictions in one file
outPredAllFileName = os.path.join(self.markerGeneWorkingDir,
str(os.path.basename(fastaFileDNA) + '_all.mP'))
outAllBuffer = OutFileBuffer(outPredAllFileName)
#run HMM search
mgList = mgFiles.getGeneNameList()
if outLog is not None:
stdoutLog = open(outLog,'w')
else:
stdoutLog = subprocess.STDOUT
#for each gene perform the analysis separately
for geneName in mgList:
domFileArray = [os.path.join(self.markerGeneWorkingDir, str(geneName + '_1.dom')),
os.path.join(self.markerGeneWorkingDir, str(geneName + '_2.dom'))]
outFileArray = [os.path.join(self.markerGeneWorkingDir, str(geneName + '_1.out')),
os.path.join(self.markerGeneWorkingDir, str(geneName + '_2.out'))]
hmmFileArray = [mgFiles.getFilePath(geneName, 'hmmPROTPrim'),
mgFiles.getFilePath(geneName, 'hmmPROTSec')]
cmdArray = list([])
#define cmd
for i in range(2):
if hmmFileArray[i] is not None:
cmdArray.append(str(os.path.join(self.hmmerBinDir, 'hmmsearch') + ' --domtblout ' + domFileArray[i] + ' -E 0.01'
+ ' -o ' + outFileArray[i] + ' ' + hmmFileArray[i] + ' ' + fastaFileProt))
else:
cmdArray.append(None)
#run cmd
for cmd in cmdArray:
if cmd is not None and os.name == 'posix':
hmmProc = subprocess.Popen(cmd, shell=True, bufsize=-1, cwd=self.hmmInstallDir, stdout=stdoutLog)
print 'run cmd:', cmd
hmmProc.wait()
print 'HMM return code:', hmmProc.returncode
if hmmProc.returncode != 0:
raise Exception("Command returned with non-zero %s status: %s" % (hmmProc.returncode, cmd))
else:
print 'Marker genes analysis, doesn`t run (no posix): ', cmd
#get regions that match to the HMM profile ()
entryDictList = []
for i in range(2):
if cmdArray[i] is not None:
entryDictList.append(forEachLine(domFileArray[i], _MgRegions()).getEntryDict())
else:
entryDictList.append(None)
entryDict1 = entryDictList[0]
entryDict2 = entryDictList[1]
#extract regions found in the protein sequences that were found by the HMM and generate corresponding DNA sequences
regionDnaFasta = os.path.join(self.markerGeneWorkingDir, str(geneName + '_dna.gff'))
outFileBuffer = OutFileBuffer(regionDnaFasta)
for seqName in entryDict1:
i = -1
for e in entryDict1[seqName]:
i += 1
from1 = entryDict1[seqName][i][0]
to1 = entryDict1[seqName][i][1]
assert ((from1 != None) and (to1 != None))
#compare the results found by the primary and secondary HMM profiles
if (entryDict2 != None) and (seqName in entryDict2):
if len(entryDict2[seqName]) >= (i+1):
from2 = entryDict2[seqName][i][0]
to2 = entryDict2[seqName][i][1]
#if from1 != from2 or to1 != to2:
# print str('Different positions in' + seqName + ' from1:' + str(from1) + ' from2:' + str(from2)
# + ' to1:' + str(to1) + ' to2:' + str(to2))
#extract regions from the DNA sequences (consider 3 ORF and reverse complements)
#name of the whole sequence
dnaSeqName = re.sub(r'([0-9]+_[0-9]+)_[pr]+[012]', r'\1', seqName)
#whole DNA sequence
dnaSeq = dnaSeqDict[dnaSeqName].seq
#reverse complement (contains "pr")
tagRev = 'p'
if re.match(r'[0-9]+_[0-9]+_pr[012]', seqName):
dnaSeq = dnaSeq.reverse_complement()
tagRev = 'pr'
#shift "0"
if re.match(r'[0-9]+_[0-9]+_[pr]+0', seqName):
tagFrom = ((from1 - 1)*3)
tagTo = (to1*3)
tagRev += '0'
dnaSeq = dnaSeq[tagFrom:tagTo]
#shift "1"
elif re.match(r'[0-9]+_[0-9]+_[pr]+1', seqName):
tagFrom = (((from1 - 1)*3) + 1)
tagTo = ((to1*3) + 1)
tagRev += '1'
dnaSeq = dnaSeq[tagFrom:tagTo]
#shift "2"
elif re.match(r'[0-9]+_[0-9]+_[pr]+2', seqName):
tagFrom = (((from1 - 1)*3) + 2)
tagTo = ((to1*3) + 2)
tagRev += '2'
dnaSeq = dnaSeq[tagFrom:tagTo]
#error
else:
sys.stderr.write('Wrong seq name: ' + seqName + ' \n')
dnaSeq = None
tag = str(str(tagFrom) + '_' + str(tagTo) + '_' + tagRev)
outFileBuffer.writeText(str('>' + dnaSeqName + '_' + tag + '\n' + dnaSeq + '\n'))
outFileBuffer.close()
#if no marker gene found
if outFileBuffer.isEmpty():
continue
#run mothur classify (bayesian? the same as for the 16S analysis)
templateFile = mgFiles.getFilePath(geneName, 'templateDNA')
taxonomyFile = mgFiles.getFilePath(geneName, 'taxonomyDNA')
assert ((templateFile is not None) and (taxonomyFile is not None))
cmd = str('time ' + self.mothur + ' "#classify.seqs(fasta=' + regionDnaFasta + ', template=' + templateFile
+ ', taxonomy=' + taxonomyFile + ', ' + self.mothurParam + ')"')
if os.name == 'posix':
mothurProc = subprocess.Popen(cmd, shell=True, bufsize=-1, cwd=self.markerGeneWorkingDir, stdout=stdoutLog)
print 'run cmd:', cmd
mothurProc.wait()
print 'mothur return code:', mothurProc.returncode
if mothurProc.returncode != 0:
raise Exception("Command returned with non-zero %s status: %s" % (mothurProc.returncode, cmd))
else:
print 'Cannot run mothur since your system is not "posix" but', str('"' + os.name + '"'), '\n', cmd
#transform the mothur output to a simple output (name, ncbid, weight)
#mothurPredFileName = os.path.join(self.markerGeneWorkingDir,
# str(geneName + '_dna.' + os.path.basename(taxonomyFile) + 'onomy')) # taxonomy
#!!!!!!!!!!!!!
mothurPredFileName = common.getMothurOutputFilePath(regionDnaFasta, taxonomyFile)
if not os.path.isfile(mothurPredFileName):
mothurPredFileName = common.getMothurOutputFilePath(regionDnaFasta, taxonomyFile, suffix='.bayesian.taxonomy')
if not os.path.isfile(mothurPredFileName):
print("Can't open file: %s" % mothurPredFileName)
outPredFileName = os.path.join(self.markerGeneWorkingDir,
str(os.path.basename(fastaFileDNA) + '_' + geneName + '.mP'))
outBuffer = OutFileBuffer(outPredFileName, bufferText=True)
forEachLine(mothurPredFileName, _MothurOutFileParser(outBuffer, geneName))
if not outAllBuffer.isEmpty():
outAllBuffer.writeText('\n')
outAllBuffer.writeText(outBuffer.getTextBuffer())
if outLog is not None:
stdoutLog.close()
outAllBuffer.close()
