def run(self):
if self.debug:
import pdb
pdb.set_trace()
outputDir = os.path.split(self.outputFname)[0]
if outputDir and not os.path.isdir(outputDir):
os.makedirs(outputDir)
reader = VCFFile(inputFname=self.inputFname)
alignmentFile = pysam.Samfile(self.alignmentFilename, "rb")
writer = VCFFile(outputFname=self.outputFname, openMode='w')
writer.metaInfoLs = reader.metaInfoLs
writer.header = reader.header
writer.writeMetaAndHeader()
statWriter = MatrixFile(self.missingStatFname, openMode='w', delimiter='\t')
header = ["sampleID", "locusID", 'chromosome', 'start', 'stop', 'occurrence', 'missingReason', \
'fractionOfGoodRead', 'medianMapQ', 'totalNoOfReads']
statWriter.writeHeader(header)
counter = 0
real_counter = 0
minDepth = self.alignmentMedianDepth/self.alignmentDepthFold
maxDepth = self.alignmentMedianDepth*self.alignmentDepthFold
for vcfRecord in reader:
locusID = "%s_%s"%(vcfRecord.chromosome, vcfRecord.position)
alignedReadLs = alignmentFile.fetch(vcfRecord.chromosome, vcfRecord.position-1, vcfRecord.position+1) #start and end in fetch() are 0-based.
locusLowMapQData = self.returnLocusLowMapQualityIndicator(alignedReadLs=alignedReadLs,\
minMapQGoodRead=self.minMapQGoodRead, minFractionOfGoodRead=self.minFractionOfGoodRead)
locusLowMapQIndicator = locusLowMapQData.locusLowMapQIndicator
depth = locusLowMapQData.totalNoOfReads
if depth>=minDepth and depth <=maxDepth:
locusOutOfDepthIndicator = 0 #good
else:
locusOutOfDepthIndicator = 1
locusLowQualityIndicator = locusOutOfDepthIndicator + locusLowMapQIndicator
data_row = [self.sampleID, locusID, vcfRecord.chromosome, vcfRecord.position, vcfRecord.position,\
1, locusLowQualityIndicator, locusLowMapQData.fractionOfGoodRead, \
locusLowMapQData.medianMapQ, locusLowMapQData.totalNoOfReads]
statWriter.writerow(data_row)
if locusLowQualityIndicator>0:
real_counter += 1
#modify the VCF record
#get sample ID column, then set its genotype missing
vcfRecord.setGenotypeCallForOneSample(sampleID=self.sampleID, genotype="./.", convertGLToPL=True)
#2014.1.4 output VCF record
writer.writeVCFRecord(vcfRecord)
counter += 1
reader.close()
statWriter.close()
writer.close()
sys.stderr.write("%s (out of %s, %s) genotypes marked missing.\n"%(real_counter, counter, \
real_counter/float(counter)))
def run(self):
if self.debug:
import pdb
pdb.set_trace()
outputDir = os.path.split(self.outputFname)[0]
if outputDir and not os.path.isdir(outputDir):
os.makedirs(outputDir)
locusID2Stat = self.getLocusID2StatFunctionDict[self.runType](
self.statFname)
reader = VCFFile(inputFname=self.inputFname)
writer = VCFFile(outputFname=self.outputFname, openMode='w')
writer.metaInfoLs = reader.metaInfoLs
writer.header = reader.header
writer.writeMetaAndHeader()
counter = 0
real_counter = 0
for vcfRecord in reader: #assuming input VCF is sorted
counter += 1
key = (vcfRecord.chromosome, vcfRecord.position,
vcfRecord.position)
stat = locusID2Stat.get(key)
if stat is None:
continue
toKeepLocus = True
if self.minValue is not None and stat < self.minValue:
toKeepLocus = False
if self.maxValue is not None and stat > self.maxValue:
toKeepLocus = False
if toKeepLocus:
real_counter += 1
writer.writeVCFRecord(vcfRecord)
reader.close()
writer.close()
if counter > 0:
fraction = real_counter / float(counter)
else:
fraction = -1
sys.stderr.write("%s out of %s records, or %s, retained.\n"%(real_counter, counter, \
fraction))
def run(self):
if self.debug:
import pdb
pdb.set_trace()
outputDir = os.path.split(self.outputFname)[0]
if outputDir and not os.path.isdir(outputDir):
os.makedirs(outputDir)
locusNewID2mapPvalue = self.getLocusNewID2mapPvalue(
self.liftOverLocusMapPvalueFname)
reader = VCFFile(inputFname=self.inputFname)
writer = VCFFile(outputFname=self.outputFname, openMode='w')
writer.metaInfoLs = reader.metaInfoLs
writer.header = reader.header
writer.writeMetaAndHeader()
counter = 0
real_counter = 0
for vcfRecord in reader: #assuming input VCF is sorted
counter += 1
key = (vcfRecord.chromosome, vcfRecord.position,
vcfRecord.position)
mapPvalue = locusNewID2mapPvalue.get(key)
if mapPvalue is None:
continue
if mapPvalue > self.minLiftOverMapPvalue:
real_counter += 1
writer.writeVCFRecord(vcfRecord)
reader.close()
writer.close()
if counter > 0:
fraction = real_counter / float(counter)
else:
fraction = -1
sys.stderr.write("%s out of %s records, or %s, retained.\n"%(real_counter, counter, \
fraction))
def filterVCFSNPCluster(self,
inputFname=None,
outputFname=None,
minNeighborDistance=10,
**keywords):
"""
#2012.8.20 locus_id2row_index from VCFFile is using (chr, pos) as key, not chr_pos
need a conversion in between
2012.5.8
"""
sys.stderr.write(
"Filtering VCF %s to get rid of SNPs that are %s distance apart ..."
% (inputFname, minNeighborDistance))
vcfFile = VCFFile(inputFname=inputFname)
outVCFFile = VCFFile(outputFname=outputFname)
outVCFFile.metaInfoLs = vcfFile.metaInfoLs
outVCFFile.header = vcfFile.header
outVCFFile.writeMetaAndHeader()
previousVCFRecord = None
previousVCFRecordIsBad = False #indicator whether previous record is bad or not. based on distance to the previous-previous record
counter = 0
for vcfRecord in vcfFile:
if previousVCFRecord is not None:
if previousVCFRecord.chr == vcfRecord.chr:
distanceToPreviousRecord = abs(vcfRecord.pos -
previousVCFRecord.pos)
if distanceToPreviousRecord < minNeighborDistance:
previousVCFRecordIsBad = True
else:
if not previousVCFRecordIsBad: #distance to current & previous-previous record is >=minNeighborDistance
outVCFFile.writeVCFRecord(previousVCFRecord)
previousVCFRecordIsBad = False
else:
#handle the last record from the previous chromosome (assuming loci are in chromosomal order)
if not previousVCFRecordIsBad: #distance to previous-previous record is >=minNeighborDistance
outVCFFile.writeVCFRecord(previousVCFRecord)
previousVCFRecordIsBad = False #reset
previousVCFRecord = vcfRecord
counter += 1
vcfFile.close()
#handle the last record
if previousVCFRecord is not None and not previousVCFRecordIsBad: #distance to previous-previous record is >=minNeighborDistance
outVCFFile.writeVCFRecord(previousVCFRecord)
outVCFFile.close()
noOfLociAfterFilter = len(outVCFFile.locus_id_ls)
delta = counter - noOfLociAfterFilter
if counter > 0:
fraction = delta / float(counter)
else:
fraction = -0.0
sys.stderr.write(" %s (%s -> %s) or %.2f%% loci filtered out.\n" %
(delta, counter, noOfLociAfterFilter, fraction * 100))
def run(self):
if self.debug:
import pdb
pdb.set_trace()
outputDir = os.path.split(self.outputFname)[0]
if outputDir and not os.path.isdir(outputDir):
os.makedirs(outputDir)
snp_pos2count = self.readInSNPID2GenotypeVectorLs(
self.inputFname, returnType=2).snp_pos2returnData
reader = VCFFile(inputFname=self.inputFname)
writer = VCFFile(outputFname=self.outputFname, openMode='w')
writer.metaInfoLs = reader.metaInfoLs
writer.header = reader.header
writer.writeMetaAndHeader()
counter = 0
real_counter = 0
for vcfRecord in reader: #assuming input VCF is sorted
counter += 1
key = (vcfRecord.chromosome, vcfRecord.position)
frequency = snp_pos2count.get(key)
if frequency == 1:
writer.writeVCFRecord(vcfRecord)
real_counter += 1
reader.close()
writer.close()
if counter > 0:
fraction = real_counter / float(counter)
else:
fraction = 0
sys.stderr.write("%s (out of %s, %s) snps are unique.\n" %
(real_counter, counter, fraction))
class LiftOverVCFBasedOnCoordinateMap(parentClass):
__doc__ = __doc__
option_default_dict = parentClass.option_default_dict.copy()
option_default_dict.update({
('coordinateMapFname', 1, ): ['', '', 1, 'file that has a map between old and new coordinates. output of FindSNPPositionOnNewRefFromFlankingBlastOutput.py', ],\
})
def __init__(self, inputFnameLs=None, **keywords):
"""
"""
parentClass.__init__(self, inputFnameLs=inputFnameLs, **keywords)
def readInCoordinateMap(self, coordinateMapFname=None):
"""
2013.07.11
querySNPID queryStrand queryChromosome queryStart queryStop queryRefBase queryAltBase queryAlignmentSpan
queryAlignmentStart queryAlignmentStop newChr newRefStart newRefStop newRefBase targetAlignmentSpan
targetAlignmentStart targetAlignmentStop
"""
sys.stderr.write("Reading in the coordinate map from %s ..." %
(coordinateMapFname))
oldCoordinate2newCoordinateDataLs = {}
reader = MatrixFile(inputFname=coordinateMapFname)
reader.constructColName2IndexFromHeader()
oldChromosomeIndex = reader.getColIndexGivenColHeader(
"queryChromosome")
oldStartIndex = reader.getColIndexGivenColHeader("queryStart")
strandIndex = reader.getColIndexGivenColHeader("queryStrand")
oldRefBaseIndex = reader.getColIndexGivenColHeader("queryRefBase")
oldAltBaseIndex = reader.getColIndexGivenColHeader("queryAltBase")
newChromosomeIndex = reader.getColIndexGivenColHeader("newChr")
newStartIndex = reader.getColIndexGivenColHeader("newRefStart")
newStopIndex = reader.getColIndexGivenColHeader("newRefStop")
newRefBaseIndex = reader.getColIndexGivenColHeader("newRefBase")
counter = 0
for row in reader:
oldChromosome = row[oldChromosomeIndex]
oldStart = int(row[oldStartIndex])
strand = row[strandIndex]
oldRefBase = row[oldRefBaseIndex]
oldAltBase = row[oldAltBaseIndex]
newChromosome = row[newChromosomeIndex]
newStart = int(row[newStartIndex])
newStop = int(row[newStopIndex])
newRefBase = row[newRefBaseIndex]
key = (oldChromosome, oldStart)
if key not in oldCoordinate2newCoordinateDataLs:
oldCoordinate2newCoordinateDataLs[key] = []
oldCoordinate2newCoordinateDataLs[key].append(PassingData(strand=strand, oldRefBase=oldRefBase, \
oldAltBase=oldAltBase, newChromosome=newChromosome, newStart=newStart,\
newStop=newStop, newRefBase=newRefBase))
counter += 1
del reader
sys.stderr.write("%s old coordinates with %s new coordinates.\n" %
(len(oldCoordinate2newCoordinateDataLs), counter))
return oldCoordinate2newCoordinateDataLs
def run(self):
if self.debug:
import pdb
pdb.set_trace()
outputDir = os.path.split(self.outputFname)[0]
if outputDir and not os.path.isdir(outputDir):
os.makedirs(outputDir)
oldCoordinate2newCoordinateDataLs = self.readInCoordinateMap(
self.coordinateMapFname)
self.reader = VCFFile(inputFname=self.inputFname)
self.writer = VCFFile(outputFname=self.outputFname, openMode='w')
self.writer.metaInfoLs = self.reader.metaInfoLs
self.writer.header = self.reader.header
self.writer.writeMetaAndHeader()
counter = 0
real_counter = 0
noOfRecordsWithMultiNewCoords = 0
for vcfRecord in self.reader: #assuming input VCF is sorted
counter += 1
key = (vcfRecord.chromosome, vcfRecord.position)
newCoordinateDataLs = oldCoordinate2newCoordinateDataLs.get(key)
if newCoordinateDataLs is None:
continue
if len(newCoordinateDataLs) > 1:
noOfRecordsWithMultiNewCoords += 1
continue
newCoordinateData = newCoordinateDataLs[0]
vcfRecord.setChromosome(newCoordinateData.newChromosome)
vcfRecord.setPosition(newCoordinateData.newStart)
if newCoordinateData.strand == '-':
newRefBase = Seq(
newCoordinateData.oldRefBase).reverse_complement()
newAltBase = Seq(
newCoordinateData.oldAltBase).reverse_complement()
else:
newRefBase = newCoordinateData.oldRefBase
newAltBase = newCoordinateData.oldAltBase
vcfRecord.setRefAllele(newRefBase)
vcfRecord.setAltAllele(newAltBase)
real_counter += 1
self.writer.writeVCFRecord(vcfRecord)
self.reader.close()
self.writer.close()
sys.stderr.write("%s (out of %s, %s) records found new coordinates. %s records with >1 new coordinates, discarded.\n"%(real_counter, counter, \
real_counter/float(counter), noOfRecordsWithMultiNewCoords))
class CombinePhasedBeagleOutputsIntoVCF(AbstractMatrixFileWalker):
__doc__ = __doc__
option_default_dict = AbstractMatrixFileWalker.option_default_dict
option_default_dict.update({
('replicateIndividualTag', 0, ): ['copy', '', 1, 'the tag that separates the true ID and its replicate count'],\
('originalVCFFname', 1, ): ['', '', 1, 'original VCF file on which both Beagle phased output and output VCF will be based. \n\
The output VCF will be same as originalVCFFname, except GT field, to be replaced by phased genotypes from Beagle-phased files' ],\
})
def __init__(self, inputFnameLs=None, **keywords):
"""
"""
AbstractMatrixFileWalker.__init__(self,
inputFnameLs=inputFnameLs,
**keywords)
#a map from one sample to specific beagle file
self.sampleID2BeagleFile = None
def setup(self, **keywords):
"""
2012.10.15
run before anything is run
"""
#2013.05.30 comment out AbstractMatrixFileWalker.setup() to open the output file differently
#AbstractMatrixFileWalker.setup(self, **keywords)
self.writer = VCFFile(outputFname=self.outputFname, openMode='w')
self.reader = VCFFile(inputFname=self.originalVCFFname, openMode='r')
self.writer.metaInfoLs = self.reader.metaInfoLs
self.writer.header = self.reader.header
self.writer.writeMetaAndHeader()
# read all the Beagle files
sampleID2BeagleFile = {}
for inputFname in self.inputFnameLs:
beagleFile = BeagleGenotypeFile(inputFname=inputFname)
beagleFile.readInAllHaplotypes()
for individualID in beagleFile.sampleIDList:
sampleID2BeagleFile[individualID] = beagleFile
# get all haplotypes , etc.
# get all sample IDs
self.sampleID2BeagleFile = sampleID2BeagleFile
def reduce(self, **keywords):
"""
2012.10.15
run after all files have been walked through
"""
#sample the data
real_counter = 0
counter = 0
no_of_loci = 0
for vcfRecord in self.reader:
for sampleID, sample_index in vcfRecord.sample_id2index.iteritems(
):
beagleFile = self.sampleID2BeagleFile.get(sampleID)
"""
if beagleFile is None:
sys.stderr.write("Warning: sampleID %s is not affiliated with any Beagle file.\n"%(sampleID)
raise
"""
beagleGenotype = beagleFile.getGenotypeOfOneSampleOneLocus(
sampleID=sampleID, locusID=None)
vcfRecord.setGenotypeCallForOneSample(
sampleID=sampleID,
genotype='%s|%s' % (beagleGenotype[0], beagleGenotype[1]))
counter += 1
self.writer.writeVCFRecord(vcfRecord)
no_of_loci += 1
sys.stderr.write("%s genotypes, %s loci.\n" % (counter, no_of_loci))
#close the self.invariantPData.writer and self.writer
AbstractMatrixFileWalker.reduce(self, **keywords)
