def run_real_quiver(cmpH5, quiverConfig, interval, depthLimit, refSeq, refWindow, seedConsensus): intStart, intEnd = interval subWin = subWindow(refWindow, interval) windowRefSeq = refSeq[intStart:intEnd] rows = readsInWindow(cmpH5, subWin, depthLimit = depthLimit, minMapQV = quiverConfig.minMapQV, strategy = "longest", stratum = None, barcode = None) spanningRows = [row for row in rows if cmpH5[row].spansReferenceRange(intStart, intEnd) ] alns = cmpH5[spanningRows] clippedAlns_ = [ aln.clippedTo(*interval) for aln in alns ] clippedAlns__ = [ aln for aln in clippedAlns_ if aln.alignedLength <= 120] clippedAlns = filterAlns(subWin, clippedAlns__, quiverConfig) consensus = consensusForAlignmentsDisregardPOA(subWin, windowRefSeq, clippedAlns, quiverConfig, "A"*100) print(str(consensus.sequence))
def consensusAndVariantsForWindow(alnFile, refWindow, referenceContig, depthLimit, arrowConfig): """ High-level routine for calling the consensus for a window of the genome given a cmp.h5. Identifies the coverage contours of the window in order to identify subintervals where a good consensus can be called. Creates the desired "no evidence consensus" where there is inadequate coverage. """ winId, winStart, winEnd = refWindow logging.info("Arrow operating on %s" % reference.windowToString(refWindow)) if options.fancyChunking: # 1) identify the intervals with adequate coverage for arrow # consensus; restrict to intervals of length > 10 alnHits = U.readsInWindow(alnFile, refWindow, depthLimit=20000, minMapQV=arrowConfig.minMapQV, strategy="longest", stratum=options.readStratum, barcode=options.barcode) starts = np.fromiter((hit.tStart for hit in alnHits), np.int) ends = np.fromiter((hit.tEnd for hit in alnHits), np.int) intervals = kSpannedIntervals(refWindow, arrowConfig.minPoaCoverage, starts, ends, minLength=10) coverageGaps = holes(refWindow, intervals) allIntervals = sorted(intervals + coverageGaps) if len(allIntervals) > 1: logging.info("Usable coverage in %s: %r" % (reference.windowToString(refWindow), intervals)) else: allIntervals = [ (winStart, winEnd) ] # 2) pull out the reads we will use for each interval # 3) call consensusForAlignments on the interval subConsensi = [] variants = [] for interval in allIntervals: intStart, intEnd = interval intRefSeq = referenceContig[intStart:intEnd] subWin = subWindow(refWindow, interval) windowRefSeq = referenceContig[intStart:intEnd] alns = U.readsInWindow(alnFile, subWin, depthLimit=depthLimit, minMapQV=arrowConfig.minMapQV, strategy="longest", stratum=options.readStratum, barcode=options.barcode) clippedAlns_ = [ aln.clippedTo(*interval) for aln in alns ] clippedAlns = U.filterAlns(subWin, clippedAlns_, arrowConfig) if len([ a for a in clippedAlns if a.spansReferenceRange(*interval) ]) >= arrowConfig.minPoaCoverage: logging.debug("%s: Reads being used: %s" % (reference.windowToString(subWin), " ".join([str(hit.readName) for hit in alns]))) css = U.consensusForAlignments(subWin, intRefSeq, clippedAlns, arrowConfig) siteCoverage = U.coverageInWindow(subWin, alns) variants_ = U.variantsFromConsensus(subWin, windowRefSeq, css.sequence, css.confidence, siteCoverage, options.aligner, ai=None) filteredVars = filterVariants(options.minCoverage, options.minConfidence, variants_) # Annotate? if options.annotateGFF: annotateVariants(filteredVars, clippedAlns) variants += filteredVars # Dump? shouldDumpEvidence = \ ((options.dumpEvidence == "all") or (options.dumpEvidence == "variants") and (len(variants) > 0)) if shouldDumpEvidence: logging.info("Arrow does not yet support --dumpEvidence") # dumpEvidence(options.evidenceDirectory, # subWin, windowRefSeq, # clippedAlns, css) else: css = ArrowConsensus.noCallConsensus(arrowConfig.noEvidenceConsensus, subWin, intRefSeq) subConsensi.append(css) # 4) glue the subwindow consensus objects together to form the # full window consensus css = join(subConsensi) # 5) Return return css, variants
def consensusAndVariantsForWindow(alnFile, refWindow, referenceContig, depthLimit, arrowConfig): """ High-level routine for calling the consensus for a window of the genome given a BAM file. Identifies the coverage contours of the window in order to identify subintervals where a good consensus can be called. Creates the desired "no evidence consensus" where there is inadequate coverage. """ winId, winStart, winEnd = refWindow logging.info("Arrow operating on %s" % reference.windowToString(refWindow)) if options.fancyChunking: # 1) identify the intervals with adequate coverage for arrow # consensus; restrict to intervals of length > 10 alnHits = U.readsInWindow(alnFile, refWindow, depthLimit=20000, minMapQV=arrowConfig.minMapQV, strategy="long-and-strand-balanced", stratum=options.readStratum, barcode=options.barcode) starts = np.fromiter((hit.tStart for hit in alnHits), np.int) ends = np.fromiter((hit.tEnd for hit in alnHits), np.int) intervals = kSpannedIntervals(refWindow, arrowConfig.minPoaCoverage, starts, ends, minLength=10) coverageGaps = holes(refWindow, intervals) allIntervals = sorted(intervals + coverageGaps) if len(allIntervals) > 1: logging.info("Usable coverage in %s: %r" % (reference.windowToString(refWindow), intervals)) else: allIntervals = [(winStart, winEnd)] # 2) pull out the reads we will use for each interval # 3) call consensusForAlignments on the interval subConsensi = [] variants = [] for interval in allIntervals: intStart, intEnd = interval intRefSeq = referenceContig[intStart:intEnd] subWin = subWindow(refWindow, interval) windowRefSeq = referenceContig[intStart:intEnd] alns = U.readsInWindow(alnFile, subWin, depthLimit=depthLimit, minMapQV=arrowConfig.minMapQV, strategy="long-and-strand-balanced", stratum=options.readStratum, barcode=options.barcode) clippedAlns_ = [aln.clippedTo(*interval) for aln in alns] clippedAlns = U.filterAlns(subWin, clippedAlns_, arrowConfig) if len([a for a in clippedAlns if a.spansReferenceRange(*interval) ]) >= arrowConfig.minPoaCoverage: logging.debug("%s: Reads being used: %s" % (reference.windowToString(subWin), " ".join( [str(hit.readName) for hit in alns]))) alnsUsed = [] if options.reportEffectiveCoverage else None css = U.consensusForAlignments(subWin, intRefSeq, clippedAlns, arrowConfig, alnsUsed=alnsUsed) # Tabulate the coverage implied by these alignments, as # well as the post-filtering ("effective") coverage siteCoverage = U.coverageInWindow(subWin, alns) effectiveSiteCoverage = U.coverageInWindow( subWin, alnsUsed) if options.reportEffectiveCoverage else None variants_, newPureCss = U.variantsFromConsensus( subWin, windowRefSeq, css.sequence, css.confidence, siteCoverage, effectiveSiteCoverage, options.aligner, ai=None, diploid=arrowConfig.polishDiploid) # Annotate? if options.annotateGFF: annotateVariants(variants_, clippedAlns) variants += variants_ # The nascent consensus sequence might contain ambiguous bases, these # need to be removed as software in the wild cannot deal with such # characters and we only use IUPAC for *internal* bookkeeping. if arrowConfig.polishDiploid: css.sequence = newPureCss else: css = ArrowConsensus.noCallConsensus( arrowConfig.noEvidenceConsensus, subWin, intRefSeq) subConsensi.append(css) # 4) glue the subwindow consensus objects together to form the # full window consensus css = join(subConsensi) # 5) Return return css, variants
def consensusAndVariantsForWindow(alnFile, refWindow, referenceContig, depthLimit, arrowConfig): """ High-level routine for calling the consensus for a window of the genome given a cmp.h5. Identifies the coverage contours of the window in order to identify subintervals where a good consensus can be called. Creates the desired "no evidence consensus" where there is inadequate coverage. """ winId, winStart, winEnd = refWindow logging.info("Arrow operating on %s" % reference.windowToString(refWindow)) if options.fancyChunking: # 1) identify the intervals with adequate coverage for arrow # consensus; restrict to intervals of length > 10 alnHits = U.readsInWindow(alnFile, refWindow, depthLimit=20000, minMapQV=arrowConfig.minMapQV, strategy="long-and-strand-balanced", stratum=options.readStratum, barcode=options.barcode) starts = np.fromiter((hit.tStart for hit in alnHits), np.int) ends = np.fromiter((hit.tEnd for hit in alnHits), np.int) intervals = kSpannedIntervals(refWindow, arrowConfig.minPoaCoverage, starts, ends, minLength=10) coverageGaps = holes(refWindow, intervals) allIntervals = sorted(intervals + coverageGaps) if len(allIntervals) > 1: logging.info("Usable coverage in %s: %r" % (reference.windowToString(refWindow), intervals)) else: allIntervals = [(winStart, winEnd)] # 2) pull out the reads we will use for each interval # 3) call consensusForAlignments on the interval subConsensi = [] variants = [] for interval in allIntervals: intStart, intEnd = interval intRefSeq = referenceContig[intStart:intEnd] subWin = subWindow(refWindow, interval) windowRefSeq = referenceContig[intStart:intEnd] alns = U.readsInWindow(alnFile, subWin, depthLimit=depthLimit, minMapQV=arrowConfig.minMapQV, strategy="long-and-strand-balanced", stratum=options.readStratum, barcode=options.barcode) clippedAlns_ = [aln.clippedTo(*interval) for aln in alns] clippedAlns = U.filterAlns(subWin, clippedAlns_, arrowConfig) if len([a for a in clippedAlns if a.spansReferenceRange(*interval) ]) >= arrowConfig.minPoaCoverage: logging.debug("%s: Reads being used: %s" % (reference.windowToString(subWin), " ".join( [str(hit.readName) for hit in alns]))) alnsUsed = [] if options.reportEffectiveCoverage else None css = U.consensusForAlignments(subWin, intRefSeq, clippedAlns, arrowConfig, alnsUsed=alnsUsed) # Tabulate the coverage implied by these alignments, as # well as the post-filtering ("effective") coverage siteCoverage = U.coverageInWindow(subWin, alns) effectiveSiteCoverage = U.coverageInWindow( subWin, alnsUsed) if options.reportEffectiveCoverage else None variants_ = U.variantsFromConsensus(subWin, windowRefSeq, css.sequence, css.confidence, siteCoverage, effectiveSiteCoverage, options.aligner, ai=None) filteredVars = filterVariants(options.minCoverage, options.minConfidence, variants_) # Annotate? if options.annotateGFF: annotateVariants(filteredVars, clippedAlns) variants += filteredVars # Dump? maybeDumpEvidence = \ ((options.dumpEvidence == "all") or (options.dumpEvidence == "outliers") or (options.dumpEvidence == "variants") and (len(variants) > 0)) if maybeDumpEvidence: refId, refStart, refEnd = subWin refName = reference.idToName(refId) windowDirectory = os.path.join(options.evidenceDirectory, refName, "%d-%d" % (refStart, refEnd)) ev = ArrowEvidence.fromConsensus(css) if options.dumpEvidence != "outliers": ev.save(windowDirectory) elif (np.max(ev.delta) > 20): # Mathematically I don't think we should be seeing # deltas > 6 in magnitude, but let's just restrict # attention to truly bonkers outliers. ev.save(windowDirectory) else: css = ArrowConsensus.noCallConsensus( arrowConfig.noEvidenceConsensus, subWin, intRefSeq) subConsensi.append(css) # 4) glue the subwindow consensus objects together to form the # full window consensus css = join(subConsensi) # 5) Return return css, variants
def consensusAndVariantsForWindow(alnFile, refWindow, referenceContig, depthLimit, arrowConfig): """ High-level routine for calling the consensus for a window of the genome given a cmp.h5. Identifies the coverage contours of the window in order to identify subintervals where a good consensus can be called. Creates the desired "no evidence consensus" where there is inadequate coverage. """ winId, winStart, winEnd = refWindow logging.info("Arrow operating on %s" % reference.windowToString(refWindow)) if options.fancyChunking: # 1) identify the intervals with adequate coverage for arrow # consensus; restrict to intervals of length > 10 alnHits = U.readsInWindow(alnFile, refWindow, depthLimit=20000, minMapQV=arrowConfig.minMapQV, strategy="long-and-strand-balanced", stratum=options.readStratum, barcode=options.barcode) starts = np.fromiter((hit.tStart for hit in alnHits), np.int) ends = np.fromiter((hit.tEnd for hit in alnHits), np.int) intervals = kSpannedIntervals(refWindow, arrowConfig.minPoaCoverage, starts, ends, minLength=10) coverageGaps = holes(refWindow, intervals) allIntervals = sorted(intervals + coverageGaps) if len(allIntervals) > 1: logging.info("Usable coverage in %s: %r" % (reference.windowToString(refWindow), intervals)) else: allIntervals = [ (winStart, winEnd) ] # 2) pull out the reads we will use for each interval # 3) call consensusForAlignments on the interval subConsensi = [] variants = [] for interval in allIntervals: intStart, intEnd = interval intRefSeq = referenceContig[intStart:intEnd] subWin = subWindow(refWindow, interval) windowRefSeq = referenceContig[intStart:intEnd] alns = U.readsInWindow(alnFile, subWin, depthLimit=depthLimit, minMapQV=arrowConfig.minMapQV, strategy="long-and-strand-balanced", stratum=options.readStratum, barcode=options.barcode) clippedAlns_ = [ aln.clippedTo(*interval) for aln in alns ] clippedAlns = U.filterAlns(subWin, clippedAlns_, arrowConfig) if len([ a for a in clippedAlns if a.spansReferenceRange(*interval) ]) >= arrowConfig.minPoaCoverage: logging.debug("%s: Reads being used: %s" % (reference.windowToString(subWin), " ".join([str(hit.readName) for hit in alns]))) alnsUsed = [] if options.reportEffectiveCoverage else None css = U.consensusForAlignments(subWin, intRefSeq, clippedAlns, arrowConfig, alnsUsed=alnsUsed) # Tabulate the coverage implied by these alignments, as # well as the post-filtering ("effective") coverage siteCoverage = U.coverageInWindow(subWin, alns) effectiveSiteCoverage = U.coverageInWindow(subWin, alnsUsed) if options.reportEffectiveCoverage else None variants_ = U.variantsFromConsensus(subWin, windowRefSeq, css.sequence, css.confidence, siteCoverage, effectiveSiteCoverage, options.aligner, ai=None) filteredVars = filterVariants(options.minCoverage, options.minConfidence, variants_) # Annotate? if options.annotateGFF: annotateVariants(filteredVars, clippedAlns) variants += filteredVars # Dump? maybeDumpEvidence = \ ((options.dumpEvidence == "all") or (options.dumpEvidence == "outliers") or (options.dumpEvidence == "variants") and (len(variants) > 0)) if maybeDumpEvidence: refId, refStart, refEnd = subWin refName = reference.idToName(refId) windowDirectory = os.path.join( options.evidenceDirectory, refName, "%d-%d" % (refStart, refEnd)) ev = ArrowEvidence.fromConsensus(css) if options.dumpEvidence != "outliers": ev.save(windowDirectory) elif (np.max(ev.delta) > 20): # Mathematically I don't think we should be seeing # deltas > 6 in magnitude, but let's just restrict # attention to truly bonkers outliers. ev.save(windowDirectory) else: css = ArrowConsensus.noCallConsensus(arrowConfig.noEvidenceConsensus, subWin, intRefSeq) subConsensi.append(css) # 4) glue the subwindow consensus objects together to form the # full window consensus css = join(subConsensi) # 5) Return return css, variants
def consensusAndVariantsForWindow(alnFile, refWindow, referenceContig, depthLimit, arrowConfig): """ High-level routine for calling the consensus for a window of the genome given a BAM file. Identifies the coverage contours of the window in order to identify subintervals where a good consensus can be called. Creates the desired "no evidence consensus" where there is inadequate coverage. """ winId, winStart, winEnd = refWindow logging.info("Arrow operating on %s" % reference.windowToString(refWindow)) if options.fancyChunking: # 1) identify the intervals with adequate coverage for arrow # consensus; restrict to intervals of length > 10 alnHits = U.readsInWindow(alnFile, refWindow, depthLimit=20000, minMapQV=arrowConfig.minMapQV, strategy="long-and-strand-balanced", stratum=options.readStratum, barcode=options.barcode) starts = np.fromiter((hit.tStart for hit in alnHits), np.int) ends = np.fromiter((hit.tEnd for hit in alnHits), np.int) intervals = kSpannedIntervals(refWindow, arrowConfig.minPoaCoverage, starts, ends, minLength=10) coverageGaps = holes(refWindow, intervals) allIntervals = sorted(intervals + coverageGaps) if len(allIntervals) > 1: logging.info("Usable coverage in %s: %r" % (reference.windowToString(refWindow), intervals)) else: allIntervals = [ (winStart, winEnd) ] # 2) pull out the reads we will use for each interval # 3) call consensusForAlignments on the interval subConsensi = [] variants = [] for interval in allIntervals: intStart, intEnd = interval intRefSeq = referenceContig[intStart:intEnd] subWin = subWindow(refWindow, interval) windowRefSeq = referenceContig[intStart:intEnd] alns = U.readsInWindow(alnFile, subWin, depthLimit=depthLimit, minMapQV=arrowConfig.minMapQV, strategy="long-and-strand-balanced", stratum=options.readStratum, barcode=options.barcode) clippedAlns_ = [ aln.clippedTo(*interval) for aln in alns ] clippedAlns = U.filterAlns(subWin, clippedAlns_, arrowConfig) if len([ a for a in clippedAlns if a.spansReferenceRange(*interval) ]) >= arrowConfig.minPoaCoverage: logging.debug("%s: Reads being used: %s" % (reference.windowToString(subWin), " ".join([str(hit.readName) for hit in alns]))) alnsUsed = [] if options.reportEffectiveCoverage else None css = U.consensusForAlignments(subWin, intRefSeq, clippedAlns, arrowConfig, alnsUsed=alnsUsed) # Tabulate the coverage implied by these alignments, as # well as the post-filtering ("effective") coverage siteCoverage = U.coverageInWindow(subWin, alns) effectiveSiteCoverage = U.coverageInWindow(subWin, alnsUsed) if options.reportEffectiveCoverage else None variants_, newPureCss = U.variantsFromConsensus(subWin, windowRefSeq, css.sequence, css.confidence, siteCoverage, effectiveSiteCoverage, options.aligner, ai=None, diploid=arrowConfig.polishDiploid) # Annotate? if options.annotateGFF: annotateVariants(variants_, clippedAlns) variants += variants_ # The nascent consensus sequence might contain ambiguous bases, these # need to be removed as software in the wild cannot deal with such # characters and we only use IUPAC for *internal* bookkeeping. if arrowConfig.polishDiploid: css.sequence = newPureCss else: css = ArrowConsensus.noCallConsensus(arrowConfig.noEvidenceConsensus, subWin, intRefSeq) subConsensi.append(css) # 4) glue the subwindow consensus objects together to form the # full window consensus css = join(subConsensi) # 5) Return return css, variants
def getReads(cmpH5, reference, interval, paddedTemplateWidth, depthLimit, real_quiver=False): minMapQV = 10 minPoaCoverage = 3 maxPoaCoverage = 11 mutationSeparation = 10 mutationNeighborhood = 20 maxIterations = 20 refineDinucleotideRepeats = True noEvidenceConsensus = "nocall" computeConfidence = True readStumpinessThreshold = 0.1 refId = [x for x in reference.enumerateIds()][0] refSeq = reference.byId[refId].sequence refWindow = (refId, 0, reference.byId[refId].length) intStart, intEnd = interval subWin = subWindow(refWindow, interval) windowRefSeq = refSeq[intStart:intEnd] rows = readsInWindow(cmpH5, subWin, depthLimit=depthLimit, minMapQV=minMapQV, strategy="longest", stratum=None, barcode=None) #print([cmpH5[row].alignedLength for row in rows if cmpH5[row].spansReferenceRange(intStart, intEnd)]) spanningRows = [ row for row in rows if cmpH5[row].spansReferenceRange(intStart, intEnd) ] alns = cmpH5[spanningRows] clippedAlns_ = [aln.clippedTo(*interval) for aln in alns] clippedAlns__ = [ aln for aln in clippedAlns_ if aln.alignedLength <= paddedTemplateWidth - 7 ] clippedAlns = filterAlns(subWin, clippedAlns__, readStumpinessThreshold) # Compute the POA consensus, which is our initial guess, and # should typically be > 99.5% accurate fwdSequences = [ a.read(orientation="genomic", aligned=False) for a in clippedAlns ] p = cc.PoaConsensus.FindConsensus(fwdSequences[:maxPoaCoverage]) template = p.Sequence() tmplSeq = np.zeros((paddedTemplateWidth), dtype=np.uint8) tmplOrds = map(ord, template) tmplSeq[:len(tmplOrds)] = tmplOrds #read pos y, read x readSeqs = np.zeros((paddedTemplateWidth, len(clippedAlns)), dtype=np.uint8) for i in xrange(len(clippedAlns)): alnOrds = map(ord, fwdSequences[i]) readSeqs[:len(alnOrds), i] = alnOrds #uint8 #metric z, read pos y, read x qvInfo = np.zeros((8, paddedTemplateWidth, len(clippedAlns)), dtype=np.uint8) for i in xrange(len(clippedAlns)): qvInfo[0, :clippedAlns[i].readLength, i] = clippedAlns[i].InsertionQV(orientation="genomic", aligned=False) qvInfo[1, :clippedAlns[i].readLength, i] = clippedAlns[i].MergeQV(orientation="genomic", aligned=False) qvInfo[2, :clippedAlns[i].readLength, i] = clippedAlns[i].DeletionQV(orientation="genomic", aligned=False) qvInfo[3, :clippedAlns[i].readLength, i] = clippedAlns[i].DeletionTag(orientation="genomic", aligned=False) qvInfo[4, :clippedAlns[i].readLength, i] = clippedAlns[i].SubstitutionQV(orientation="genomic", aligned=False) if real_quiver: return template, len(tmplOrds), fwdSequences, qvInfo else: return tmplSeq, len(tmplOrds), readSeqs, qvInfo
def getReads(cmpH5, reference, interval, paddedTemplateWidth, depthLimit, real_quiver=False): minMapQV = 10 minPoaCoverage = 3 maxPoaCoverage = 11 mutationSeparation = 10 mutationNeighborhood = 20 maxIterations = 20 refineDinucleotideRepeats = True noEvidenceConsensus = "nocall" computeConfidence = True readStumpinessThreshold = 0.1 refId = [x for x in reference.enumerateIds()][0] refSeq = reference.byId[refId].sequence refWindow = (refId, 0, reference.byId[refId].length) intStart, intEnd = interval subWin = subWindow(refWindow, interval) windowRefSeq = refSeq[intStart:intEnd] rows = readsInWindow(cmpH5, subWin, depthLimit = depthLimit, minMapQV = minMapQV, strategy = "longest", stratum = None, barcode = None) #print([cmpH5[row].alignedLength for row in rows if cmpH5[row].spansReferenceRange(intStart, intEnd)]) spanningRows = [row for row in rows if cmpH5[row].spansReferenceRange(intStart, intEnd) ] alns = cmpH5[spanningRows] clippedAlns_ = [ aln.clippedTo(*interval) for aln in alns ] clippedAlns__ = [ aln for aln in clippedAlns_ if aln.alignedLength <= paddedTemplateWidth - 7] clippedAlns = filterAlns(subWin, clippedAlns__, readStumpinessThreshold) # Compute the POA consensus, which is our initial guess, and # should typically be > 99.5% accurate fwdSequences = [ a.read(orientation="genomic", aligned=False) for a in clippedAlns] p = cc.PoaConsensus.FindConsensus(fwdSequences[:maxPoaCoverage]) template = p.Sequence() tmplSeq = np.zeros((paddedTemplateWidth), dtype=np.uint8) tmplOrds = map(ord, template) tmplSeq[:len(tmplOrds)] = tmplOrds #read pos y, read x readSeqs = np.zeros((paddedTemplateWidth, len(clippedAlns)), dtype=np.uint8) for i in xrange(len(clippedAlns)): alnOrds = map(ord, fwdSequences[i]) readSeqs[:len(alnOrds), i] = alnOrds #uint8 #metric z, read pos y, read x qvInfo = np.zeros((8, paddedTemplateWidth, len(clippedAlns)), dtype=np.uint8) for i in xrange(len(clippedAlns)): qvInfo[0, :clippedAlns[i].readLength, i] = clippedAlns[i].InsertionQV(orientation="genomic", aligned=False) qvInfo[1, :clippedAlns[i].readLength, i] = clippedAlns[i].MergeQV(orientation="genomic", aligned=False) qvInfo[2, :clippedAlns[i].readLength, i] = clippedAlns[i].DeletionQV(orientation="genomic", aligned=False) qvInfo[3, :clippedAlns[i].readLength, i] = clippedAlns[i].DeletionTag(orientation="genomic", aligned=False) qvInfo[4, :clippedAlns[i].readLength, i] = clippedAlns[i].SubstitutionQV(orientation="genomic", aligned=False) if real_quiver: return template, len(tmplOrds), fwdSequences, qvInfo else: return tmplSeq, len(tmplOrds), readSeqs, qvInfo