def _getReferenceIds(self, alignedReferences, referenceIds):
"""
Figure out which reference ids we can process.
@param alignedReferences: A C{set} of C{str} reference ids found in
the passed reference files.
@param referenceIds: A C{list} of C{str} reference ids for which
processing has specifically been requested, or C{None}.
@return: A C{set} of C{str} reference ids to process.
"""
if referenceIds:
# Specific reference ids were given. Check that each appears in
# some alignment file and that we have a genome for each. Any
# error here causes a message to stderr and exit.
missing = set(referenceIds) - alignedReferences
if missing:
print(
'Alignments against the following reference id%s are not '
'present in any alignment file:\n%s' %
(s(len(missing)), '\n'.join(' %s' % id_
for id_ in sorted(missing))),
file=sys.stderr)
sys.exit(1)
missing = set(referenceIds) - set(self.referenceGenomes)
if missing:
print('Reference id%s %s not present in any reference genome '
'file.' % (s(len(missing)), commas(missing)),
file=sys.stderr)
sys.exit(1)
else:
# We weren't told which reference ids to specifically examine
# the alignments of, so examine all available references
# mentioned in any alignment file and that we also have a
# genome for. Mention any references from alignment files that
# we can't process due to lack of genome.
missing = alignedReferences - set(self.referenceGenomes)
if missing:
self.report(
'No analysis will be performed on reference%s %s '
'(found in SAM/BAM alignment file(s) headers) because no '
'corresponding reference genome was found.' %
(s(len(missing)), commas(missing)))
referenceIds = alignedReferences & set(self.referenceGenomes)
if referenceIds:
self.report('Examining %d reference%s: %s' %
(len(referenceIds), s(
len(referenceIds)), commas(referenceIds)))
else:
print(
'Nothing to do! No genome could be found for any aligned '
'reference. Found reference%s: %s' %
(s(len(alignedReferences)), commas(alignedReferences)),
file=sys.stderr)
sys.exit(1)
return referenceIds
def _readReferenceGenomes(self, referenceGenomeFiles):
"""
Read reference genomes from files and check that any duplicates have
identical sequences.
@param referenceGenomeFiles: A C{list} of C{str} names of FASTA files
containing reference genomes.
@raise ValueError: If a reference genome is found in more than one file
and the sequences are not identical.
@return: A C{dict} keyed by C{str} sequence id with C{dark.Read}
values holding reference genomes.
"""
result = {}
seen = {}
for filename in referenceGenomeFiles:
for read in FastaReads(filename):
id_ = read.id
if id_ in seen:
if result[id_].sequence != read.sequence:
raise ValueError(
'Reference genome id %r was found in two files '
'(%r and %r) but with different sequences.' %
(id_, seen[id_], filename))
else:
seen[id_] = filename
result[id_] = read
self.report('Read %d reference genome%s:\n%s' %
(len(result), s(len(result)), '\n'.join(
' %s' % id_ for id_ in sorted(result))),
requiredVerbosityLevel=2)
return result
def __str__(self):
result = [
'Cluster with %d read%s:' % (len(self.reads), s(len(self.reads)))
]
for read in self.reads:
result.append(' %s' % read)
result.append(nucleotidesToStr(self.nucleotides, prefix=' '))
return '\n'.join(result)
def _removePreExistingTopLevelOutputDirFiles(self):
"""
Remove all pre-existing files from the top-level output directory.
"""
paths = list(
map(str, chain(Path(self.outputDir).glob('result-summary.txt'))))
if paths:
self.report(' Removing %d pre-existing output file%s from '
'top-level output directory %s.' %
(len(paths), s(len(paths)), self.outputDir),
requiredVerbosityLevel=2)
list(map(unlink, paths))
def summarize(self, fp, count, componentOffsets, referenceSequence):
"""
Write out a summary of this consistent component.
@param fp: The file pointer to write to.
@param count: The C{int} number of this component.
@param componentOffsets: The C{set} of offsets in this component.
@param referenceSequence: The C{str} reference sequence.
"""
plural = s(len(self.reads))
print(' Component %d: %d read%s, covering %d offset%s' %
(count, len(self.reads), plural, len(
self.nucleotides), s(len(self.nucleotides))),
file=fp)
print(' Nucleotide counts for each offset:', file=fp)
print(nucleotidesToStr(self.nucleotides, ' '), file=fp)
print(' Consensus sequence: %s' %
self.consensusSequence(componentOffsets, referenceSequence, fp),
file=fp)
print(' Read%s:' % plural, file=fp)
for read in sorted(self.reads):
print(' ', read, file=fp)
def plotBaseFrequencies(significantOffsets,
baseCountAtOffset,
readCountAtOffset,
outfile,
title=None,
sampleName=None,
valuesFile=None,
minReads=5,
homogeneousCutoff=0.9,
sortOn=None,
histogram=False,
show=False,
titleFontSize=12,
axisFontSize=12):
"""
Plot sorted base frequencies at signifcant sites.
"""
subtitle = (
'<br>%d significant sites. Min %d read%s per site. '
'%.2f homogeneity cutoff.' %
(len(significantOffsets), minReads, s(minReads), homogeneousCutoff))
if sortOn is None:
title = title or 'Base frequencies (sorted)'
_plotBaseFrequencies(significantOffsets, baseCountAtOffset,
readCountAtOffset, outfile, title + subtitle,
show, titleFontSize, axisFontSize)
elif sortOn == 'max':
title = title or 'Maximum base frequency'
result = _plotSortedMaxBaseFrequencies(
significantOffsets, baseCountAtOffset, readCountAtOffset, outfile,
title + subtitle, histogram, show, titleFontSize, axisFontSize)
else:
assert sortOn == 'entropy', ('Unknown --sortOn value: %r' % sortOn)
title = title or 'Base frequency entropy'
result = _plotBaseFrequenciesEntropy(significantOffsets,
baseCountAtOffset,
readCountAtOffset, outfile,
title + subtitle, histogram, show,
titleFontSize, axisFontSize)
if valuesFile:
# The following will fail if sortOn is None (no result, above).
with open(valuesFile, 'w') as fp:
dump(
{
'sampleName': sampleName,
'text': [text for _, text in result],
'values': [value for value, _ in result],
}, fp)
def plotSignificantOffsets(fig, row, col, significantOffsets, genomeLength):
"""
Plot the genome offsets that are significant.
"""
n = len(significantOffsets)
trace = go.Scatter(x=[i + 1 for i in significantOffsets],
y=[1.0] * n,
mode='markers',
showlegend=False)
fig.append_trace(trace, row, col)
fig['layout']['annotations'][1]['text'] = (
'%d significant genome location%s' % (n, s(n)))
fig['layout']['xaxis'].update({
'range': (0, genomeLength + 1),
})
def _removePreExistingReferenceDirFiles(self, directory):
"""
Remove all pre-existing files from the output directory for a
particular reference sequence alignment.
@param directory: The C{str} directory to examine.
"""
# This prevents us from doing a run that results in (say) 6
# component files and then later doing a run that results in
# only 5 components and erroneously thinking that
# component-6-2.fasta etc. are from the most recent run.
paths = list(
map(
str,
chain(
Path(directory).glob('*.fasta'),
Path(directory).glob('*.html'),
Path(directory).glob('*.txt'))))
if paths:
self.report(' Removing %d pre-existing output file%s from %s '
'directory.' % (len(paths), s(len(paths)), directory),
requiredVerbosityLevel=2)
list(map(unlink, paths))
def initialReferenceIdAnalysis(self, referenceId, alignmentFile,
outputDir):
"""
Analyze the given reference id in the given alignment file (if an
alignment to the reference id is present).
@param referenceId: The C{str} id of the reference sequence to analyze.
@param alignmentFile: The C{str} name of an alignment file.
@param outputDir: The C{str} name of the output directory.
@return: C{None} if C{referenceId} is not present in C{alignmentFile}
or if no significant offsets are found. Else, a C{dict} containing
the signifcant offsets and the consensus sequence that best matches
C{referenceId}.
"""
# Make sure this reference id is in this alignment file and if so
# get its length (and check it's the same as the length of the
# sequence given in the reference file).
with samfile(alignmentFile) as sam:
tid = sam.get_tid(referenceId)
if tid == -1:
# This referenceId is not in this alignment file.
self.report(' Reference %s not in alignment file.' %
referenceId)
return
else:
genomeLength = sam.lengths[tid]
# Sanity check.
assert genomeLength == len(self.referenceGenomes[referenceId])
if self.plotSAM:
filename = join(outputDir, 'reads.html')
self.report(' Saving reads alignment plot to %s' % filename)
plotSAM(SAMFilter(alignmentFile, referenceIds={referenceId}),
filename,
title=referenceId,
jitter=0.45)
alignedReads = []
samFilter = SAMFilter(
alignmentFile,
referenceIds={referenceId},
dropDuplicates=True,
dropSupplementary=True,
# dropSecondary=True,
storeQueryIds=True)
paddedSAM = PaddedSAM(samFilter)
for query in paddedSAM.queries(addAlignment=True):
assert len(query) == genomeLength
alignedReads.append(
AlignedRead(query.id, query.sequence, query.alignment))
# Sanity check that all aligned reads have different ids. This
# should be the case because the padded SAM queries method adds /2,
# /3 etc to queries that have more than one alignment.
assert len(alignedReads) == len(set(read.id for read in alignedReads))
readCountAtOffset, baseCountAtOffset, readsAtOffset = gatherData(
genomeLength, alignedReads)
significantOffsets = list(
findSignificantOffsets(baseCountAtOffset, readCountAtOffset,
self.minReads, self.homogeneousCutoff))
self.report(
' %d alignment%s (of %d unique %s) read from %s' %
(samFilter.alignmentCount, s(
samFilter.alignmentCount), len(samFilter.queryIds), 'query'
if len(samFilter.queryIds) == 1 else 'queries', alignmentFile))
self.report(' %d of which %s aligned to %s' %
(len(alignedReads),
'was' if len(alignedReads) == 1 else 'were', referenceId))
self.report(' Reference genome length %d' % genomeLength)
self.report(' Found %d significant location%s' %
(len(significantOffsets), s(len(significantOffsets))))
self.saveBaseFrequencies(outputDir, genomeLength, baseCountAtOffset)
if not significantOffsets:
self.report(' No significant locations found.')
return
if self.saveReducedFASTA:
self.saveReducedFasta(significantOffsets, outputDir)
self._plotCoverageAndSignificantLocations(referenceId, alignmentFile,
readCountAtOffset,
genomeLength,
significantOffsets,
outputDir)
self.saveSignificantOffsets(significantOffsets, outputDir)
for read in alignedReads:
read.setSignificantOffsets(significantOffsets)
self.saveReferenceBaseFrequencyPlot(referenceId, genomeLength,
significantOffsets,
baseCountAtOffset,
readCountAtOffset, outputDir)
# Save the reference.
filename = join(outputDir, 'reference.fasta')
self.report(' Saving reference to', filename)
reference = self.referenceGenomes[referenceId]
Reads([reference]).save(filename)
# Extract a consensus according to bcftools.
self.writeBcftoolsConsensus(referenceId, alignmentFile, outputDir)
return (genomeLength, alignedReads, readCountAtOffset,
baseCountAtOffset, readsAtOffset, significantOffsets,
samFilter, paddedSAM)
'--idSuffix',
default='',
help=('Add this string to the end of the read ids. This is added '
'after the string added by --editIds (if also used).'))
parser.add_argument(
'--editIds',
action='store_true',
default=False,
help=('Add "-mutations:N" to the end of each read id, where N '
'is the number of mutations introduced to the read.'))
addFASTACommandLineOptions(parser)
args = parser.parse_args()
reads = parseFASTACommandLineOptions(args)
rate = args.rate
verbose = args.verbose
editIds = args.editIds
idSuffix = args.idSuffix
format_ = 'fastq' if args.fastq else 'fasta'
for read in reads:
count = len(mutateRead(read, rate))
if verbose:
print('%d mutation%s made in read (len %d) %s' %
(count, s(count), len(read), read.id),
file=sys.stderr)
read.id = (read.id + (('-mutations:%d' % count) if editIds else '') +
idSuffix)
print(read.toString(format_), end='')
def plotConsistency(significantOffsets, baseCountAtOffset, readsAtOffset,
minCommonReads, outfile, title, show):
"""
"""
scores = []
text = []
for offset1 in significantOffsets:
rowScores = []
rowText = []
for offset2 in significantOffsets:
if offset2 == offset1:
rowScores.append(1.0)
rowText.append('%d identity (%d read%s)' %
(offset1 + 1, len(readsAtOffset[offset1]),
s(len(readsAtOffset[offset1]))))
else:
readsCoveringBoth = (readsAtOffset[offset1]
& readsAtOffset[offset2])
# print('analyzeConsistency', offset1, offset2)
# print('%d reads cover 1, %d cover 2, %d cover both' % (
# len(readsAtOffset[offset1]),
# len(readsAtOffset[offset2]),
# len(readsCoveringBoth)))
if readsCoveringBoth:
commonCount = len(readsCoveringBoth)
if commonCount < minCommonReads:
rowScores.append(0.0)
rowText.append(
'%d (%d read%s) vs %d (%d read%s)<br>Too few (%d) '
'reads in common' %
(offset2 + 1, len(readsAtOffset[offset2]),
s(len(readsAtOffset[offset2])), offset1 + 1,
len(readsAtOffset[offset1]),
s(len(readsAtOffset[offset1])), commonCount))
else:
bases1 = []
bases2 = []
for read in readsCoveringBoth:
bases1.append(read.base(offset1))
bases2.append(read.base(offset2))
if offset1 == 1895 and offset2 == 1816:
print('offset1 == 1896 and offset2 == 1817')
print(bases1)
print(bases2)
# f = (adjusted_mutual_info_score if offset2 > offset1
# else adjusted_rand_score)
f = (normalized_information_distance
if offset2 > offset1 else adjusted_rand_score)
rowScores.append(f(bases1, bases2))
rowText.append(
'%d (%d read%s) vs %d (%d read%s)<br>%d read%s in '
'common' %
(offset2 + 1, len(readsAtOffset[offset2]),
s(len(readsAtOffset[offset2])), offset1 + 1,
len(readsAtOffset[offset1]),
s(len(readsAtOffset[offset1])), commonCount,
s(commonCount)))
# print('offset %d vs %d = %.4f (%d)' %
# (offset1, offset2, rowScores[-1], commonCount))
else:
rowScores.append(-0.25)
rowText.append('%d vs %d, no reads in common' %
(offset1 + 1, offset2 + 1))
scores.append(rowScores)
text.append(rowText)
data = [
# go.Heatmap(x=x, y=x, z=scores, name='ARI'),
go.Heatmap(z=scores, name='ARI', text=text) # , colorscale='Viridis'),
]
layoutDict = dict(
xaxis={
'title': 'Significant location index',
},
yaxis={
'title': 'Score',
},
)
if title:
layoutDict['title'] = title
layout = go.Layout(layoutDict)
fig = go.Figure(data=data, layout=layout)
plotly.offline.plot(fig, filename=outfile, auto_open=show, show_link=False)
def plotConsistentComponents(referenceId,
genomeLength,
components,
significantOffsets,
outfile,
infoFile,
outputDir,
title='xxx',
show=False,
titleFontSize=12,
axisFontSize=12):
"""
Plot consistent connected components.
"""
def offsetsToLocationsStr(offsets):
return ', '.join(map(lambda i: str(i + 1), sorted(offsets)))
data = []
with open(infoFile, 'w') as fp:
print('There are %d significant location%s: %s' %
(len(significantOffsets), s(len(significantOffsets)),
offsetsToLocationsStr(significantOffsets)),
file=fp)
for count, component in enumerate(components, start=1):
print('Processing component %d, with %d consistent component%s' %
(count, len(component.consistentComponents),
s(len(component.consistentComponents))),
file=fp)
# Get the reference sequence for the component.
reads = list(
FastaReads(
join(outputDir, 'component-%d-consensuses.fasta' % count)))
reference = reads[0]
length = len(reference)
minOffset = min(component.offsets)
maxOffset = max(component.offsets)
print(' Offset range: %d to %d' % (minOffset + 1, maxOffset + 1),
file=fp)
# Add a top line to represent the reference.
data.append(
go.Scatter(x=(minOffset + 1, maxOffset + 1),
y=(1.05, 1.05),
hoverinfo='text',
name=('Reference component %s' % count),
text=('Reference component %s, %d offsets' %
(count, len(component.offsets)))))
# Add vertical lines at the start and end of this component.
data.append(
go.Scatter(
x=(minOffset + 1, minOffset + 1),
y=(-0.05, 1.05),
mode='lines',
hoverinfo='none',
line={
'color': '#eee',
},
showlegend=False,
))
data.append(
go.Scatter(
x=(maxOffset + 1, maxOffset + 1),
y=(-0.05, 1.05),
mode='lines',
hoverinfo='none',
line={
'color': '#eee',
},
showlegend=False,
))
for ccCount, cc in enumerate(component.consistentComponents,
start=1):
ccSummary = ('Component read count %d, offsets covered %d/%d' %
(len(cc.reads), len(
cc.nucleotides), len(component.offsets)))
# Get the consistent connected component consensus.
consensus = reads[ccCount]
assert ('consistent-component-%d' % ccCount) in consensus.id
print(' Processing consistent component', ccCount, file=fp)
print(' Component sequence:', consensus.sequence, file=fp)
print(' %d offset%s: %s' %
(len(cc.nucleotides), s(len(cc.nucleotides)),
offsetsToLocationsStr(cc.nucleotides)),
file=fp)
match = compareDNAReads(reference, consensus)
print(matchToString(match, reference, consensus,
indent=' '),
file=fp)
identicalMatchCount = match['match']['identicalMatchCount']
ambiguousMatchCount = match['match']['ambiguousMatchCount']
# The match fraction will ignore gaps in the consensus
# sequence as it is padded with '-' chars to align it to
# the reference.
fraction = (identicalMatchCount + ambiguousMatchCount) / (
length - len(match['read2']['gapOffsets']))
x = []
y = [fraction] * len(cc.nucleotides)
text = []
identical = []
for index, offset in enumerate(sorted(component.offsets)):
if offset in cc.nucleotides:
consensusBase = consensus.sequence[index]
referenceBase = reference.sequence[index]
if consensusBase == referenceBase:
identical.append(len(x))
# x axis values are 1-based (locations, not offsets)
x.append(offset + 1)
text.append(
'Location: %d, component: %s, reference: %s'
'<br>Component nucleotides: %s<br>%s' %
(offset + 1, consensusBase, referenceBase,
baseCountsToStr(
cc.nucleotides[offset]), ccSummary))
data.append(
go.Scatter(x=x,
y=y,
hoverinfo='text',
selectedpoints=identical,
showlegend=False,
text=text,
mode='markers',
selected={'marker': {
'color': 'blue',
}},
unselected={'marker': {
'color': 'red',
}}))
# Add the significant offsets.
n = len(significantOffsets)
data.append(
go.Scatter(x=[i + 1 for i in significantOffsets],
y=[-0.05] * n,
text=[
'Location %d' % (offset + 1)
for offset in significantOffsets
],
hoverinfo='text',
mode='markers',
name='Significant locations'))
layout = go.Layout(
title=title,
titlefont={
'size': titleFontSize,
},
xaxis={
'range': (0, genomeLength + 1),
'title': 'Genome location',
'titlefont': {
'size': axisFontSize,
},
},
yaxis={
'range': (-0.1, 1.1),
'title': 'Nucleotide identity with reference sequence',
'titlefont': {
'size': axisFontSize,
},
},
hovermode='closest',
)
fig = go.Figure(data=data, layout=layout)
plotly.offline.plot(fig, filename=outfile, auto_open=show, show_link=False)
def saveClosestReferenceConsensus(self, referenceId, components,
baseCountAtOffset, genomeLength,
alignedReads, referenceInsertions,
outputDir):
"""
Calculate and save the best consensus to a reference genome.
@param referenceId: The C{str} id of the reference sequence.
@param components: A C{list} of C{ComponentByOffsets} instances.
@param baseCountAtOffset: A C{list} of C{Counter} instances giving
the count of each nucleotide at each genome offset.
@param genomeLength: The C{int} length of the genome the reads were
aligned to.
@param alignedReads: A list of C{AlignedRead} instances.
@param referenceInsertions: A C{dict} keyed by read id (the read
that would cause a reference insertion). The values are lists
of 2-tuples, with each 2-tuple containing an offset into the
reference sequence and the C{str} of nucleotide that would be
inserted starting at that offset.
@param outputDir: A C{str} directory path.
@return: A tuple of (consensus, unwantedReads, wantedCcReadCount,
wantedReadsCountAtOffset, wantedReadsBaseCountAtOffset).
"""
def ccMatchCount(cc, reference, drawFp, drawMessage):
"""
Count the matches between a consistent component and a reference
genome.
@param cc: A C{ConsistentComponent} instance.
@param reference: A C{Read} instance.
@param drawFp: A file pointer to write information about draws (if
any) to.
@param drawMessage: A C{str} message to write to C{drawFp}. If the
string contains '%(baseCounts)s' that will be replaced by a
string representation of the base counts (in C{counts})
obtained from C{baseCountsToStr}. If not, the base count info
will be printed after the message.
@return: The C{int} count of bases that match the reference
for the offsets covered by the consistent component.
"""
referenceSequence = reference.sequence
nucleotides = cc.nucleotides
count = 0
for offset in nucleotides:
message = (drawMessage + (' location %d: base counts' %
(offset + 1)) + ' %(baseCounts)s.')
referenceBase = referenceSequence[offset]
componentBase = commonest(nucleotides[offset],
referenceBase,
drawFp=drawFp,
drawMessage=message)
count += int(componentBase == referenceBase)
return count
def sortedConsistentComponent(component, reference, fp):
"""
Sort the consistent components in the given C{ComponentByOffsets}
instance according to how well they match the passed reference.
The sort order is by increasing match score, so the best
consistent component is last.
@param component: A C{ComponentByOffsets} instance.
@param reference: A C{Read} instance.
@param fp: A file pointer to write information to.
@return: The C{int} index of the best consistent component.
"""
result = []
for index, cc in enumerate(component.consistentComponents):
matchCount = ccMatchCount(
cc, reference, fp,
' Consistent component %d base draw' % (index + 1))
score = matchCount / len(cc.nucleotides)
print(' Consistent component %d (%d reads) has %d exact '
'matches with the reference, out of the %d offsets it '
'covers (%.2f%%).' %
(index + 1, len(cc.reads), matchCount, len(
cc.nucleotides), score * 100.0),
file=fp)
result.append((score, len(cc.nucleotides), index, cc))
result.sort()
return result
reference = self.referenceGenomes[referenceId]
fields = reference.id.split(maxsplit=1)
if len(fields) == 1:
referenceIdRest = ''
else:
referenceIdRest = ' ' + fields[1]
infoFile = join(outputDir, 'reference-consensus.txt')
self.report(' Saving closest consensus to reference info to',
infoFile)
with open(infoFile, 'w') as infoFp:
print('Building consensus at significant offsets.', file=infoFp)
consensus = [None] * genomeLength
offsetsDone = set()
wantedReads = set()
unwantedReads = set()
for count, component in enumerate(components, start=1):
print('\nExamining component %d with %d locations: %s' %
(count, len(component.offsets),
commas(map(lambda offset: offset + 1,
component.offsets))),
file=infoFp)
componentOffsets = set(component.offsets)
sortedCcs = sortedConsistentComponent(component, reference,
infoFp)
while componentOffsets - offsetsDone:
# The following pop call will raise an IndexError if
# the sorted cc list is empty. But if it's empty we
# shouldn't be here, because the set of included
# offsets should at that point include everything in
# this component. Having the naked pop here ensures we
# get an exception if this assumption is incorrect.
# It's like having an assert to test that we found all
# the component's offsets following the loop.
score, _, ccIndex, cc = sortedCcs.pop()
print(' Incorporating nucleotides from consistent '
'component %d (%d reads, score %.2f, covering %d '
'locations (%d still undecided in consensus)) to '
'consensus.' %
(ccIndex + 1, len(
cc.reads), score, len(cc.nucleotides),
len(set(cc.nucleotides) - offsetsDone)),
file=infoFp)
wantedReads |= cc.reads
for offset in sorted(cc.nucleotides):
if offset in offsetsDone:
continue
nucleotides = cc.nucleotides[offset]
referenceBase = reference.sequence[offset]
base = commonest(
nucleotides,
referenceBase,
drawFp=infoFp,
drawMessage=(' WARNING: base count draw at '
'location %d ' %
(offset + 1)) + ' %(baseCounts)s.')
assert consensus[offset] is None
consensus[offset] = base
offsetsDone.add(offset)
# Do some reporting on the base just added.
if base == referenceBase:
mismatch = ''
else:
consensusBase = commonest(
baseCountAtOffset[offset],
referenceBase,
drawFp=infoFp,
drawMessage=(
' WARNING: consensus base count '
'draw at location %d ' % (offset + 1)) +
' %(baseCounts)s.')
mismatch = (
' (mismatch: reference has %s, all-read '
'consensus has %s)' %
(referenceBase, consensusBase))
print(' Location %d: %s from nucleotides %s%s' %
(offset + 1, base, nucleotides.baseCountsToStr(),
mismatch),
file=infoFp)
# Print info about the cccs that were not needed to cover
# all the offsets in this cc. Reverse the list so we print
# them in decreasing match score order.
for score, _, ccIndex, cc in reversed(sortedCcs):
unwantedReads |= cc.reads
print(' Will NOT incorporate nucleotides from consistent '
'component %d (%d reads, score %.2f, covering %d '
'locations) to consensus.' %
(ccIndex + 1, len(
cc.reads), score, len(cc.nucleotides)),
file=infoFp)
# Get the base counts at each offset, from the full set of
# aligned reads minus the reads in cccs we're not using.
(wantedReadsCountAtOffset, wantedReadsBaseCountAtOffset,
_) = gatherData(genomeLength,
set(alignedReads) - unwantedReads)
# Process the insignificant offsets, based on all reads EXCEPT
# those not used in the connected components.
offsetsToTry = sorted(set(range(genomeLength)) - offsetsDone)
print('\nAttempting to add bases from %d non-significant '
'consensus locations, using all reads, EXCEPT those '
'belonging to unused consistent components:' %
len(offsetsToTry),
file=infoFp)
for offset in offsetsToTry:
assert consensus[offset] is None
baseCount = wantedReadsBaseCountAtOffset[offset]
if baseCount:
referenceBase = reference.sequence[offset]
base = commonest(
baseCount,
referenceBase,
drawFp=infoFp,
drawMessage=(
' WARNING: consensus base count draw at '
'location %d' % (offset + 1)) + ' %(baseCounts)s.')
print(' Location %d: %s from nucleotides %s' %
(offset + 1, base, baseCountsToStr(baseCount)),
file=infoFp,
end='')
if base == referenceBase:
print(file=infoFp)
else:
print(' (mismatch: reference has %s)' % referenceBase,
file=infoFp)
consensus[offset] = base
offsetsDone.add(offset)
# Process remaining insignificant offsets, using ALL reads
# (i.e., including those in cccs that we wanted to avoid
# using). At this point, this is the best we can do with these
# final offsets (otherwise we will get gaps - which in some
# cases may actually might be preferable because the reference
# sequence may not be fully covered by the actual infection
# sequence).
offsetsToTry = sorted(set(range(genomeLength)) - offsetsDone)
print('\nAttempting to add bases from %d non-significant '
'consensus locations, using all reads, INCLUDING those '
'belonging to unused consistent components:' %
len(offsetsToTry),
file=infoFp)
for offset in offsetsToTry:
assert consensus[offset] is None
referenceBase = reference.sequence[offset]
baseCount = baseCountAtOffset[offset]
if baseCount:
base = commonest(
baseCount,
referenceBase,
drawFp=infoFp,
drawMessage=(
' WARNING: consensus base count draw at '
'location %d' % (offset + 1)) + ' %(baseCounts)s.')
print(' Location %d: %s from nucleotides %s' %
(offset + 1, base, baseCountsToStr(baseCount)),
file=infoFp,
end='')
else:
# The reads did not cover this offset.
base = '-'
print(' Location %d: -' % (offset + 1),
file=infoFp,
end='')
if base == referenceBase:
print(file=infoFp)
else:
print(' (mismatch: reference has %s)' % referenceBase,
file=infoFp)
consensus[offset] = base
offsetsDone.add(offset)
# Sanity check: make sure we processed all offsets.
assert offsetsDone == set(range(genomeLength))
consensusId = (
'%s-consensus%s' %
(self.shortReferenceId[referenceId], referenceIdRest))
consensus = Read(consensusId, ''.join(consensus))
# Print details of the match of the consensus to the reference.
match = compareDNAReads(reference, consensus)
print('\nOVERALL match with reference:', file=infoFp)
print(matchToString(match, reference, consensus, indent=' '),
file=infoFp)
# Print any insertions to the reference.
wantedReadsWithInsertions = (set(referenceInsertions) &
(set(alignedReads) - unwantedReads))
if wantedReadsWithInsertions:
print('\nReference insertions present in %d read%s:' %
(len(wantedReadsWithInsertions),
s(len(wantedReadsWithInsertions))),
file=infoFp)
nucleotides = defaultdict(Counter)
for readId in wantedReadsWithInsertions:
for (offset, sequence) in referenceInsertions[readId]:
for index, base in enumerate(sequence):
nucleotides[offset + index][base] += 1
print(nucleotidesToStr(nucleotides, prefix=' '), file=infoFp)
else:
print('\nReference insertions: none.', file=infoFp)
filename = join(outputDir, 'reference-consensus.fasta')
self.report(' Saving consensus to', filename)
Reads([consensus]).save(filename)
wantedCcReadCount = 0
filename = join(outputDir, 'cc-wanted.fastq')
with open(filename, 'w') as fp:
for wantedCcRead in wantedReads:
alignment = wantedCcRead.alignment
if not (alignment.is_secondary or alignment.is_supplementary):
wantedCcReadCount += 1
print(Read(alignment.query_name, alignment.query_sequence,
alignmentQuality(alignment)).toString('fastq'),
end='',
file=fp)
self.report(
' Saved %d read%s wanted in consistent connected components '
'to %s' % (wantedCcReadCount, s(wantedCcReadCount), filename))
unwantedReads = set(alignedReads) - wantedReads
return (consensus, unwantedReads, wantedCcReadCount,
wantedReadsCountAtOffset, wantedReadsBaseCountAtOffset)
def mergeDescription(self, a, b, distance):
"""
Make a textual description of a cluster merge.
@param a: An C{int} cluster number.
@param b: An C{int} cluster number.
@param distance: The C{float} [0.0, 1.0] distance between the clusters.
@return: A C{str} side-by-side descriptions of clusters C{a} and C{b}.
"""
cluster1 = self.readClusters[a]
cluster2 = self.readClusters[b]
result1 = []
result2 = []
matches = []
sharedCount = matchCount = 0
allOffsets = sorted(
set(cluster1.nucleotides) | set(cluster2.nucleotides))
for offset in allOffsets:
inCount = 0
if offset in cluster1.nucleotides:
result1.append(cluster1.nucleotides[offset].baseCountsToStr())
inCount += 1
else:
result1.append('-')
if offset in cluster2.nucleotides:
result2.append(cluster2.nucleotides[offset].baseCountsToStr())
inCount += 1
else:
result2.append('-')
if inCount == 2:
sharedCount += 1
if (cluster1.nucleotides[offset].commonest
& cluster2.nucleotides[offset].commonest):
matches.append('*')
matchCount += 1
else:
multiple = OffsetBases.highestFrequenciesMultiple(
cluster1.nucleotides[offset],
cluster2.nucleotides[offset])
# Sanity: the multiple cannot be None because that
# would mean only one nucleotide is present, and that
# case is dealt with by the first part of this if/then.
assert multiple is not None
if multiple >= ReadCluster.MIN_COMMONEST_MULTIPLE:
matchCount += 1
matches.append('+')
else:
matches.append('')
else:
matches.append('')
result1Width = max(len(line) for line in result1)
result2Width = max(len(line) for line in result2)
return '\n'.join([
('Merging clusters %d and %d with distance %.2f' %
(a, b, distance)),
('Cluster %d has %d read%s, covering %d offset%s' %
(a, len(cluster1.reads), s(len(cluster1.reads), ),
len(cluster1.nucleotides), s(len(cluster1.nucleotides)))),
('Cluster %d has %d read%s, covering %d offset%s' %
(b, len(cluster2.reads), s(len(cluster2.reads)),
len(cluster2.nucleotides), s(len(cluster2.nucleotides)))),
('%d matches out of %d shared offsets' %
(matchCount, sharedCount)),
] + [
' %d: %*s %*s %s' %
(offset + 1, result1Width, line1, result2Width, line2, match)
for (offset, line1, line2,
match) in zip(allOffsets, result1, result2, matches)
])
def analyzeReferenceId(self, referenceId, alignmentFile, outputDir):
"""
Analyze the given reference id in the given alignment file (if an
alignment to the reference id is present).
@param referenceId: The C{str} id of the reference sequence to analyze.
@param alignmentFile: The C{str} name of an alignment file.
@param outputDir: The C{str} name of the output directory.
@return: C{None} if C{referenceId} is not present in C{alignmentFile}
or if no significant offsets are found. Else, a C{dict} containing
the signifcant offsets and the consensus sequence that best matches
C{referenceId}.
"""
analysis = self.initialReferenceIdAnalysis(referenceId, alignmentFile,
outputDir)
if analysis:
(genomeLength, alignedReads, readCountAtOffset, baseCountAtOffset,
readsAtOffset, significantOffsets, samFilter,
paddedSAM) = analysis
else:
return
insignificantOffsets = set(
range(genomeLength)) - set(significantOffsets)
reference = self.referenceGenomes[referenceId]
referenceSequence = reference.sequence
consensus = []
for base in referenceSequence:
ob = OffsetBases()
ob.incorporateBase(base)
consensus.append(ob)
readQueue = PriorityQueue()
self.updatePriorityQueue(readQueue, alignedReads, consensus,
significantOffsets)
consensusFilename = join(outputDir, 'reference-consensus.sam')
nonConsensusFilename = join(outputDir, 'reference-non-consensus.sam')
self.report(' Writing consensus SAM to', consensusFilename)
self.report(' Writing non-consensus SAM to', nonConsensusFilename)
with samfile(alignmentFile) as sam:
consensusAlignment = AlignmentFile(consensusFilename,
mode='w',
template=sam)
nonConsensusAlignment = AlignmentFile(nonConsensusFilename,
mode='w',
template=sam)
# Reads with no significant offsets get written to both output files.
readsWithNoSignificantOffsetsCount = 0
for read in alignedReads:
if not read.significantOffsets:
readsWithNoSignificantOffsetsCount += 1
consensusAlignment.write(read.alignment)
nonConsensusAlignment.write(read.alignment)
for offset in insignificantOffsets:
base = read.base(offset)
if base is not None:
consensus[offset].incorporateBase(base)
self.report(' %d read%s did not overlap any significant offsets' %
(readsWithNoSignificantOffsetsCount,
s(readsWithNoSignificantOffsetsCount)))
readsMatchingConsensusCount = readsNotMatchingConsensusCount = 0
cutoff = self.cutoff
while readQueue:
mismatchFraction, _ = readQueue.lowestPriority()
read = readQueue.pop()
if mismatchFraction <= cutoff:
# We want this read. Incorporate it into the consensus.
readsMatchingConsensusCount += 1
consensusAlignment.write(read.alignment)
affectedReads = set()
for offset in read.significantOffsets:
readBase = read.base(offset)
consensus[offset].incorporateBase(readBase)
for readAtOffset in readsAtOffset[offset]:
if readAtOffset in readQueue:
affectedReads.add(readAtOffset)
self.updatePriorityQueue(readQueue, affectedReads, consensus,
significantOffsets)
else:
readsNotMatchingConsensusCount += 1
nonConsensusAlignment.write(read.alignment)
consensusAlignment.close()
nonConsensusAlignment.close()
self.report(
' %d read%s matched the consensus, %d did not.' %
(readsMatchingConsensusCount, s(readsMatchingConsensusCount),
readsNotMatchingConsensusCount))
# Remove the reference bases from the consensus.
for offset, base in enumerate(referenceSequence):
consensus[offset].unincorporateBase(base)
consensusInfoFilename = join(outputDir, 'reference-consensus.txt')
self.report(' Writing consensus info to', consensusInfoFilename)
with open(consensusInfoFilename, 'w') as fp:
consensusSequence = []
for offset in range(genomeLength):
# Take a copy of the commonest set because we may pop from
# it below.
commonest = set(consensus[offset].commonest)
referenceBase = referenceSequence[offset]
if len(commonest) > 1:
nucleotides = ' Nucleotides: %s' % (
consensus[offset].baseCountsToStr())
else:
nucleotides = ''
if referenceBase in commonest:
consensusBase = referenceBase
else:
if len(commonest) == 1:
# Nothing in the included reads covers this offset.
consensusBase = '-'
elif len(commonest) > 1:
# Report a draw (in which the reference base is not
# included and so cannot be used to break the draw).
commonest.pop()
else:
consensusBase = commonest.pop()
consensusSequence.append(consensusBase)
mismatch = '' if referenceBase == consensusBase else (
' Mismatch (reference has %s)' % referenceBase)
print('%d: %s%s%s' %
(offset + 1, consensusBase, mismatch, nucleotides),
file=fp)
consensusRead = Read('gready-consensus-%s' % referenceId,
''.join(consensusSequence))
consensusFilename = join(outputDir, 'reference-consensus.fasta')
self.report(' Writing gready consensus info to', consensusFilename)
Reads([consensusRead]).save(consensusFilename)
return {
'consensusRead': consensusRead,
'significantOffsets': significantOffsets,
}
def mergeDescriptionWithOffsetScores(self, a, b, distance):
"""
Make a textual description of a cluster merge, including per-offset
score information.
@param a: An C{int} cluster number.
@param b: An C{int} cluster number.
@param distance: The C{float} [0.0, 1.0] distance between the clusters.
@return: A C{str} side-by-side descriptions of clusters C{a} and C{b}.
"""
cluster1 = self.readClusters[a]
cluster2 = self.readClusters[b]
result1 = []
result2 = []
offsetScores = []
matches = []
sharedCount = matchCount = 0
allOffsets = sorted(
set(cluster1.nucleotides) | set(cluster2.nucleotides))
for offset in allOffsets:
inCount = 0
if offset in cluster1.nucleotides:
result1.append(cluster1.nucleotides[offset].baseCountsToStr())
inCount += 1
else:
result1.append('-')
if offset in cluster2.nucleotides:
result2.append(cluster2.nucleotides[offset].baseCountsToStr())
inCount += 1
else:
result2.append('-')
if inCount == 2:
sharedCount += 1
if (cluster1.nucleotides[offset].commonest
& cluster2.nucleotides[offset].commonest):
matches.append('*')
matchCount += 1
else:
matches.append('')
offsetScores.append('%.3f' % min(
OffsetBases.multiplicativeDistance(
cluster1.nucleotides[offset],
cluster2.nucleotides[offset]),
OffsetBases.homogeneousDistance(
cluster1.nucleotides[offset],
cluster2.nucleotides[offset])))
else:
matches.append('')
offsetScores.append('')
result1Width = max(len(line) for line in result1)
result2Width = max(len(line) for line in result2)
offsetScoresWidth = max(len(line) for line in offsetScores)
return '\n'.join([
('Merging clusters %d and %d with distance %.2f' %
(a, b, distance)),
('Cluster %d has %d read%s, covering %d offset%s' %
(a, len(cluster1.reads), s(len(cluster1.reads), ),
len(cluster1.nucleotides), s(len(cluster1.nucleotides)))),
('Cluster %d has %d read%s, covering %d offset%s' %
(b, len(cluster2.reads), s(len(cluster2.reads)),
len(cluster2.nucleotides), s(len(cluster2.nucleotides)))),
('%d matches out of %d shared offsets' %
(matchCount, sharedCount)),
] + [
' %d: %*s %*s %*s %s' %
(offset + 1, result1Width, line1, result2Width, line2,
offsetScoresWidth, offsetScore, match)
for (offset, line1, line2, offsetScore, match
) in zip(allOffsets, result1, result2, offsetScores, matches)
])
parser.add_argument(
'--alignReads',
action='store_true',
default=False,
help=('If specified, print the reads aligned (with "-" characters) '
'to the genome.'))
addFASTACommandLineOptions(parser)
args = parser.parse_args()
reads = list(parseFASTACommandLineOptions(args))
# There should only be one "read", the sequence we are to create other
# reads from.
assert len(reads) == 1, (
'FASTA input contained %d sequence%s (expected just one).' %
(len(reads), s(len(reads))))
genome = reads[0]
genomeLen = len(genome)
meanLength = args.meanLength
if meanLength > genomeLen:
raise ValueError('The mean read length (%d) is greater than the '
'genome length (%d)' % (int(meanLength), genomeLen))
if meanLength <= 0:
raise ValueError('The mean read length must be greater than zero')
sdLength = args.sdLength
if sdLength <= 0.0:
raise ValueError('The read length standard deviation must be > 0.0')
