def testDistanceMax(self):
"""Verify computation of distances between genomic signatures."""
gs = GenomicSignatures(K=2, threads=1)
sig1 = gs.seqSignature('AAAA')
sig2 = gs.seqSignature('GGGG')
dist = gs.distance(sig1, sig2)
self.assertAlmostEqual(dist, 2.0)
def testDistanceZero(self):
"""Verify computation of distances between genomic signatures."""
gs = GenomicSignatures(K=2, threads=1)
sig1 = gs.seqSignature('AACC')
sig2 = gs.seqSignature('AACC')
dist = gs.distance(sig1, sig2)
self.assertEqual(dist, 0)
def testDistanceMax(self):
"""Verify computation of distances between genomic signatures."""
gs = GenomicSignatures(K = 2, threads = 1)
sig1 = gs.seqSignature('AAAA')
sig2 = gs.seqSignature('GGGG')
dist = gs.distance(sig1, sig2)
self.assertAlmostEqual(dist, 2.0)
def testDistanceZero(self):
"""Verify computation of distances between genomic signatures."""
gs = GenomicSignatures(K = 2, threads = 1)
sig1 = gs.seqSignature('AACC')
sig2 = gs.seqSignature('AACC')
dist = gs.distance(sig1, sig2)
self.assertEqual(dist, 0)
def __calculateResults(self, windowSizes, numWindows, genomeDir, queueIn,
queueOut):
while True:
genomeId = queueIn.get(block=True, timeout=None)
if genomeId == None:
break
start = time.time()
seqs = readGenomicSeqsFromFasta(
os.path.join(genomeDir, genomeId, genomeId + '.fna'))
genomeScaffold = 'NNNN'.join(seqs.values())
# calculate tetranucleotide signature of genome
gsCalculator = GenomicSignatures(4)
genomeSig = gsCalculator.seqSignature(genomeScaffold)
fout = open('./deltaTD/' + genomeId + '.tsv', 'w')
fout.write('# Tetra signature = ' + str(genomeSig) + '\n')
fout.close()
sys.exit()
# calculate tetranucleotide distance distribution for different window sizes
startW = time.time()
for windowSize in windowSizes:
endWindowPos = len(genomeScaffold) - windowSize
if endWindowPos <= 0:
# This might occur for the largest window sizes and smallest genomes
break
deltaTDs = []
while len(deltaTDs) != numWindows:
# pick random window
startWindow = randint(0, endWindowPos)
windowSig = gsCalculator.seqSignature(
genomeScaffold[startWindow:(startWindow + windowSize)])
dist = gsCalculator.distance(genomeSig, windowSig)
deltaTDs.append(dist)
fout.write('Windows Size = ' + str(windowSize) + '\n')
fout.write(','.join(map(str, deltaTDs)) + '\n')
fout.close()
endW = time.time()
print endW - startW
queueOut.put(genomeId)
end = time.time()
print end - start
def __calculateResults(self, windowSizes, numWindows, genomeDir, queueIn, queueOut):
while True:
genomeId = queueIn.get(block=True, timeout=None)
if genomeId == None:
break
start = time.time()
seqs = readGenomicSeqsFromFasta(os.path.join(genomeDir, genomeId, genomeId + '.fna'))
genomeScaffold = 'NNNN'.join(seqs.values())
# calculate tetranucleotide signature of genome
gsCalculator = GenomicSignatures(4)
genomeSig = gsCalculator.seqSignature(genomeScaffold)
fout = open('./deltaTD/' + genomeId + '.tsv', 'w')
fout.write('# Tetra signature = ' + str(genomeSig) + '\n')
fout.close()
sys.exit()
# calculate tetranucleotide distance distribution for different window sizes
startW = time.time()
for windowSize in windowSizes:
endWindowPos = len(genomeScaffold) - windowSize
if endWindowPos <= 0:
# This might occur for the largest window sizes and smallest genomes
break
deltaTDs = []
while len(deltaTDs) != numWindows:
# pick random window
startWindow = randint(0, endWindowPos)
windowSig = gsCalculator.seqSignature(genomeScaffold[startWindow:(startWindow+windowSize)])
dist = gsCalculator.distance(genomeSig, windowSig)
deltaTDs.append(dist)
fout.write('Windows Size = ' + str(windowSize) + '\n')
fout.write(','.join(map(str, deltaTDs)) + '\n')
fout.close()
endW = time.time()
print endW - startW
queueOut.put(genomeId)
end = time.time()
print end - start
def plotOnAxes(self, fastaFile, tetraSigs, distributionsToPlot, axesHist,
axesDeltaTD):
# Read reference distributions from file
dist = readDistribution('td_dist')
# get tetranucleotide signature for bin
seqs = readFasta(fastaFile)
binTools = BinTools()
binSig = binTools.binTetraSig(seqs, tetraSigs)
# get tetranucleotide distances for windows
genomicSig = GenomicSignatures(K=4, threads=1)
data = []
seqLens = []
deltaTDs = []
for seqId, seq in seqs.iteritems():
start = 0
end = self.options.td_window_size
seqLen = len(seq)
seqLens.append(seqLen)
deltaTDs.append(genomicSig.distance(tetraSigs[seqId], binSig))
while (end < seqLen):
windowSig = genomicSig.seqSignature(seq[start:end])
data.append(genomicSig.distance(windowSig, binSig))
start = end
end += self.options.td_window_size
if len(data) == 0:
axesHist.set_xlabel(
'[Error] No seqs >= %d, the specified window size' %
self.options.td_window_size)
return
deltaTDs = np.array(deltaTDs)
# Histogram plot
bins = [0.0]
binWidth = self.options.td_bin_width
binEnd = binWidth
while binEnd <= 1.0:
bins.append(binEnd)
binEnd += binWidth
axesHist.hist(data, bins=bins, normed=True, color=(0.5, 0.5, 0.5))
axesHist.set_xlabel(r'$\Delta$ TD')
axesHist.set_ylabel('% windows (' + str(self.options.td_window_size) +
' bp)')
# Prettify plot
for a in axesHist.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in axesHist.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in axesHist.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in axesHist.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in axesHist.spines.iteritems():
if loc in ['right', 'top']:
spine.set_color('none')
else:
spine.set_color(self.axesColour)
# get CD bin statistics
meanTD, deltaTDs = binTools.tetraDiffDist(seqs, genomicSig, tetraSigs,
binSig)
# Delta-TD vs Sequence length plot
axesDeltaTD.scatter(deltaTDs,
seqLens,
c=abs(deltaTDs),
s=10,
lw=0.5,
cmap='gray_r')
axesDeltaTD.set_xlabel(r'$\Delta$ TD (mean TD = %.2f)' % meanTD)
axesDeltaTD.set_ylabel('Sequence length (kbp)')
_, yMaxSeqs = axesDeltaTD.get_ylim()
xMinSeqs, xMaxSeqs = axesDeltaTD.get_xlim()
# plot reference distributions
for distToPlot in distributionsToPlot:
boundKey = findNearest(dist[dist.keys()[0]].keys(), distToPlot)
x = []
y = []
for windowSize in dist:
x.append(dist[windowSize][boundKey])
y.append(windowSize)
# sort by y-values
sortIndexY = np.argsort(y)
x = np.array(x)[sortIndexY]
y = np.array(y)[sortIndexY]
# make sure x-values are strictly decreasing as y increases
# as this is conservative and visually satisfying
for i in xrange(0, len(x) - 1):
for j in xrange(i + 1, len(x)):
if x[j] > x[i]:
if j == len(x) - 1:
x[j] = x[i]
else:
x[j] = (x[j - 1] + x[j + 1]
) / 2 # interpolate values from neighbours
if x[j] > x[i]:
x[j] = x[i]
axesDeltaTD.plot(x, y, 'r--', lw=0.5, zorder=0)
# ensure y-axis include zero and covers all sequences
axesDeltaTD.set_ylim([0, yMaxSeqs])
# ensure x-axis is set appropriately for sequences
axesDeltaTD.set_xlim([xMinSeqs, xMaxSeqs])
# draw vertical line at x=0
axesDeltaTD.vlines(0,
0,
yMaxSeqs,
linestyle='dashed',
color=self.axesColour,
zorder=0)
# Change sequence lengths from bp to kbp
yticks = axesDeltaTD.get_yticks()
kbpLabels = []
for seqLen in yticks:
label = '%.1f' % (float(seqLen) / 1000)
label = label.replace('.0', '') # remove trailing zero
kbpLabels.append(label)
axesDeltaTD.set_yticklabels(kbpLabels)
# Prettify plot
for a in axesDeltaTD.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in axesDeltaTD.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in axesDeltaTD.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in axesDeltaTD.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in axesDeltaTD.spines.iteritems():
if loc in ['right', 'top']:
spine.set_color('none')
else:
spine.set_color(self.axesColour)
def plotOnAxes(self, fastaFile, tetraSigs, distributionsToPlot, axesHist, axesDeltaTD):
# Read reference distributions from file
dist = readDistribution("td_dist")
# get tetranucleotide signature for bin
seqs = readFasta(fastaFile)
binTools = BinTools()
binSig = binTools.binTetraSig(seqs, tetraSigs)
# get tetranucleotide distances for windows
genomicSig = GenomicSignatures(K=4, threads=1)
data = []
seqLens = []
deltaTDs = []
for seqId, seq in seqs.iteritems():
start = 0
end = self.options.td_window_size
seqLen = len(seq)
seqLens.append(seqLen)
deltaTDs.append(genomicSig.distance(tetraSigs[seqId], binSig))
while end < seqLen:
windowSig = genomicSig.seqSignature(seq[start:end])
data.append(genomicSig.distance(windowSig, binSig))
start = end
end += self.options.td_window_size
if len(data) == 0:
axesHist.set_xlabel("[Error] No seqs >= %d, the specified window size" % self.options.td_window_size)
return
deltaTDs = np.array(deltaTDs)
# Histogram plot
bins = [0.0]
binWidth = self.options.td_bin_width
binEnd = binWidth
while binEnd <= 1.0:
bins.append(binEnd)
binEnd += binWidth
axesHist.hist(data, bins=bins, normed=True, color=(0.5, 0.5, 0.5))
axesHist.set_xlabel(r"$\Delta$ TD")
axesHist.set_ylabel("% windows (" + str(self.options.td_window_size) + " bp)")
# Prettify plot
for a in axesHist.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in axesHist.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in axesHist.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in axesHist.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in axesHist.spines.iteritems():
if loc in ["right", "top"]:
spine.set_color("none")
else:
spine.set_color(self.axesColour)
# get CD bin statistics
meanTD, deltaTDs = binTools.tetraDiffDist(seqs, genomicSig, tetraSigs, binSig)
# Delta-TD vs Sequence length plot
axesDeltaTD.scatter(deltaTDs, seqLens, c=abs(deltaTDs), s=10, lw=0.5, cmap="gray_r")
axesDeltaTD.set_xlabel(r"$\Delta$ TD (mean TD = %.2f)" % meanTD)
axesDeltaTD.set_ylabel("Sequence length (kbp)")
_, yMaxSeqs = axesDeltaTD.get_ylim()
xMinSeqs, xMaxSeqs = axesDeltaTD.get_xlim()
# plot reference distributions
for distToPlot in distributionsToPlot:
boundKey = findNearest(dist[dist.keys()[0]].keys(), distToPlot)
x = []
y = []
for windowSize in dist:
x.append(dist[windowSize][boundKey])
y.append(windowSize)
# sort by y-values
sortIndexY = np.argsort(y)
x = np.array(x)[sortIndexY]
y = np.array(y)[sortIndexY]
# make sure x-values are strictly decreasing as y increases
# as this is conservative and visually satisfying
for i in xrange(0, len(x) - 1):
for j in xrange(i + 1, len(x)):
if x[j] > x[i]:
if j == len(x) - 1:
x[j] = x[i]
else:
x[j] = (x[j - 1] + x[j + 1]) / 2 # interpolate values from neighbours
if x[j] > x[i]:
x[j] = x[i]
axesDeltaTD.plot(x, y, "r--", lw=0.5, zorder=0)
# ensure y-axis include zero and covers all sequences
axesDeltaTD.set_ylim([0, yMaxSeqs])
# ensure x-axis is set appropriately for sequences
axesDeltaTD.set_xlim([xMinSeqs, xMaxSeqs])
# draw vertical line at x=0
axesDeltaTD.vlines(0, 0, yMaxSeqs, linestyle="dashed", color=self.axesColour, zorder=0)
# Change sequence lengths from bp to kbp
yticks = axesDeltaTD.get_yticks()
kbpLabels = []
for seqLen in yticks:
label = "%.1f" % (float(seqLen) / 1000)
label = label.replace(".0", "") # remove trailing zero
kbpLabels.append(label)
axesDeltaTD.set_yticklabels(kbpLabels)
# Prettify plot
for a in axesDeltaTD.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in axesDeltaTD.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in axesDeltaTD.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in axesDeltaTD.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in axesDeltaTD.spines.iteritems():
if loc in ["right", "top"]:
spine.set_color("none")
else:
spine.set_color(self.axesColour)
