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GC_content_explore.py
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GC_content_explore.py
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#!/usr/bin/env python
import pysam
import re
import matplotlib
import matplotlib.pyplot as plt
import numpy
from pbcore.io import CmpH5Reader
from pbcore.io import CmpH5Alignment
def IdentityFromCIGAR(cigar):
nMatch = 0
nIns = 0
nDel = 0
for cig in cigar:
if (cig[0] == 0):
nMatch += cig[1]
elif(cig[0] == 1):
nIns += cig[1]
elif(cig[0] == 2):
nDel += cig[1]
denom = float(nMatch + nIns + nDel)
return nMatch / denom
class AlignmentSummary:
def __init__(self, identity, length):
self.identity = identity
self.length = length
self.zmw = 0
self.sub = 0
def SamToMap(samFileName, samMap):
sf = pysam.Samfile( samFileName, "r" )
for aln in sf.fetch():
if (aln.rname == "*"):
continue
ident = IdentityFromCIGAR(aln.cigar)
samMap[aln.qname] = AlignmentSummary(ident, aln.qlen)
def GetSubreadGC(subread):
return (float(subread.basecalls().count('G') + subread.basecalls().count('C')) / len(subread.basecalls()))
def GetGC(read):
maxLen = 0
maxS = 0
for s in range(0,len(read.subreads)):
l = len(read.subreads[s].basecalls())
if (l > maxLen):
maxLen = l
maxS = s
return (float(read.subreads[maxS].basecalls().count('G') + read.subreads[maxS].basecalls().count("C"))) / len(read.subreads[maxS].basecalls())
#dh5 = "/net/eichler/vol20/projects/pacbio/backups/incoming/130625_MYD_eee_20kb_368/D01_1/Analysis_Results/m130626_034031_42134_c100534392550000001823079711101324_s1_p0.bas.h5"
#dsam = "/net/eichler/vol20/projects/pacbio/nobackups/results/130625_MYD_eee_20kb_368/D01_1/D.sam"
dh5 = "/mnt/pacbio/D01_1/Analysis_Results/m130626_034031_42134_c100534392550000001823079711101324_s1_p0.bas.h5"
dsam = "/mnt/pacbio_analysis/D01_1/D.sam"
from pbcore.io import BasH5Reader
dReader = BasH5Reader(dh5)
#
# key:
# rs read score
# rl read length
# mi mapped identity
# ml mapped length
# m indices of mapped reads
# um indices of unmapped reads
# s mapped subreads
# us unmapped subreads
class Count:
def __init__(self):
self.fields = ["rs", "rl", "mi", "ml", "m", "um", "s", "us"]
self.data = { f: [] for f in self.fields }
self.npdata = {}
def ToNumpy(self):
self.npdata = { f: numpy.array(self.data[f]) for f in self.fields }
def StoreMapped(fileNames, alnMap, stats):
for fileName in fileNames:
reader = BasH5Reader(fileName)
for zmw in reader.sequencingZmws:
for s in reader[zmw].subreads:
stats.data["rs"].append(reader[zmw].readScore)
stats.data["rl"].append(s.readEnd - s.readStart)
if (s.readName in alnMap):
stats.data["m"].append(len(stats.data["rs"]) - 1 )
stats.data["ml"].append(alnMap[s.readName].length)
stats.data["mi"].append(alnMap[s.readName].identity)
stats.data["s"].append(s)
else:
stats.data["um"].append(len(stats.data["rs"]) - 1 )
stats.data["ml"].append(0)
stats.data["mi"].append(0)
stats.data["us"].append(s)
dfn = ["/mnt/pacbio/D01_1/Analysis_Results/m130626_034031_42134_c100534392550000001823079711101324_s1_p0.bas.h5"]
dsam = "/mnt/pacbio_analysis/D01_1/D.sam"
dcmp = "/mnt/pacbio_analysis/D01_1/D.cmp.h5"
gfn = ["/mnt/pacbio/G01_1/Analysis_Results/m130626_103730_42134_c100534392550000001823079711101327_s1_p0.bas.h5","/mnt/pacbio/G01_1/Analysis_Results/m130626_103730_42134_c100534392550000001823079711101327_s2_p0.bas.h5"]
gsam = "/mnt/pacbio_analysis/G01_1/G.sam"
hfn = ["/mnt/pacbio/H01_1/Analysis_Results/m130626_125440_42134_c100534382550000001823079711101330_s1_p0.bas.h5","/mnt/pacbio/H01_1/Analysis_Results/m130626_125440_42134_c100534382550000001823079711101330_s2_p0.bas.h5"]
hsam = "/mnt/pacbio_analysis/H01_1/H.sam"
ffn = ["/mnt/pacbio/F01_1/Analysis_Results/m130626_081902_42134_c100534392550000001823079711101326_s1_p0.bas.h5","/mnt/pacbio/F01_1/Analysis_Results/m130626_081902_42134_c100534392550000001823079711101326_s2_p0.bas.h5"]
fsam = "/mnt/pacbio_analysis/F01_1/F.sam"
dStats = Count()
dh5Files = [dh5]
dSamMap = {}
SamToMap(dsam, dSamMap)
StoreMapped(dfn, dSamMap, dStats)
dStats.ToNumpy()
fStats = Count()
fSamMap = {}
SamToMap(fsam, fSamMap)
StoreMapped(ffn, fSamMap, fStats)
fStats.ToNumpy()
gStats = Count()
gSamMap = {}
SamToMap(gsam, gSamMap)
StoreMapped(gfn, gSamMap, gStats)
gStats.ToNumpy()
hStats = Count()
hSamMap = {}
SamToMap(hsam, hSamMap)
StoreMapped(hfn, hSamMap, hStats)
hStats.ToNumpy()
def ArrayHist(array, nbins=30):
h = numpy.histogram(array, bins=nbins)
return (h[1][0:-1], h[0])
def StatsHist(stats, dataset="rs", which="m", minValue=None):
d = stats.npdata[dataset][stats.npdata[which]]
if (minValue is not None):
d = d[d > minValue]
h = numpy.histogram(d, bins=30)
return (h[1][0:-1], h[0])
dh = StatsHist(dStats, dataset="rs", which="m", minValue = 0.25)
fh = StatsHist(fStats, dataset="rs", which="m", minValue = 0.25)
duh =StatsHist(dStats, dataset="rs", which="um", minValue = 0.25)
fuh =StatsHist(fStats, dataset="rs", which="um", minValue = 0.25)
ax = plt.axes
plt.scatter(dh[0], dh[1], axes=ax)
plt.scatter(fh[0], fh[1], axes=ax, color="red")
plt.scatter(duh[0], duh[1], axes=ax, color="LightBlue")
plt.scatter(fuh[0], fuh[1], axes=ax, color="pink")
plt.show()
dCmpR = CmpH5Reader(dcmp)
mgc = numpy.array([GetSubreadGC(sr) for sr in gStats.npdata["s"]])
umgc = numpy.array([GetSubreadGC(sr) for sr in gStats.npdata["us"]])
dmgc = numpy.array([GetSubreadGC(sr) for sr in dStats.npdata["s"]])
dumgc = numpy.array([GetSubreadGC(sr) for sr in dStats.npdata["us"]])
hmgc = numpy.array([GetSubreadGC(sr) for sr in hStats.npdata["s"]])
humgc = numpy.array([GetSubreadGC(sr) for sr in hStats.npdata["us"]]
)
def GetLengths(subreads):
return numpy.array([len(sr.basecalls()) for sr in subreads])
def IMean(array, indices):
return np.mean(array[indices])
def LimitIndices(array, minValue = 0, maxValue=10000000):
lowi = array > minValue
highi = array < maxValue
return lowi & highi
hl = GetLengths(hStats.npdata["s"])
hul = GetLengths(hStats.npdata["us"])
dl = GetLengths(dStats.npdata["s"])
dul = GetLengths(dStats.npdata["us"])
gmgch = ArrayHist(mgc)
gumgch = ArrayHist(umgc)
dmgch = ArrayHist(dmgc)
dumgch = ArrayHist(umgc)
ax1 = plt.subplot(121)
ax1.scatter(dl, dmgc, color="DarkRed", alpha=0.10)
ax1.scatter(hl, hmgc, color="DarkBlue", alpha=0.10)
ax2 = plt.subplot(122)
ax2.scatter(dul, dumgc, color="HotPink", alpha=0.10)
ax2.scatter(hul, humgc, color="DodgerBlue", alpha=0.10)
plt.show()
hi = LimitIndices(hmgc, 0.1, 0.7)
hui = LimitIndices(humgc, 0.1, 0.7)
di = LimitIndices(dmgc, 0.1, 0.7)
dui = LimitIndices(dumgc, 0.1, 0.7)
hi = hl > 1000
hui = hul > 1000
di =dl > 1000
dui = dul > 1000
ax1 = plt.subplot(121)
ax1.scatter(dl[di], dmgc[di], color="DarkRed", alpha=0.10)
ax1.scatter(hl[hi], hmgc[hi], color="DarkBlue", alpha=0.10)
ax2 = plt.subplot(122)
ax2.scatter(dul[dui], dumgc[dui], color="HotPink", alpha=0.10)
ax2.scatter(hul[hui], humgc[hui], color="DodgerBlue", alpha=0.10)
plt.show()
print numpy.mean(hmgc[hi])
print numpy.mean(humgc[hui])
print numpy.mean(dmgc[di])
print numpy.mean(dumgc[dui])
def GetGCContentByLength(lens, gc, nBins = 100):
maxLength = np.max(lens)
binSize = maxLength/nBins
gcBins = [ [] for i in range(0,nBins)]
for i in range(0,len(lens)):
binIndex = min(int(lens[i]/binSize), nBins-1)
gcBins[binIndex].append(gc[i])
means = [ np.mean(gcBins[i]) if (len(gcBins[i]) > 0) else 0 for i in range(0,nBins) ]
sds = [ np.std(gcBins[i]) if (len(gcBins[i]) > 0) else 0 for i in range(0,nBins) ]
x = [ binSize * i for i in range(0,nBins) ]
return (x, np.array(means), np.array(sds))
(dx,dm,ds) = GetGCContentByLength(dl, dmgc)
(dux,dum,dus) = GetGCContentByLength(dul, dumgc)
(hx,hm,hs) = GetGCContentByLength(hl, hmgc)
(hux,hum,hus) = GetGCContentByLength(hul, humgc)
fig = plt.figure(figsize=(12,6))
ax1 = plt.subplot(121)
ax1.errorbar(hx,hm,yerr=hs, ecolor="DodgerBlue", color="blue")
ax1.errorbar(dx,dm,yerr=ds, ecolor="HotPink", color="red")
ax1.legend(("Dra1", "control"))
ax1.set(title="GC content of mapped reads by length")
ax1.axis([-1000,20000,0.1,0.7])
ax2 = plt.subplot(122)
ax2.errorbar(hux,hum,yerr=hus, ecolor="DodgerBlue", color="blue")
ax2.errorbar(dux,dum,yerr=dus, ecolor="HotPink", color="red")
ax2.set(title="GC content of unmapped reads by length")
ax2.axis([-1000,20000,0.1,0.7])
ax2.legend(("Dra1", "control"))
plt.show()