def bias_correction(bam, signal, fBiasDict, rBiasDict, genome_file_name, chrName, start, end):
# Parameters
window = 50
defaultKmerValue = 1.0
# Initialization
fastaFile = Fastafile(genome_file_name)
k_nb = len(fBiasDict.keys()[0])
p1 = start; p2 = end
p1_w = p1 - (window/2); p2_w = p2 + (window/2)
p1_wk = p1_w - (k_nb/2); p2_wk = p2_w + (k_nb/2)
# Raw counts
nf = [0.0] * (p2_w-p1_w); nr = [0.0] * (p2_w-p1_w)
for r in bam.fetch(chrName, p1_w, p2_w):
if((not r.is_reverse) and (r.pos > p1_w)): nf[r.pos-p1_w] += 1.0
if((r.is_reverse) and ((r.aend-1) < p2_w)): nr[r.aend-1-p1_w] += 1.0
# Smoothed counts
Nf = []; Nr = [];
fSum = sum(nf[:window]); rSum = sum(nr[:window]);
fLast = nf[0]; rLast = nr[0]
for i in range((window/2),len(nf)-(window/2)):
Nf.append(fSum)
Nr.append(rSum)
fSum -= fLast; fSum += nf[i+(window/2)]; fLast = nf[i-(window/2)+1]
rSum -= rLast; rSum += nr[i+(window/2)]; rLast = nr[i-(window/2)+1]
# Fetching sequence
currStr = str(fastaFile.fetch(chrName, p1_wk-1, p2_wk-2)).upper()
currRevComp = revcomp(str(fastaFile.fetch(chrName,p1_wk+2, p2_wk+1)).upper())
# Iterating on sequence to create signal
af = []; ar = []
for i in range((k_nb/2),len(currStr)-(k_nb/2)+1):
fseq = currStr[i-(k_nb/2):i+(k_nb/2)]
rseq = currRevComp[len(currStr)-(k_nb/2)-i:len(currStr)+(k_nb/2)-i]
try: af.append(fBiasDict[fseq])
except Exception: af.append(defaultKmerValue)
try: ar.append(rBiasDict[rseq])
except Exception: ar.append(defaultKmerValue)
# Calculating bias and writing to wig file
fSum = sum(af[:window]); rSum = sum(ar[:window]);
fLast = af[0]; rLast = ar[0]
bias_corrected_signal = []
for i in range((window/2),len(af)-(window/2)):
nhatf = Nf[i-(window/2)]*(af[i]/fSum)
nhatr = Nr[i-(window/2)]*(ar[i]/rSum)
zf = log(nf[i]+1)-log(nhatf+1)
zr = log(nr[i]+1)-log(nhatr+1)
bias_corrected_signal.append(zf+zr)
fSum -= fLast; fSum += af[i+(window/2)]; fLast = af[i-(window/2)+1]
rSum -= rLast; rSum += ar[i+(window/2)]; rLast = ar[i-(window/2)+1]
# Termination
fastaFile.close()
return bias_corrected_signal
outputFile.write("fixedStep chrom="+chrName+" start="+str(p1+1)+" step=1\n")
fSum = sum(af[:window]); rSum = sum(ar[:window]);
fLast = af[0]; rLast = ar[0]
for i in range((window/2),len(af)-(window/2)):
nhatf = Nf[i-(window/2)]*(af[i]/fSum)
nhatr = Nr[i-(window/2)]*(ar[i]/rSum)
zf = log(nf[i]+1)-log(nhatf+1)
zr = log(nr[i]+1)-log(nhatr+1)
outputFile.write(str(round(zf+zr,4))+"\n")
#print i+p1+1-(window/2), af[i], ar[i], fSum, rSum, Nf[i-(window/2)], Nr[i-(window/2)]
fSum -= fLast; fSum += af[i+(window/2)]; fLast = af[i-(window/2)+1]
rSum -= rLast; rSum += ar[i+(window/2)]; rLast = ar[i-(window/2)+1]
#for i in range(p1, p2):
# print i+1, z[i-p1]
except Exception: continue
# Closing files
bamFile.close()
fastaFile.close()
coordFile.close()
outputFile.close()
# Converting to bigwig
os.system(" ".join(["wigToBigWig",outputFileName,csFileName,outputFileName[:-3]+"bw"]))
os.system(" ".join(["wigToBigWig",outputFileNameRaw,csFileName,outputFileNameRaw[:-3]+"bw"]))
#os.system(" ".join(["rm",outputFileName]))
def bias_correction(chrom, start, end, bam, bias_table, genome_file_name, forward_shift, reverse_shift):
# Parameters
window = 50
defaultKmerValue = 1.0
# Initialization
fastaFile = Fastafile(genome_file_name)
fBiasDict = bias_table[0]
rBiasDict = bias_table[1]
k_nb = len(fBiasDict.keys()[0])
p1 = start
p2 = end
p1_w = p1 - (window / 2)
p2_w = p2 + (window / 2)
p1_wk = p1_w - int(floor(k_nb / 2.))
p2_wk = p2_w + int(ceil(k_nb / 2.))
if p1 <= 0 or p1_w <= 0 or p2_wk <= 0:
# Return raw counts
bc_signal = [0.0] * (p2 - p1)
for read in bam.fetch(chrom, p1, p2):
# check if the read is unmapped, according to issue #112
if read.is_unmapped:
continue
if not read.is_reverse:
cut_site = read.pos + forward_shift
if p1 <= cut_site < p2:
bc_signal[cut_site - p1] += 1.0
else:
cut_site = read.aend + reverse_shift - 1
if p1 <= cut_site < p2:
bc_signal[cut_site - p1] += 1.0
return bc_signal
# Raw counts
nf = [0.0] * (p2_w - p1_w)
nr = [0.0] * (p2_w - p1_w)
for read in bam.fetch(chrom, p1_w, p2_w):
# check if the read is unmapped, according to issue #112
if read.is_unmapped:
continue
if not read.is_reverse:
cut_site = read.pos + forward_shift
if p1_w <= cut_site < p2_w:
nf[cut_site - p1_w] += 1.0
else:
cut_site = read.aend + reverse_shift - 1
if p1_w <= cut_site < p2_w:
nr[cut_site - p1_w] += 1.0
# Smoothed counts
Nf = []
Nr = []
f_sum = sum(nf[:window])
r_sum = sum(nr[:window])
f_last = nf[0]
r_last = nr[0]
for i in range((window / 2), len(nf) - (window / 2)):
Nf.append(f_sum)
Nr.append(r_sum)
f_sum -= f_last
f_sum += nf[i + (window / 2)]
f_last = nf[i - (window / 2) + 1]
r_sum -= r_last
r_sum += nr[i + (window / 2)]
r_last = nr[i - (window / 2) + 1]
# Fetching sequence
currStr = str(fastaFile.fetch(chrom, p1_wk, p2_wk - 1)).upper()
currRevComp = AuxiliaryFunctions.revcomp(str(fastaFile.fetch(chrom, p1_wk + 1, p2_wk)).upper())
# Iterating on sequence to create signal
af = []
ar = []
for i in range(int(ceil(k_nb / 2.)), len(currStr) - int(floor(k_nb / 2)) + 1):
fseq = currStr[i - int(floor(k_nb / 2.)):i + int(ceil(k_nb / 2.))]
rseq = currRevComp[len(currStr) - int(ceil(k_nb / 2.)) - i:len(currStr) + int(floor(k_nb / 2.)) - i]
try:
af.append(fBiasDict[fseq])
except Exception:
af.append(defaultKmerValue)
try:
ar.append(rBiasDict[rseq])
except Exception:
ar.append(defaultKmerValue)
# Calculating bias and writing to wig file
f_sum = sum(af[:window])
r_sum = sum(ar[:window])
f_last = af[0]
r_last = ar[0]
bc_signal = []
for i in range((window / 2), len(af) - (window / 2)):
nhatf = Nf[i - (window / 2)] * (af[i] / f_sum)
nhatr = Nr[i - (window / 2)] * (ar[i] / r_sum)
bc_signal.append(nhatf + nhatr)
f_sum -= f_last
f_sum += af[i + (window / 2)]
f_last = af[i - (window / 2) + 1]
r_sum -= r_last
r_sum += ar[i + (window / 2)]
r_last = ar[i - (window / 2) + 1]
# Termination
fastaFile.close()
return bc_signal
def bias_correction(bam, signal, fBiasDict, rBiasDict, genome_file_name,
chrName, start, end):
# Parameters
window = 50
defaultKmerValue = 1.0
# Initialization
fastaFile = Fastafile(genome_file_name)
k_nb = len(fBiasDict.keys()[0])
p1 = start
p2 = end
p1_w = p1 - (window / 2)
p2_w = p2 + (window / 2)
p1_wk = p1_w - (k_nb / 2)
p2_wk = p2_w + (k_nb / 2)
# Raw counts
nf = [0.0] * (p2_w - p1_w)
nr = [0.0] * (p2_w - p1_w)
for r in bam.fetch(chrName, p1_w, p2_w):
if ((not r.is_reverse) and (r.pos > p1_w)): nf[r.pos - p1_w] += 1.0
if ((r.is_reverse) and ((r.aend - 1) < p2_w)):
nr[r.aend - 1 - p1_w] += 1.0
# Smoothed counts
Nf = []
Nr = []
fSum = sum(nf[:window])
rSum = sum(nr[:window])
fLast = nf[0]
rLast = nr[0]
for i in range((window / 2), len(nf) - (window / 2)):
Nf.append(fSum)
Nr.append(rSum)
fSum -= fLast
fSum += nf[i + (window / 2)]
fLast = nf[i - (window / 2) + 1]
rSum -= rLast
rSum += nr[i + (window / 2)]
rLast = nr[i - (window / 2) + 1]
# Fetching sequence
currStr = str(fastaFile.fetch(chrName, p1_wk - 1, p2_wk - 2)).upper()
currRevComp = revcomp(
str(fastaFile.fetch(chrName, p1_wk + 2, p2_wk + 1)).upper())
# Iterating on sequence to create signal
af = []
ar = []
for i in range((k_nb / 2), len(currStr) - (k_nb / 2) + 1):
fseq = currStr[i - (k_nb / 2):i + (k_nb / 2)]
rseq = currRevComp[len(currStr) - (k_nb / 2) - i:len(currStr) +
(k_nb / 2) - i]
try:
af.append(fBiasDict[fseq])
except Exception:
af.append(defaultKmerValue)
try:
ar.append(rBiasDict[rseq])
except Exception:
ar.append(defaultKmerValue)
# Calculating bias and writing to wig file
fSum = sum(af[:window])
rSum = sum(ar[:window])
fLast = af[0]
rLast = ar[0]
bias_corrected_signal = []
for i in range((window / 2), len(af) - (window / 2)):
nhatf = Nf[i - (window / 2)] * (af[i] / fSum)
nhatr = Nr[i - (window / 2)] * (ar[i] / rSum)
zf = log(nf[i] + 1) - log(nhatf + 1)
zr = log(nr[i] + 1) - log(nhatr + 1)
bias_corrected_signal.append(zf + zr)
fSum -= fLast
fSum += af[i + (window / 2)]
fLast = af[i - (window / 2) + 1]
rSum -= rLast
rSum += ar[i + (window / 2)]
rLast = ar[i - (window / 2) + 1]
# Termination
fastaFile.close()
return bias_corrected_signal
def estimate_bias_kmer(args):
# Parameters
maxDuplicates = 100
pseudocount = 1.0
# Initializing bam and fasta
bamFile = Samfile(args.reads_file, "rb")
genome_data = GenomeData(args.organism)
fastaFile = Fastafile(genome_data.get_genome())
regions = GenomicRegionSet("regions")
regions.read(args.regions_file)
# Initializing dictionaries
obsDictF = dict()
obsDictR = dict()
expDictF = dict()
expDictR = dict()
ct_reads_r = 0
ct_reads_f = 0
ct_kmers = 0
# Iterating on HS regions
for region in regions:
# Initialization
prevPos = -1
trueCounter = 0
# Evaluating observed frequencies ####################################
# Fetching reads
for r in bamFile.fetch(region.chrom, region.initial, region.final):
# Calculating positions
if not r.is_reverse:
cut_site = r.pos + args.forward_shift - 1
p1 = cut_site - int(floor(args.k_nb / 2))
else:
cut_site = r.aend + args.reverse_shift + 1
p1 = cut_site - int(floor(args.k_nb / 2))
p2 = p1 + args.k_nb
# Verifying PCR artifacts
if p1 == prevPos:
trueCounter += 1
else:
prevPos = p1
trueCounter = 0
if trueCounter > maxDuplicates: continue
# Fetching k-mer
try:
currStr = str(fastaFile.fetch(region.chrom, p1, p2)).upper()
except Exception:
continue
if r.is_reverse: currStr = AuxiliaryFunctions.revcomp(currStr)
# Counting k-mer in dictionary
if not r.is_reverse:
ct_reads_f += 1
try:
obsDictF[currStr] += 1
except Exception:
obsDictF[currStr] = 1
else:
ct_reads_r += 1
try:
obsDictR[currStr] += 1
except Exception:
obsDictR[currStr] = 1
# Evaluating expected frequencies ####################################
# Fetching whole sequence
try:
currStr = str(fastaFile.fetch(region.chrom, region.initial, region.final)).upper()
except Exception:
continue
currRevComp = AuxiliaryFunctions.revcomp(currStr)
# Iterating on each sequence position
for i in range(0, len(currStr) - args.k_nb):
ct_kmers += 1
# Counting k-mer in dictionary
s = currStr[i:i + args.k_nb]
try:
expDictF[s] += 1
except Exception:
expDictF[s] = 1
# Counting k-mer in dictionary for reverse complement
s = currRevComp[i:i + args.k_nb]
try:
expDictR[s] += 1
except Exception:
expDictR[s] = 1
# Closing files
bamFile.close()
fastaFile.close()
# Creating bias dictionary
alphabet = ["A", "C", "G", "T"]
kmerComb = ["".join(e) for e in product(alphabet, repeat=args.k_nb)]
bias_table_F = dict([(e, 0.0) for e in kmerComb])
bias_table_R = dict([(e, 0.0) for e in kmerComb])
for kmer in kmerComb:
try:
obsF = obsDictF[kmer] + pseudocount
except Exception:
obsF = pseudocount
try:
expF = expDictF[kmer] + pseudocount
except Exception:
expF = pseudocount
if ct_reads_f == 0:
bias_table_F[kmer] = 1
else:
bias_table_F[kmer] = round(float(obsF / ct_reads_f) / float(expF / ct_kmers), 6)
try:
obsR = obsDictR[kmer] + pseudocount
except Exception:
obsR = pseudocount
try:
expR = expDictR[kmer] + pseudocount
except Exception:
expR = pseudocount
if ct_reads_r == 0:
bias_table_R[kmer] = 1
else:
bias_table_R[kmer] = round(float(obsR / ct_reads_r) / float(expR / ct_kmers), 6)
write_table(args.output_location, args.output_prefix, [bias_table_F, bias_table_R])
def bias_correction(self, signal, bias_table, genome_file_name, chrName,
start, end, forward_shift, reverse_shift,
strands_specific):
"""
Performs bias correction.
Keyword arguments:
signal -- Input signal.
bias_table -- Bias table.
Return:
bias_corrected_signal -- Bias-corrected sequence.
"""
if (not bias_table): return signal
# Parameters
window = 50
defaultKmerValue = 1.0
# Initialization
fastaFile = Fastafile(genome_file_name)
fBiasDict = bias_table[0]
rBiasDict = bias_table[1]
k_nb = len(fBiasDict.keys()[0])
p1 = start
p2 = end
p1_w = p1 - (window / 2)
p2_w = p2 + (window / 2)
p1_wk = p1_w - int(floor(k_nb / 2.))
p2_wk = p2_w + int(ceil(k_nb / 2.))
if (p1 <= 0 or p1_w <= 0 or p1_wk <= 0): return signal
# Raw counts
nf = [0.0] * (p2_w - p1_w)
nr = [0.0] * (p2_w - p1_w)
for read in self.bam.fetch(chrName, p1_w, p2_w):
if (not read.is_reverse):
cut_site = read.pos + forward_shift
if cut_site >= start and cut_site < end:
nf[cut_site - p1_w] += 1.0
# for i in range(max(read.pos + forward_shift, start), min(read.pos + forward_shift + 1, end - 1)):
# nf[i - start] += 1.0
else:
cut_site = read.aend + reverse_shift - 1
if cut_site >= start and cut_site < end:
nr[cut_site - p1_w] += 1.0
# for i in range(max(read.aend + reverse_shift - 1, start), min(read.aend + reverse_shift, end - 1)):
# nr[i - start] += 1.0
# if ((not read.is_reverse) and (read.pos > p1_w)): nf[read.pos - p1_w] += 1.0
# if ((read.is_reverse) and ((read.aend - 1) < p2_w)): nr[read.aend - 1 - p1_w] += 1.0
# Smoothed counts
Nf = []
Nr = []
fSum = sum(nf[:window])
rSum = sum(nr[:window])
fLast = nf[0]
rLast = nr[0]
for i in range((window / 2), len(nf) - (window / 2)):
Nf.append(fSum)
Nr.append(rSum)
fSum -= fLast
fSum += nf[i + (window / 2)]
fLast = nf[i - (window / 2) + 1]
rSum -= rLast
rSum += nr[i + (window / 2)]
rLast = nr[i - (window / 2) + 1]
# Fetching sequence
currStr = str(fastaFile.fetch(chrName, p1_wk - 1, p2_wk - 2)).upper()
currRevComp = AuxiliaryFunctions.revcomp(
str(fastaFile.fetch(chrName, p1_wk + 2, p2_wk + 1)).upper())
#currStr = str(fastaFile.fetch(chrName, p1_wk, p2_wk - 1)).upper()
#currRevComp = AuxiliaryFunctions.revcomp(str(fastaFile.fetch(chrName, p1_wk + 1,
# p2_wk)).upper())
# Iterating on sequence to create signal
af = []
ar = []
for i in range(int(ceil(k_nb / 2.)),
len(currStr) - int(floor(k_nb / 2)) + 1):
fseq = currStr[i - int(floor(k_nb / 2.)):i + int(ceil(k_nb / 2.))]
rseq = currRevComp[len(currStr) - int(ceil(k_nb / 2.)) -
i:len(currStr) + int(floor(k_nb / 2.)) - i]
try:
af.append(fBiasDict[fseq])
except Exception:
af.append(defaultKmerValue)
try:
ar.append(rBiasDict[rseq])
except Exception:
ar.append(defaultKmerValue)
# Calculating bias and writing to wig file
fSum = sum(af[:window])
rSum = sum(ar[:window])
fLast = af[0]
rLast = ar[0]
bias_corrected_signal = []
bias_corrected_signal_forward = []
bias_corrected_signal_reverse = []
for i in range((window / 2), len(af) - (window / 2)):
nhatf = Nf[i - (window / 2)] * (af[i] / fSum)
nhatr = Nr[i - (window / 2)] * (ar[i] / rSum)
zf = log(nf[i] + 1) - log(nhatf + 1)
zr = log(nr[i] + 1) - log(nhatr + 1)
bias_corrected_signal_forward.append(zf)
bias_corrected_signal_reverse.append(zr)
bias_corrected_signal.append(zf + zr)
fSum -= fLast
fSum += af[i + (window / 2)]
fLast = af[i - (window / 2) + 1]
rSum -= rLast
rSum += ar[i + (window / 2)]
rLast = ar[i - (window / 2) + 1]
# Fixing the negative number in bias corrected signal
min_value = abs(min(bias_corrected_signal_forward))
bias_fixed_signal_forward = [
e + min_value for e in bias_corrected_signal_forward
]
min_value = abs(min(bias_corrected_signal_reverse))
bias_fixed_signal_reverse = [
e + min_value for e in bias_corrected_signal_reverse
]
min_value = abs(min(bias_corrected_signal))
bias_fixed_signal = [e + min_value for e in bias_corrected_signal]
# Termination
fastaFile.close()
if not strands_specific:
return bias_corrected_signal
else:
return bias_fixed_signal_forward, bias_fixed_signal_reverse
def bias_correction_atac(self, bias_table, genome_file_name, chrName, start, end,
forward_shift, reverse_shift):
# Parameters
window = 50
defaultKmerValue = 1.0
# Initialization
fastaFile = Fastafile(genome_file_name)
fBiasDict = bias_table[0]
rBiasDict = bias_table[1]
k_nb = len(fBiasDict.keys()[0])
p1 = start
p2 = end
p1_w = p1 - (window / 2)
p2_w = p2 + (window / 2)
p1_wk = p1_w - int(floor(k_nb / 2.))
p2_wk = p2_w + int(ceil(k_nb / 2.))
if (p1 <= 0 or p1_w <= 0 or p2_wk <= 0):
# Return raw counts
nf = [0.0] * (p2 - p1)
nr = [0.0] * (p2 - p1)
for read in self.bam.fetch(chrName, p1, p2):
if not read.is_reverse:
cut_site = read.pos + forward_shift
if p1 <= cut_site < p2:
nf[cut_site - p1] += 1.0
else:
cut_site = read.aend + reverse_shift - 1
if p1 <= cut_site < p2:
nr[cut_site - p1] += 1.0
return nf, nr
# Raw counts
nf = [0.0] * (p2_w - p1_w)
nr = [0.0] * (p2_w - p1_w)
for read in self.bam.fetch(chrName, p1_w, p2_w):
if not read.is_reverse:
cut_site = read.pos + forward_shift
if p1_w <= cut_site < p2_w:
nf[cut_site - p1_w] += 1.0
else:
cut_site = read.aend + reverse_shift - 1
if p1_w <= cut_site < p2_w:
nr[cut_site - p1_w] += 1.0
# Smoothed counts
Nf = []
Nr = []
fSum = sum(nf[:window])
rSum = sum(nr[:window])
fLast = nf[0]
rLast = nr[0]
for i in range((window / 2), len(nf) - (window / 2)):
Nf.append(fSum)
Nr.append(rSum)
fSum -= fLast
fSum += nf[i + (window / 2)]
fLast = nf[i - (window / 2) + 1]
rSum -= rLast
rSum += nr[i + (window / 2)]
rLast = nr[i - (window / 2) + 1]
# Fetching sequence
currStr = str(fastaFile.fetch(chrName, p1_wk, p2_wk - 1)).upper()
currRevComp = AuxiliaryFunctions.revcomp(str(fastaFile.fetch(chrName, p1_wk + 1,
p2_wk)).upper())
# Iterating on sequence to create signal
af = []
ar = []
for i in range(int(ceil(k_nb / 2.)), len(currStr) - int(floor(k_nb / 2)) + 1):
fseq = currStr[i - int(floor(k_nb / 2.)):i + int(ceil(k_nb / 2.))]
rseq = currRevComp[len(currStr) - int(ceil(k_nb / 2.)) - i:len(currStr) + int(floor(k_nb / 2.)) - i]
try:
af.append(fBiasDict[fseq])
except Exception:
af.append(defaultKmerValue)
try:
ar.append(rBiasDict[rseq])
except Exception:
ar.append(defaultKmerValue)
# Calculating bias and writing to wig file
fSum = sum(af[:window])
rSum = sum(ar[:window])
fLast = af[0]
rLast = ar[0]
bias_corrected_signal_forward = []
bias_corrected_signal_reverse = []
for i in range((window / 2), len(af) - (window / 2)):
nhatf = Nf[i - (window / 2)] * (af[i] / fSum)
nhatr = Nr[i - (window / 2)] * (ar[i] / rSum)
bias_corrected_signal_forward.append(nhatf)
bias_corrected_signal_reverse.append(nhatr)
fSum -= fLast
fSum += af[i + (window / 2)]
fLast = af[i - (window / 2) + 1]
rSum -= rLast
rSum += ar[i + (window / 2)]
rLast = ar[i - (window / 2) + 1]
# Termination
fastaFile.close()
return bias_corrected_signal_forward, bias_corrected_signal_reverse
def bias_correction(self, signal, bias_table, genome_file_name, chrName, start, end,
forward_shift, reverse_shift, strands_specific):
"""
Performs bias correction.
Keyword arguments:
signal -- Input signal.
bias_table -- Bias table.
Return:
bias_corrected_signal -- Bias-corrected sequence.
"""
if (not bias_table): return signal
# Parameters
window = 50
defaultKmerValue = 1.0
# Initialization
fastaFile = Fastafile(genome_file_name)
fBiasDict = bias_table[0]
rBiasDict = bias_table[1]
k_nb = len(fBiasDict.keys()[0])
p1 = start
p2 = end
p1_w = p1 - (window / 2)
p2_w = p2 + (window / 2)
p1_wk = p1_w - int(floor(k_nb / 2.))
p2_wk = p2_w + int(ceil(k_nb / 2.))
if (p1 <= 0 or p1_w <= 0 or p1_wk <= 0): return signal
# Raw counts
nf = [0.0] * (p2_w - p1_w)
nr = [0.0] * (p2_w - p1_w)
for read in self.bam.fetch(chrName, p1_w, p2_w):
if (not read.is_reverse):
cut_site = read.pos + forward_shift
if cut_site >= start and cut_site < end:
nf[cut_site - p1_w] += 1.0
# for i in range(max(read.pos + forward_shift, start), min(read.pos + forward_shift + 1, end - 1)):
# nf[i - start] += 1.0
else:
cut_site = read.aend + reverse_shift - 1
if cut_site >= start and cut_site < end:
nr[cut_site - p1_w] += 1.0
# for i in range(max(read.aend + reverse_shift - 1, start), min(read.aend + reverse_shift, end - 1)):
# nr[i - start] += 1.0
# if ((not read.is_reverse) and (read.pos > p1_w)): nf[read.pos - p1_w] += 1.0
# if ((read.is_reverse) and ((read.aend - 1) < p2_w)): nr[read.aend - 1 - p1_w] += 1.0
# Smoothed counts
Nf = []
Nr = []
fSum = sum(nf[:window])
rSum = sum(nr[:window])
fLast = nf[0]
rLast = nr[0]
for i in range((window / 2), len(nf) - (window / 2)):
Nf.append(fSum)
Nr.append(rSum)
fSum -= fLast
fSum += nf[i + (window / 2)]
fLast = nf[i - (window / 2) + 1]
rSum -= rLast
rSum += nr[i + (window / 2)]
rLast = nr[i - (window / 2) + 1]
# Fetching sequence
currStr = str(fastaFile.fetch(chrName, p1_wk-1, p2_wk-2)).upper()
currRevComp = AuxiliaryFunctions.revcomp(str(fastaFile.fetch(chrName,p1_wk+2, p2_wk+1)).upper())
#currStr = str(fastaFile.fetch(chrName, p1_wk, p2_wk - 1)).upper()
#currRevComp = AuxiliaryFunctions.revcomp(str(fastaFile.fetch(chrName, p1_wk + 1,
# p2_wk)).upper())
# Iterating on sequence to create signal
af = []
ar = []
for i in range(int(ceil(k_nb / 2.)), len(currStr) - int(floor(k_nb / 2)) + 1):
fseq = currStr[i - int(floor(k_nb / 2.)):i + int(ceil(k_nb / 2.))]
rseq = currRevComp[len(currStr) - int(ceil(k_nb / 2.)) - i:len(currStr) + int(floor(k_nb / 2.)) - i]
try:
af.append(fBiasDict[fseq])
except Exception:
af.append(defaultKmerValue)
try:
ar.append(rBiasDict[rseq])
except Exception:
ar.append(defaultKmerValue)
# Calculating bias and writing to wig file
fSum = sum(af[:window])
rSum = sum(ar[:window])
fLast = af[0]
rLast = ar[0]
bias_corrected_signal = []
bias_corrected_signal_forward = []
bias_corrected_signal_reverse = []
for i in range((window / 2), len(af) - (window / 2)):
nhatf = Nf[i - (window / 2)] * (af[i] / fSum)
nhatr = Nr[i - (window / 2)] * (ar[i] / rSum)
zf = log(nf[i] + 1) - log(nhatf + 1)
zr = log(nr[i] + 1) - log(nhatr + 1)
bias_corrected_signal_forward.append(zf)
bias_corrected_signal_reverse.append(zr)
bias_corrected_signal.append(zf + zr)
fSum -= fLast
fSum += af[i + (window / 2)]
fLast = af[i - (window / 2) + 1]
rSum -= rLast
rSum += ar[i + (window / 2)]
rLast = ar[i - (window / 2) + 1]
# Fixing the negative number in bias corrected signal
min_value = abs(min(bias_corrected_signal_forward))
bias_fixed_signal_forward = [e + min_value for e in bias_corrected_signal_forward]
min_value = abs(min(bias_corrected_signal_reverse))
bias_fixed_signal_reverse = [e + min_value for e in bias_corrected_signal_reverse]
min_value = abs(min(bias_corrected_signal))
bias_fixed_signal = [e + min_value for e in bias_corrected_signal]
# Termination
fastaFile.close()
if not strands_specific:
return bias_corrected_signal
else:
return bias_fixed_signal_forward, bias_fixed_signal_reverse
def estimate_table_pwm(self, regions, dnase_file_name, genome_file_name,
k_nb, forward_shift, reverse_shift):
"""
Estimates bias based on HS regions, DNase-seq signal and genomic sequences.
Keyword arguments:
regions -- DNase-seq HS regions.
atac_file_name -- DNase-seq file name.
genome_file_name -- Genome to fetch genomic sequences from.
Return:
bias_table_F, bias_table_R -- Bias tables.
"""
# Initializing bam and fasta
if (dnase_file_name.split(".")[-1].upper() != "BAM"):
return None # TODO ERROR
bamFile = Samfile(dnase_file_name, "rb")
fastaFile = Fastafile(genome_file_name)
obsSeqsF = []
obsSeqsR = []
expSeqsF = []
expSeqsR = []
# Iterating on HS regions
for region in regions:
# Evaluating observed frequencies
# Fetching reads
for r in bamFile.fetch(region.chrom, region.initial, region.final):
# Calculating positions
# if(not r.is_reverse): p1 = r.pos - (k_nb/2) - 1 + shift
# else: p1 = r.aend - (k_nb/2) + 1 - shift
if (not r.is_reverse):
cut_site = r.pos + forward_shift - 1
p1 = cut_site - int(floor(k_nb / 2))
else:
cut_site = r.aend + reverse_shift + 1
p1 = cut_site - int(floor(k_nb / 2))
p2 = p1 + k_nb
# Fetching k-mer
try:
currStr = str(fastaFile.fetch(region.chrom, p1,
p2)).upper()
except Exception:
continue
if (r.is_reverse):
currStr = AuxiliaryFunctions.revcomp(currStr)
# Counting k-mer in dictionary
if 'N' not in currStr:
if (not r.is_reverse):
obsSeqsF.append(Seq(currStr))
else:
obsSeqsR.append(Seq(currStr))
# Evaluating expected frequencies
# Fetching whole sequence
try:
currStr = str(
fastaFile.fetch(region.chrom, region.initial,
region.final)).upper()
except Exception:
continue
currRevComp = AuxiliaryFunctions.revcomp(currStr)
# Iterating on each sequence position
for i in range(0, len(currStr) - k_nb):
s = currStr[i:i + k_nb]
if 'N' not in currStr:
# Counting k-mer in dictionary
expSeqsF.append(Seq(s))
# Counting k-mer in dictionary for reverse complement
s = currRevComp[i:i + k_nb]
expSeqsR.append(Seq(s))
# Closing files
bamFile.close()
fastaFile.close()
obsMotifsF = motifs.create(obsSeqsF)
obsMotifsR = motifs.create(obsSeqsR)
expMotifsF = motifs.create(expSeqsF)
expMotifsR = motifs.create(expSeqsR)
obsPwmF = obsMotifsF.pwm
obsPwmR = obsMotifsR.pwm
expPwmF = expMotifsF.pwm
expPwmR = expMotifsR.pwm
# Output logos
logo_obs_f = os.path.join(
self.output_loc, "Bias", "logo",
"obs_{}_{}_f.pdf".format(str(k_nb), str(forward_shift)))
logo_obs_r = os.path.join(
self.output_loc, "Bias", "logo",
"obs_{}_{}_r.pdf".format(str(k_nb), str(forward_shift)))
logo_exp_f = os.path.join(
self.output_loc, "Bias", "logo",
"exp_{}_{}_f.pdf".format(str(k_nb), str(forward_shift)))
logo_exp_r = os.path.join(
self.output_loc, "Bias", "logo",
"exp_{}_{}_r.pdf".format(str(k_nb), str(forward_shift)))
obsMotifsF.weblogo(logo_obs_f,
format="pdf",
stack_width="large",
color_scheme="color_classic",
yaxis_scale=0.2,
yaxis_tic_interval=0.1)
obsMotifsR.weblogo(logo_obs_r,
format="pdf",
stack_width="large",
color_scheme="color_classic",
yaxis_scale=0.2,
yaxis_tic_interval=0.1)
expMotifsF.weblogo(logo_exp_f,
format="pdf",
stack_width="large",
color_scheme="color_classic",
yaxis_scale=0.02,
yaxis_tic_interval=0.01)
expMotifsR.weblogo(logo_exp_r,
format="pdf",
stack_width="large",
color_scheme="color_classic",
yaxis_scale=0.02,
yaxis_tic_interval=0.01)
# Output pwms
pwm_data_list = [obsPwmF, obsPwmR, expPwmF, expPwmR]
pwm_file_list = []
pwm_obs_f = os.path.join(
self.output_loc, "Bias", "pwm",
"obs_{}_{}_f.pwm".format(str(k_nb), str(forward_shift)))
pwm_obs_r = os.path.join(
self.output_loc, "Bias", "pwm",
"obs_{}_{}_r.pwm".format(str(k_nb), str(forward_shift)))
pwm_exp_f = os.path.join(
self.output_loc, "Bias", "pwm",
"exp_{}_{}_f.pwm".format(str(k_nb), str(forward_shift)))
pwm_exp_r = os.path.join(
self.output_loc, "Bias", "pwm",
"exp_{}_{}_r.pwm".format(str(k_nb), str(forward_shift)))
pwm_file_list.append(pwm_obs_f)
pwm_file_list.append(pwm_obs_r)
pwm_file_list.append(pwm_exp_f)
pwm_file_list.append(pwm_exp_r)
for i in range(len(pwm_data_list)):
with open(pwm_file_list[i], "w") as f:
f.write(str(pwm_data_list[i]))
# Creating bias dictionary
alphabet = ["A", "C", "G", "T"]
k_mer_comb = ["".join(e) for e in product(alphabet, repeat=k_nb)]
bias_table_F = dict([(e, 0.0) for e in k_mer_comb])
bias_table_R = dict([(e, 0.0) for e in k_mer_comb])
for k_mer in k_mer_comb:
obsF = self.get_pwm_score(k_mer, obsPwmF, k_nb)
expF = self.get_pwm_score(k_mer, expPwmF, k_nb)
bias_table_F[k_mer] = round(obsF / expF, 6)
obsR = self.get_pwm_score(k_mer, obsPwmR, k_nb)
expR = self.get_pwm_score(k_mer, expPwmR, k_nb)
bias_table_R[k_mer] = round(obsR / expR, 6)
# Return
return [bias_table_F, bias_table_R]
def estimate_table(self, regions, dnase_file_name, genome_file_name, k_nb,
forward_shift, reverse_shift):
"""
Estimates bias based on HS regions, DNase-seq signal and genomic sequences.
Keyword arguments:
regions -- DNase-seq HS regions.
dnase_file_name -- DNase-seq file name.
genome_file_name -- Genome to fetch genomic sequences from.
Return:
bias_table_F, bias_table_R -- Bias tables.
"""
# Parameters
maxDuplicates = 100
pseudocount = 1.0
# Initializing bam and fasta
if (dnase_file_name.split(".")[-1].upper() != "BAM"):
return None # TODO ERROR
bamFile = Samfile(dnase_file_name, "rb")
fastaFile = Fastafile(genome_file_name)
# Initializing dictionaries
obsDictF = dict()
obsDictR = dict()
expDictF = dict()
expDictR = dict()
ct_reads_r = 0
ct_reads_f = 0
ct_kmers = 0
# Iterating on HS regions
for region in regions:
# Initialization
prevPos = -1
trueCounter = 0
# Evaluating observed frequencies ####################################
# Fetching reads
for r in bamFile.fetch(region.chrom, region.initial, region.final):
# Calculating positions
if (not r.is_reverse):
cut_site = r.pos + forward_shift - 1
p1 = cut_site - int(floor(k_nb / 2))
else:
cut_site = r.aend + reverse_shift + 1
p1 = cut_site - int(floor(k_nb / 2))
p2 = p1 + k_nb
# Verifying PCR artifacts
if (p1 == prevPos):
trueCounter += 1
else:
prevPos = p1
trueCounter = 0
if (trueCounter > maxDuplicates): continue
# Fetching k-mer
try:
currStr = str(fastaFile.fetch(region.chrom, p1,
p2)).upper()
except Exception:
continue
if (r.is_reverse):
currStr = AuxiliaryFunctions.revcomp(currStr)
# Counting k-mer in dictionary
if (not r.is_reverse):
ct_reads_f += 1
try:
obsDictF[currStr] += 1
except Exception:
obsDictF[currStr] = 1
else:
ct_reads_r += 1
try:
obsDictR[currStr] += 1
except Exception:
obsDictR[currStr] = 1
# Evaluating expected frequencies ####################################
# Fetching whole sequence
try:
currStr = str(
fastaFile.fetch(region.chrom, region.initial,
region.final)).upper()
except Exception:
continue
currRevComp = AuxiliaryFunctions.revcomp(currStr)
# Iterating on each sequence position
for i in range(0, len(currStr) - k_nb):
ct_kmers += 1
# Counting k-mer in dictionary
s = currStr[i:i + k_nb]
try:
expDictF[s] += 1
except Exception:
expDictF[s] = 1
# Counting k-mer in dictionary for reverse complement
s = currRevComp[i:i + k_nb]
try:
expDictR[s] += 1
except Exception:
expDictR[s] = 1
# Closing files
bamFile.close()
fastaFile.close()
# Creating bias dictionary
alphabet = ["A", "C", "G", "T"]
kmerComb = ["".join(e) for e in product(alphabet, repeat=k_nb)]
bias_table_F = dict([(e, 0.0) for e in kmerComb])
bias_table_R = dict([(e, 0.0) for e in kmerComb])
for kmer in kmerComb:
try:
obsF = obsDictF[kmer] + pseudocount
except Exception:
obsF = pseudocount
try:
expF = expDictF[kmer] + pseudocount
except Exception:
expF = pseudocount
if ct_reads_f == 0:
bias_table_F[kmer] = 1
else:
bias_table_F[kmer] = round(
float(obsF / ct_reads_f) / float(expF / ct_kmers), 6)
try:
obsR = obsDictR[kmer] + pseudocount
except Exception:
obsR = pseudocount
try:
expR = expDictR[kmer] + pseudocount
except Exception:
expR = pseudocount
if ct_reads_r == 0:
bias_table_R[kmer] = 1
else:
bias_table_R[kmer] = round(
float(obsR / ct_reads_r) / float(expR / ct_kmers), 6)
# Return
return [bias_table_F, bias_table_R]
def estimate_table(self, regions, dnase_file_name, genome_file_name, k_nb, shift):
"""
Estimates bias based on HS regions, DNase-seq signal and genomic sequences.
Keyword arguments:
regions -- DNase-seq HS regions.
dnase_file_name -- DNase-seq file name.
genome_file_name -- Genome to fetch genomic sequences from.
Return:
bias_table_F, bias_table_R -- Bias tables.
"""
# Parameters
maxDuplicates = 100
pseudocount = 1.0
# Initializing bam and fasta
if(dnase_file_name.split(".")[-1].upper() != "BAM"): return None # TODO ERROR
bamFile = Samfile(dnase_file_name, "rb")
fastaFile = Fastafile(genome_file_name)
# Initializing dictionaries
obsDictF = dict(); obsDictR = dict()
expDictF = dict(); expDictR = dict()
ct_reads_r=0
ct_reads_f=0
ct_kmers=0
# Iterating on HS regions
for region in regions:
# Initialization
prevPos = -1
trueCounter = 0
# Evaluating observed frequencies ####################################
# Fetching reads
for r in bamFile.fetch(region.chrom, region.initial, region.final):
# Calculating positions
if(not r.is_reverse): p1 = r.pos - (k_nb/2) - 1 + shift
else: p1 = r.aend - (k_nb/2) + 1 - shift
p2 = p1 + k_nb
# Verifying PCR artifacts
if(p1 == prevPos): trueCounter += 1
else:
prevPos = p1
trueCounter = 0
if(trueCounter > maxDuplicates): continue
# Fetching k-mer
try: currStr = str(fastaFile.fetch(region.chrom, p1, p2)).upper()
except Exception: continue
if(r.is_reverse): currStr = AuxiliaryFunctions.revcomp(currStr)
# Counting k-mer in dictionary
if(not r.is_reverse):
ct_reads_r+=1
try: obsDictF[currStr] += 1
except Exception: obsDictF[currStr] = 1
else:
ct_reads_f+=1
try: obsDictR[currStr] += 1
except Exception: obsDictR[currStr] = 1
# Evaluating expected frequencies ####################################
# Fetching whole sequence
try: currStr = str(fastaFile.fetch(region.chrom, region.initial, region.final)).upper()
except Exception: continue
currRevComp = AuxiliaryFunctions.revcomp(currStr)
# Iterating on each sequence position
for i in range(0,len(currStr)-k_nb):
ct_kmers+=1
# Counting k-mer in dictionary
s = currStr[i:i+k_nb]
try: expDictF[s] += 1
except Exception: expDictF[s] = 1
# Counting k-mer in dictionary for reverse complement
s = currRevComp[i:i+k_nb]
try: expDictR[s] += 1
except Exception: expDictR[s] = 1
# Closing files
bamFile.close()
fastaFile.close()
# Creating bias dictionary
alphabet = ["A","C","G","T"]
kmerComb = ["".join(e) for e in product(alphabet, repeat=k_nb)]
bias_table_F = dict([(e,0.0) for e in kmerComb])
bias_table_R = dict([(e,0.0) for e in kmerComb])
for kmer in kmerComb:
try: obsF = obsDictF[kmer] + pseudocount
except Exception: obsF = pseudocount
try: expF = expDictF[kmer] + pseudocount
except Exception: expF = pseudocount
bias_table_F[kmer] = round(float(obsF/ct_reads_f)/float(expF/ct_kmers),6)
try: obsR = obsDictR[kmer] + pseudocount
except Exception: obsR = pseudocount
try: expR = expDictR[kmer] + pseudocount
except Exception: expR = pseudocount
bias_table_R[kmer] = round(float(obsR/ct_reads_r)/float(expR/ct_kmers),6)
# Return
return [bias_table_F, bias_table_R]
def estimate_bias_kmer(args):
# Parameters
maxDuplicates = 100
pseudocount = 1.0
# Initializing bam and fasta
bamFile = Samfile(args.reads_file, "rb")
genome_data = GenomeData(args.organism)
fastaFile = Fastafile(genome_data.get_genome())
regions = GenomicRegionSet("regions")
regions.read(args.regions_file)
# Initializing dictionaries
obsDictF = dict()
obsDictR = dict()
expDictF = dict()
expDictR = dict()
ct_reads_r = 0
ct_reads_f = 0
ct_kmers = 0
# Iterating on HS regions
for region in regions:
# Initialization
prevPos = -1
trueCounter = 0
# Evaluating observed frequencies ####################################
# Fetching reads
for r in bamFile.fetch(region.chrom, region.initial, region.final):
# Calculating positions
if not r.is_reverse:
cut_site = r.pos + args.forward_shift - 1
p1 = cut_site - int(floor(args.k_nb / 2))
else:
cut_site = r.aend + args.reverse_shift + 1
p1 = cut_site - int(floor(args.k_nb / 2))
p2 = p1 + args.k_nb
# Verifying PCR artifacts
if p1 == prevPos:
trueCounter += 1
else:
prevPos = p1
trueCounter = 0
if trueCounter > maxDuplicates: continue
# Fetching k-mer
try:
currStr = str(fastaFile.fetch(region.chrom, p1, p2)).upper()
except Exception:
continue
if r.is_reverse: currStr = AuxiliaryFunctions.revcomp(currStr)
# Counting k-mer in dictionary
if not r.is_reverse:
ct_reads_f += 1
try:
obsDictF[currStr] += 1
except Exception:
obsDictF[currStr] = 1
else:
ct_reads_r += 1
try:
obsDictR[currStr] += 1
except Exception:
obsDictR[currStr] = 1
# Evaluating expected frequencies ####################################
# Fetching whole sequence
try:
currStr = str(fastaFile.fetch(region.chrom, region.initial, region.final)).upper()
except Exception:
continue
currRevComp = AuxiliaryFunctions.revcomp(currStr)
# Iterating on each sequence position
for i in range(0, len(currStr) - args.k_nb):
ct_kmers += 1
# Counting k-mer in dictionary
s = currStr[i:i + args.k_nb]
try:
expDictF[s] += 1
except Exception:
expDictF[s] = 1
# Counting k-mer in dictionary for reverse complement
s = currRevComp[i:i + args.k_nb]
try:
expDictR[s] += 1
except Exception:
expDictR[s] = 1
# Closing files
bamFile.close()
fastaFile.close()
# Creating bias dictionary
alphabet = ["A", "C", "G", "T"]
kmerComb = ["".join(e) for e in product(alphabet, repeat=args.k_nb)]
bias_table_F = dict([(e, 0.0) for e in kmerComb])
bias_table_R = dict([(e, 0.0) for e in kmerComb])
for kmer in kmerComb:
try:
obsF = obsDictF[kmer] + pseudocount
except Exception:
obsF = pseudocount
try:
expF = expDictF[kmer] + pseudocount
except Exception:
expF = pseudocount
if ct_reads_f == 0:
bias_table_F[kmer] = 1
else:
bias_table_F[kmer] = round(float(obsF / ct_reads_f) / float(expF / ct_kmers), 6)
try:
obsR = obsDictR[kmer] + pseudocount
except Exception:
obsR = pseudocount
try:
expR = expDictR[kmer] + pseudocount
except Exception:
expR = pseudocount
if ct_reads_r == 0:
bias_table_R[kmer] = 1
else:
bias_table_R[kmer] = round(float(obsR / ct_reads_r) / float(expR / ct_kmers), 6)
write_table(args.output_location, args.output_prefix, [bias_table_F, bias_table_R])
def estimate_bias_pwm(args):
# Parameters
max_duplicates = 100
# Initializing bam and fasta
bamFile = Samfile(args.reads_file, "rb")
genome_data = GenomeData(args.organism)
fastaFile = Fastafile(genome_data.get_genome())
regions = GenomicRegionSet("regions")
regions.read(args.regions_file)
obs_f_pwm_dict = dict([("A", [0.0] * args.k_nb), ("C", [0.0] * args.k_nb),
("G", [0.0] * args.k_nb), ("T", [0.0] * args.k_nb), ("N", [0.0] * args.k_nb)])
exp_f_pwm_dict = dict([("A", [0.0] * args.k_nb), ("C", [0.0] * args.k_nb),
("G", [0.0] * args.k_nb), ("T", [0.0] * args.k_nb), ("N", [0.0] * args.k_nb)])
obs_r_pwm_dict = dict([("A", [0.0] * args.k_nb), ("C", [0.0] * args.k_nb),
("G", [0.0] * args.k_nb), ("T", [0.0] * args.k_nb), ("N", [0.0] * args.k_nb)])
exp_r_pwm_dict = dict([("A", [0.0] * args.k_nb), ("C", [0.0] * args.k_nb),
("G", [0.0] * args.k_nb), ("T", [0.0] * args.k_nb), ("N", [0.0] * args.k_nb)])
# Iterating on HS regions
for region in regions:
# Initialization
prev_pos = -1
true_counter = 0
# Evaluating observed frequencies
# Fetching reads
for r in bamFile.fetch(region.chrom, region.initial, region.final):
# Calculating positions
if not r.is_reverse:
cut_site = r.pos + args.forward_shift - 1
p1 = cut_site - int(floor(args.k_nb / 2))
else:
cut_site = r.aend + args.reverse_shift + 1
p1 = cut_site - int(floor(args.k_nb / 2))
p2 = p1 + args.k_nb
# Verifying PCR artifacts
if p1 == prev_pos:
true_counter += 1
else:
prev_pos = p1
true_counter = 0
if true_counter > max_duplicates: continue
# Fetching k-mer
try:
currStr = str(fastaFile.fetch(region.chrom, p1, p2)).upper()
except Exception:
continue
if r.is_reverse: currStr = AuxiliaryFunctions.revcomp(currStr)
# Counting k-mer in dictionary
if not r.is_reverse:
for i in range(0, len(currStr)):
obs_f_pwm_dict[currStr[i]][i] += 1
else:
for i in range(0, len(currStr)):
obs_r_pwm_dict[currStr[i]][i] += 1
# Evaluating expected frequencies
# Fetching whole sequence
try:
currStr = str(fastaFile.fetch(region.chrom, region.initial, region.final)).upper()
except Exception:
continue
# Iterating on each sequence position
s = None
for i in range(0, len(currStr) - args.k_nb):
# Counting k-mer in dictionary
s = currStr[i:i + args.k_nb]
for i in range(0, len(s)):
exp_f_pwm_dict[s[i]][i] += 1
# Counting k-mer in dictionary for reverse complement
s = AuxiliaryFunctions.revcomp(s)
for i in range(0, len(s)):
exp_r_pwm_dict[s[i]][i] += 1
# Closing files
bamFile.close()
fastaFile.close()
# Output pwms
os.system("mkdir -p " + os.path.join(args.output_location, "pfm"))
pwm_dict_list = [obs_f_pwm_dict, obs_r_pwm_dict, exp_f_pwm_dict, exp_r_pwm_dict]
pwm_file_list = []
pwm_obs_f = os.path.join(args.output_location, "pfm", "obs_{}_f.pfm".format(str(args.k_nb)))
pwm_obs_r = os.path.join(args.output_location, "pfm", "obs_{}_r.pfm".format(str(args.k_nb)))
pwm_exp_f = os.path.join(args.output_location, "pfm", "exp_{}_f.pfm".format(str(args.k_nb)))
pwm_exp_r = os.path.join(args.output_location, "pfm", "exp_{}_r.pfm".format(str(args.k_nb)))
pwm_file_list.append(pwm_obs_f)
pwm_file_list.append(pwm_obs_r)
pwm_file_list.append(pwm_exp_f)
pwm_file_list.append(pwm_exp_r)
for i in range(len(pwm_dict_list)):
with open(pwm_file_list[i], "w") as pwm_file:
for e in ["A", "C", "G", "T"]:
pwm_file.write(" ".join([str(int(f)) for f in pwm_dict_list[i][e]]) + "\n")
motif_obs_f = motifs.read(open(pwm_obs_f), "pfm")
motif_obs_r = motifs.read(open(pwm_obs_r), "pfm")
motif_exp_f = motifs.read(open(pwm_exp_f), "pfm")
motif_exp_r = motifs.read(open(pwm_exp_r), "pfm")
# Output logos
os.system("mkdir -p " + os.path.join(args.output_location, "logo"))
logo_obs_f = os.path.join(args.output_location, "logo", "obs_{}_f.pdf".format(str(args.k_nb)))
logo_obs_r = os.path.join(args.output_location, "logo", "obs_{}_r.pdf".format(str(args.k_nb)))
logo_exp_f = os.path.join(args.output_location, "logo", "exp_{}_f.pdf".format(str(args.k_nb)))
logo_exp_r = os.path.join(args.output_location, "logo", "exp_{}_r.pdf".format(str(args.k_nb)))
motif_obs_f.weblogo(logo_obs_f, format="pdf", stack_width="large", color_scheme="color_classic",
yaxis_scale=0.2, yaxis_tic_interval=0.1)
motif_obs_r.weblogo(logo_obs_r, format="pdf", stack_width="large", color_scheme="color_classic",
yaxis_scale=0.2, yaxis_tic_interval=0.1)
motif_exp_f.weblogo(logo_exp_f, format="pdf", stack_width="large", color_scheme="color_classic",
yaxis_scale=0.02, yaxis_tic_interval=0.01)
motif_exp_r.weblogo(logo_exp_r, format="pdf", stack_width="large", color_scheme="color_classic",
yaxis_scale=0.02, yaxis_tic_interval=0.01)
# Creating bias dictionary
alphabet = ["A", "C", "G", "T"]
k_mer_comb = ["".join(e) for e in product(alphabet, repeat=args.k_nb)]
bias_table_F = dict([(e, 0.0) for e in k_mer_comb])
bias_table_R = dict([(e, 0.0) for e in k_mer_comb])
for k_mer in k_mer_comb:
obs_f = get_ppm_score(k_mer, motif_obs_f.pwm, args.k_nb)
exp_f = get_ppm_score(k_mer, motif_exp_f.pwm, args.k_nb)
bias_table_F[k_mer] = round(obs_f / exp_f, 6)
obs_r = get_ppm_score(k_mer, motif_obs_r.pwm, args.k_nb)
exp_r = get_ppm_score(k_mer, motif_exp_r.pwm, args.k_nb)
bias_table_R[k_mer] = round(obs_r / exp_r, 6)
write_table(args.output_location, args.output_prefix, [bias_table_F, bias_table_R])
outputFile4.write("\t".join(header4) + "\n")
for i in range(0, maxV):
vec = []
for j in range(0, len(vectorTable4)):
try:
vec.append(vectorTable4[j][i])
except Exception:
vec.append("NA")
try:
vec.append(vectorTable5[j][i])
except Exception:
vec.append("NA")
outputFile4.write("\t".join(vec) + "\n")
stagFile.close()
outputFile1.close()
outputFile2.close()
outputFile3.close()
outputFile4.close()
genomeFile.close()
regionsFile.close()
#chrommHmmFile.close()
enhancersFile.close()
[e.close() for e in signalFileList]
[e.close() for e in controlFileList]
[e.close() for e in motifFileList]
# Removing all files
command = "rm -rf " + tempLocation
os.system(command)
def estimate_bias_pwm(args):
# Parameters
max_duplicates = 100
# Initializing bam and fasta
bamFile = Samfile(args.reads_file, "rb")
genome_data = GenomeData(args.organism)
fastaFile = Fastafile(genome_data.get_genome())
regions = GenomicRegionSet("regions")
regions.read(args.regions_file)
obs_f_pwm_dict = dict([("A", [0.0] * args.k_nb), ("C", [0.0] * args.k_nb),
("G", [0.0] * args.k_nb), ("T", [0.0] * args.k_nb), ("N", [0.0] * args.k_nb)])
exp_f_pwm_dict = dict([("A", [0.0] * args.k_nb), ("C", [0.0] * args.k_nb),
("G", [0.0] * args.k_nb), ("T", [0.0] * args.k_nb), ("N", [0.0] * args.k_nb)])
obs_r_pwm_dict = dict([("A", [0.0] * args.k_nb), ("C", [0.0] * args.k_nb),
("G", [0.0] * args.k_nb), ("T", [0.0] * args.k_nb), ("N", [0.0] * args.k_nb)])
exp_r_pwm_dict = dict([("A", [0.0] * args.k_nb), ("C", [0.0] * args.k_nb),
("G", [0.0] * args.k_nb), ("T", [0.0] * args.k_nb), ("N", [0.0] * args.k_nb)])
# Iterating on HS regions
for region in regions:
# Initialization
prev_pos = -1
true_counter = 0
# Evaluating observed frequencies
# Fetching reads
for r in bamFile.fetch(region.chrom, region.initial, region.final):
# Calculating positions
if not r.is_reverse:
cut_site = r.pos + args.forward_shift - 1
p1 = cut_site - int(floor(args.k_nb / 2))
else:
cut_site = r.aend + args.reverse_shift + 1
p1 = cut_site - int(floor(args.k_nb / 2))
p2 = p1 + args.k_nb
# Verifying PCR artifacts
if p1 == prev_pos:
true_counter += 1
else:
prev_pos = p1
true_counter = 0
if true_counter > max_duplicates: continue
# Fetching k-mer
try:
currStr = str(fastaFile.fetch(region.chrom, p1, p2)).upper()
except Exception:
continue
if r.is_reverse: currStr = AuxiliaryFunctions.revcomp(currStr)
# Counting k-mer in dictionary
if not r.is_reverse:
for i in range(0, len(currStr)):
obs_f_pwm_dict[currStr[i]][i] += 1
else:
for i in range(0, len(currStr)):
obs_r_pwm_dict[currStr[i]][i] += 1
# Evaluating expected frequencies
# Fetching whole sequence
try:
currStr = str(fastaFile.fetch(region.chrom, region.initial, region.final)).upper()
except Exception:
continue
# Iterating on each sequence position
s = None
for i in range(0, len(currStr) - args.k_nb):
# Counting k-mer in dictionary
s = currStr[i:i + args.k_nb]
for i in range(0, len(s)):
exp_f_pwm_dict[s[i]][i] += 1
# Counting k-mer in dictionary for reverse complement
s = AuxiliaryFunctions.revcomp(s)
for i in range(0, len(s)):
exp_r_pwm_dict[s[i]][i] += 1
# Closing files
bamFile.close()
fastaFile.close()
# Output pwms
os.system("mkdir -p " + os.path.join(args.output_location, "pfm"))
pwm_dict_list = [obs_f_pwm_dict, obs_r_pwm_dict, exp_f_pwm_dict, exp_r_pwm_dict]
pwm_file_list = []
pwm_obs_f = os.path.join(args.output_location, "pfm", "obs_{}_f.pfm".format(str(args.k_nb)))
pwm_obs_r = os.path.join(args.output_location, "pfm", "obs_{}_r.pfm".format(str(args.k_nb)))
pwm_exp_f = os.path.join(args.output_location, "pfm", "exp_{}_f.pfm".format(str(args.k_nb)))
pwm_exp_r = os.path.join(args.output_location, "pfm", "exp_{}_r.pfm".format(str(args.k_nb)))
pwm_file_list.append(pwm_obs_f)
pwm_file_list.append(pwm_obs_r)
pwm_file_list.append(pwm_exp_f)
pwm_file_list.append(pwm_exp_r)
for i in range(len(pwm_dict_list)):
with open(pwm_file_list[i], "w") as pwm_file:
for e in ["A", "C", "G", "T"]:
pwm_file.write(" ".join([str(int(f)) for f in pwm_dict_list[i][e]]) + "\n")
motif_obs_f = motifs.read(open(pwm_obs_f), "pfm")
motif_obs_r = motifs.read(open(pwm_obs_r), "pfm")
motif_exp_f = motifs.read(open(pwm_exp_f), "pfm")
motif_exp_r = motifs.read(open(pwm_exp_r), "pfm")
# Output logos
os.system("mkdir -p " + os.path.join(args.output_location, "logo"))
logo_obs_f = os.path.join(args.output_location, "logo", "obs_{}_f.pdf".format(str(args.k_nb)))
logo_obs_r = os.path.join(args.output_location, "logo", "obs_{}_r.pdf".format(str(args.k_nb)))
logo_exp_f = os.path.join(args.output_location, "logo", "exp_{}_f.pdf".format(str(args.k_nb)))
logo_exp_r = os.path.join(args.output_location, "logo", "exp_{}_r.pdf".format(str(args.k_nb)))
motif_obs_f.weblogo(logo_obs_f, format="pdf", stack_width="large", color_scheme="color_classic",
yaxis_scale=0.2, yaxis_tic_interval=0.1)
motif_obs_r.weblogo(logo_obs_r, format="pdf", stack_width="large", color_scheme="color_classic",
yaxis_scale=0.2, yaxis_tic_interval=0.1)
motif_exp_f.weblogo(logo_exp_f, format="pdf", stack_width="large", color_scheme="color_classic",
yaxis_scale=0.02, yaxis_tic_interval=0.01)
motif_exp_r.weblogo(logo_exp_r, format="pdf", stack_width="large", color_scheme="color_classic",
yaxis_scale=0.02, yaxis_tic_interval=0.01)
# Creating bias dictionary
alphabet = ["A", "C", "G", "T"]
k_mer_comb = ["".join(e) for e in product(alphabet, repeat=args.k_nb)]
bias_table_F = dict([(e, 0.0) for e in k_mer_comb])
bias_table_R = dict([(e, 0.0) for e in k_mer_comb])
for k_mer in k_mer_comb:
obs_f = get_ppm_score(k_mer, motif_obs_f.pwm, args.k_nb)
exp_f = get_ppm_score(k_mer, motif_exp_f.pwm, args.k_nb)
bias_table_F[k_mer] = round(obs_f / exp_f, 6)
obs_r = get_ppm_score(k_mer, motif_obs_r.pwm, args.k_nb)
exp_r = get_ppm_score(k_mer, motif_exp_r.pwm, args.k_nb)
bias_table_R[k_mer] = round(obs_r / exp_r, 6)
write_table(args.output_location, args.output_prefix, [bias_table_F, bias_table_R])
def bias_correction(chrom, start, end, bam, bias_table, genome_file_name,
forward_shift, reverse_shift):
# Parameters
window = 50
defaultKmerValue = 1.0
# Initialization
fastaFile = Fastafile(genome_file_name)
fBiasDict = bias_table[0]
rBiasDict = bias_table[1]
k_nb = len(list(fBiasDict.keys())[0])
p1 = start
p2 = end
p1_w = p1 - (window // 2)
p2_w = p2 + (window // 2)
p1_wk = p1_w - int(floor(k_nb / 2.))
p2_wk = p2_w + int(ceil(k_nb / 2.))
if p1 <= 0 or p1_w <= 0 or p1_wk <= 0 or p2_wk <= 0:
# Return raw counts
bc_signal = [0.0] * (p2 - p1)
for read in bam.fetch(chrom, p1, p2):
# check if the read is unmapped, according to issue #112
if read.is_unmapped:
continue
if not read.is_reverse:
cut_site = read.pos + forward_shift
if p1 <= cut_site < p2:
bc_signal[cut_site - p1] += 1.0
else:
cut_site = read.aend + reverse_shift - 1
if p1 <= cut_site < p2:
bc_signal[cut_site - p1] += 1.0
return bc_signal
# Raw counts
nf = [0.0] * (p2_w - p1_w)
nr = [0.0] * (p2_w - p1_w)
for read in bam.fetch(chrom, p1_w, p2_w):
# check if the read is unmapped, according to issue #112
if read.is_unmapped:
continue
if not read.is_reverse:
cut_site = read.pos + forward_shift
if p1_w <= cut_site < p2_w:
nf[cut_site - p1_w] += 1.0
else:
cut_site = read.aend + reverse_shift - 1
if p1_w <= cut_site < p2_w:
nr[cut_site - p1_w] += 1.0
# Smoothed counts
Nf = []
Nr = []
f_sum = sum(nf[:window])
r_sum = sum(nr[:window])
f_last = nf[0]
r_last = nr[0]
for i in range(int(window / 2), len(nf) - int(window / 2)):
Nf.append(f_sum)
Nr.append(r_sum)
f_sum -= f_last
f_sum += nf[i + int(window / 2)]
f_last = nf[i - int(window / 2) + 1]
r_sum -= r_last
r_sum += nr[i + int(window / 2)]
r_last = nr[i - int(window / 2) + 1]
# Fetching sequence
currStr = str(fastaFile.fetch(chrom, p1_wk, p2_wk - 1)).upper()
currRevComp = AuxiliaryFunctions.revcomp(
str(fastaFile.fetch(chrom, p1_wk + 1, p2_wk)).upper())
# Iterating on sequence to create signal
af = []
ar = []
for i in range(int(ceil(k_nb / 2.)),
len(currStr) - int(floor(k_nb / 2)) + 1):
fseq = currStr[i - int(floor(k_nb / 2.)):i + int(ceil(k_nb / 2.))]
rseq = currRevComp[len(currStr) - int(ceil(k_nb / 2.)) -
i:len(currStr) + int(floor(k_nb / 2.)) - i]
try:
af.append(fBiasDict[fseq])
except Exception:
af.append(defaultKmerValue)
try:
ar.append(rBiasDict[rseq])
except Exception:
ar.append(defaultKmerValue)
# Calculating bias and writing to wig file
f_sum = sum(af[:window])
r_sum = sum(ar[:window])
f_last = af[0]
r_last = ar[0]
bc_signal = []
for i in range(int(window / 2), len(af) - int(window / 2)):
nhatf = Nf[i - int(window / 2)] * (af[i] / f_sum)
nhatr = Nr[i - int(window / 2)] * (ar[i] / r_sum)
bc_signal.append(nhatf + nhatr)
f_sum -= f_last
f_sum += af[i + int(window / 2)]
f_last = af[i - int(window / 2) + 1]
r_sum -= r_last
r_sum += ar[i + int(window / 2)]
r_last = ar[i - int(window / 2) + 1]
# Termination
fastaFile.close()
return bc_signal
def bias_correction_dnase(signal_class, signal, chrName, start, end,
forward_shift, reverse_shift):
table_file_name_F = os.path.join(os.path.dirname(__file__), '../data/single_hit_bias_table_F.txt')
table_file_name_R = os.path.join(os.path.dirname(__file__), '../data/single_hit_bias_table_R.txt')
bias_table = load_table(table_file_name_F, table_file_name_R)
if not bias_table: return signal
# Parameters
window = 50
defaultKmerValue = 1.0
genome_file_name = signal_class.fastaFile
# Initialization
fastaFile = Fastafile(genome_file_name)
fBiasDict = bias_table[0]
rBiasDict = bias_table[1]
k_nb = len(list(fBiasDict.keys())[0])
p1 = start
p2 = end
p1_w = int(p1 - (window / 2))
p2_w = int(p2 + (window / 2))
p1_wk = p1_w - int(floor(k_nb / 2.))
p2_wk = p2_w + int(ceil(k_nb / 2.))
if p1 <= 0 or p1_w <= 0 or p1_wk <= 0: return signal
# Raw counts
nf = [0.0] * int(p2_w - p1_w)
nr = [0.0] * int(p2_w - p1_w)
for read in signal_class.bam.fetch(chrName, p1_w, p2_w):
# check if the read is unmapped, according to issue #112
if read.is_unmapped:
continue
if not read.is_reverse:
cut_site = read.pos + forward_shift
if p1_w <= cut_site < p2_w:
nf[cut_site - p1_w] += 1.0
else:
cut_site = read.aend + reverse_shift - 1
if p1_w <= cut_site < p2_w:
nr[cut_site - p1_w] += 1.0
# Smoothed counts
Nf = []
Nr = []
fSum = sum(nf[:window])
rSum = sum(nr[:window])
fLast = nf[0]
rLast = nr[0]
for i in range(int(window / 2), int(len(nf) - (window / 2))):
Nf.append(fSum)
Nr.append(rSum)
fSum -= fLast
fSum += nf[i + int(window / 2)]
fLast = nf[i - int(window / 2) + 1]
rSum -= rLast
rSum += nr[i + int(window / 2)]
rLast = nr[i - int(window / 2) + 1]
# Fetching sequence
currStr = str(fastaFile.fetch(chrName, p1_wk, p2_wk - 1)).upper()
currRevComp = revcomp(str(fastaFile.fetch(chrName, p1_wk + 1, p2_wk)).upper())
# Iterating on sequence to create signal
af = []
ar = []
for i in range(int(ceil(k_nb / 2.)), len(currStr) - int(floor(k_nb / 2)) + 1):
fseq = currStr[i - int(floor(k_nb / 2.)):i + int(ceil(k_nb / 2.))]
rseq = currRevComp[
len(currStr) - int(ceil(k_nb / 2.)) - i:len(currStr) + int(
floor(k_nb / 2.)) - i]
try:
af.append(fBiasDict[fseq])
except Exception:
af.append(defaultKmerValue)
try:
ar.append(rBiasDict[rseq])
except Exception:
ar.append(defaultKmerValue)
# Calculating bias and writing to wig file
fSum = sum(af[:window])
rSum = sum(ar[:window])
fLast = af[0]
rLast = ar[0]
bias_corrected_signal = []
for i in range(int(window / 2), int(len(af) - (window / 2))):
nhatf = Nf[i - int(window / 2)] * (af[i] / fSum)
nhatr = Nr[i - int(window / 2)] * (ar[i] / rSum)
zf = log(nf[i] + 1) - log(nhatf + 1)
zr = log(nr[i] + 1) - log(nhatr + 1)
bias_corrected_signal.append(zf + zr)
fSum -= fLast
fSum += af[i + int(window / 2)]
fLast = af[i - int(window / 2) + 1]
rSum -= rLast
rSum += ar[i + int(window / 2)]
rLast = ar[i - int(window / 2) + 1]
# Termination
fastaFile.close()
return bias_corrected_signal
