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pmdtools.0.60.py
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#!/usr/bin/env python
"""
Author: Pontus Skoglund
Contact: [email protected]
Date: January 23, 2014
Citation: P Skoglund, BH Northoff, MV Shunkov, AP Derevianko, S Paabo, J Krause, M Jakobsson (2014) Separating endogenous ancient DNA from modern day contamination in a Siberian Neandertal, PNAS, advance online 27 January
Usage: python pmdtools.py <SAM formatted data with MD field present from stdin> [options]
Example: #remove all sequence reads with PMD score <3
samtools view -q 30 mybamfile.bam | python pmdtools.py --header --threshold 3 > samtools view -Sb - > mybamfile_filtered.bam
#for more options:
python pmdtools.py --help
(for specification on the SAM format and a the samtools suite, see Li, Handsaker et al. 2009, Bioinformatics)
"""
import sys
from optparse import OptionParser
import math
import subprocess
usage = "usage: python %prog <SAM formatted data with MD field present from stdin> [options] "
parser = OptionParser(usage=usage, version="%prog v0.50")
parser.add_option("--adjustss", action="store_true", dest="adjustss",help="strand orientation aware penalization of base qualities for single stranded libraries (use with --CpG for udg-treated ss libs)",default=False)
parser.add_option("-n", "--number", action="store", type="int", dest="maxreads",help="stop after these many reads have been processed",default=(10**20))
parser.add_option("-c", "--chromosome", action="store", type="string", dest="chromosome",help="only process data from this chromosome",default=False)
parser.add_option("-m", "--requiremapq", action="store", type="int", dest="mapq",help="only process sequences with mapping quality at least this great",default=0)
parser.add_option("--readlength", action="store", type="int", dest="readlength",help="only process sequences with this read length",default=0)
parser.add_option("--maxlength", action="store", type="int", dest="maxlength",help="only process sequences with max this read length",default=300)
parser.add_option("--minlength", action="store", type="int", dest="minlength",help="only process sequences with min this read length",default=0)
parser.add_option("--maxGC", action="store", type="float", dest="maxGC",help="only process sequences with max this GC content of the aligning reference sequence",default=1.0)
parser.add_option("--minGC", action="store", type="float", dest="minGC",help="only process sequences with min this GC content of the aligning reference sequence",default=0.0)
parser.add_option("-q", "--requirebaseq", action="store", type="int", dest="baseq",help="only process bases with base quality at least this great",default=0)
parser.add_option("-d", "--deamination", action="store_true", dest="deamination",help="output base frequencies in the read at positions where there are C or G in the reference",default=False)
parser.add_option("--CpG","--UDGplus", action="store_true", dest="cpg",help="only use Cs and Gs in CpG context",default=False)
parser.add_option("--ss", action="store_true", dest="ss",help="single stranded",default=False)
parser.add_option("--PMDSprim", action="store_true", dest="PMDSprim",help="PMDSprim",default=False)
parser.add_option("--PMDSprimthreshold", action="store",type="float", dest="PMDSprimthreshold",help="PMDSprimthreshold",default=False)
parser.add_option("--UDGhalf", action="store_true", dest="UDGhalf",help="only use Cs and Gs in CpG context, the first and last base are used regardless of dinucleotide context",default=False)
#parser.add_option("--UDGplus", action="store_true", dest="UDGplus",help="only use Cs and Gs in CpG context (synonymous with option --CpG)",default=False)
parser.add_option("--UDGminus", action="store_true", dest="UDGminus",help="use all bases (placeholder)",default=False)
parser.add_option("--EcoliCpG", action="store_true", dest="EcoliCpG",help="Ecoli CpG (first 5' position and two 3' positions and CpG context)",default=False)
parser.add_option("--Ecoli", action="store_true", dest="Ecoli",help="Ecoli (first 5' position and two 3' positions only)",default=False)
parser.add_option("--maskss", action="store_true", dest="maskss",help="mask to lower case bases where ref is C (taking strand into account)",default=False)
parser.add_option("--Leipzigsimple", action="store_true", dest="Leipzigsimple",help="Leipzigsimple",default=False)
parser.add_option("--customterminus", action="store",type="string", dest="customterminus",help="customterminus",default=False)
parser.add_option("--flagss", action="store_true", dest="flagss",help="flag SNPs (taking strand into account)",default=False)
parser.add_option("--noCpG", action="store_true", dest="nocpg",help="dont use Cs and Gs in CpG context",default=False)
parser.add_option("--first", action="store_true", dest="first",help="outputs the deamination rate at the first position only, but with a standard error",default=False)
parser.add_option("--range", action="store", type="int", dest="range",help="output deamination patterns for this many positions from the sequence terminus (default=30)",default=30)
parser.add_option("--polymorphism_ancient", action="store", type="float", dest="polymorphism_ancient",help="True biological polymorphism between the ancient individual and the reference sequence",default=0.001)
parser.add_option("--polymorphism_contamination", action="store", type="float", dest="polymorphism_contamination",help="True biological polymorphism between the contaminants and the reference sequence",default=0.001)
parser.add_option("--PMDpparam", action="store", type="float", dest="PMDpparam",help="parameter p in geometric probability distribution of PMD",default=0.3)
parser.add_option("--PMDconstant", action="store", type="float", dest="PMDconstant",help="constant C in geometric probability distribution of PMD",default=0.01)
parser.add_option("--noclips", action="store_true", dest="noclips",help="no clips",default=False)
parser.add_option("--noindels", action="store_true", dest="noindels",help="no indels",default=False)
parser.add_option("--onlyclips", action="store_true", dest="onlyclips",help="only clips",default=False)
parser.add_option("--onlydeletions", action="store_true", dest="onlydeletions",help="only deletions",default=False)
parser.add_option("--onlyinsertions", action="store_true", dest="onlyinsertions",help="only insertions",default=False)
parser.add_option("--nodeletions", action="store_true", dest="nodeletions",help="no deletions",default=False)
parser.add_option("--noinsertions", action="store_true", dest="noinsertions",help="no insertions",default=False)
parser.add_option("--notreverse", action="store_true", dest="notreverse",help="no reverse complement alignments",default=False)
parser.add_option("-p", "--printDS", action="store_true", dest="printDS",help="print PMD scores",default=False)
parser.add_option("--verbose", action="store_true", dest="verbose",help="verbose",default=False)
parser.add_option("--printalignments", action="store_true", dest="printalignments",help="print human readable alignments",default=False)
parser.add_option("--maskterminaldeaminations", action="store",type="int", dest="maskterminaldeaminations",help="mask terminal deaminations",default=False)
parser.add_option("--maskterminalbases", action="store",type="int", dest="maskterminalbases",help="mask terminal bases",default=False)
parser.add_option("-t", "--threshold", type="float", dest="threshold",help="only output sequences with PMD score above this threshold",default=(-20000.0))
parser.add_option("--upperthreshold", type="float", dest="upperthreshold",help="only output sequences with PMD score below this threshold",default=(1000000.0))
parser.add_option("--perc_identity", type="float", dest="perc_identity",help="only output sequences with percent identity above this threshold",default=0.0)
parser.add_option("-a", "--adjustbaseq", action="store_true", dest="adjustbaseq",help="apply PMD-aware adjustment of base quality scores specific to C>T and G>A mismatches to the reference",default=False)
parser.add_option("--adjustbaseq_all", action="store_true", dest="adjustbaseq_all",help="apply PMD-aware adjustment of base quality scores regardless of observed bases",default=False)
parser.add_option("--dry", action="store_true", dest="dry",help="print SAM input without any filters",default=False)
parser.add_option("--samtoolspath", action="store", dest="samtoolspath",help="full path to samtools",default='samtools')
parser.add_option("--basecomposition", action="store_true", dest="basecomposition",help="basecomposition",default=False)
parser.add_option("-r","--refseq", action="store", dest="refseq",help="refseq",default=False)
parser.add_option("--header", action="store_true", dest="header",help="output the SAM header",default=False)
parser.add_option("--estimate", action="store_true", dest="estimate",help="two-terminus estimate of contamination",default=False)
parser.add_option("--estimatebase", action="store",type="int", dest="estimatebase",help="position of base used fortwo-terminus estimate of contamination",default=0)
parser.add_option("--platypus", action="store_true", dest="platypus",help="output big list of base frequencies for platypus",default=False)
parser.add_option("--writesamfield", action="store_true", dest="writesamfield",help="add 'DS:Z:<PMDS>' field to SAM output, will overwrite if already present",default=False)
parser.add_option("-b", "--basic", action="store", type="int", dest="basic",help="only output reads with a C>T mismatch this many basepairs from the 5' end",default=0)
parser.add_option("--terminal", action="store_true", dest="terminal",help="only output reads with a C>T mismatch in either 3' or 5' end",default=False)
parser.add_option("--stats", action="store_true", dest="stats",help="output summarizing statistics to stderr",default=False)
(options, args) = parser.parse_args()
#if options.UDGplus:
# options.CpG=True
def translate(inbase):
if inbase == 'A': outbase = 'T'
elif inbase == 'T': outbase = 'A'
elif inbase == 'G': outbase = 'C'
elif inbase == 'C': outbase = 'G'
elif inbase == 'N': outbase = 'N'
elif inbase == 'Y': outbase = 'Y'
elif inbase == 'R': outbase = 'R'
elif inbase == '-': newseq += '-'
return outbase
def revcomp(inseq):
inseq= inseq[::-1]#.upper()
newseq=''
for inbase in inseq:
if inbase == 'A': newseq += 'T'
elif inbase == 'T': newseq += 'A'
elif inbase == 'G': newseq += 'C'
elif inbase == 'C': newseq += 'G'
elif inbase == 'Y': newseq += 'Y'
elif inbase == 'R': newseq += 'R'
elif inbase == 'a': newseq += 't'
elif inbase == 't': newseq += 'a'
elif inbase == 'g': newseq += 'c'
elif inbase == 'c': newseq += 'g'
elif inbase == 'N': newseq += 'N'
elif inbase == '-': newseq += '-'
return newseq
def phred2prob(Q):
return 10.0 ** (-Q/10.0)
def prob2phred(P):
return -10.0*math.log(P,10)
def L_match(fposition,fmodel,fquals,fpoly):
P_damage= float(fmodel[fposition])
P_error= phred2prob( (ord(fquals[fposition])-33))/3.0
P_poly= fpoly
P_match= (1.0-P_damage)*(1.0-P_error)*(1.0-P_poly) + (P_damage*P_error*(1.0-P_poly)) + (P_error*P_poly * (1.0-P_damage))
return P_match
def L_mismatch(fposition,fmodel,fquals,fpoly):
P_damage= float(fmodel[fposition])
P_error= phred2prob( (ord(fquals[fposition])-33))/3.0
P_poly= fpoly
P_match= (1.0-P_damage)*(1.0-P_error)*(1.0-P_poly) + (P_damage*P_error*(1.0-P_poly)) + (P_error*P_poly * (1.0-P_damage))
P_mismatch= 1.0-P_match
return P_mismatch
def L_match_SS(fposition,zposition,fmodel,fquals,fpoly):
P_damage= float(fmodel[fposition])
P_damage2= float(fmodel[zposition])
P_error= phred2prob( (ord(fquals[fposition])-33))/3.0
P_poly= fpoly
P_match= (1.0-P_damage)*(1.0-P_error)*(1.0-P_poly)*(1.0-P_damage2) + (P_damage*P_error*(1.0-P_poly)*(1.0-P_damage2) ) + (P_damage2*P_error*(1.0-P_poly)*(1.0-P_damage) ) + (P_error*P_poly*(1.0-P_damage2) * (1.0-P_damage))
return P_match
def L_mismatch_SS(fposition,zposition,fmodel,fquals,fpoly):
P_damage= float(fmodel[fposition])
P_damage2= float(fmodel[zposition])
P_error= phred2prob( (ord(fquals[fposition])-33))/3.0
P_poly= fpoly
P_match= (1.0-P_damage)*(1.0-P_error)*(1.0-P_poly)*(1.0-P_damage2) + (P_damage*P_error*(1.0-P_poly)*(1.0-P_damage2) ) + (P_damage2*P_error*(1.0-P_poly)*(1.0-P_damage) ) + (P_error*P_poly*(1.0-P_damage2) * (1.0-P_damage))
P_mismatch= 1.0-P_match
return P_mismatch
def Newbaseq(fposition,fmodel,fquals):
P_damage= float(fmodel[fposition])
P_error= phred2prob( (ord(fquals[fposition])-33))/3.0
NewErrorP= 1.0 - ((1.0-P_damage) * (1.0-P_error))
return NewErrorP
def geometric(pval,kval,constant):
return ((1.0-pval)**(kval-1))*pval + constant
FNULL = open('/dev/null', 'w')
def fa_get (ffile,chrom,fstart,fend):
chrom=str(chrom)#'chr'+str(chrom)
location=str(chrom)+':'+str(fstart)+'-'+str(fend)
cmd_line=[options.samtoolspath, 'faidx', ffile,location]
outp_file=subprocess.Popen(cmd_line, stdout=subprocess.PIPE,stderr=FNULL)
pileupline = outp_file.stdout.read().split()
pileupline =''.join(pileupline[1:])
if len(pileupline)<1:
print 'no such reference sequence',cmd_line
if options.verbose:
print ' '.join(cmd_line)
return pileupline
maxlen=options.maxlength
modern_model_deam='0.001'
modern_model_deam=modern_model_deam.split('\n')*1000
modern_model_deam=[float(l) for l in modern_model_deam]
ancient_model_deam=[geometric(options.PMDpparam,l,options.PMDconstant) for l in range(1,1000)]
if options.adjustbaseq_all:
adjustment_model_deam=[geometric(options.PMDpparam,l,0.0) for l in range(1,1000)] ###constant is 0.0 here in contrast to the model used to compute PMD scores
start_dict= {}
###base composition
start_count = 0
rev_start_count = 0
not_counted = 0
imperfect = 0
mismatch_dict={}
import re
cigarparser = re.compile("([0-9]*)([A-Z])")
start_dict_rev= {}
mismatch_dict_rev={}
mismatch_dict_CpG={}
mismatch_dict_CpG_rev={}
mismatch_dict_CpG_rev_ss={}
firstC=0
firstT=0
clipexcluded=0
indelexcluded=0
noMD=0
noGCexcluded=0
excluded_threshold=0
passed=0
noquals=0
maskings=0
CCandCC=0
CTandCC=0
CCandCT=0
CTandCT=0
estimator_list=[]
composition_dict={}
composition_dict_rev={}
line_counter = 0
for line in sys.stdin:
if '@' in line[0]:
if options.header:
print line.rstrip('\n')
continue
line_counter +=1
line=line.rstrip('\n')
col = line.split('\t')
readname = col[0]
position = int(col[3])
chromosome = col[2]
if options.chromosome:
if chromosome != options.chromosome:continue
MAPQ = int(col[4])
read = col[9]
readlen = len(read)
quals= col[10]
flag = col[1]
position = int(col[3])
cigar=col[5]
if len(quals) <2:
noquals+=1
continue
if options.noinsertions:
if 'I' in cigar:continue
if options.nodeletions:
if 'D' in cigar:continue
if options.onlyinsertions:
if 'I' not in cigar:continue
if options.onlydeletions:
if 'D' not in cigar:continue
if options.noindels:
if 'I' in cigar or 'D' in cigar:
indelexcluded +=1
continue
if options.noclips:
if 'S' in cigar or 'H' in cigar or 'N' in cigar or 'P' in cigar:
clipexcluded +=1
continue
if options.onlyclips:
if 'S' not in cigar:
continue
if 'H' in cigar or 'P' in cigar or 'N' in cigar:
print >>sys.stderr,'cigar found:',cigar,'PMDtools only supports cigar operations M, I, S and D, the alignment has been excluded'
continue
if MAPQ < options.mapq:
continue
if options.readlength >0:
if options.readlength != len(read):continue
if options.minlength >0:
if options.minlength > len(read):continue
if options.maxlength != 300:
if options.maxlength < len(read):continue
if options.chromosome:
if chromosome != options.chromosome: continue
if flag.isdigit():
if int(flag) & 16:
reverse = True
else:
reverse = False
else:
if 'r' in flag: reverse = True
elif 'r' not in flag: reverse = False
if options.notreverse:
if reverse: continue
DSfield=False
if 'DS:Z:' in line:
DSfield=True
PMDS= float(line.split('DS:Z:')[1].rstrip('\n').split()[0])
LR=PMDS
#print PMDS
"""
Recreate reference sequence from MD field
"""
if (DSfield == False) or (options.writesamfield) or (options.basic > 0) or (options.terminal) or (options.perc_identity > 0.01) or (options.printalignments) or options.adjustss or (options.adjustbaseq) or (options.adjustbaseq_all) or options.deamination or options.dry or options.estimate or options.first:
read=col[9]
ref_seq=''
newread=''
import re
try:
MD=line.split('MD:Z:')[1].split()[0].rstrip('\n')
except IndexError:
noMD+=1
continue
MDlist=re.findall('(\d+|\D+)',MD)
MDcounter=0
alignment=''
for e in MDlist:
if e.isdigit():
e=int(e)
alignment += '.'*e
ref_seq+= read[MDcounter:MDcounter+e]
newread+= read[MDcounter:MDcounter+e]
MDcounter+= int(e)
elif '^' in e:
ef=e.lstrip('^')
alignment += ef
continue
elif e.isalpha():
alignment += e
ref_seq+= e
newread+= read[MDcounter]
MDcounter+=len(e)
if 'I' in cigar or 'S' in cigar:
# find insertions and clips in cigar
insertions=[]
deletions=[]
softclips=[]
hardclips=[]
paddings=[]
cigarcount=0
cigarcomponents=cigarparser.findall(cigar)
for p in cigarcomponents:
cigaraddition=int(p[0])
if 'I' in p[1]:
for c in range(cigarcount,cigarcount+cigaraddition): insertions.append(c)
elif 'S' in p[1]:
for c in range(cigarcount,cigarcount+cigaraddition): softclips.append(c)
cigarcount += int(p[0])
# end cigar parsing
# redo the read and ref using indel and clip info
ref_seq=''
newread =''
alignmentcounter=0
for x,r in zip(xrange(0,len(col[9])),read):
if x in insertions:
ref_seq += '-'
newread += read[x]
elif x in softclips:
ref_seq += '-'
newread += read[x]
else:
newread += read[x]
refbasealn=alignment[alignmentcounter]
if refbasealn == '.':
ref_seq += read[x]
else:
ref_seq += refbasealn
alignmentcounter +=1
if reverse:
read = revcomp(read)
ref_seq = revcomp(ref_seq)
quals = quals[::-1]
real_read=read
real_ref_seq=ref_seq
if options.maskterminaldeaminations or options.maskterminalbases:
maskedseq=real_read
GCcontent=1.0*(real_ref_seq.count('G')+real_ref_seq.count('C'))/readlen
if GCcontent > options.maxGC:continue
elif GCcontent < options.minGC:continue
if 'G' not in real_ref_seq and 'C' not in real_ref_seq and 'T' not in real_ref_seq and 'A' not in real_ref_seq:
print >>sys.stderr,'bad reference sequence reconstruction:',real_ref_seq
print >>sys.stderr,'SAM line:',line
continue
#exit(1)
if options.basecomposition:
backoffset=10
if reverse:
endpos=position
startpos=position+len(real_read)
else:
startpos=position
endpos=position+len(real_read)
"""
5' end
"""
largerefseq=fa_get(options.refseq,chromosome,startpos-backoffset,startpos+options.range)
largerefseq=largerefseq.upper()
if len(largerefseq)<1:continue
#print largerefseq
#if reverse:
# largerefseq=revcomp(largerefseq)
#print largerefseq,real_read
for i in range(-backoffset,options.range):
#print i+backoffset, len(largerefseq)
base=largerefseq[min([i+backoffset,len(largerefseq)])]
thekey='5'+base+str(i)
if thekey in composition_dict.keys():
addition = composition_dict[thekey]
addition += 1
composition_dict[thekey] = addition
else:
composition_dict[thekey] = 1
"""
3' end
"""
largerefseq=fa_get(options.refseq,chromosome,endpos-options.range,endpos+backoffset)
largerefseq=largerefseq.upper()[::-1]
if len(largerefseq)<1:continue
#if reverse:
# largerefseq=revcomp(largerefseq)
for i in range(-backoffset,options.range):
base=largerefseq[min([i+backoffset,len(largerefseq)])]
thekey='3'+base+str(i)
if thekey in composition_dict_rev.keys():
addition = composition_dict_rev[thekey]
addition += 1
composition_dict_rev[thekey] = addition
else:
composition_dict_rev[thekey] = 1
#continue
"""
basic filter
prints the SAM line if a C>T mismatch with sufficient base quality is observed in the first n bases, where n is specified
"""
if options.basic > 0:
#start_position = len(real_read) - len(real_read.lstrip('-'))
for a,b,x in zip(real_read,real_ref_seq,range(0,len(real_ref_seq))):
if a == 'N': break
elif b == 'N': break
elif a=='-':break
elif b=='-':break
i = x #- start_position
if i >= readlen: break ###20
if i >= options.basic: break
if options.cpg:
if b == 'C' and a=='T' and ((ord(quals[i])-33) > options.baseq) and (real_ref_seq[i+1] == 'G'):
print line.rstrip('\n')
break
elif b == 'C' and a=='T' and ((ord(quals[i])-33) > options.baseq):
print line.rstrip('\n')
break
"""
basic terminal
prints the SAM line if a C>T mismatch with sufficient base quality is observed in the first and last base
"""
if options.terminal:
a,b = real_read[0],real_ref_seq[0]
if b == 'C' and a=='T' and ((ord(quals[0])-33) > options.baseq):
#print a,b
print line.rstrip('\n')
continue
#break
a,b = real_read[::-1][0],real_ref_seq[::-1][0]
#i=len(real_read)-1
if b == 'G' and a=='A' and ((ord(quals[::-1][0])-33) > options.baseq):
print line.rstrip('\n')
continue
#break
#continue
"""
first base
prints the deamination rate at the first base and a standard error computed by jackknife over reads
"""
if options.first:
b=real_ref_seq[0]
a=real_read[0]
if options.cpg:
if b == 'C' and a=='T' and ((ord(quals[0])-33) >= options.baseq) and (real_ref_seq[1] == 'G'):
firstT +=1
if b == 'C' and a=='C' and ((ord(quals[0])-33) >= options.baseq) and (real_ref_seq[1] == 'G'):
firstC +=1
else:
if b == 'C' and a=='T' and ((ord(quals[0])-33) >= options.baseq):
firstT +=1
if b == 'C' and a=='C' and ((ord(quals[0])-33) >= options.baseq):
firstC +=1
if options.Leipzigsimple:
Leipzigsimple=False
for leipnum in [0,-1,-2]:
b=real_ref_seq[leipnum]
a=real_read[leipnum]
if b == 'C' and a=='T' and ((ord(quals[leipnum])-33) >= options.baseq):
Leipzigsimple =True
if Leipzigsimple:
line=line.rstrip('\n')+'\t'+'LS:Z:'+'1'
else:
line=line.rstrip('\n')+'\t'+'LS:Z:'+'0'
if options.customterminus != False:
Leipzigsimple=False
customterminus_list=[int(l) for l in options.customterminus.split(',')]
if len(real_ref_seq) < max(customterminus_list) or len(real_ref_seq) < abs(min(customterminus_list)):
continue
for leipnum in customterminus_list:
b=real_ref_seq[leipnum]
a=real_read[leipnum]
if leipnum <0:
if options.ss:
if b == 'C' and a=='T' and ((ord(quals[leipnum])-33) >= options.baseq):
Leipzigsimple =True
else:
if b == 'G' and a=='A' and ((ord(quals[leipnum])-33) >= options.baseq):
Leipzigsimple =True
else:
if b == 'C' and a=='T' and ((ord(quals[leipnum])-33) >= options.baseq):
Leipzigsimple =True
if Leipzigsimple:
print line
else:
line=line.rstrip('\n')+'\t'+'LS:Z:'+'0'
continue
if options.perc_identity > 0.01 or options.printalignments:
"""
divergence filter
"""
match=0
mismatch=0
mismatch_string=''
for a,b in zip(real_read,real_ref_seq):
thesebases=[a,b]
if '-' in thesebases:
mismatch_string+='-'
continue
if a == 'N': continue
if b == 'N': continue
if a == b:
mismatch_string+='|'
match +=1
elif a!=b:
mismatch_string+='x'
if 'C' == b and 'T' == a: continue
if 'G' == b and 'A' == a: continue
mismatch +=1
try:
perc_identity=1.0*match/(match+mismatch)
except ZeroDivisionError:
continue
if perc_identity < options.perc_identity:continue
"""
start PMD score computations
"""
if (DSfield == False) or (DSfield == True and options.writesamfield == True) or (options.basic > 0) or options.adjustss or options.adjustbaseq or options.adjustbaseq_all or options.deamination or options.dry:
L_D=1.0
L_M=1.0
L_D_max=1.0
L_M_max=1.0
L_D_min=1.0
L_M_min=1.0
"""
Check for informative sites (does nothing at the moment)
"""
#print real_ref_seq[-1]
if options.cpg:
if 'CG' not in real_ref_seq:
#print 'no informative'
L_D=1.0
L_M=1.0
elif options.UDGhalf:
if 'CG' not in real_ref_seq and real_ref_seq[0] !='C' and real_ref_seq[-1] !='G':
#print 'no informative'
L_D=1.0
L_M=1.0
else:
if 'C' not in real_ref_seq and 'G' not in real_ref_seq:
#print 'no informative'
L_D=1.0
L_M=1.0
newquals=quals
start_position = 0
back_start_position = len(real_read)-1
for a,b,x in zip(real_read,real_ref_seq,range(0,len(real_ref_seq))):
if 'N' in [a,b]: continue
i = x - start_position
z = back_start_position - x
qualsrev=quals[::-1]
if i >= readlen: break ###20
if options.adjustbaseq_all:
newprob= adjustment_model_deam[i]+adjustment_model_deam[z] + phred2prob(ord(quals[i])-33)
#newprob=min(newprob,1.0)
newphred=int(prob2phred(newprob))
newqual=chr(int(newphred)+33)
newquals=quals[0:i]+newqual+quals[(i+1):]
if options.adjustss:
if b == 'C' and reverse==False:
if options.cpg:
if i+1 >= readlen: break
if real_ref_seq[i+1] != 'G': continue
#quality is set to 2
newqual=chr(2+33)
newquals=quals[0:i]+newqual+quals[(i+1):]
elif b == 'A' and reverse==True:
if options.cpg:
if i-1 >= readlen: break
if real_ref_seq[i-1] != 'C': continue
#quality is set to 2
newqual=chr(2+33)
newquals=quals[0:i]+newqual+quals[(i+1):]
if (ord(quals[i])-33) < options.baseq:
#make sure that quality is adjusted even if baseq is below threshold
if options.adjustbaseq:
if b == 'C' and a == 'T':
if options.cpg:
if i+1 >= readlen: break
if real_ref_seq[i+1] != 'G': continue
elif options.nocpg:
if i+1 >= readlen: break
if real_ref_seq[i+1] == 'G': continue
elif options.UDGhalf:
if i+1 >= readlen: break
if real_ref_seq[i+1] != 'G' and i != 0: continue
newprob= Newbaseq(i,ancient_model_deam,quals)
newphred=int(prob2phred(newprob))
newqual=chr(int(newphred)+33)
newquals=quals[0:i]+newqual+quals[(i+1):]
if (b == 'G' and a == 'A') or (options.ss and b == 'C' and a == 'T'):
if options.cpg:
if i-1 >= readlen: break
if real_ref_seq[i-1] != 'C': continue
elif options.nocpg:
if i-1 >= readlen: break
if real_ref_seq[i-1] == 'C': continue
elif options.UDGhalf:
if real_ref_seq[i-1] != 'C' and z != 0: continue
newprob= Newbaseq(z,ancient_model_deam,qualsrev)
newphred=int(prob2phred(newprob))
newqual=chr(int(newphred)+33)
newquals=quals[0:i]+newqual+quals[(i+1):]
continue
"""
platypus
"""
if options.platypus:
#count from 5' end
cpgcheck=False
if i+1 <= len(real_ref_seq):
if real_ref_seq[i:i+2] == 'CG':
cpgcheck=True
#if (i-1 > -1):
# if real_ref_seq[i-1:i+1] == 'CG':
# cpgcheck=True
if cpgcheck==True:
thekey=b+a+str(i)
if thekey in mismatch_dict_CpG.keys():
addition = mismatch_dict_CpG[thekey]
addition += 1
mismatch_dict_CpG[thekey] = addition
else:
mismatch_dict_CpG[thekey] = 1
else:
thekey=b+a+str(i)
if thekey in mismatch_dict.keys():
addition = mismatch_dict[thekey]
addition += 1
mismatch_dict[thekey] = addition
else:
mismatch_dict[thekey] = 1
#count from 3' end
cpgcheck=False
if (i-1 > -1):
if real_ref_seq[i-1:i+1] == 'CG':
cpgcheck=True
#if i+1 <= len(real_ref_seq):
# if real_ref_seq[i:i+2] == 'CG':
# cpgcheck=True
if cpgcheck==True:
thekey=b+a+str(z)
if thekey in mismatch_dict_CpG_rev.keys():
addition = mismatch_dict_CpG_rev[thekey]
addition += 1
mismatch_dict_CpG_rev[thekey] = addition
else:
mismatch_dict_CpG_rev[thekey] = 1
else:
thekey=b+a+str(z)
if thekey in mismatch_dict_rev.keys():
addition = mismatch_dict_rev[thekey]
addition += 1
mismatch_dict_rev[thekey] = addition
else:
mismatch_dict_rev[thekey] = 1
if options.deamination:
if b == 'C':
if options.cpg:
if i+1 >= readlen: break
if real_read[i+1] != 'G': continue
elif options.nocpg:
if i+1 >= readlen: break
if real_read[i+1] == 'G': continue
elif options.UDGhalf:
if i+1 >= readlen: break
if real_ref_seq[i+1] != 'G' and i != 0: continue
thekey=b+a+str(i)
if thekey in mismatch_dict.keys():
addition = mismatch_dict[thekey]
addition += 1
mismatch_dict[thekey] = addition
else:
mismatch_dict[thekey] = 1
if b == 'G':
if options.cpg:
if i-1 >= readlen: break
if real_ref_seq[i-1] != 'C': continue
elif options.nocpg:
if i-1 >= readlen: break
if real_ref_seq[i-1] == 'C': continue
elif options.UDGhalf:
if i-1 >= readlen: break
if real_ref_seq[i-1] != 'C' and z != 0: continue
thekey=b+a+str(z)
if thekey in mismatch_dict_rev.keys():
addition = mismatch_dict_rev[thekey]
addition += 1
mismatch_dict_rev[thekey] = addition
else:
mismatch_dict_rev[thekey] = 1
continue
"""
compute degradation score
"""
if True:
if i >= readlen:continue
if b == 'C':
if options.cpg:
if i+1 >= readlen: break
if real_ref_seq[i+1] != 'G': continue
elif options.cpg:
if i+1 >= readlen: break
if real_ref_seq[i+1] == 'G': continue
elif options.UDGhalf:
if i+1 >= readlen: break
if real_ref_seq[i+1] != 'G' and i != 0: continue
elif options.EcoliCpG or ('UDG' in line and 'noUDG' not in line):
#print 'RGdetected',line
if i+1 >= readlen: break
if real_ref_seq[i+1] != 'G' and i !=0 and z not in [0,1]: continue
elif options.Ecoli:
if i+1 >= readlen: break
if i !=0 and z not in [0,1]: continue
if a=='T':
L_D = L_D * L_mismatch(i,ancient_model_deam,quals,options.polymorphism_ancient)
L_M = L_M * L_mismatch(i,modern_model_deam,quals,options.polymorphism_contamination)
if options.ss:
L_D = L_D * L_mismatch_SS(i,z,ancient_model_deam,quals,options.polymorphism_ancient)
L_M = L_M * L_mismatch_SS(i,z,modern_model_deam,quals,options.polymorphism_contamination)
if options.adjustbaseq:
newprob= Newbaseq(i,ancient_model_deam,quals)
newphred=int(prob2phred(newprob))
newqual=chr(int(newphred)+33)
"""
print phred2prob(ord(quals[i])),newprob
print ord(quals[i]),newphred
print quals[i],newqual
print quals[0:i],quals[i],quals[(i+1):]
print quals
"""
quals=quals[0:i]+newqual+quals[(i+1):]
if options.maskterminaldeaminations != False:
if (i <= options.maskterminaldeaminations):
maskedseq=real_read[0:i]+'N'+real_read[(i+1):]
elif (z <= options.maskterminaldeaminations) and options.ss:
maskedseq=real_read[0:i]+'N'+real_read[(i+1):]
elif a=='C':
L_D = L_D * L_match(i,ancient_model_deam,quals,options.polymorphism_ancient)
L_M = L_M * L_match(i,modern_model_deam,quals,options.polymorphism_contamination)
if options.ss:
L_D = L_D * L_match_SS(i,z,ancient_model_deam,quals,options.polymorphism_ancient)
L_M = L_M * L_match_SS(i,z,modern_model_deam,quals,options.polymorphism_contamination)
if options.PMDSprim and a in ['C','T']:
L_D_max=L_D_max * L_mismatch(i,ancient_model_deam,quals,options.polymorphism_ancient)
L_M_max=L_M_max * L_mismatch(i,modern_model_deam,quals,options.polymorphism_ancient)
L_D_min=L_D_min * L_match(i,ancient_model_deam,quals,options.polymorphism_ancient)
L_M_min=L_M_min * L_match(i,modern_model_deam,quals,options.polymorphism_ancient)
if b == 'G' and (options.ss ==False):
if options.cpg:
if real_ref_seq[i-1] != 'C': continue
elif options.nocpg:
if real_ref_seq[i-1] == 'C': continue
elif options.UDGhalf:
if real_ref_seq[i-1] != 'C' and z != 0: continue
if a=='A':
L_D = L_D * L_mismatch(z,ancient_model_deam,qualsrev,options.polymorphism_ancient)
L_M = L_M * L_mismatch(z,modern_model_deam,qualsrev,options.polymorphism_contamination)
if options.adjustbaseq:
newprob= Newbaseq(z,ancient_model_deam,qualsrev)
newphred=int(prob2phred(newprob))
newqual=chr(int(newphred)+33)
newquals=quals[0:i]+newqual+quals[(i+1):]
if options.maskterminaldeaminations != False and options.ss ==False:
if (z <= options.maskterminaldeaminations):
maskedseq=real_read[0:i]+'N'+real_read[(i+1):]
elif a=='G':
#try:
L_D = L_D * L_match(z,ancient_model_deam,qualsrev,options.polymorphism_ancient)
L_M = L_M * L_match(z,modern_model_deam,qualsrev,options.polymorphism_contamination)
if options.PMDSprim and a in ['G','A']:
L_D_max=L_D_max * L_mismatch(z,ancient_model_deam,quals,options.polymorphism_ancient)
L_M_max=L_M_max * L_mismatch(z,modern_model_deam,quals,options.polymorphism_ancient)
L_D_min=L_D_min * L_match(z,ancient_model_deam,qualsrev,options.polymorphism_ancient)
L_M_min=L_M_min * L_match(z,modern_model_deam,qualsrev,options.polymorphism_ancient)
LR= (math.log(L_D/L_M) )
if options.PMDSprim:
maxPMDSval=(math.log(L_D_max/L_M_max) )
maxPMDSval=maxPMDSval/readlen
if LR >0.0:
LRnumerator=(math.log(L_D_max/L_M_max) )
elif LR <0.0:
LRnumerator=(math.log(L_D_max/L_M_max) )
if options.PMDSprimthreshold:
if maxPMDSval < options.PMDSprimthreshold:
continue
if options.printDS:
print LR,maxPMDSval,maxPMDSval,maxPMDSval*readlen,readlen
#LR=LR/LRnumerator
quals=newquals
if options.adjustbaseq:
if reverse:
qualsp=quals[::-1]
else:
qualsp=quals
line='\t'.join(col[0:10])+'\t'+qualsp+'\t'+'\t'.join(col[11:])
if options.maskterminaldeaminations or options.maskterminalbases:
readp=maskedseq
if reverse:
readp = revcomp(readp)
real_read=maskedseq
line='\t'.join(col[0:9])+'\t'+readp+'\t'+'\t'.join(col[10:])
"""
add PMDS tag
"""
if options.writesamfield == True:
# remove DS field if present
if DSfield==True:
newline=''
for col in line.split('\t'):
if 'DS:Z:' in col:
continue
else:
newline += col+'\t'
line=newline.rstrip('\t')
line=line.rstrip('\n')+'\t'+'DS:Z:'+str(round(LR,3))