def adderrortoread(readstr,readcigar,readqualitydeteriorationrate,indelrate):
switchdict={'A':'t','T':'a','C':'g','G':'c'}
outstr=''
errorrate=0
indellist=[0]
indelflaglist=[-1]
for i in range(len(readstr)):
if common.toss(indelrate) and i!=0 and indellist[-1]!=(i-1):
indellist.append(i)
if common.toss():
outstr+=outstr[-1]
outstr+=readstr[i]
indelflaglist.append(1)
else:
indelflaglist.append(0)
else:
if common.toss(errorrate):
outstr+=switchdict[readstr[i]]
else:
outstr+=readstr[i]
errorrate+=readqualitydeteriorationrate
indellist.append(len(readstr))
indelflaglist.append(-1)
outcigarstr=''
for i in range(1,len(indellist)):
subcig=common.cigarsubstr(readcigar,indellist[i-1],indellist[i]-indellist[i-1])
#print 'here',readcigar,indellist[i-1],indellist[i],subcig
outcigarstr+=subcig
if indelflaglist[i]==1:
outcigarstr+='1I'
elif indelflaglist[i]==0:
outcigarstr+='1D'
return [outstr,outcigarstr]
def RNAmetasource2source(parameterlist):
"""
Output:
Gene Dict: Transcript Dict: chromosome, strand, startbase, cigar, txsize
"""
# empirical parameter
genewithnovelskippedexonpct=50
metasourcefilename=parameterlist[0][0]
filetype=parameterlist[1][0]
if parameterlist[1][1]=='no_novel_transcript':
novel_skipped_exon_flg=0
else:
novel_skipped_exon_flg=1
numgenes=int(parameterlist[1][2])
genelist=parameterlist[1][3]
if filetype=='gtf':
metasource=gtffile.gtfFile(metasourcefilename)
genetranscriptdict=metasource.getgenetranscriptdict()
elif filetype=='pck':
genetranscriptdict=cPickle.load(open(metasourcefilename))
allgenes=genetranscriptdict.keys()
if len(genelist)==0:
if numgenes==0:
numgenes=len(genetranscriptdict)
else:
numgenes=min(numgenes,len(genetranscriptdict))
choosegenes=random.sample(allgenes,numgenes)
else:
choosegenes=genelist[0:]
#print len(genelist),len(choosegenes)
outtranscriptdict={}
for gene in choosegenes:
txdict=genetranscriptdict[gene]
txexonslist=[txdict[tx][2] for tx in txdict]
if novel_skipped_exon_flg==1 and common.toss(genewithnovelskippedexonpct/100.0):
newtxflg=0
trys=0
while not newtxflg and trys<10:
trys+=1
tx=random.choice(txdict.keys())
txexons=txdict[tx][2]
if len(txexons)>2:
skipped=random.randint(1,len(txexons)-2)
newtx=txexons[:skipped-1]+txexons[skipped:]
if newtx not in txexonslist:
newtxflg=1
txdict['%s_skip'%tx]=[txdict[tx][0],txdict[tx][1],newtx]
outtranscriptdict[gene]={}
for tx in txdict:
cigartup=common.exonlist2cigar(txdict[tx][2])
outtranscriptdict[gene][tx]=[txdict[tx][0],txdict[tx][1],cigartup[0],cigartup[1],cigartup[2]]
return outtranscriptdict
def fragment2read(allparameterlist):
"""
input:
transcript string, transcript cigar, fragmentlist
output:
list of (readstr,readcigar)
"""
#read_extract_parameters=SE,100,300,400 ; single:size and fragment range
#read_extract_parameters=PE,100,300,400 ; paired:size and fragment range
#read_extract_parameters=PB,500,1000 ; pacbio fragment range
txstr, txcigar, fragmentlist, parameterlist = allparameterlist
readlist = []
if parameterlist[0] == 'SE':
substrsize = int(parameterlist[1])
for fragment in fragmentlist:
if common.toss():
substrstart = fragment[0]
else:
substrstart = fragment[1] - substrsize
readcigar = common.cigarsubstr(txcigar, substrstart, substrsize)
readstr = txstr[substrstart:substrstart + substrsize]
readlist.append([substrstart, readstr, readcigar])
if parameterlist[0] == 'PE':
substrsize = int(parameterlist[1])
for fragment in fragmentlist:
substrstart1 = fragment[0]
readcigar1 = common.cigarsubstr(txcigar, substrstart1, substrsize)
readstr1 = txstr[substrstart1:substrstart1 + substrsize]
#readlist.append([substrstart,readstr,readcigar])
substrstart2 = fragment[1] - substrsize
readcigar2 = common.cigarsubstr(txcigar, substrstart2, substrsize)
readstr2 = txstr[substrstart2:substrstart2 + substrsize]
readlist.append([[substrstart1, readstr1, readcigar1],
[substrstart2, readstr2, readcigar2]])
if parameterlist[0] == 'PB':
for fragment in fragmentlist:
substrstart = fragment[0]
substrsize = fragment[1] - fragment[0]
readcigar = common.cigarsubstr(txcigar, substrstart, substrsize)
readstr = txstr[substrstart:substrstart + substrsize]
readlist.append([substrstart, readstr, readcigar])
readlist.sort()
if parameterlist[0] == 'PE':
pe_readlist = readlist[0:]
readlist = []
for pe_read in pe_readlist:
read1, read2 = pe_read
readlist.append(read1)
readlist.append(read2)
return readlist
def fragment2read(allparameterlist):
"""
input:
transcript string, transcript cigar, fragmentlist
output:
list of (readstr,readcigar)
"""
#read_extract_parameters=SE,100,300,400 ; single:size and fragment range
#read_extract_parameters=PE,100,300,400 ; paired:size and fragment range
#read_extract_parameters=PB,500,1000 ; pacbio fragment range
txstr,txcigar,fragmentlist,parameterlist=allparameterlist
readlist=[]
if parameterlist[0]=='SE':
substrsize=int(parameterlist[1])
for fragment in fragmentlist:
if common.toss():
substrstart=fragment[0]
else:
substrstart=fragment[1]-substrsize
readcigar=common.cigarsubstr(txcigar,substrstart,substrsize)
readstr=txstr[substrstart:substrstart+substrsize]
readlist.append([substrstart,readstr,readcigar])
if parameterlist[0]=='PE':
substrsize=int(parameterlist[1])
for fragment in fragmentlist:
substrstart1=fragment[0]
readcigar1=common.cigarsubstr(txcigar,substrstart1,substrsize)
readstr1=txstr[substrstart1:substrstart1+substrsize]
#readlist.append([substrstart,readstr,readcigar])
substrstart2=fragment[1]-substrsize
readcigar2=common.cigarsubstr(txcigar,substrstart2,substrsize)
readstr2=txstr[substrstart2:substrstart2+substrsize]
readlist.append([[substrstart1,readstr1,readcigar1],[substrstart2,readstr2,readcigar2]])
if parameterlist[0]=='PB':
for fragment in fragmentlist:
substrstart=fragment[0]
substrsize=fragment[1]-fragment[0]
readcigar=common.cigarsubstr(txcigar,substrstart,substrsize)
readstr=txstr[substrstart:substrstart+substrsize]
readlist.append([substrstart,readstr,readcigar])
readlist.sort()
if parameterlist[0]=='PE':
pe_readlist=readlist[0:]
readlist=[]
for pe_read in pe_readlist:
read1,read2=pe_read
readlist.append(read1)
readlist.append(read2)
return readlist
def adderrortoread(readstr, readcigar, readqualitydeteriorationrate,
indelrate):
switchdict = {'A': 't', 'T': 'a', 'C': 'g', 'G': 'c'}
outstr = ''
errorrate = 0
indellist = [0]
indelflaglist = [-1]
for i in range(len(readstr)):
if common.toss(indelrate) and i != 0 and indellist[-1] != (i - 1):
indellist.append(i)
if common.toss():
outstr += outstr[-1]
outstr += readstr[i]
indelflaglist.append(1)
else:
indelflaglist.append(0)
else:
if common.toss(errorrate):
outstr += switchdict[readstr[i]]
else:
outstr += readstr[i]
errorrate += readqualitydeteriorationrate
indellist.append(len(readstr))
indelflaglist.append(-1)
outcigarstr = ''
for i in range(1, len(indellist)):
subcig = common.cigarsubstr(readcigar, indellist[i - 1],
indellist[i] - indellist[i - 1])
#print 'here',readcigar,indellist[i-1],indellist[i],subcig
outcigarstr += subcig
if indelflaglist[i] == 1:
outcigarstr += '1I'
elif indelflaglist[i] == 0:
outcigarstr += '1D'
return [outstr, outcigarstr]
def getsimulatedtwoexpressions(genetxcountdict,covdistparam,JSDdistparam,numreads,fold_flg=0):
"""
Computes gene and transcript coverages for two datasets
input:
genetxcountdict: count of transcripts for each gene, make sure than
covdistparam: the parameters describing truncated normal distribution - mu,sigma,left,right
of ratio of rpkm of gene between two datasets
JSDdistparam: the parameters describing uniform plus triangular distribution - left, right, mode
of JSD of trascript expression of a gene between two datasets
output:
genepairexpdict=gene:genereads1,genereads2,txexp1,txexp2
"""
numgenes=len(genetxcountdict)
genereadslist1=getsimulatedgenereadcounts(numgenes,numreads)
#print covdistparam
covratiolisttemp1=[random.gauss(covdistparam[0],covdistparam[1]) for i in range(10*numgenes)]
covratiolisttemp2=[x for x in covratiolisttemp1 if covdistparam[2]<=x<=covdistparam[3]]
covratiolist=random.sample(covratiolisttemp2,numgenes)
# 1: gene read count <= 5% of total is acceptable
# 2: gene read count >= 60% of total is not acceptable
# 3: if between 1 and 2, gene read count < 10 times fair share is acceptable
genefairshare=numreads*1.0/numgenes
genereadslist2temp1=[genereadslist1[i]*pow(10,covratiolist[i]) for i in range(numgenes)]
genereadslist2temp2=[min(x,numreads*0.60) for x in genereadslist2temp1]
genereadslist2temp3=[min(x,max(10*genefairshare,numreads*0.05)) for x in genereadslist2temp2]
genereadslist2=[max(1,int(x*numreads/sum(genereadslist2temp3))) for x in genereadslist2temp3]
JSDlist=[]
for gene in genetxcountdict:
if genetxcountdict[gene]>1:
if common.toss():
JSDlist.append(random.uniform(JSDdistparam[0],JSDdistparam[1]))
else:
JSDlist.append(random.triangular(JSDdistparam[0],JSDdistparam[1],JSDdistparam[2]))
numbins=50
JSDbins=np.linspace(0,1,numbins+1)
JSDbinscount=[]
for i in range(numbins):
JSDbinscount.append(len([x for x in JSDlist if JSDbins[i]<x<=JSDbins[i+1]]))
print JSDbinscount
outdict={}
genelist=genetxcountdict.keys()
for i in range(numgenes):
gene=genelist[i]
if genetxcountdict[gene]==1:
outdict[gene]=[genereadslist1[i],genereadslist2[i],[1.0],[1.0],0.0]
else:
binned=0; numattempts=0
while binned==0:
numattempts+=1
if fold_flg==0:
v1=getsimulatedtxexpressionpct(genetxcountdict[gene])
v2=getsimulatedtxexpressionpct(genetxcountdict[gene])
genejsd=common.JSD(v1,v2)
binidx=int(genejsd*numbins)
else:
v1=getsimulatedtxexpressionpct(genetxcountdict[gene])
v2=v1[:]
v2[0],v2[1]=v1[1],v1[0]
genejsd=abs(v2[1]-v2[0])
binidx=min(int(genejsd*numbins),numbins-1)
if JSDbinscount[binidx]>0:
binned=1
JSDbinscount[binidx]-=1
if numattempts>10000 and binidx>1:
binned=1
JSDbinscount[binidx]-=1
outdict[gene]=[genereadslist1[i],genereadslist2[i],v1,v2,genejsd]
print JSDbinscount
return outdict
def RNAmetasource2source(parameterlist):
"""
Output:
Gene Dict: Transcript Dict: chromosome, strand, startbase, cigar, txsize
"""
# empirical parameter
genewithnovelskippedexonpct = 50
metasourcefilename = parameterlist[0][0]
filetype = parameterlist[1][0]
if parameterlist[1][1] == 'no_novel_transcript':
novel_skipped_exon_flg = 0
else:
novel_skipped_exon_flg = 1
numgenes = int(parameterlist[1][2])
genelist = parameterlist[1][3]
if filetype == 'gtf':
metasource = gtffile.gtfFile(metasourcefilename)
genetranscriptdict = metasource.getgenetranscriptdict()
elif filetype == 'pck':
genetranscriptdict = cPickle.load(open(metasourcefilename))
allgenes = genetranscriptdict.keys()
if len(genelist) == 0:
if numgenes == 0:
numgenes = len(genetranscriptdict)
else:
numgenes = min(numgenes, len(genetranscriptdict))
choosegenes = random.sample(allgenes, numgenes)
else:
choosegenes = genelist[0:]
#print len(genelist),len(choosegenes)
outtranscriptdict = {}
for gene in choosegenes:
txdict = genetranscriptdict[gene]
txexonslist = [txdict[tx][2] for tx in txdict]
if novel_skipped_exon_flg == 1 and common.toss(
genewithnovelskippedexonpct / 100.0):
newtxflg = 0
trys = 0
while not newtxflg and trys < 10:
trys += 1
tx = random.choice(txdict.keys())
txexons = txdict[tx][2]
if len(txexons) > 2:
skipped = random.randint(1, len(txexons) - 2)
newtx = txexons[:skipped - 1] + txexons[skipped:]
if newtx not in txexonslist:
newtxflg = 1
txdict['%s_skip' %
tx] = [txdict[tx][0], txdict[tx][1], newtx]
outtranscriptdict[gene] = {}
for tx in txdict:
cigartup = common.exonlist2cigar(txdict[tx][2])
outtranscriptdict[gene][tx] = [
txdict[tx][0], txdict[tx][1], cigartup[0], cigartup[1],
cigartup[2]
]
return outtranscriptdict
def getsimulatedtwoexpressions(genetxcountdict,
covdistparam,
JSDdistparam,
numreads,
fold_flg=0):
"""
Computes gene and transcript coverages for two datasets
input:
genetxcountdict: count of transcripts for each gene, make sure than
covdistparam: the parameters describing truncated normal distribution - mu,sigma,left,right
of ratio of rpkm of gene between two datasets
JSDdistparam: the parameters describing uniform plus triangular distribution - left, right, mode
of JSD of trascript expression of a gene between two datasets
output:
genepairexpdict=gene:genereads1,genereads2,txexp1,txexp2
"""
numgenes = len(genetxcountdict)
genereadslist1 = getsimulatedgenereadcounts(numgenes, numreads)
#print covdistparam
covratiolisttemp1 = [
random.gauss(covdistparam[0], covdistparam[1])
for i in range(10 * numgenes)
]
covratiolisttemp2 = [
x for x in covratiolisttemp1 if covdistparam[2] <= x <= covdistparam[3]
]
covratiolist = random.sample(covratiolisttemp2, numgenes)
# 1: gene read count <= 5% of total is acceptable
# 2: gene read count >= 60% of total is not acceptable
# 3: if between 1 and 2, gene read count < 10 times fair share is acceptable
genefairshare = numreads * 1.0 / numgenes
genereadslist2temp1 = [
genereadslist1[i] * pow(10, covratiolist[i]) for i in range(numgenes)
]
genereadslist2temp2 = [
min(x, numreads * 0.60) for x in genereadslist2temp1
]
genereadslist2temp3 = [
min(x, max(10 * genefairshare, numreads * 0.05))
for x in genereadslist2temp2
]
genereadslist2 = [
max(1, int(x * numreads / sum(genereadslist2temp3)))
for x in genereadslist2temp3
]
JSDlist = []
for gene in genetxcountdict:
if genetxcountdict[gene] > 1:
if common.toss():
JSDlist.append(random.uniform(JSDdistparam[0],
JSDdistparam[1]))
else:
JSDlist.append(
random.triangular(JSDdistparam[0], JSDdistparam[1],
JSDdistparam[2]))
numbins = 50
JSDbins = np.linspace(0, 1, numbins + 1)
JSDbinscount = []
for i in range(numbins):
JSDbinscount.append(
len([x for x in JSDlist if JSDbins[i] < x <= JSDbins[i + 1]]))
print JSDbinscount
outdict = {}
genelist = genetxcountdict.keys()
for i in range(numgenes):
gene = genelist[i]
if genetxcountdict[gene] == 1:
outdict[gene] = [
genereadslist1[i], genereadslist2[i], [1.0], [1.0], 0.0
]
else:
binned = 0
numattempts = 0
while binned == 0:
numattempts += 1
if fold_flg == 0:
v1 = getsimulatedtxexpressionpct(genetxcountdict[gene])
v2 = getsimulatedtxexpressionpct(genetxcountdict[gene])
genejsd = common.JSD(v1, v2)
binidx = int(genejsd * numbins)
else:
v1 = getsimulatedtxexpressionpct(genetxcountdict[gene])
v2 = v1[:]
v2[0], v2[1] = v1[1], v1[0]
genejsd = abs(v2[1] - v2[0])
binidx = min(int(genejsd * numbins), numbins - 1)
if JSDbinscount[binidx] > 0:
binned = 1
JSDbinscount[binidx] -= 1
if numattempts > 10000 and binidx > 1:
binned = 1
JSDbinscount[binidx] -= 1
outdict[gene] = [
genereadslist1[i], genereadslist2[i], v1, v2, genejsd
]
print JSDbinscount
return outdict
