def pair_hmm_align_unaligned_seqs(seqs,moltype,params={}):
"""
This needs to be moved to cogent.align.align
"""
seqs = LoadSeqs(data=seqs,moltype=moltype,aligned=False)
try:
s1, s2 = seqs.values()
except ValueError:
raise ValueError,\
"Pairwise aligning of seqs requires exactly two seqs."
try:
gap_open = params['gap_open']
except KeyError:
gap_open = 5
try:
gap_extend = params['gap_extend']
except KeyError:
gap_extend = 2
try:
score_matrix = params['score_matrix']
except KeyError:
score_matrix = make_dna_scoring_dict(\
match=1,transition=-1,transversion=-1)
return global_pairwise(s1,s2,score_matrix,gap_open,gap_extend)
def pair_hmm_align_unaligned_seqs(seqs, moltype, params={}):
"""
This needs to be moved to cogent.align.align
"""
seqs = LoadSeqs(data=seqs, moltype=moltype, aligned=False)
try:
s1, s2 = seqs.values()
except ValueError:
raise ValueError,\
"Pairwise aligning of seqs requires exactly two seqs."
try:
gap_open = params['gap_open']
except KeyError:
gap_open = 5
try:
gap_extend = params['gap_extend']
except KeyError:
gap_extend = 2
try:
score_matrix = params['score_matrix']
except KeyError:
score_matrix = make_dna_scoring_dict(\
match=1,transition=-1,transversion=-1)
return global_pairwise(s1, s2, score_matrix, gap_open, gap_extend)
def test_pairwise_returns_score(self):
"""exercise pairwise local/global returns alignment score"""
S = make_dna_scoring_dict(10, -1, -8)
aln, score = local_pairwise(seq1, seq2, S, 10, 2, return_score=True)
self.assertTrue(score > 100)
aln, score = global_pairwise(seq1, seq2, S, 10, 2, return_score=True)
self.assertTrue(score > 100)
def test_pairwise_returns_score(self):
"""exercise pairwise local/global returns alignment score"""
S = make_dna_scoring_dict(10, -1, -8)
aln, score = local_pairwise(seq1, seq2, S, 10, 2, return_score=True)
self.assertTrue(score > 100)
aln, score = global_pairwise(seq1, seq2, S, 10, 2, return_score=True)
self.assertTrue(score > 100)
def makeSampleAlignment():
# must be an esier way to make an alignment of annotated sequences!
from cogent.align.align import global_pairwise, make_dna_scoring_dict
DNA = make_dna_scoring_dict(10, -8, -8)
seq1 = makeSampleSequence()[:-2]
seq2 = makeSampleSequence()[2:]
seq1.Name = 'FAKE01'
seq2.Name = 'FAKE02'
names = (seq1.getName(), seq2.getName())
align = global_pairwise(seq1, seq2, DNA, 2, 1)
align.addAnnotation(annotation.Variable, 'redline', 'align', [((0,15),1),((15,30),2),((30,45),3)])
align.addAnnotation(annotation.Variable, 'blueline', 'align', [((0,15),1.5),((15,30),2.5),((30,45),3.5)])
return align
def makeSampleAlignment():
# must be an esier way to make an alignment of annotated sequences!
from cogent.align.align import global_pairwise, make_dna_scoring_dict
DNA = make_dna_scoring_dict(10, -8, -8)
seq1 = makeSampleSequence()[:-2]
seq2 = makeSampleSequence()[2:]
seq1.Name = 'FAKE01'
seq2.Name = 'FAKE02'
names = (seq1.getName(), seq2.getName())
align = global_pairwise(seq1, seq2, DNA, 2, 1)
align.addAnnotation(annotation.Variable, 'redline', 'align',
[((0, 15), 1), ((15, 30), 2), ((30, 45), 3)])
align.addAnnotation(annotation.Variable, 'blueline', 'align',
[((0, 15), 1.5), ((15, 30), 2.5), ((30, 45), 3.5)])
return align
def RemoveError(log,seqs,seqsnp,sfreq,readerror,meanerror,ofracerr,indelprob,indelmax,pyroseq):
""" Deblur the reads
Input:
log - a LogMe log module to write the debluring info
seqs - the list of sequences
seqsnp - a list of numpy arrays of the sequences (for faster comparison) - from SeqToArray()
sfreq - dictionary (based on the sequence) of the number of reads for each sequence
readerror - the maximal read error expected (fraction - typically 0.01)
meanerror - the mean read error used for peak spread normalization - typically 0.01
ofracerr - the error distribution array, or 0 if use default
indelprob - the probability for an indel (currently constant for number of indels until max is reached)
indelmax - the maximal number of indels expected by errors (error cutoff)
pyroseq - if set, use pairwise alignment for pyrosequencing data
Output:
sfreq - the deblurred number of reads for each sequence (0 if not present)
debugdata - a list of strings
Notes:
meanerror is used only for normalizing the peak height before deblurring, whereas readerror
is used for calculating the expected number of errors for each position
error distribution array X should be of length >10, where Xi = max frequency of error hamming i
if it is 0, we use the default distribution
"""
# take the list values so it won't change
fracerr=list(ofracerr)
# we assume all sequences are of equal length
commonlen=len(seqs[0])
for cseq in seqs:
if not(commonlen==len(cseq)):
print("Not all sequences are same length!!!!")
print(commonlen)
print(len(cseq))
print(cseq)
print ("processing",len(seqs),"sequences")
numreal=0
for cchar in seqs[0]:
if not (cchar=='-'):
numreal+=1
modfactor=pow((1-meanerror),numreal)
# create the error profile from the read error
# exponential independent
# fracerr=[]
# for a in range(10):
# fracerr.append(pow(readerror,a)/modfactor)
# empirical
# fracerr=[1.0/modfactor,pow(readerror,1)/modfactor,2*pow(readerror,2)/modfactor,pow(readerror,2)/modfactor,pow(readerror,2)/modfactor,pow(readerror,2)/modfactor,pow(readerror,2)/modfactor,pow(readerror,2)/modfactor,pow(readerror,2)/modfactor,pow(readerror,2)/modfactor]
# used for the 22 mock mixture
# fracerr=[1.0/modfactor,pow(readerror,1)/modfactor,0.01,0.01,0.01,0.005,0.005,0.005,0.005,0.005,0.005,0.001,0.001,0.001,0.001,0.001,0.001,0.0005,0.0001,0.0001]
# used for the 44 mock mixture
# e1=pow(readerror,1)/modfactor
# fracerr=[1.0/modfactor,e1,e1/4,e1/5,e1/6,e1/8,e1/10,e1/15,e1/20,e1/30,e1/40,e1/50,e1/50,e1/50,e1/50,e1/50,e1/50,e1/100,e1/500,e1/500]
# if fracerr not supplied, use the default (22 mock mixture setup)
log.log("original fracer parameter:",fracerr)
if fracerr==0:
fracerr=[1.0/modfactor,pow(readerror,1)/modfactor,0.01,0.01,0.01,0.005,0.005,0.005,0.005,0.005,0.005,0.001,0.001,0.001,0.001]
log.log("modified fracerr because it was 0")
else:
for idx,val in enumerate(fracerr):
fracerr[idx]=fracerr[idx]/modfactor
maxhdist=len(fracerr)-1
print "fracerr"
print fracerr
print "readerror"
print readerror
print "modfactor"
print modfactor
log.log("indel prob:",indelprob)
log.log("indel max:",indelmax)
log.log("readerror:",readerror)
log.log("meanerror:",meanerror)
log.log("mod factor:",modfactor)
log.log("fracerr:",fracerr)
# for pairwise alignment:
DNAm = make_dna_scoring_dict(10, -8, -8)
for idx,cseq in enumerate(seqs):
csfreq=sfreq[cseq]
# no need to remove neighbors if freq. is <=0
if csfreq<=0:
continue
# correct for the fact that many reads are expected to be mutated
numerr=[]
for a in range(len(fracerr)):
numerr.append(fracerr[a]*csfreq)
# if it's low level, just continue
if numerr[1]<0.1:
continue
# compare to all other sequences and calculate hamming dist
cseqnp=seqsnp[idx]
oseqlen=len(seqs[idx].rstrip('-'))
for idxtmp,seqnptmp in enumerate(seqsnp):
# don't compare to ourselves (dist=0)
if idxtmp==idx:
continue
# calculate the hamming distance
hdist=np.count_nonzero(np.not_equal(seqnptmp,cseqnp))
# if far away, don't need to correct
if hdist>maxhdist:
continue
# close, so lets calculate exact distance
numsub=0
numindel=0
# experimental try 2
# s1=seqs[idx].replace('-','')
# s2=seqs[idxtmp].replace('-','')
# cseq1,cseq2=nw_align(s1,s2)
# experimental: pairwise align the sequences
if pyroseq:
s0=DNA.makeSequence(seqs[idx])
s0=s0.degap()
s1=DNA.makeSequence(seqs[idxtmp])
s1=s1.degap()
print s0._seq
print s1._seq
align = global_pairwise(s0, s1, DNAm, 10, 9)
a0=align.getGappedSeq('seq_0')
a1=align.getGappedSeq('seq_1')
cseq1=a0._seq
cseq2=a1._seq
len1=len(cseq1.rstrip('-'))
len2=len(cseq2.rstrip('-'))
oseqlen=len(cseq1)
for cpos in range(oseqlen):
if not (cseq1[cpos]==cseq2[cpos]):
if cseq1[cpos]=='-':
if cpos<len1:
numindel+=1
else:
if cseq2[cpos]=='-':
if cpos<len2:
numindel+=1
else:
numsub+=1
# not pyrosequencing so use the faster global alignment
else:
for cpos in range(oseqlen):
if not (cseqnp[cpos]==seqnptmp[cpos]):
# 4 is '-'
if seqnptmp[cpos]==4:
numindel+=1
else:
if cseqnp[cpos]==4:
numindel+=1
else:
numsub+=1
nerr=numerr[numsub]
# remove errors due to (PCR?) indels (saw in 22 mock mixture)
if numindel>0:
nerr=nerr*indelprob
if numindel>indelmax:
nerr=0
# if the effect is small - don't do anything
if nerr<0.1:
continue
# met all the criteria - so correct the frequency of the neighbor
sfreq[seqs[idxtmp]]-=nerr
# if sfreq[seqs[idxtmp]]<=0:
# if sfreq[seqs[idxtmp]]+nerr>0:
# log.log("Removed sequence ",idxtmp," due to sequence ",idx)
# log.log("seq:",idx," and ",idxtmp," have ",numindel," indels and ",numsub,"substitutions")
# log.log(cseq1)
# log.log(cseq2)
# log.log("true seq freq:",csfreq)
# log.log("freq from ",sfreq[seqs[idxtmp]]+nerr," to ",sfreq[seqs[idxtmp]])
# else:
# if numindel>0:
# log.log("====indels but no delete!!!!")
# log.log("seq:",idx," and ",idxtmp," have ",numindel," indels and ",numsub,"substitutions")
# log.log(cseq1)
# log.log(cseq2)
# log.log("true seq freq:",csfreq)
# log.log("freq from ",sfreq[seqs[idxtmp]]+nerr," to ",sfreq[seqs[idxtmp]])
return(sfreq)
