def build_fwtrack(self, opt):
"""Build FWTrackII from all lines, return a FWTrackII object.
Handle multi-reads here by building a probability and enrichment index
or select only one alignment from each multi-read.
Initial alignment probabilities are set from read/mismatch qualities
or from a uniform distribution.
"""
fwtrack = FWTrackII()
i = 0
m = 0
read_total = 0
recent_tags = []
random_select_one_multi = opt.random_select_one_multi
no_multi_reads = opt.no_multi_reads
min_score = opt.min_score
prior_prob_snp = opt.prior_prob_snp
no_prior_prob_map = opt.no_prior_prob_map
if opt.qual_scale == 'auto':
opt.qual_scale = self._guess_qual_scale()
if opt.qual_scale == 'sanger+33':
qual_offset = 33
elif opt.qual_scale == 'illumina+64':
qual_offset = 64
group_starts_append = fwtrack.group_starts.append
fwtrack_add_loc = fwtrack.add_loc
match_probs = {
} # {(1,30):p(match|phred=30), (0,30):p(mismatch|phred=30)}
for grouplines in self._group_by_name(self.fhd):
read_total += 1 # in ratios, only count reads, not total alignments
if len(grouplines) == 1:
# uniquely mapping reads
i += 1
if i == 1000000:
m += 1
logging.info(" %d alignments read." % (m * 1000000))
i = 0
chromosome, fpos, strand, qualstr, mismatches = grouplines[0]
fwtrack_add_loc(chromosome, fpos, strand,
0) # 0'th index => unique
else:
if no_multi_reads: # throw away multi-reads
fwtrack.total -= 1
continue
elif random_select_one_multi: # choose one alignment at random
i += 1
if i == 1000000:
m += 1
logging.info(" %d alignments read." % (m * 1000000))
i = 0
randline = grouplines[random_range(len(grouplines))]
chromosome, fpos, strand, qualstr, mismatches = randline
fwtrack_add_loc(chromosome, fpos, strand, 0)
else: # use all alignments probabilistically
group_starts_append(
fwtrack.total_multi +
1) # starts at 1 (0 reserved for unique reads)
if no_prior_prob_map:
# don't use map quality; just assume uniform priors
for (chromosome, fpos, strand, qualstr,
mismatches) in grouplines:
i += 1
if i == 1000000:
m += 1
logging.info(" %d alignments read." %
(m * 1000000))
i = 0
fwtrack.total_multi += 1
fwtrack_add_loc(chromosome, fpos, strand,
fwtrack.total_multi)
normed_probs = [1. / len(grouplines)] * len(grouplines)
else:
# TODO: might want to be working in log space-- if many mismatches, we'll lose precision
qualstr = grouplines[0][
3] # all quality strings are shared across the group
group_total_prob = 0.
group_probs = []
group_probs_append = group_probs.append
for (chromosome, fpos, strand, qualstr,
mismatches) in grouplines:
i += 1
if i == 1000000:
m += 1
logging.info(" %d alignments read." %
(m * 1000000))
i = 0
fwtrack.total_multi += 1
fwtrack_add_loc(chromosome, fpos, strand,
fwtrack.total_multi)
mismatches = set(mismatches)
read_prob = 1.
# P(SNP) = prior probability a SNP occurs at any base
# P(SE) = probability there was a sequencing error (from PHRED)
# _P(Map|SNP,SE)__MATCH__SNP__SE_
# 0 0 0 0 # can't map here without explanation
# 1 0 0 1
# 1 0 1 0
# 1 0 1 1
# 1 1 0 0
# 1 1 0 1
# 0 1 1 0 # wouldn't map here if SNP, but sequencer read reference
# 1 1 1 1
# we are interested in P(Mapping | Match), which is equivalent to:
# \Sum_{SNP \in {0,1}, SE \in {0,1}} p(SNP) * p(SE) * p(Map|SE,SNP), or:
# p(Map|match = 0):
# p(SE) + p(SNP) + p(SE)*p(SNP)
# p(Map|match = 1):
# 1 - (p(SE) + p(SE)*p(SNP))
for b in xrange(len(qualstr)):
tup = (b in mismatches, qualstr[b])
if tup in match_probs:
prob = match_probs[tup]
elif tup[0]: # mismatch
p_seq_error = 10.**(
(qualstr[b] - qual_offset) / -10.)
prob = p_seq_error + prior_prob_snp + p_seq_error * prior_prob_snp
match_probs[tup] = prob
else: # match
p_seq_error = 10.**(
(qualstr[b] - qual_offset) / -10.)
prob = 1. - (p_seq_error +
p_seq_error * prior_prob_snp)
match_probs[tup] = prob
read_prob *= prob
# quick & dirty check-- only looking at last base
assert qualstr[
b] >= qual_offset # Specified quality scale yielded a negative phred score! You probably have the wrong PHRED scale!
assert 0. <= read_prob <= 1. # error with map qualities
#raise BaseQualityError("Specified quality scale yielded a negative phred score! You probably have the wrong PHRED scale!")
group_probs_append(read_prob)
group_total_prob += read_prob
normed_probs = [
p / group_total_prob for p in group_probs
]
fwtrack.prob_aligns.extend(normed_probs)
fwtrack.prior_aligns.extend(normed_probs)
fwtrack.enrich_scores.extend([min_score] * len(grouplines))
fwtrack.total = read_total # overwrite the running total, counting each read once
return fwtrack
def build_fwtrack (self, opt):
"""Build FWTrackII from all lines, return a FWTrackII object.
Handle multi-reads here by building a probability and enrichment index
or select only one alignment from each multi-read.
Initial alignment probabilities are set from read/mismatch qualities
or from a uniform distribution.
"""
fwtrack = FWTrackII()
i = 0
m = 0
read_total = 0
recent_tags = []
random_select_one_multi = opt.random_select_one_multi
no_multi_reads = opt.no_multi_reads
min_score = opt.min_score
prior_prob_snp = opt.prior_prob_snp
no_prior_prob_map = opt.no_prior_prob_map
if opt.qual_scale == 'auto':
opt.qual_scale = self._guess_qual_scale()
if opt.qual_scale == 'sanger+33':
qual_offset = 33
elif opt.qual_scale == 'illumina+64':
qual_offset = 64
group_starts_append = fwtrack.group_starts.append
fwtrack_add_loc = fwtrack.add_loc
match_probs = {} # {(1,30):p(match|phred=30), (0,30):p(mismatch|phred=30)}
for grouplines in self._group_by_name(self.fhd):
read_total += 1 # in ratios, only count reads, not total alignments
if len(grouplines) == 1:
# uniquely mapping reads
i+=1
if i == 1000000:
m += 1
logging.info(" %d alignments read." % (m*1000000))
i=0
chromosome, fpos, strand, qualstr, mismatches = grouplines[0]
fwtrack_add_loc(chromosome,fpos,strand,0) # 0'th index => unique
else:
if no_multi_reads: # throw away multi-reads
fwtrack.total -= 1
continue
elif random_select_one_multi: # choose one alignment at random
i+=1
if i == 1000000:
m += 1
logging.info(" %d alignments read." % (m*1000000))
i=0
randline = grouplines[random_range(len(grouplines))]
chromosome,fpos,strand,qualstr,mismatches = randline
fwtrack_add_loc(chromosome,fpos,strand,0)
else: # use all alignments probabilistically
group_starts_append(fwtrack.total_multi + 1) # starts at 1 (0 reserved for unique reads)
if no_prior_prob_map:
# don't use map quality; just assume uniform priors
for (chromosome,fpos,strand, qualstr,mismatches) in grouplines:
i+=1
if i == 1000000:
m += 1
logging.info(" %d alignments read." % (m*1000000))
i=0
fwtrack.total_multi += 1
fwtrack_add_loc(chromosome,fpos,strand,
fwtrack.total_multi)
normed_probs = [1./len(grouplines)] * len(grouplines)
else:
# TODO: might want to be working in log space-- if many mismatches, we'll lose precision
qualstr = grouplines[0][3] # all quality strings are shared across the group
group_total_prob = 0.
group_probs = []
group_probs_append = group_probs.append
for (chromosome,fpos,strand, qualstr, mismatches) in grouplines:
i+=1
if i == 1000000:
m += 1
logging.info(" %d alignments read." % (m*1000000))
i=0
fwtrack.total_multi += 1
fwtrack_add_loc(chromosome,fpos,strand,
fwtrack.total_multi)
mismatches = set(mismatches)
read_prob = 1.
# P(SNP) = prior probability a SNP occurs at any base
# P(SE) = probability there was a sequencing error (from PHRED)
# _P(Map|SNP,SE)__MATCH__SNP__SE_
# 0 0 0 0 # can't map here without explanation
# 1 0 0 1
# 1 0 1 0
# 1 0 1 1
# 1 1 0 0
# 1 1 0 1
# 0 1 1 0 # wouldn't map here if SNP, but sequencer read reference
# 1 1 1 1
# we are interested in P(Mapping | Match), which is equivalent to:
# \Sum_{SNP \in {0,1}, SE \in {0,1}} p(SNP) * p(SE) * p(Map|SE,SNP), or:
# p(Map|match = 0):
# p(SE) + p(SNP) + p(SE)*p(SNP)
# p(Map|match = 1):
# 1 - (p(SE) + p(SE)*p(SNP))
for b in xrange(len(qualstr)):
tup = (b in mismatches,qualstr[b])
if tup in match_probs:
prob = match_probs[tup]
elif tup[0]: # mismatch
p_seq_error = 10. ** ((qualstr[b]-qual_offset)/-10.)
prob = p_seq_error + prior_prob_snp + p_seq_error * prior_prob_snp
match_probs[tup] = prob
else: # match
p_seq_error = 10. ** ((qualstr[b]-qual_offset)/-10.)
prob = 1. - (p_seq_error + p_seq_error * prior_prob_snp)
match_probs[tup] = prob
read_prob *= prob
# quick & dirty check-- only looking at last base
assert qualstr[b] >= qual_offset # Specified quality scale yielded a negative phred score! You probably have the wrong PHRED scale!
assert 0.<=read_prob<=1. # error with map qualities
#raise BaseQualityError("Specified quality scale yielded a negative phred score! You probably have the wrong PHRED scale!")
group_probs_append(read_prob)
group_total_prob += read_prob
normed_probs = [p / group_total_prob for p in group_probs]
fwtrack.prob_aligns.extend(normed_probs)
fwtrack.prior_aligns.extend(normed_probs)
fwtrack.enrich_scores.extend([min_score] * len(grouplines))
fwtrack.total = read_total # overwrite the running total, counting each read once
return fwtrack
