def get_offset(self,
ref_file='~/References/HIV-HXB2.fasta',
gene='protease'):
'''
'''
from pythonlib import Alignment
import os
outfile = 'ppp.tmp'
start, stop = gene_coord[gene]
usa_seq = ref_file + '[%d:%d]' % (start, stop)
Alignment.needle_align(usa_seq,
'asis:%s' % self.cons,
outfile,
go=10.0,
ge=0.5)
tal = Alignment.alignfile2dict([outfile], 'get_offset', 10.0, 0.5)
os.remove(outfile)
ka = tal.keys()[0]
this = tal[ka]['asis']
this.summary()
self.offset = this.start
print('Offset consensus w.r.t',
ref_file,
'is',
self.offset,
file=sys.stderr)
return
def find_closest_here(reads_file):
'''
The diff_thresh has been set to 0.025 because even when aligning error-free reads
to the original haplotypes, the distribution of differences of the best 2
identities goes from 0.023 to 0.091 (~9%)
'''
from pythonlib import Alignment
import tempfile
import subprocess
import heapq
import operator
# diff_thresh = 0.025
# abs_thresh = 0.85
ref_file = 'ref.fasta'
out = tempfile.NamedTemporaryFile()
outname = out.name
cmline = 'needle -asequence %s -bsequence %s \
-gapopen 6.0 -gapextend 3.0 -auto -adesshow3 -out %s -aformat3 markx10' \
% (ref_file, reads_file, outname)
subprocess.call(cmline, shell=True)
dd = Alignment.alignfile2dict([outname], 'n', 6.0, 3.0, Verbose=False)
kh = dd.keys()[0]
d = Alignment.alignfile2dict([outname], 'n', 6.0, 3.0, Verbose=False)[kh]
out.close()
this = []
mm = []
ident_2 = []
ig = []
for k, v in d.items():
v.summary()
ig.append(v.int_gaps)
this.append(float(v.ident) / (v.stop - v.start + 1))
mm.append(v.mismatch) #v.stop - v.start + 1 - v.ident
ident_2.append(float(v.ident) / (v.stop - v.start + 1 - v.int_gaps))
return ig, this, mm, ident_2
def get_cons(self, plurality=0.1):
'''Consensus by running EMBOSS cons
'''
import subprocess
import os
import itertools
from pythonlib import Alignment
cline = 'cons -sequence %s -stdout -auto' % self.sup_file
cline += ' -plurality %f' % plurality
p = subprocess.Popen(cline, shell=True, bufsize=1024, \
stdin=subprocess.PIPE, stdout=subprocess.PIPE, \
close_fds=True)
sc = list(SeqIO.parse(p.stdout, 'fasta'))[0].seq.tostring().upper()
strcons = sc.replace('N', '')
outfile = 'tmp.tmp'
Alignment.needle_align(self.ref_file, 'asis:%s' % strcons, \
outfile, go=10.0, ge=0.5)
tal = Alignment.alignfile2dict([outfile], 'ref_cons_alignment', 10.0,
0.5)
os.remove(outfile)
ka = tal.keys()[0]
this = tal[ka]['asis']
it_pair = itertools.izip(this.seq_a, this.seq_b)
this_seq = []
while True:
try:
p = it_pair.next()
except StopIteration:
break
if p is None:
break
if p[1] == '-':
assert p[0] != '-', 'gap-gap?'
this_seq.append(p[0])
elif p[0] != '-':
this_seq.append(p[1])
ws = ''.join(this_seq)
return ws
def find_closest(hr):
'''
The diff_thresh has been set to 0.025 because even when aligning error-free reads
to the original haplotypes, the distribution of differences of the best 2
identities goes from 0.023 to 0.091 (~9%)
'''
from pythonlib import Alignment
import tempfile
import subprocess
import heapq
import operator
diff_thresh = 0.0125
abs_thresh = 0.1
# ref_file = './ref.fas'
out = tempfile.NamedTemporaryFile()
outname = out.name
hap, ref_file = hr
cmline = 'needle -asequence asis:\'%s\' -bsequence %s \
-gapopen 10.0 -gapextend 1.0 -auto -adesshow3 -out %s -aformat3 markx10' \
% (hap, ref_file, outname)
subprocess.call(cmline, shell=True)
d = Alignment.alignfile2dict([outname], 'n', 6.0, 3.0, Verbose = False)['asis']
out.close()
this = {}
mm = {}
gaps = {}
for k, v in d.items():
v.summary()
this[v.id_b] = float(v.mismatch)/(v.stop - v.start + 1) #float(v.ident)/(v.stop - v.start + 1)
mm[v.id_b] = v.mismatch # v.stop - v.start + 1 - v.ident
gaps[v.id_b] = v.int_gaps
best2 = heapq.nsmallest(2, this.items(), operator.itemgetter(1))
rel_delta = (best2[1][1] - best2[0][1])#/best2[0][1]
if rel_delta >= diff_thresh and best2[0][1] <= abs_thresh:
return best2[0][0], best2[0][1], mm[best2[0][0]], gaps[best2[0][0]]
else:
return None, gaps[best2[0][0]]
def alignedvariants(self, threshold=0.9):
import subprocess
import re
import itertools
import hashlib
from Bio.Emboss.Applications import NeedleCommandline
from pythonlib import Alignment
files = []
var_dict = {}
for i, s in enumerate(self.seq_obj):
m_obj = re.search('posterior=(.*)\s*ave_reads=(.*)', s.description)
post, ave_reads = map(float, (m_obj.group(1), m_obj.group(2)))
if post < threshold or ave_reads < 1.:
continue
if post > 1.0:
print('WARNING: posterior=', post, file=sys.stderr)
outfile = 'tmp%d.needle' % i
files.append(outfile)
needle_cline = NeedleCommandline(asequence='asis:%s' % self.ref, bsequence='asis:%s' % s.seq.tostring().strip('-'), \
outfile=outfile, gapopen=10.0, gapextend=0.5, aformat='markx10')
needle_cline.auto = True
try:
retcode = subprocess.call(str(needle_cline), shell=True)
if retcode < 0:
sys.exit('Child needle was terminated by signal %d' %
-retcode)
# else:
# print >> sys.stderr, 'Child needle returned %i' % retcode
except OSError:
sys.exit('Execution of needle failed: %s' % ee)
pass
tal = Alignment.alignfile2dict([outfile],
'support_seqs%d' % i,
10.0,
0.5,
Verbose=False)
os.remove(outfile)
ka = tal.keys()[0]
this = tal[ka]['asis']
it_pair = itertools.izip(this.seq_a, this.seq_b)
#this.summary()
#start, stop = this.start, this.stop
#it_pair = itertools.izip(this.seq_a[start-1:stop], this.seq_b[start-1:stop])
this_seq = []
while True:
try:
p = it_pair.next()
except StopIteration:
break
if p is None:
break
if p[1] == '-':
assert p[0] != '-', 'gap-gap?'
this_seq.append(p[0])
elif p[0] != '-':
this_seq.append(p[1])
ws = ''.join(this_seq)
var_dict[ws] = var_dict.get(ws, 0) + ave_reads
for k, v in var_dict.items():
ts = Seq(k, IUPAC.unambiguous_dna)
tsr = SeqRecord(ts, id = hashlib.sha224(k).hexdigest(), \
name='Reconstructed local hap')
tsr.description = 'ave_reads=%f' % v
self.dna_seqs.append(tsr)
print('%d haplotypes have support >=%f'\
% (len(files), threshold), file=sys.stderr)
return self.dna_seqs
def count_codons(haps):
import pickle
from Bio.Seq import translate
from operator import itemgetter
from pythonlib import Alignment
from pythonlib import mystats
latex = False # print latex table
count = [{} for i in range(102)]
oh = open('all.dat', 'w')
hap_freq = {}
degeneracy = {}
mask_mupos = [] #[10, 11, 22, 25, 32, 46, 58, 62, 67, 74, 89]
mupos = []
# These sequences are HXB2 proteases
wt_protease = 'PQVTLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF'
wt_protease_nt = 'CCTCAGGTCACTCTTTGGCAACGACCCCTCGTCACAATAAAGATAGGGGGGCAACTAAAGGAAGCTCTATTAGATACAGGAGCAGATGATACAGTA\
TTAGAAGAAATGAGTTTGCCAGGAAGATGGAAACCAAAAATGATAGGGGGAATTGGAGGTTTTATCAAAGTAAGACAGTATGATCAGATACTCATAGAAATCTGTGGACATAAAGCTA\
TAGGTACAGTATTAGTAGGACCTACACCTGTCAACATAATTGGAAGAAATCTGTTGACTCAGATTGGTTGCACTTTAAATTTT'
ac_res = map(align_codons, haps)
protease = wt_protease
for ar in ac_res:
start, residues, freq = ar # start here is human (from 1)
start -= 1 # start here is pythonic (from 0)
if start == None and residues == None: continue
oh.write('%d %s\n' % (round(freq), wt_protease_nt[:start] + residues +
wt_protease_nt[len(residues) + start:]))
if start % 3 == 0:
read = residues
elif start % 3 == 1:
read = residues[2:]
elif start % 3 == 2:
read = residues[1:]
try:
aa = translate(read) # Biopython
except:
print 'error: read', read
continue
if start % 3 == 0:
start_a = start / 3 + 1
if start % 3:
start_a = start / 3 + 2
stop_a = len(aa) + start_a + 1
this_hap = str(protease[:start_a - 1] + aa + protease[stop_a - 2:])
print this_hap.ljust(100), str(freq).ljust(
8
) # this is used for resistance prediction, whole haplotype and reads
for i, c in enumerate(this_hap):
count[i + 1][c] = count[i + 1].get(c, 0) + freq
Alignment.needle_align('asis:%s' % wt_protease, 'asis:%s ' % this_hap,
'tmp', 10.0, 0.5)
d = Alignment.alignfile2dict(['tmp'], 'n', 10.0, 0.5,
Verbose=False)['asis']['asis']
os.remove('tmp')
mutations = []
for i, c in enumerate(zip(d.seq_a, d.seq_b)):
pos = i + 1
if '-' in c:
continue
if c[0] != c[1]:
mutations.append(c[0] + str(pos) + c[1])
if pos not in mask_mupos: mupos.append(pos)
signature = ', '.join(mutations)
hap_freq[signature] = hap_freq.get(signature, 0.0) + freq
degeneracy[signature] = degeneracy.get(signature, 0) + 1
print ''
for k, v in hap_freq.items():
print str(v).ljust(15), ' ', k
mupos = sorted(mupos)
spos = {}
for i, j in enumerate(mupos):
spos[j] = i
hf_sorted = sorted(hap_freq.items(), key=itemgetter(1), reverse=True)
tot_reads = sum([h[1] for h in haps])
tot_hap = sum(hap_freq.values())
print 'Tot reads after', tot_reads
print 'Tot', tot_hap
print 'Simpson\'s index on amino acid sequences = %f +/- %f' % mystats.Simpson(
hap_freq.values())
oh = open('degeneracy.pck', 'w')
pickle.dump(degeneracy, oh)
oh.close()
for c in count:
ts = sum(c.values())
for k in c.keys():
c[k] /= ts
plot_variation(count)
if not latex:
return hf_sorted
print ''
print '|c' * (1 + len(spos))
for i in mupos:
print '%s%d & ' % (wt_protease[i - 1], i),
print ''
return hf_sorted