def lookForORF(self, seq, rec) :
coordinates, edges = rec['coordinates'], rec['flanking'][:]
seq = self.get_seq(seq, *coordinates)
#if (len(seq) - sum(edges)) % 3 == 0 :
startCodon, stopCodon = 0, 0
for s in xrange(edges[0]%3, len(seq), 3) :
c = seq[s:s+3]
if c in {'ATG', 'TTG', 'GTG'} :
startCodon, edges[0] = 1, 0
e0 = s+3
new_s = coordinates[1] + s if coordinates[3] == '+' else coordinates[2] - s
break
if not startCodon :
return 6
for e in xrange(e0, len(seq), 3) :
c = seq[e:e+3]
if c in {'TAG', 'TAA', 'TGA'} :
stopCodon, edges[1] = 1, 0
new_e = coordinates[1] + e+2 if coordinates[3] == '+' else coordinates[2] - e-2
break
if startCodon and stopCodon and abs(e-s) + 1 >= 0.6 * (abs(coordinates[2]-coordinates[1])+1 - sum(rec['flanking'])) :
c2 = (new_s, new_e) if coordinates[3] == '+' else (new_e, new_s)
if c2[0] != coordinates[1] or c2[1] != coordinates[2] :
rec['CIGAR'] = rec['CIGAR'].rsplit(':', 1)[0] + ':EXEMPT'
coordinates[1:3] = c2
rec['flanking'] = edges
return 0
return 6
def get_similar(bsn, ortho_pairs):
key = tuple(sorted([bsn[0][0], bsn[0][1]]))
if key in ortho_pairs:
return
matched_aa = {}
len_aa = int(int(bsn[0][12]) / 3)
for part in bsn:
s_i, e_i, s_j, e_j = [int(x) for x in part[6:10]]
for s, t in re.findall(r'(\d+)([A-Z])', part[14]):
frame_i, frame_j = s_i % 3, s_j % 3
s = int(s)
if t == 'M':
if frame_i == frame_j or params['incompleteCDS']:
matched_aa.update({
(s_i + x): 1
for x in xrange((3 - (frame_i - 1)) % 3, s)
})
s_i += s
s_j += s
if len(matched_aa) * 3 >= min(
params['match_len2'],
params['match_len']) or len(matched_aa) >= (
min(params['match_prop'],
params['match_prop2']) - 0.1) * len_aa:
ortho_pairs[key] = 1
return
elif t == 'I':
s_i += s
else:
s_j += s
def do_polish_with_SNPs(self, reference, snp_file):
sequence = readFasta(reference)
snps = {n: [] for n in sequence}
if snp_file != '':
with open(snp_file) as fin:
for line in fin:
part = line.strip().split()
snps[part[0]].append([int(part[1]), part[-1]])
self.snps = snps
for n, s in sequence.items():
sequence[n] = list(s)
for cont, sites in snps.items():
for site, base in reversed(sites):
if base.startswith('+'):
sequence[cont][site - 1:site - 1] = base[1:]
elif base.startswith('-'):
sequence[cont][site - 1:(site + len(base) - 2)] = []
else:
sequence[cont][site - 1] = base
with open('{0}.fasta'.format(prefix), 'w') as fout:
for n, s in sorted(sequence.items()):
s = ''.join(s)
fout.write('>{0}\n{1}\n'.format(
n, '\n'.join([
s[site:(site + 100)]
for site in xrange(0, len(s), 100)
])))
return '{0}.fasta'.format(prefix)
def write_down(filename, regions, repeats, mutations, reference, query, tag) :
with uopen(filename, 'w') as fout:
fout.write('##gff-version 3\n')
fout.write('## Reference: {0}\n'.format(reference))
fout.write('## Query: {0}\n'.format(query))
fout.write('## Tag: {0}\n'.format(tag))
fout.write('\n'.join(['{0}\trefMapper\tmisc_feature\t{1}\t{2}\t{3}\t{4}\t.\t{5}'.format(r[1], r[2], r[3], r[0], r[10], '/inference="Aligned%20with%20{0}:{1}-{2}"'.format(*r[7:10])) for r in regions]) + '\n')
fout.write('\n'.join(['{0}\trefMapper\tunsure\t{1}\t{2}\t.\t+\t.\t/inference="{3}"'.format(r[0], r[1], r[2], 'Repetitive%20region' if r[3] == 0 else 'Uncertain%20base%20calling%20or%20ambigious%20alignment') for r in repeats]) + '\n')
for contig, variation in sorted(mutations.items()):
for site, alters in sorted(variation.items()) :
for alter, source in alters.items() :
if source[6][0] == '-' :
difference = '+{0}'.format(source[7])
origin = '.'
elif source[7][0] == '-' :
difference = '-{0}'.format(source[6])
origin = source[6]
else :
difference = source[7]
origin = source[6]
compare = ''
for id in xrange(0, len(source), 9) :
compare += '{0}:{1}-{2}:{3};'.format(source[id+0], abs(source[id+4]), abs(source[id+5]), source[id+1])
fout.write('{0}\trefMapper\tvariation\t{1}\t{2}\t.\t+\t.\t{3}\n'.format(contig, source[2], source[3], '/replace="{0}";/compare="{1}";/origin="{2}"'.format(difference, compare[:-1], origin)))
def inter_loci_overlap(self, alleles, parameters) :
regions = [reg for region in alleles.values() for reg in region]
# sort with contig name and start points
regions.sort(key=itemgetter(2,3))
for id, regi in enumerate(regions) :
if regi[0] == '' : continue
todel, deleted = [], 0
for jd in xrange(id+1, len(regions)) :
regj = regions[jd]
if regj[0] == '' or regi[0] == regj[0]: continue
if regi[2] != regj[2] or regj[3] > regi[4] :
break
overlap = min(regi[4], regj[4]) - regj[3] + 1
if (regi[-1] != '' and float(overlap) >= parameters['merging_prop'] * (regi[4]-regi[3]+1)) or \
(regj[-1] != '' and float(overlap) >= parameters['merging_prop'] * (regj[4]-regj[3]+1)) :
delta = regi[1] - regj[1]
if delta > 0.05 :
todel.append(jd)
elif delta <= -0.05 :
deleted = 1
break
if deleted == 0 :
for jd in todel:
regions[jd][0] = ''
else :
regi[0] = ''
return [{'locus':reg[0], 'identity':reg[1], 'CIGAR':reg[9], 'coordinates':[reg[2], int(reg[3]), int(reg[4]), reg[5]], 'flanking':reg[6:8], 'status':reg[8], 'accepted':(0 if reg[8] == '' else 128)} for reg in regions if reg[0] != '' and reg[-1] != '']
def reScore(self, ref, qry, blastab, mode, perBatch=10000):
if not self.qrySeq:
self.qrySeq, self.qryQual = readFastq(qry)
if not self.refSeq:
self.refSeq, self.refQual = readFastq(ref)
for k, v in self.qrySeq.items():
self.qrySeq[k] = nucEncoder[np.array(list(v)).view(asc2int)]
for k, v in self.refSeq.items():
self.refSeq[k] = nucEncoder[np.array(list(v)).view(asc2int)]
nTab = len(blastab)
for bId in xrange(0, blastab.shape[0], perBatch):
logger('Update scores: {0} / {1}'.format(bId, nTab))
tabs = blastab[bId:bId + perBatch]
#scores = np.array([ cigar2score([t[14], self.refSeq[str(t[1])][t[8]-1:t[9]] if t[8] < t[9] else 4 - self.refSeq[str(t[1])][t[9]-1:t[8]][::-1], self.qrySeq[str(t[0])][t[6]-1:t[7]], t[6], mode, 6, 1]) for t in tabs ])
scores = np.array(
list(
map(cigar2score, ([
t[14],
self.refSeq[str(t[1])][t[8] - 1:t[9]] if t[8] < t[9]
else 4 - self.refSeq[str(t[1])][t[9] - 1:t[8]][::-1],
self.qrySeq[str(t[0])][t[6] - 1:t[7]], t[6], mode, 6, 1
] for t in tabs))))
tabs.T[2], tabs.T[11] = scores.T
return blastab
def main(mgs):
for mg in mgs:
res = []
with uopen(mg) as fin:
for line in fin:
logp = re.findall('logp:\t([-eE\d\.]+)', line)
if len(logp):
logp = float(logp[0])
res.append([logp])
else:
genotype = re.findall(
'Genotype (\d+):\tMean proportion:\t([eE\d\.]+)\tCI95%:\t(\[ [eE\d\.]+ - [eE\d\.]+ \])',
line)
if len(genotype):
res[-1].append(
[genotype[0][1], genotype[0][2], '', '', ''])
elif len(res) and len(
res[-1]) > 1 and res[-1][-1][-1] == '':
part = line.strip().split('\t')
res[-1][-1][2:] = [
part[0], part[1], part[3] + ' ' + part[5]
]
try:
res = max(res)
res[1:] = sorted(res[1:], key=lambda x: -float(x[0]))
for i in xrange(1, len(res)):
r = res[i]
print('{0}\t{1}\t{2}'.format(mg, i, '\t'.join(r)))
except:
pass
def assignment3(dists, res, encode, presence):
n_loci = mat.shape[1] - 1
for id in xrange(len(dists)):
idx, ref, _, s, ql = dists[id]
gl = presence[encode[res[ref, 1:]], np.arange(1, res.shape[1])]
gl[gl > ql] = ql
d = (n_loci * (gl - s).astype(float) / gl + 0.5).astype(int)
jd = np.argmax((d - np.arange(n_loci)) <= 0) + 1
res[idx, jd:] = res[ref, jd:]
gl = presence[encode[res[idx, jd:]], np.arange(jd, presence.shape[1])]
presence[encode[res[idx, jd:]][gl < ql],
np.arange(jd, presence.shape[1])[gl < ql]] = ql
def assignment(dists, res):
for id in xrange(len(dists)):
idx, ref, d1, d2 = dists[id]
for d in xrange(d1, n_loci + 1):
if res[idx, d] != res[ref, d]:
if d >= res[idx, 0]:
if d >= d2:
if res[idx, d] < res[ref, d]:
grps = [res[idx, d], res[ref, d]]
else:
grps = [res[ref, d], res[idx, d]]
res[:idx, d][res[:idx, d] == grps[1]] = grps[0]
res[idx, d] = grps[0]
else:
if res[idx, d] < res[ref, d]:
res[:idx, d][res[:idx, d] == res[ref, d]] = res[idx, d]
else:
res[idx, d:] = res[ref, d:]
break
else:
break
if res[idx, 0] > d1:
res[idx, 0] = d1
return
def tab2overlaps(tabs, ovl_l, ovl_p, nTab, overlaps):
ovlId = 0
for i1 in xrange(overlaps[-1, 0], nTab):
t1 = tabs[i1]
ovl_l2 = min(ovl_l, ovl_p * (t1[3] - t1[2] + 1))
if i1 > overlaps[-1, 0]:
i2r = xrange(i1 + 1, nTab)
else:
i2r = xrange(overlaps[-1, 1], nTab)
for i2 in i2r:
t2 = tabs[i2]
if t1[0] != t2[0] or t2[2] > t1[3]: break
ovl = min(t1[3], t2[3]) - t2[2] + 1
if ovl >= ovl_l2 or ovl >= ovl_p * (t2[3] - t2[2] + 1):
overlaps[ovlId, :] = [t1[1], t2[1], ovl]
ovlId += 1
if ovlId == 1000000:
overlaps[-1, :2] = [i1, i2]
break
if ovlId == 1000000:
break
if ovlId < 1000000:
overlaps[-1, :] = -1
return overlaps
def runDiamond(self, ref, qry, nhits=10, frames='7') :
logger('Run diamond starts')
refAA = os.path.join(self.dirPath, 'refAA')
qryAA = os.path.join(self.dirPath, 'qryAA')
aaMatch = os.path.join(self.dirPath, 'aaMatch')
if not self.qrySeq :
self.qrySeq, self.qryQual = readFastq(qry)
if not self.refSeq :
self.refSeq, self.refQual = readFastq(ref)
qryAASeq = transeq(self.qrySeq, frame='F', transl_table=self.table_id)
with open(qryAA, 'w') as fout :
for n, ss in sorted(qryAASeq.items()) :
_, id, s = min([ (len(s[:-1].split('X')), id, s) for id, s in enumerate(ss) ])
fout.write('>{0}:{1}\n{2}\n'.format(n, id+1, s))
diamond_fmt = '{diamond} makedb --db {qryAA} --in {qryAA}'.format(
diamond=diamond, qryAA=qryAA)
p = Popen(diamond_fmt.split(), stderr=PIPE, stdout=PIPE, universal_newlines=True).communicate()
refAASeq = transeq(self.refSeq, frames, transl_table=self.table_id)
toWrite = []
for n, ss in sorted(refAASeq.items()) :
for id, s in enumerate(ss) :
cdss = re.findall('.{1000,}?X|.{1,1000}$', s + 'X')
cdss[-1] = cdss[-1][:-1]
cdsi = np.cumsum([0]+list(map(len, cdss[:-1])))
for ci, cs in zip(cdsi, cdss) :
if len(cs) :
toWrite.append('>{0}:{1}:{2}\n{3}\n'.format(n, id+1, ci, cs))
for id in xrange(5) :
with open('{0}.{1}'.format(refAA, id), 'w') as fout :
for line in toWrite[id::5] :
fout.write(line)
diamond_cmd = '{diamond} blastp --no-self-hits --threads {n_thread} --db {refAA} --query {qryAA} --out {aaMatch} --id {min_id} --query-cover {min_ratio} --evalue 1 -k {nhits} --dbsize 5000000 --outfmt 101'.format(
diamond=diamond, refAA='{0}.{1}'.format(refAA, id), qryAA=qryAA, aaMatch='{0}.{1}'.format(aaMatch, id), n_thread=self.n_thread, min_id=self.min_id*100., nhits=nhits, min_ratio=self.min_ratio*100.)
Popen(diamond_cmd.split(), stdout=PIPE, stderr=PIPE, universal_newlines=True).communicate()
blastab = []
for r in self.pool.imap_unordered(parseDiamond, [ ['{0}.{1}'.format(aaMatch, id), self.refSeq, self.qrySeq, self.min_id, self.min_cov, self.min_ratio] for id in xrange(5) ]) :
if r is not None :
blastab.append(np.load(r, allow_pickle=True))
os.unlink(r)
blastab = np.vstack(blastab)
logger('Run diamond finishes. Got {0} alignments'.format(blastab.shape[0]))
return blastab
def global_difference2(g):
_, idx, cnt = np.unique(g.T[1], return_counts=True, return_index=True)
idx = idx[cnt == 1]
names, seqs = g[idx, 1], np.vstack(g[idx, 4])
comparable = np.zeros(shape=[seqs.shape[0], seqs.shape[0]])
seqs[np.in1d(seqs, [65, 67, 71, 84], invert=True).reshape(seqs.shape)] = 45
diff = compare_seq(
seqs, np.zeros(shape=[seqs.shape[0], seqs.shape[0], 2],
dtype=np.uint8))
res = {}
for i, n1 in enumerate(names):
for j in xrange(i + 1, len(names)):
n2 = names[j]
if diff[i, j, 1] >= min(params['match_len2'],
seqs.shape[1] * params['match_prop2']):
res[(n1, n2)] = diff[i, j, :]
return res
def ovlFilter(self, blastab, params):
coverage, delta = params[1:]
logger('Run filtering. Start with {0} hits.'.format(len(blastab)))
blastab[blastab.T[8] > blastab.T[9], 8:10] *= -1
blastab = pd.DataFrame(blastab).sort_values(by=[1, 0, 8, 6]).values
for i, t1 in enumerate(blastab):
if t1[2] < 0: continue
toDel = []
for j in xrange(i + 1, blastab.shape[0]):
t2 = blastab[j]
if t2[2] < 0: continue
if np.any(t1[:2] != t2[:2]) or t1[9] < t2[8]:
break
c = min(t1[9], t2[9]) - t2[8] + 1
if (c >= coverage * (t1[9] - t1[8] + 1)
and t2[11] - t1[11] >= delta):
t1[2] = -1.
break
elif (c >= coverage * (t2[9] - t2[8] + 1)
and t1[11] - t2[11] >= delta):
toDel.append(j)
elif c >= (t1[9] - t1[8] +
1) and c < coverage * (t2[9] - t2[8] + 1):
c2 = min(t1[7], t2[7]) - max(t2[6], t1[6]) + 1
if c2 >= (t1[7] - t1[6] +
1) and c2 < coverage * (t2[7] - t2[6] + 1):
t1[2] == -1
break
elif c >= (t2[9] - t2[8] +
1) and c < coverage * (t1[9] - t1[8] + 1):
c2 = min(t1[7], t2[7]) - max(t2[6], t1[6]) + 1
if c2 >= (t2[7] - t2[6] +
1) and c2 < coverage * (t1[7] - t1[6] + 1):
toDel.append(j)
if t1[2] >= 0:
for j in toDel:
blastab[j][2] = -1.
blastab = blastab[blastab.T[2] >= 0]
blastab[blastab.T[8] < 0, 8:10] *= -1
logger('Done filtering. End with {0} hits.'.format(blastab.shape[0]))
return blastab
def global_difference(bsn_file, orthoGroup, counts=3000):
groups = np.load(bsn_file)
genes = []
for gene, g in groups.items():
_, idx, cnt = np.unique(g.T[1], return_counts=True, return_index=True)
score = (np.sum(cnt == 1) - 1) * (2**41) - np.sum(g[idx[cnt == 1], 2],
dtype=int)
if score > 0:
genes.append([score, gene])
genes = sorted(genes, reverse=True)
og = np.array(list(orthoGroup.keys()))
grp_order, all_useds = [], set([])
for score, gene in genes:
tag = groups[gene][0][0]
if tag not in all_useds:
grp_order.append(gene)
used = og[og.T[0] == tag, 1]
all_useds |= set(used.tolist())
genes = grp_order[:counts]
global_differences = {}
for iter in xrange(0, len(genes), 100):
logger('finding ANIs between genomes. {0}/{1}'.format(
iter, len(genes)))
#diffs = list(map(global_difference2, [groups[i] for i in genes[iter:iter+100]]))
diffs = pool.map(global_difference2,
[groups[i] for i in genes[iter:iter + 100]])
for diff in diffs:
for pair, (mut, aln) in diff.items():
if pair not in global_differences:
global_differences[pair] = []
if aln:
global_differences[pair].append(max(float(mut), .5) / aln)
for pair, info in global_differences.items():
diff = np.log(info)
mean_diff = max(np.mean(diff), -4.605)
sigma = min(max(np.sqrt(np.mean((diff - mean_diff)**2)) * 3, 0.693),
1.386)
global_differences[pair] = (np.exp(mean_diff), np.exp(sigma))
return pd.DataFrame(list(global_differences.items())).values
def __readAssembly(self, assembly):
seq = {}
with uopen(assembly) as fin:
header = fin.read(1)
with uopen(assembly) as fin:
if header == '@':
for id, line in enumerate(fin):
if id % 4 == 0:
part = line[1:].strip().split()
name = part[0]
seq[name] = [
0,
float(part[2]) if len(part) > 2 else 0., None, None
]
elif id % 4 == 1:
seq[name][2] = line.strip()
seq[name][0] = len(seq[name][2])
elif id % 4 == 3:
seq[name][3] = np.array(list(line.strip()))
fasfile = assembly.rsplit('.', 1)[0] + '.fasta'
logger('Write fasta sequences into {0}'.format(fasfile))
with open(fasfile, 'w') as fout:
for n, s in sorted(seq.items()):
fout.write('>{0}\n{1}\n'.format(
n, '\n'.join([
s[2][site:(site + 100)]
for site in xrange(0, len(s[2]), 100)
])))
else:
fasfile = assembly
for id, line in enumerate(fin):
if line.startswith('>'):
name = line[1:].strip().split()[0]
seq[name] = [0, 0., []]
else:
seq[name][2].extend(line.strip().split())
for n, s in seq.items():
s[2] = ''.join(s[2])
s[0] = len(s[2])
return seq, fasfile
def runUBlast(self, ref, qry, nhits=6, frames='7'):
logger('Run uBLAST starts')
def parseUBlast(fin, refseq, qryseq, min_id, min_cov, min_ratio):
blastab = pd.read_csv(fin, sep='\t', header=None)
blastab[2] /= 100.
blastab = blastab[blastab[2] >= min_id]
blastab[3], blastab[4] = blastab[3] * 3, blastab[4] * 3
qf, rf = blastab[0].str.rsplit(
':', 1, expand=True), blastab[1].str.rsplit(':',
1,
expand=True)
if np.all(qf[0].str.isdigit()):
qf[0] = qf[0].astype(int)
if np.all(rf[0].str.isdigit()):
rf[0] = rf[0].astype(int)
blastab[0], qf = qf[0], qf[1].astype(int)
blastab[1], rf = rf[0], rf[1].astype(int)
blastab[6], blastab[
7] = blastab[6] * 3 + qf - 3, blastab[7] * 3 + qf - 1
blastab[14] = [[
[3 * vv[0], vv[1]] for vv in v
] for v in map(getCIGAR, zip(blastab[15], blastab[14]))]
blastab[12], blastab[13] = blastab[0].apply(lambda x: len(qryseq[
str(x)])), blastab[1].apply(lambda x: len(refseq[str(x)]))
rf3 = (rf <= 3)
blastab.loc[rf3,
8], blastab.loc[rf3, 9] = blastab.loc[rf3, 8] * 3 + rf[
rf3] - 3, blastab.loc[rf3, 9] * 3 + rf[rf3] - 1
blastab.loc[~rf3, 8], blastab.loc[
~rf3, 9] = blastab.loc[~rf3, 13] - (
blastab.loc[~rf3, 8] * 3 + rf[~rf3] - 3 -
3) + 1, blastab.loc[~rf3, 13] - (blastab.loc[~rf3, 9] * 3 +
rf[~rf3] - 3 - 1) + 1
d = np.max([
blastab[7] - blastab[12], blastab[9] - blastab[13],
1 - blastab[9],
np.zeros(blastab.shape[0], dtype=int)
],
axis=0)
blastab[7] -= d
def ending(x, y):
x[-1][0] -= y
np.vectorize(ending)(blastab[14], d)
d[~rf3] *= -1
blastab[9] -= d
blastab = blastab[
(blastab[7] - blastab[6] + 1 >= min_ratio * blastab[12])
& (blastab[7] - blastab[6] + 1 >= min_cov)]
return blastab.drop(columns=[15, 16])
refAA = os.path.join(self.dirPath, 'refAA')
qryAA = os.path.join(self.dirPath, 'qryAA')
aaMatch = os.path.join(self.dirPath, 'aaMatch')
if not self.qrySeq:
self.qrySeq, self.qryQual = readFastq(qry)
if not self.refSeq:
self.refSeq, self.refQual = readFastq(ref)
qryAASeq = transeq(self.qrySeq, frame='F')
with open(qryAA, 'w') as fout:
for n, ss in sorted(qryAASeq.items()):
_, id, s = min([(len(s[:-1].split('X')), id, s)
for id, s in enumerate(ss)])
fout.write('>{0}:{1}\n{2}\n'.format(n, id + 1, s))
refAASeq = transeq(self.refSeq, frames)
toWrite = []
for n, ss in sorted(refAASeq.items()):
for id, s in enumerate(ss):
toWrite.append('>{0}:{1}\n{2}\n'.format(n, id + 1, s))
blastab = []
for id in xrange(5):
with open(refAA, 'w') as fout:
for line in toWrite[id::4]:
fout.write(line)
ublast_cmd = '{usearch} -self -threads {n_thread} -db {refAA} -ublast {qryAA} -mid {min_id} -query_cov {min_ratio} -evalue 1 -accel 0.9 -maxhits {nhits} -userout {aaMatch} -ka_dbsize 5000000 -userfields query+target+id+alnlen+mism+opens+qlo+qhi+tlo+thi+evalue+raw+ql+tl+qrow+trow+qstrand'.format(
usearch=usearch,
refAA=refAA,
qryAA=qryAA,
aaMatch=aaMatch,
n_thread=self.n_thread,
min_id=self.min_id * 100.,
nhits=nhits,
min_ratio=self.min_ratio)
p = Popen(ublast_cmd.split(),
stderr=PIPE,
stdout=PIPE,
universal_newlines=True).communicate()
if os.path.getsize(aaMatch) > 0:
blastab.append(
parseUBlast(open(aaMatch), self.refSeq, self.qrySeq,
self.min_id, self.min_cov, self.min_ratio))
blastab = pd.concat(blastab)
logger('Run uBLAST finishes. Got {0} alignments'.format(
blastab.shape[0]))
return blastab
def _linearMerge(data):
matches, params = data
grpCol = pd.Series(data=[[]] * matches.shape[0])
matches = np.hstack([matches, grpCol.values[:, np.newaxis]])
gapDist, lenDiff = params[1:]
gene, geneLen = matches[0][0], matches[0][12]
tailing = 20
def resolve_edges(edges):
grps = []
for id, m1 in edges[0]:
for jd, m2 in edges[1]:
if (m1[1] == m2[1] and max(abs(m1[8]), abs(m1[9])) > min(abs(m2[8]), abs(m2[9])) ) or \
abs(m1[2]-m2[2]) > 0.3 or m1[6] >= m2[6] or m1[7] >= m2[7] or m2[6]-m1[7]-1 >= gapDist:
continue
rLen = m2[7] - m1[6] + 1
g1 = -m1[9] - 1 if m1[9] < 0 else m1[13] - m1[9]
g2 = m1[8] - 1 if m1[8] > 0 else m1[13] + m1[8]
qLen = m1[9] - m1[8] + 1 + m2[9] - m2[8] + 1 + g1 + g2
if g1 + g2 >= gapDist or min(rLen, qLen) * lenDiff < max(
rLen, qLen):
continue
overlap = sorted([m1[7] - m2[6] + 1, -g1 - g2], reverse=True)
rLen1, rLen2 = m1[7] - m1[6] + 1, m2[7] - m2[6] + 1
if overlap[0] > 0:
score = m1[11] + m2[11] - overlap[0] * min(
float(m1[11]) / rLen1,
float(m2[11]) / rLen2)
ident = (m1[2] * rLen1 + m2[2] * rLen2 - overlap[0] *
min(m1[2], m2[2])) / (rLen1 + rLen2 - overlap[0])
else:
score = m1[11] + m2[11]
ident = (m1[2] * rLen1 + m2[2] * rLen2) / (rLen1 + rLen2)
if overlap[1] < 0:
score += overlap[1] / 3.
if score > m1[11] and score > m2[11]:
grps.append([score, ident, rLen, 1, id, jd])
return grps
groups = []
prev, edges = matches[0][1], [[], []]
nSave = len(matches)
for id, m1 in enumerate(matches):
rLen1 = m1[7] - m1[6] + 1
groups.append([m1[11], m1[2], rLen1, 0, id])
if m1[6] > tailing and (
(m1[8] > 0 and m1[8] - 1 <= gapDist) or
(m1[8] < 0 and m1[13] + m1[8] < gapDist)
): # any hit within the last 150 bps to either end of a scaffold is a potential fragmented gene
edges[1].append([id, m1])
if m1[7] <= m1[12] - tailing:
if (m1[8] > 0 and m1[13] - m1[9] <= gapDist) or (
m1[8] < 0 and -1 - m1[9] < gapDist):
edges[0].append([id, m1])
for jd in xrange(id + 1, nSave):
m2 = matches[jd]
if m1[1] != m2[2] or (m1[8] < 0 and m2[8] > 0) or m2[8] - m1[
9] - 1 >= gapDist: # maximum 300bps between two continuous hits in the same scaffold
break
rLen, qLen = m2[7] - m1[6] + 1, m2[9] - m1[8] + 1
if abs(m1[2]-m2[2]) > 0.3 or m1[9] >= m2[9] or m1[6] >= m2[6] or m1[7] >= m2[7] or m2[6] - m1[7] -1 >= gapDist \
or min(rLen, qLen)*lenDiff < max(rLen, qLen) :
continue
rLen2 = m2[7] - m2[6] + 1
overlap = sorted([m1[7] - m2[6] + 1, m1[9] - m2[8] + 1],
reverse=True)
if overlap[0] > 0:
score = m1[11] + m2[11] - overlap[0] * min(
float(m1[11]) / rLen1,
float(m2[11]) / rLen2)
ident = (m1[2] * rLen1 + m2[2] * rLen2 - overlap[0] *
min(m1[2], m2[2])) / (rLen1 + rLen2 - overlap[0])
else:
score = m1[11] + m2[11]
ident = (m1[2] * rLen1 + m2[2] * rLen2) / (rLen1 + rLen2)
if overlap[1] < 0:
score += overlap[1] / 3.
if score > m1[11] and score > m2[11]:
groups.append([score, ident, rLen, 0, id, jd])
if len(edges[0]) and len(edges[1]):
groups.extend(resolve_edges(edges))
if len(groups) > len(matches):
groups.sort(reverse=True)
usedMatches, usedGroups = {}, []
for grp in groups:
if (grp[4], 4) in usedMatches or (grp[-1], 5) in usedMatches:
continue
if grp[3] > 0:
if (grp[4], 5) in usedMatches or (grp[-1], 4) in usedMatches:
continue
if grp[4] != grp[-1]:
lMat, rMat = matches[grp[4]], matches[grp[-1]]
il, im = sorted([grp[4], grp[-1]])
skp = 0
for i in xrange(il + 1, im):
if matches[i][1] in {lMat[1], rMat[1]}:
if (i, 4) in usedMatches or (i, 5) in usedMatches:
skp = 1
break
if skp:
continue
for i in xrange(il + 1, im):
if matches[i][1] in {lMat[1], rMat[1]}:
usedMatches[(i, 4)] = usedMatches[(i, 5)] = 0
usedGroups.append(grp)
usedMatches[(grp[4], 4)] = usedMatches[(grp[-1], 5)] = 1
if grp[3] > 0:
usedMatches[(grp[4], 5)] = usedMatches[(grp[-1], 4)] = 1
usedGroups.sort(key=itemgetter(4), reverse=True)
for gId in xrange(len(usedGroups) - 1):
g1, g2 = usedGroups[gId:gId + 2]
if g1[4] == g2[-1]:
m = matches[g1[4]]
score = g1[0] + g2[0] - m[11]
length = g1[2] + g2[2] - (m[7] - m[6] + 1)
iden = (g1[1] * g1[2] + g2[1] * g2[2] - min(g1[1], g2[1]) *
(m[7] - m[6] + 1)) / length
usedGroups[gId + 1] = [score, iden, length, 0, g2[4]] + g1[4:]
g1[1] = -1
else:
usedGroups = groups
usedMatches = {(k, k): 1 for k in np.arange(matches.shape[0])}
for g in usedGroups:
if g[1] >= 0:
ids = [matches[i][15] for i in g[4:]]
for i in g[4:]:
matches[i, -1] = g[:3] + ids
ids = {k[0] for k, v in usedMatches.items() if v == 1}
matches = matches[np.array(list(ids))]
return matches
def hierCC(args):
params = get_args(args)
ot = time.time()
profile_file, cluster_file, old_cluster = params.profile, params.output + '.npz', params.incremental
global mat, n_loci
mat = pd.read_csv(profile_file, sep='\t', header=None, dtype=str).values
allele_columns = np.array(
[i == 0 or (not h.startswith('#')) for i, h in enumerate(mat[0])])
mat = mat[1:, allele_columns].astype(int)
n_loci = mat.shape[1] - 1
logger(
'{0}: Loaded in allelic profiles with dimension: {1} and {2}. The first column is assumed to be type id.'
.format(time.time() - ot, *mat.shape))
if not params.immutable:
absence = np.sum(mat <= 0, 1)
mat = mat[np.argsort(absence, kind='mergesort')]
if os.path.isfile(old_cluster):
od = np.load(old_cluster, allow_pickle=True)
cls = od['hierCC']
typed = {c[0]: id for id, c in enumerate(cls) if c[0] > 0}
if len(typed) > 0:
logger('{0}: Loaded in {1} old hierCC assignments.'.format(
time.time() - ot, len(typed)))
mat_idx = np.array([t in typed for t in mat.T[0]])
mat[:] = np.vstack([mat[mat_idx], mat[(mat_idx) == False]])
else:
typed = {}
logger('{0}: Start hierCC assignments'.format(time.time() - ot))
pool = Pool(10)
res = np.repeat(mat.T[0], mat.shape[1]).reshape(mat.shape)
res[1:, 0] = n_loci + 1
for index in xrange(0, mat.shape[0], 100):
to_run = []
for idx in np.arange(index, index + 100):
if idx < mat.shape[0]:
if mat[idx, 0] in typed:
res[idx, :] = cls[typed[mat[idx, 0]], :]
else:
to_run.append(idx)
if len(to_run) == 0:
continue
if not params.immutable:
dists = np.vstack(pool.map(get_distance, to_run))
assignment(dists, res)
else:
dists = np.vstack(pool.map(get_distance2, to_run))
assignment2(dists, res)
logger('{0}: Assigned {1} of {2} types into hierCC.'.format(
time.time() - ot, index, mat.shape[0]))
res.T[0] = mat.T[0]
np.savez_compressed(cluster_file, hierCC=res)
if not params.delta:
with uopen(params.output + '.hierCC.gz', 'w') as fout:
fout.write('#ST_id\t{0}\n'.format('\t'.join(
['d' + str(id) for id in np.arange(n_loci)])))
for r in res[np.argsort(res.T[0])]:
fout.write('\t'.join([str(rr) for rr in r]) + '\n')
else:
deltas = map(int, params.delta.split(','))
with uopen(params.output + '.hierCC.gz', 'w') as fout:
fout.write('#ST_id\t{0}\n'.format('\t'.join(
['d' + str(id) for id in deltas])))
for r in res[np.argsort(res.T[0])]:
fout.write('\t'.join([str(r[id + 1]) for id in deltas]) + '\n')
del res
logger('NUMPY clustering result (for incremental hierCC): {0}.npz'.format(
params.output))
logger('TEXT clustering result (for visual inspection): {0}.hierCC.gz'.
format(params.output))
def assignment2(dists, res):
for id in xrange(len(dists)):
idx, ref, jd = dists[id]
res[idx, jd:] = res[ref, jd:]
def runDiamond(self, ref, qry, nhits=10, frames='7'):
logger('Run diamond starts')
def parseDiamond(fin, refseq, qryseq, min_id, min_cov, min_ratio):
blastab = []
for line in fin:
if line.startswith('@'):
continue
part = line.strip().split('\t')
if part[2] == '*': continue
qn, qf = part[0].rsplit(':', 1)
rn, rf, rx = part[2].rsplit(':', 2)
rs = int(part[3]) + int(rx)
ql, rl = len(qryseq[str(qn)]), len(refseq[str(rn)])
qm = len(part[9])
if qm * 3 < min_cov: continue
cov_ratio = qm * 3. / ql
if cov_ratio < min_ratio: continue
cigar = [[int(n) * 3, t]
for n, t in re.findall(r'(\d+)([A-Z])', part[5])]
cl = np.sum([c[0] for c in cigar])
variation = float(part[12][5:]) * 3 if part[12].startswith(
'NM:') else float(re.findall('NM:i:(\d+)', line)[0]) * 3
iden = 1 - round(variation / cl, 3)
if iden < min_id: continue
qf, rf = int(qf), int(rf)
qs = int(part[18][5:]) if part[18].startswith('ZS:') else int(
re.findall('ZS:i:(\d+)', line)[0])
rm = int(
np.sum([c[0] for c in cigar if c[1] in {'M', 'D'}]) / 3)
if rf <= 3:
rs, r_e = rs * 3 + rf - 3, (rs + rm - 1) * 3 + rf - 1
else:
rs, r_e = rl - (rs * 3 + rf - 6) + 1, rl - (
(rs + rm - 1) * 3 + rf - 4) + 1
if qf <= 3:
qs, qe = qs * 3 + qf - 3, (qs + qm - 1) * 3 + qf - 1
else:
qs, qe = ql - (qs * 3 + qf - 6) + 1, ql - (
(qs + qm - 1) * 3 + qf - 4) + 1
qs, qe, rs, r_e = qe, qs, r_e, rs
cigar = list(reversed(cigar))
cd = [c[0] for c in cigar if c[1] != 'M']
score = int(
part[14][5:]) if part[14].startswith('ZR:') else int(
re.findall('ZR:i:(\d+)', line)[0])
blastab.append([
qn, rn, iden, cl,
int(variation - sum(cd)),
len(cd), qs, qe, rs, r_e, 0.0, score, ql, rl, cigar
])
blastab = pd.DataFrame(blastab)
blastab[[0, 1]] = blastab[[0, 1]].astype(str)
return blastab
refAA = os.path.join(self.dirPath, 'refAA')
qryAA = os.path.join(self.dirPath, 'qryAA')
aaMatch = os.path.join(self.dirPath, 'aaMatch')
if not self.qrySeq:
self.qrySeq, self.qryQual = readFastq(qry)
if not self.refSeq:
self.refSeq, self.refQual = readFastq(ref)
qryAASeq = transeq(self.qrySeq, frame='F', transl_table=self.table_id)
with open(qryAA, 'w') as fout:
for n, ss in sorted(qryAASeq.items()):
_, id, s = min([(len(s[:-1].split('X')), id, s)
for id, s in enumerate(ss)])
fout.write('>{0}:{1}\n{2}\n'.format(n, id + 1, s))
diamond_fmt = '{diamond} makedb --db {qryAA} --in {qryAA}'.format(
diamond=diamond, qryAA=qryAA)
p = Popen(diamond_fmt.split(),
stderr=PIPE,
stdout=PIPE,
universal_newlines=True).communicate()
refAASeq = transeq(self.refSeq, frames, transl_table=self.table_id)
toWrite = []
for n, ss in sorted(refAASeq.items()):
for id, s in enumerate(ss):
cdss = re.findall('.{1000,}?X|.{1,1000}$', s + 'X')
cdss[-1] = cdss[-1][:-1]
cdsi = np.cumsum([0] + list(map(len, cdss[:-1])))
for ci, cs in zip(cdsi, cdss):
if len(cs):
toWrite.append('>{0}:{1}:{2}\n{3}\n'.format(
n, id + 1, ci, cs))
blastab = []
for id in xrange(5):
#logger('{0}'.format(id))
with open(refAA, 'w') as fout:
for line in toWrite[id::5]:
fout.write(line)
diamond_cmd = '{diamond} blastp --no-self-hits --threads {n_thread} --db {refAA} --query {qryAA} --out {aaMatch} --id {min_id} --query-cover {min_ratio} --evalue 1 -k {nhits} --dbsize 5000000 --outfmt 101'.format(
diamond=diamond,
refAA=refAA,
qryAA=qryAA,
aaMatch=aaMatch,
n_thread=self.n_thread,
min_id=self.min_id * 100.,
nhits=nhits,
min_ratio=self.min_ratio * 100.)
p = Popen(diamond_cmd.split(),
stdout=PIPE,
stderr=PIPE,
universal_newlines=True).communicate()
if os.path.getsize(aaMatch) > 0:
tab = parseDiamond(open(aaMatch), self.refSeq, self.qrySeq,
self.min_id, self.min_cov, self.min_ratio)
os.unlink(aaMatch)
if tab is not None:
blastab.append(tab)
blastab = pd.concat(blastab)
logger('Run diamond finishes. Got {0} alignments'.format(
blastab.shape[0]))
return blastab
def loadBam(prefix, reference, bams, sequences, snps):
sites = []
p = subprocess.Popen('samtools mpileup -ABQ0 {0}'.format(
' '.join(bams)).split(),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
universal_newlines=True)
for line in p.stdout:
part = line.strip().split('\t')
s = int(part[1]) - 1
if s % 100000 == 0:
sys.stdout.write('# {0}\n'.format(s))
if sequences[part[0]][s] > 0 or s % 5 == 0:
bases = ''.join(part[4::3])
bases = re.sub('[\*\$\+-]', '', re.sub(r'\^.', '', bases.upper()))
bases = re.split('(\d+)', bases)
for i in range(1, len(bases), 2):
bases[i + 1] = bases[i + 1][int(bases[i]):]
types, cnts = np.unique(list(''.join(bases[::2])),
return_counts=True)
if np.sum(cnts) >= 3:
if types.size > 1:
cnts.sort()
sites.append([cnts[-1], np.sum(cnts[:-1])])
else:
sites.append([cnts[0], 0])
sites = np.array(sites)
ave_depth = np.max([np.median(np.sum(sites, 1)), 2.])
sys.stdout.write(
'{3}: Average read depth: {0}; Sites between {1} and {2} will be used for hybrid estimation.\n'
.format(ave_depth, ave_depth / 2., ave_depth * 3., prefix))
sites = sites[(ave_depth / 2. <= np.sum(sites, 1))
& (np.sum(sites, 1) <= ave_depth * 3)]
m = GaussianMixture(n_components=1, covariance_type='tied')
m.fit(sites)
best_model = [m.bic(sites), m]
for n_components in xrange(2, 6):
sys.stdout.write('# Testing {0} components.\n'.format(n_components))
m = GaussianMixture(n_components=n_components, covariance_type='tied')
for i in xrange(20):
m.fit(sites)
bic = m.bic(sites)
if bic < best_model[0]:
best_model = [bic, m]
m = GaussianMixture(n_components=n_components,
covariance_type='tied')
m = best_model[1]
mId = np.argmax(m.means_.T[1] / np.sum(m.means_, 1))
sys.stdout.write(
'{3}: Find {0} GMM components. The most divergent group is {1} and counts for {2} of total sites.\n'
.format(m.n_components, m.means_[mId].tolist(), m.weights_[mId],
prefix))
mDiv = m.means_[mId][0] / np.sum(m.means_[mId])
mDiv = 10 * np.log10([[mDiv, 1 - mDiv], [1 - mDiv, mDiv]])
seq = {n: list(s) for n, s in readFasta(reference).items()}
qual = {n: [0] * len(s) for n, s in seq.items()}
lowQ, lowC, highQ = 0, 0, 0
p = subprocess.Popen('samtools mpileup -ABQ0 {0}'.format(
' '.join(bams)).split(),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
universal_newlines=True)
for line in p.stdout:
part = line.strip().split('\t')
s = int(part[1]) - 1
if s % 100000 == 0:
sys.stdout.write('# {0}\n'.format(s))
bases = ''.join(part[4::3])
bases = re.sub('[\*\$\+-]', '', re.sub(r'\^.', '', bases.upper()))
bases = re.split('(\d+)', bases)
for i in range(1, len(bases), 2):
bases[i + 1] = bases[i + 1][int(bases[i]):]
types, cnts = np.unique(list(''.join(bases[::2])), return_counts=True)
if types.size > 0:
depth = np.sum(cnts)
if cnts.size == 1:
g, mId = [cnts[0], 0], 0
elif cnts.size > 1:
mId = np.argmax(cnts)
g = [cnts[mId], depth - cnts[mId]]
seq[part[0]][s] = types[mId]
if depth >= 3 and depth / 3. <= ave_depth <= depth * 3.:
q = min(
40,
max(
1,
int(round(
np.sum(g * mDiv[0]) - np.sum(g * mDiv[1]), 0))))
qual[part[0]][s] = q
if q < 10:
lowQ += 1
else:
highQ += 1
else:
lowC += 1
qual = {n: ''.join([chr(ss + 33) for ss in s]) for n, s in qual.items()}
with open(prefix + '.fastq', 'w') as fout:
for n, s in seq.items():
fout.write('@{0}\n{1}\n+\n{2}\n'.format(n, ''.join(s), qual[n]))
sys.stdout.write(
'{0}: {1} good sites; {2} low covered sites; {3} low quality sites;\n'.
format(prefix, highQ, lowC, lowQ))
return
def getClust(prefix, genes, params):
groups = {}
dirPath = tempfile.mkdtemp(prefix='NS_', dir='.')
try:
if not params['translate']:
geneFile = genes
else:
na_seqs = readFasta(genes)
aa_seqs = transeq(na_seqs, frame='1', transl_table='starts')
with open(os.path.join(dirPath, 'seq.aa'), 'w') as fout:
for n, s in aa_seqs:
fout.write('>{0}\n{1}\n'.format(n, s[0]))
geneFile = os.path.join(dirPath, 'seq.aa')
seqDb = os.path.join(dirPath, 'seq.db')
tmpDb = os.path.join(dirPath, 'tmp')
lcDb = os.path.join(dirPath, 'seq.lc')
tabFile = os.path.join(dirPath, 'clust.tab')
refFile = os.path.join(dirPath, 'seq.ref')
nRef = 999999999999999
for ite in xrange(3):
if os.path.isdir(tmpDb):
shutil.rmtree(tmpDb)
os.makedirs(tmpDb)
if os.path.isfile(seqDb):
list(map(os.unlink, glob.glob(seqDb + '*')))
if os.path.isfile(lcDb):
list(map(os.unlink, glob.glob(lcDb + '*')))
subprocess.Popen('{0} createdb {2} {1} -v 0'.format(
externals['mmseqs'], seqDb, geneFile).split()).communicate()
subprocess.Popen('{0} linclust {1} {2} {3} --min-seq-id {4} -c {5} --threads {6} -v 0'.format( \
externals['mmseqs'], seqDb, lcDb, tmpDb, params['identity'], params['coverage'], params['n_thread']).split(), stdout=subprocess.PIPE).communicate()
subprocess.Popen('{0} createtsv {1} {1} {2} {3}'.format(\
externals['mmseqs'], seqDb, lcDb, tabFile).split(), stdout = subprocess.PIPE).communicate()
with open(tabFile) as fin:
for line in fin:
part = line.strip().split()
groups[part[1]] = part[0]
tmp = []
with open(geneFile) as fin:
toWrite, used_grps = False, {None: 1}
for line in fin:
if line.startswith('>'):
name = line[1:].strip().split()[0]
grp = groups.get(name, None)
toWrite = False if grp in used_grps else True
if toWrite:
used_grps[grp] = name
if toWrite:
tmp.append(line)
for gene, grp in groups.items():
if grp in used_grps:
groups[gene] = used_grps[grp]
with open(refFile, 'w') as fout:
for line in tmp:
fout.write(line)
if nRef <= len(used_grps):
break
nRef = len(used_grps)
geneFile = refFile
if not params['translate']:
shutil.copy2(refFile, '{0}.clust.exemplar'.format(prefix))
else:
rSeq = readFasta(refFile)
na_seqs = dict(na_seqs)
with open('{0}.clust.exemplar'.format(prefix), 'w') as fout:
for n, s in rSeq:
fout.write('>{0}\n{1}\n'.format(n, na_seqs[n]))
finally:
shutil.rmtree(dirPath)
with open('{0}.clust.tab'.format(prefix), 'w') as fout:
for gene, grp in sorted(groups.items()):
g = gene
while g != grp:
g, grp = grp, groups[grp]
groups[gene] = grp
fout.write('{0}\t{1}\n'.format(gene, grp))
return '{0}.clust.exemplar'.format(prefix), '{0}.clust.tab'.format(prefix)
def linear_merge(self, blasttab, min_iden, min_frag_prop, min_frag_len, max_dist=300, diag_diff=1.5, max_diff=200, **params) :
for part in blasttab :
if part[8] > part[9] :
part[8], part[9] = -part[8], -part[9]
blasttab.sort(key=itemgetter(0,1,6,8,-11))
nB = len(blasttab)
for id, p1 in enumerate(blasttab) :
if p1[0] == '' : continue
for jd in xrange(id+1, nB) :
p2 = blasttab[jd]
if p2[0] == '' : continue
if (p1[0], p1[1]) != (p2[0], p2[1]) or p2[6] - p1[6] > 4 :
break
d = abs(p1[6]-p2[6]) + abs(p1[7]-p2[7]) + abs(p1[8]-p2[8]) + abs(p1[9]-p2[9])
if d <= 5 :
if p1[2] >= p2[2] :
p2[0] = ''
else :
p1[0] = ''
break
blasttab = [p for p in blasttab if p[0] != '']
nB = len(blasttab)
syntenies = []
for id, p1 in enumerate(blasttab) :
for jd in xrange(id+1, nB) :
p2 = blasttab[jd]
if p1[0] != p2[0] or p1[1] != p2[1] or p2[6] - p1[7] > max_dist :
break
elif p1[8] < 0 < p2[8] or p2[8] < p1[8] + 15 or p2[9] < p1[9] + 15 or p2[7]<p1[7]+15 or p2[6] < p1[6]+15 or p2[8] - p1[9] > max_dist :
continue
m, n = p2[7] - p1[6] + 1, p2[9] - p1[8] + 1
if m < min_frag_len or m < min_frag_prop*p1[12] or max(m, n) - min(m, n) > max_diff or max(m,n) > diag_diff * min(m, n) :
continue
o_len = 0 if p2[6] > p1[7] else p1[7] - p2[6] + 1
p1_len = p1[7] - p1[6] + 1 - o_len
p2_len = p2[7] - p2[6] + 1 - o_len
iden = (p1[2]*p1_len + p2[2]*p2_len +max(p1[2], p2[2])*o_len)/(p1_len+p2_len+o_len)
if iden < min_iden :
continue
p1s, p2s = p1[11]/(p1[7]-p1[6]+1), p2[11]/(p2[7]-p2[6]+1)
dist = max(p2[6]-p1[7]-1, p2[8]-p1[9]-1, 0)
score = p1s*p1_len +p2s*p2_len+max(p1s, p2s)*o_len - dist
if score > 0 :
syntenies.append([id, jd, iden, score])
syn_score = {}
for id , syn in enumerate(syntenies) :
if syn[0] not in syn_score and syn[1] not in syn_score :
p1, p2 = blasttab[syn[0]][:], blasttab[syn[1]]
c1, c2 = p1[-1], p2[-1]
r_dist, q_dist = p2[6]-p1[7]-1, p2[8]-p1[9]-1
if min(r_dist, q_dist) < 0 :
p1s, p2s = p1[11]/(p1[7]-p1[6]+1), p2[11]/(p2[7]-p2[6]+1)
if p1s <= p2s :
cc = [ [int(n), t] for n,t in re.findall(r'(\d+)([A-Z])', c1)]
i = -1
else :
cc = [ [int(n), t] for n,t in re.findall(r'(\d+)([A-Z])', c2)]
i = 0
while min(r_dist, q_dist) < 0 :
if cc[i][1] == 'M' :
d = min(cc[i][0], max(-r_dist, -q_dist, 0))
r_dist, q_dist = d + r_dist, d + q_dist
elif cc[i][1] == 'D' :
d = min(cc[i][0], max(-r_dist, -q_dist, 0))
r_dist += d
elif cc[i][1] == 'I' :
d = min(cc[i][0], max(-r_dist, -q_dist, 0))
q_dist += d
else :
raise 'unknown'
if d >= cc[i][0] :
cc = cc[1:] if i == 0 else cc[:-1]
else :
cc[i][0] -= d
if i == -1 :
c1 = ''.join([ '{0}{1}'.format(*c) for c in cc ])
else :
c2 = ''.join([ '{0}{1}'.format(*c) for c in cc ])
gap=[]
if r_dist > 0 :
gap.append('{0}D'.format(r_dist))
if q_dist > 0 :
gap.append('{0}I'.format(q_dist))
if len(gap) == 0 :
cc1 = re.findall(r'(^.*?)(\d+)([A-Z]$)', c1)[0]
cc2 = re.findall(r'(^\d+)([A-Z])(.*$)', c2)[0]
if cc1[2] == cc2[1] :
c1 = '{0}{1}{2}'.format(cc1[0], int(cc1[1])+int(cc2[0]), cc1[2])
c2 = cc2[2]
p1[7], p1[9], p1[2], p1[11], p1[14] = p2[7], p2[9], syn[2], syn[3], ''.join([c1] + gap + [c2])
blasttab.append(p1)
syn_score[syn[0]] = id
syn_score[syn[1]] = id
for part in blasttab :
if part[8] < 0 :
part[8], part[9] = -part[8], -part[9]
x = ['{0}D'.format(part[6]-1), part[-1]] if part[6] > 1 else [part[-1]]
if part[7] < part[12] :
x.append('{0}D'.format(part[12]-part[7]))
if len(x) : part[-1] = ''.join(x)
return blasttab
def alignAgainst(data) :
prefix, aligner, db, (rtag, reference), (tag, query) = data
if isinstance(aligner, list) :
return lastAgainst(tag, query, db, prefix, reference, aligner[1])
try :
qrySeq, qryQual = readFastq(query)
except :
return [tag, query]
refSeq, refQual = readFastq(reference)
proc = subprocess.Popen('{0} -c -t1 --frag=yes -A1 -B14 -O24,60 -E2,1 -r100 -g1000 -P -N5000 -f1000,5000 -n2 -m50 -s200 -z200 -2K10m --heap-sort=yes --secondary=yes {1} {2}'.format(
aligner, db, query).split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True)
alignments = []
for lineId, line in enumerate(proc.stdout) :
part = line.strip().split('\t')
part[1:4] = [int(p) for p in part[1:4]]
part[6:11] = [int(p) for p in part[6:11]]
part[11] = float(part[13][5:])
part[12], part[13] = lineId, part[11]/part[10]
part[14:17] = [[], [], []]
alignments.append(part)
proc.wait()
deleteChain = {}
nItem = len(alignments)
alignments.sort(key=lambda x:x[:4])
for i1, p1 in enumerate(alignments) :
for i2 in xrange(i1+1, nItem) :
p2 = alignments[i2]
if p1[0] != p2[0] : break
s, e = max(p1[2], p2[2]), min(p1[3], p2[3])
if s > e+10 :
break
if (e-s) >= 0.9 * (p1[3]-p1[2]) and p2[13] - 0.1 >= p1[13] :
deleteChain[p1[12]] = deleteChain.get(p1[12], set([])) | set([p2[12]])
if (e-s) >= 0.9 * (p2[3]-p2[2]) and p1[13] - 0.1 >= p2[13] :
deleteChain[p2[12]] = deleteChain.get(p2[12], set([])) | set([p1[12]])
alignments.sort(key=lambda x:x[5:9])
for i1, p1 in enumerate(alignments) :
for i2 in xrange(i1+1, nItem) :
p2 = alignments[i2]
if p1[5] != p2[5] : break
s, e = max(p1[7], p2[7]), min(p1[8], p2[8])
if s > e+10 :
break
if (e-s) >= 0.9 * (p1[8]-p1[7]) and p2[13] - 0.05 >= p1[13] :
deleteChain[p1[12]] = deleteChain.get(p1[12], set([])) | set([p2[12]])
if (e-s) >= 0.9 * (p2[8]-p2[7]) and p1[13] - 0.05 >= p2[13] :
deleteChain[p2[12]] = deleteChain.get(p2[12], set([])) | set([p1[12]])
deleted = {}
for p in sorted(alignments, key=lambda x:x[11], reverse=True) :
id = p[12]
if id in deleteChain :
for jd in deleteChain[id] :
if jd not in deleted :
deleted[id] = 1
break
alignments = [p for p in alignments if p[12] not in deleted]
# repeats in qry
nItem = len(alignments)
alignments.sort(key=lambda x:x[:4])
for i1, p1 in enumerate(alignments) :
for i2 in xrange(i1+1, nItem) :
p2 = alignments[i2]
if p1[0] != p2[0] : break
s, e = max(p1[2], p2[2]), min(p1[3], p2[3])
if e > s :
p1[16].append([s, e])
p2[16].append([s, e])
else :
break
# repeats in ref
alignments.sort(key=lambda x:x[5:9])
for i1, p1 in enumerate(alignments) :
for i2 in xrange(i1+1, nItem) :
p2 = alignments[i2]
if p1[5] != p2[5] : break
s, e = max(p1[7], p2[7]), min(p1[8], p2[8])
if e > s :
p1[15].append([s, e])
p2[15].append([s, e])
else :
break
maskedRegion = {}
refRepeat = []
for p in alignments :
# prepare a unique set of repeat region
qryRepeat = []
if len(p[16]) > 0 :
qryRepeat.append(p[16][0])
for pp in p[16][1:] :
if pp[0] > qryRepeat[-1][1]+20 :
qryRepeat.append(pp)
elif pp[1] > qryRepeat[-1][1]:
qryRepeat[-1][1] = pp[1]
ref = [refSeq[p[5]], refQual[p[5]]]
qry = [qrySeq[p[0]], qryQual[p[0]]]
cigar = p[-1][5:]
d = 1 if p[4] == '+' else -1
if d < 0 :
qryRepeat = [[q[1], q[0], -1, -1] for q in qryRepeat]
else :
qryRepeat = [[q[0], q[1], -1, -1] for q in reversed(qryRepeat)]
mut = []
alnSite = [p[7], p[2] if d > 0 else p[3]-1]
for cl, ct in re.findall(r'(\d+)([MID])', cigar) :
cl = int(cl)
if ct == 'M' :
# extract aligned sequences
r = ref[0][alnSite[0]:alnSite[0]+cl]
r1 = ref[1][alnSite[0]:alnSite[0]+cl]
q = qry[0][alnSite[1]:alnSite[1]+cl] if d > 0 else rc(qry[0][(alnSite[1]-cl+1):(alnSite[1]+1)])
q1 = qry[1][alnSite[1]:alnSite[1]+cl] if d > 0 else ''.join(reversed(qry[1][(alnSite[1]-cl+1):(alnSite[1]+1)]))
e =[alnSite[0]+cl, alnSite[1]+cl*d]
for qid in xrange(len(qryRepeat)-1, -1, -1) :
qr = qryRepeat[qid]
if d*qr[0] <= d*e[1] :
if qr[2] == -1 :
qr[2] = alnSite[0] + d*(qr[0] - alnSite[1])
if d*qr[1] <= d*e[1] :
qr[3] = alnSite[0] + d*(qr[1] - alnSite[1])
p[15].append(qr[2:])
del qryRepeat[qid]
else :
break
for id, (rr, rr1, qq, qq1) in enumerate(np.array([list(r), list(r1), list(q), list(q1)]).T) :
if ord(rr1) < 43 or ord(qq1) < 43 :
maskedRegion[(p[5], alnSite[0]+id)] = 0
if rr != qq and rr != 'N' and qq != 'N' :
mut.append([alnSite[0]+id, alnSite[1]+id*d, rr, qq, p[4]])
alnSite = e
elif ct == 'I' :
q = qry[0][alnSite[1]:alnSite[1]+cl] if d < 0 else rc(qry[0][(alnSite[1]-cl+1):(alnSite[1]+1)] )
q1 = qry[1][alnSite[1]:alnSite[1]+cl] if d > 0 else ''.join(reversed(qry[0][(alnSite[1]-cl+1):(alnSite[1]+1)] ))
e = alnSite[1] + cl*d
for qid in xrange(len(qryRepeat)-1, -1, -1) :
qr = qryRepeat[qid]
if d*qr[0] <= d*e :
if qr[2] == -1 :
qr[2] = alnSite[0]
if d*qr[1] <= d*e :
qr[3] = alnSite[0]
p[15].append(qr[2:])
del qryRepeat[qid]
else :
break
if ord(min(list(q1))) >= 43 :
mut.append([alnSite[0], min(alnSite[1], e), '.', '+' + q, p[4]])
for site in xrange(alnSite[0], alnSite[0]+2) :
maskedRegion[(p[5], site)] = 0
alnSite[1] = e
elif ct == 'D' :
r = ref[0][alnSite[0]:alnSite[0]+cl]
r1 = ref[1][alnSite[0]:alnSite[0]+cl]
if ord(min(list(r1))) >= 43 :
mut.append([alnSite[0], int(alnSite[1]+0.5*d), '.', '-' + r, p[4]])
for site in xrange(alnSite[0], alnSite[0]+2) :
maskedRegion[(p[5], site)] = 0
alnSite[0]+=cl
p[14] = mut
refRepeat.extend([ [p[5], pp[0], pp[1]] for pp in p[15] ])
repeats = []
if len(refRepeat) :
refRepeat.sort()
repeats = [refRepeat[0]]
for p in refRepeat[1:] :
if p[0] != repeats[-1][0] or p[1] - 20 > repeats[-1][2] :
repeats.append(p)
elif p[2] > repeats[-1][2] :
repeats[-1][2] = p[2]
for p in repeats :
for site in xrange(p[1], p[2]) :
maskedRegion[(p[0], site)] = 1
repeats = []
for cont, site in sorted(maskedRegion) :
if len(repeats) == 0 or repeats[-1][0] != cont or repeats[-1][2]+1 < site :
repeats.append([cont, site, site])
else :
repeats[-1][2] = site
mutations = []
alignments = [aln for aln in alignments if aln[9] >= 100]
for aln in alignments :
for m in aln[14] :
if len(m[3]) == 1 :
if (aln[5], m[0]) not in maskedRegion :
mutations.append([aln[5], aln[0]] + m)
elif maskedRegion.get((aln[5], m[0]), 0) != 1 :
if m[3].startswith('-') and maskedRegion.get((aln[5], m[0]+len(m[3])-2), 0) > 0 :
continue
mutations.append([aln[5], aln[0]] + m)
with uopen(prefix + '.gff.gz', 'w') as fout :
fout.write('##gff-version 3\n')
fout.write('## Reference: {0}\n'.format(reference))
fout.write('## Query: {0}\n'.format(query))
fout.write('## Tag: {0}\n'.format(tag))
for aln in alignments :
if aln[5] == aln[0] and aln[2] == aln[7] and aln[3] == aln[8] :
fout.write('{0}\trefMapper\tmisc_feature\t{1}\t{2}\t{3}\t{4}\t.\t/inference="Self%20Alignments"\n'.format(
aln[5], aln[7]+1, aln[8], aln[9], aln[4], aln[0], aln[2]+1, aln[3],
))
else :
fout.write('{0}\trefMapper\tmisc_feature\t{1}\t{2}\t{3}\t{4}\t.\t/inference="Aligned%20with%20{5}:{6}-{7}"\n'.format(
aln[5], aln[7]+1, aln[8], aln[9], aln[4], aln[0], aln[2]+1, aln[3],
))
for p in repeats :
fout.write('{0}\trefMapper\tunsure\t{1}\t{2}\t.\t+\t.\t/inference="Uncertain%20base%20calling%20or%20ambigious%20alignment"\n'.format(
p[0], p[1]+1, p[2]+1,
))
for mut in mutations :
e1 = mut[2] if not mut[5].startswith('-') else mut[2] + len(mut[5]) - 2
e2 = mut[3] if not mut[5].startswith('+') else mut[3] + len(mut[5]) - 2
if len(mut[5]) > 26 :
mut[5] = '{0}[{1}bps]'.format(mut[5][0], len(mut[5])-1)
fout.write('{0}\trefMapper\tvariation\t{1}\t{2}\t.\t+\t.\t/replace="{7}";/compare="{3}:{4}-{5}:{8}";/origin="{6}"\n'.format(
mut[0], mut[2]+1, e1+1, mut[1], mut[3]+1, e2+1, mut[4], mut[5], mut[6]
))
return [tag, prefix + '.gff.gz']
def loadBam(prefix, reference, bams, sequences, snps):
sequence = readFasta(reference)
sequence = {n: [s, [0] * len(s)] for n, s in sequence.items()}
sites = {}
for bam in bams:
if bam is not None:
depth = subprocess.Popen('{samtools} depth -q 0 -Q 0 {bam}'.format(
bam=bam, **externals).split(),
stdout=subprocess.PIPE,
universal_newlines=True)
try:
d = pd.read_csv(depth.stdout, sep='\t').values
sites.update({cName: 1 for cName in np.unique(d.T[0])})
except:
pass
sequence = {n: s for n, s in sequence.items() if n in sites}
with open('{0}.mapping.reference.fasta'.format(prefix), 'w') as fout:
for n, s in sorted(sequence.items()):
fout.write('>{0}\n{1}\n'.format(
n, '\n'.join([
s[0][site:(site + 100)]
for site in xrange(0, len(s[0]), 100)
])))
bam_opt = ' '.join(['--bam {0}'.format(b) for b in bams if b is not None])
pilon_cmd = '{pilon} --fix snps,indels,gaps --vcf --output {prefix}.mapping --genome {prefix}.mapping.reference.fasta {bam_opt}'.format(
prefix=prefix, bam_opt=bam_opt, **externals)
subprocess.Popen(pilon_cmd.split(),
stdout=subprocess.PIPE,
universal_newlines=True).communicate()
uncertains = []
with open('{0}.mapping.vcf'.format(prefix)) as fin:
for line in fin:
if line.startswith('#'): continue
part = line.strip().split('\t')
if sequences[part[0]][int(part[1]) - 1] >= 0:
if len(part[3]) == 1 and len(part[4]) == 1:
pp = part[7].split(';')
dp = float(pp[0][3:])
if dp >= 3:
qd = int(pp[4][3:])
if part[-1] == '0/1' or qd < 10:
bcs = sorted(
[float(bc) for bc in pp[5][3:].split(',')])
uncertains.append([bcs[-1], np.sum(bcs[:-1])])
uncertains = np.array(uncertains)
p = np.sum(uncertains.T[0]) / np.sum(uncertains)
qPerRead = 10 * (np.log10(p) - np.log10(1 - p))
for n in sequence:
sequence[n][0] = list(sequence[n][0])
highQ, lowQ, lowC = 0, 0, 0
with open('{0}.mapping.vcf'.format(prefix)) as fin:
for line in fin:
if line.startswith('#'): continue
part = line.strip().split('\t')
if len(part[3]) == 1 and len(part[4]) == 1:
s = int(part[1]) - 1
pp = part[7].split(';')
dp = float(pp[0][3:])
qd = int(pp[4][3:])
if part[-1] == '0/1' or qd < 10:
bcs = np.array([int(bc) for bc in pp[5][3:].split(',')])
if np.sum(bcs) > 0:
sequence[part[0]][0][s] = ['A', 'C', 'G',
'T'][np.argmax(bcs)]
else:
sequence[part[0]][0][s] = part[3]
if dp < 3:
lowC += 1
else:
bcs.sort()
bcs = [bcs[-1], np.sum(bcs[:-1])]
q1 = binom.cdf(bcs[0], bcs[0] + bcs[1], p)
q2 = qPerRead * (bcs[0] - bcs[1]) if q1 >= 0.05 else 1
if q2 >= 10:
highQ += 1
else:
lowQ += 1
sequence[part[0]][1][s] = min(40, max(1, int(q2)))
else:
if dp < 3:
lowC += 1
else:
if qd >= 10:
highQ += 1
else:
lowQ += 1
sequence[part[0]][1][s] = qd
if part[-1] == '1/1':
sequence[part[0]][0][s] = part[4]
logger(
'{0}: Expected mix-up: {1} {2} ; Got highQ {3} ; lowQ {4} ; lowC {5}'.
format(prefix, uncertains.shape[0], p, highQ, lowQ, lowC))
with open('{0}.metaCaller.fastq'.format(prefix), 'w') as fout:
p = prefix.rsplit('/', 1)[-1]
for n, (s, q) in sequence.items():
fout.write('@{0}\n{1}\n+\n{2}\n'.format(
p + '_' + n, ''.join(s), ''.join([chr(qq + 33) for qq in q])))
os.unlink('{0}.mapping.vcf'.format(prefix))
os.unlink('{0}.mapping.fasta'.format(prefix))
os.unlink('{0}.mapping.reference.fasta'.format(prefix))
return '{0}.metaCaller.fastq'.format(prefix)
def make_alignment( filename ) :
comparisons = []
with open(filename, 'r') as fin:
for line in fin:
if line[0] == 'a' :
comparison = [ int(line.split(' ', 2)[1][6:]) ]
elif line[0] == 's' :
part = line.strip().split()[1:]
part[1:5] = [int(part[1]), int(part[2]), part[3], int(part[4])]
if part[3] == '+' :
part[1:3] = [part[1]+1, part[1]+part[2]]
else :
part[1:3] = [part[4]-part[1], part[4]-part[1]-part[2]+1]
comparison.extend(part)
if len(comparison) >= 13 :
comparison.append([int((m in 'nN') or (n in 'Nn')) for m, n in zip(comparison[6], comparison[12])])
elif line[0] in 'pq' :
part = line.strip().split()
comparison[13] = [ max(comparison[13][id], int(b in '!"#$%&\'()*+,-./')) for id, b in enumerate(part[-1])]
elif len(line.strip()) == 0 :
if comparison[0] >= 200 :
comparisons.append( last_package.call_mutation(comparison) )
# remove significant low identity regions in query
comparisons.sort(key=lambda x: min(x[8:10]) )
comparisons.sort(key=lambda x: x[7] )
low_q = []
for id, regi in enumerate(comparisons) :
if len(regi) == 0 : continue
for jd in xrange(id+1, len(comparisons)) :
regj = comparisons[jd]
if len(regj) == 0 : continue
if regi[7] != regj[7] : break
si, ei = sorted(regi[8:10])
sj, ej = sorted(regj[8:10])
s = max(si, sj)
e = min(ei, ej)
if e >= s :
overlap_i = last_package.sub_comparison(regi, qry_coords=[s, e])
overlap_j = last_package.sub_comparison(regj, qry_coords=[s, e])
if overlap_i[0] < 0.95 * overlap_j[0] and ( regi[0] < regj[0] or ei < ej ) :
if s - si >= 30 :
comparisons[id] = last_package.sub_comparison(regi, qry_coords=[si, s-1])
#if overlap_i[3] < overlap_i[2] and overlap_i[4] == '+' :
#print comparisons[id]
overlap_i[12] = 'E'
low_q.append(overlap_i)
if overlap_i[3] >= overlap_i[2] :
regi = comparisons[id]
if len(regi) == 0: break
else :
comparisons[id][12] = 'E'
break
elif overlap_i[0] * 0.95 > overlap_j[0] :
if ej - e >= 30 :
comparisons[jd] = last_package.sub_comparison(regj, qry_coords=[e+1, ej])
overlap_j[12] = 'E'
if overlap_j[3] >= overlap_j[2] :
low_q.append(overlap_j)
else :
comparisons[jd][12] = 'E'
elif s == si and e == ei and regj[0] > regi[0]*3 and overlap_i[0] <= overlap_j[0] :
comparisons[id][12] = 'E'
break
elif s == sj and e == ej and regi[0] > regj[0]*3 and overlap_i[0] >= overlap_j[0] :
comparisons[jd][12] = 'E'
else :
comparisons[id][12] = 'D'
comparisons[jd][12] = 'D'
else :
break
# remove significant low identity regions in reference
comparisons = sorted([x for x in comparisons if len(x) > 0] + low_q, key=lambda x: x[2] )
comparisons.sort(key=lambda x: x[1] )
for id, regi in enumerate(comparisons) :
if len(regi) == 0 : continue
for jd in xrange(id+1, len(comparisons)) :
regj = comparisons[jd]
if len(regj) == 0 : continue
if regi[1] != regj[1] : break
si, ei = regi[2:4]
sj, ej = regj[2:4]
s = max(si, sj)
e = min(ei, ej)
if e >= s :
overlap_i = last_package.sub_comparison(regi, ref_coords=[s, e])
overlap_j = last_package.sub_comparison(regj, ref_coords=[s, e])
if overlap_i[0] < 0.95 * overlap_j[0] and ( regi[0] < regj[0] or ei < ej ) :
if s - si >= 30 :
comparisons[id] = last_package.sub_comparison(regi, ref_coords=[si, s-1])
regi = comparisons[id]
if len(regi) == 0: break
else :
comparisons[id] = []
break
elif overlap_i[0] * 0.95 > overlap_j[0] :
if ej - e >= 30 :
comparisons[jd] = last_package.sub_comparison(regj, ref_coords=[e+1, ej])
else :
comparisons[jd] = []
elif overlap_i[0] == overlap_j[0] and len(overlap_i) == len(overlap_j) :
if si == sj and ei == ej:
diff = 0
for i, i_snp in enumerate(overlap_i[13:]) :
j_snp = overlap_j[13+i]
if i_snp[0] != j_snp[0] or i_snp[5] != j_snp[5] :
diff = 1
break
if diff == 0 :
if comparisons[id][12] in 'DE':
comparisons[id] = []
break
else:
comparisons[jd] = []
elif si <= sj and ei >= ej :
diff = 0
for i, i_snp in enumerate(overlap_i[13:]) :
j_snp = overlap_j[13+i]
if i_snp[0] != j_snp[0] or i_snp[5] != j_snp[5] :
diff = 1
break
if diff == 0 :
comparisons[jd] = []
elif si >= sj and ei <= ej:
diff = 0
for i, i_snp in enumerate(overlap_i[13:]) :
j_snp = overlap_j[13+i]
if i_snp[0] != j_snp[0] or i_snp[5] != j_snp[5] :
diff = 1
break
if diff == 0 :
comparisons[id] = []
break
else :
break
if len(comparisons[id]) > 0 :
regi = comparisons[id]
regi[6] = [lq for lq in regi[6] if lq[1] >= regi[2] and lq[0] <= regi[3]]
# mark repetitive regions in query
repeats = []
mutations = {}
comparisons = sorted([x for x in comparisons if len(x) > 0 and x[12] != 'E'], key=lambda x: min(x[8:10]) )
comparisons.sort(key=lambda x: x[7] )
for id, regi in enumerate(comparisons) :
for jd in xrange(id+1, len(comparisons)) :
regj = comparisons[jd]
if regi[7] != regj[7] : break
si, ei = sorted(regi[8:10])
sj, ej = sorted(regj[8:10])
s = max(si, sj)
e = min(ei, ej)
if e >= s :
for mut in regi[13:] :
if abs(min(mut[2:4])) <= e and abs(max(mut[2:4])) >= s :
mut[6] = 1
for mut in regj[13:] :
if abs(min(mut[2:4])) <= e and abs(max(mut[2:4])) >= s :
mut[6] = 1
overlap_i = last_package.sub_comparison(regi, qry_coords=[s, e])
overlap_j = last_package.sub_comparison(regj, qry_coords=[s, e])
regi[6] = [lq for lq in regi[6] if lq[0] <overlap_i[2] or lq[1] > overlap_i[3]]
regj[6] = [lq for lq in regj[6] if lq[0] <overlap_j[2] or lq[1] > overlap_j[3]]
repeats.append(overlap_i[1:4] + [0])
repeats.append(overlap_j[1:4] + [0])
# identify repetitive regions in the reference
comparisons.sort(key=lambda x: x[2] )
comparisons.sort(key=lambda x: x[1] )
for id, regi in enumerate(comparisons) :
if len(regi) == 0 : continue
for jd in xrange(id+1, len(comparisons)) :
regj = comparisons[jd]
if regi[1] != regj[1] : break
si, ei = sorted(regi[2:4])
sj, ej = sorted(regj[2:4])
s = max(si, sj)
e = min(ei, ej)
if e >= s :
overlap_i = last_package.sub_comparison(regi, ref_coords=[s, e])
overlap_j = last_package.sub_comparison(regj, ref_coords=[s, e])
if len(overlap_i) == len(overlap_j) :
diff = 0
for i, i_snp in enumerate(overlap_i[13:]) :
j_snp = overlap_j[13+i]
if i_snp[0] != j_snp[0] or i_snp[5] != j_snp[5] :
diff = 1
break
if diff == 1 :
for mut in regi[13:] :
if abs(mut[0]) <= e and abs(mut[1]) >= s :
mut[6] = 1
for mut in regj[13:] :
if abs(mut[0]) <= e and abs(mut[1]) >= s :
mut[6] = 1
regi[6] = [lq for lq in regi[6] if lq[0] <s or lq[1] > e]
regj[6] = [lq for lq in regj[6] if lq[0] <s or lq[1] > e]
repeats.append([regi[1], s, e, 0])
else :
for mut in regi[13:] :
if abs(mut[0]) <= e and abs(mut[1]) >= s :
mut[6] = 1
for mut in regj[13:] :
if abs(mut[0]) <= e and abs(mut[1]) >= s :
mut[6] = 1
regi[6] = [lq for lq in regi[6] if lq[0] <s or lq[1] > e]
regj[6] = [lq for lq in regj[6] if lq[0] <s or lq[1] > e]
repeats.append([regi[1], s, e, 0])
for mut in regi[13:] :
if mut[6] == 0 :
if regi[1] not in mutations :
mutations[ regi[1] ] = {}
if mut[0] not in mutations[ regi[1] ]:
mutations[ regi[1] ] [ mut[0] ] = {}
if mut[5] not in mutations[ regi[1] ] [ mut[0] ] :
mutations[ regi[1] ] [ mut[0] ] [ mut[5] ] = [regi[7], regi[10]] + mut
else :
mutations[ regi[1] ] [ mut[0] ] [ mut[5] ].extend([regi[7], regi[10]] + mut)
repeats.extend([[regi[1]]+ lq[:2] + [1] for lq in regi[6]])
repeats.sort(key=lambda x:x[1])
repeats.sort(key=lambda x:x[0])
repetitive_regions = []
for rep in repeats:
if len(repetitive_regions) == 0 or repetitive_regions[-1][0] != rep[0] or repetitive_regions[-1][2]+1 < rep[1] :
repetitive_regions.append(rep)
elif rep[2] > repetitive_regions[-1][2] :
repetitive_regions[-1][2] = rep[2]
if repetitive_regions[-1][3] > 0 :
repetitive_regions[-1][3] = rep[3]
nocall = {}
for r in repetitive_regions + [c[1:] for c in comparisons if float(c[0])/(abs(c[3]-c[2])+1) < 0.7 or c[0] < 200] :
for s in xrange(r[1], r[2]+1) :
nocall[(r[0], s)] = 1
mutations = { contig:{ site:alters for site, alters in variation.items() if (contig, site) not in nocall } for contig, variation in mutations.items() }
comparisons = [c for c in comparisons if float(c[0])/(abs(c[3]-c[2])+1) >= 0.7 and c[0] >= 200]
return comparisons, repetitive_regions, mutations
def write_output(prefix, prediction, genomes, clust_ref, old_prediction):
predictions, alleles = {}, {}
allele_file = open('{0}.allele.fna'.format(prefix), 'w')
prediction = pd.read_csv(prediction, sep='\t', header=None).values
for part in prediction:
#with open(prediction) as fin :
#for line in fin :
#part = line.strip().split()
if part[0] not in alleles:
alleles[part[0]] = {clust_ref[part[0]]: 1}
allele_file.write('>{0}_{1}\n{2}\n'.format(part[0], 1,
clust_ref[part[0]]))
if part[9] < part[10]:
l, r, d = min(part[7] - 1,
part[9] - 1), min(part[12] - part[8],
part[13] - part[10]), 1
else:
l, r, d = min(part[7] - 1,
part[13] - part[9]), min(part[12] - part[8],
part[10] - 1), -1
if l <= 6 and part[7] - l == 1:
part[7], part[9] = part[7] - l, part[9] - l * d
else:
ll = (part[7] - 1) % 3
if ll > 0:
part[7], part[9] = part[7] + 3 - ll, part[9] + (3 - ll) * d
if r <= 6 and part[8] + r == part[12]:
part[8], part[10] = part[8] + r, part[10] + r * d
else:
rr = (part[12] - part[8]) % 3
if rr > 0:
part[8], part[10] = part[8] - 3 + rr, part[10] - (3 + rr) * d
if part[9] < part[10]:
part[9:12] = part[9], part[10], '+'
else:
part[9:12] = part[10], part[9], '-'
if part[4] not in predictions:
predictions[part[4]] = []
elif predictions[part[4]][-1][2] == part[2]:
prev = predictions[part[4]][-1]
if prev[5] == part[5] and part[7] - prev[8] < 500:
if part[11] == '+' and part[9] - prev[10] < 500:
prev[8], prev[10] = part[8], part[10]
continue
elif part[11] == '-' and prev[9] - part[10] < 500:
prev[8], prev[9] = part[8], part[9]
continue
predictions[part[4]][-1][1], part[1] = -1, -1
predictions[part[4]].append(part)
op = ['', 0, []]
with open('{0}.EToKi.gff'.format(prefix), 'w') as fout:
for gid, (g, predict) in enumerate(predictions.items()):
predict.sort(key=itemgetter(5, 9, 10))
for pid, pred in enumerate(predict):
if pred[1] == -1 or (pred[10] - pred[9] + 1) <= 0.8 * pred[12]:
cds, allele_id = 'fragment:{0:.2f}%'.format(
(pred[10] - pred[9] + 1) * 100 / pred[12]), 'uncertain'
start, stop = pred[9:11]
else:
s, e = pred[9:11]
if pred[11] == '+':
s2, e2 = s - min(int(3 * ((s - 1) / 3)), 60), e + min(
3 * int((pred[13] - e) / 3), 600)
seq = genomes[pred[5]][1][(s2 - 1):e2]
lp, rp = s - s2, e2 - e
else:
s2, e2 = s - min(int(3 * ((s - 1) / 3)), 600), e + min(
3 * int((pred[13] - e) / 3), 60)
seq = rc(genomes[pred[5]][1][(s2 - 1):e2])
rp, lp = s - s2, e2 - e
seq2 = seq[(lp):(len(seq) - rp)]
if seq2 not in alleles[pred[0]]:
if pred[3] == pred[0] and pred[7] == 1 and pred[
8] == pred[12]:
alleles[pred[0]][seq2] = len(alleles[pred[0]]) + 1
else:
alleles[pred[0]][seq2] = 'LowQ{0}'.format(
len(alleles[pred[0]]) + 1)
allele_id = str(alleles[pred[0]][seq2])
allele_file.write('>{0}_{1}\n{2}\n'.format(
pred[0], allele_id, seq2))
else:
allele_id = str(alleles[pred[0]][seq2])
frames = sorted(set([0, len(seq) % 3]))
for frame, aa_seq in zip(
frames,
transeq({'n': seq},
transl_table='starts',
frame=','.join(
[str(f + 1) for f in frames]))['n']):
cds = 'CDS'
s0, s1 = aa_seq.find('M', int(lp / 3),
int(lp / 3 + 30)), aa_seq.rfind(
'M', 0, int(lp / 3))
start = s0 if s0 >= 0 else s1
if start < 0:
cds, start = 'nostart', int(lp / 3)
stop = aa_seq.find('X', start)
if 0 <= stop < lp / 3 + 30:
s0 = aa_seq.find('M', stop, int(lp / 3 + 30))
if s0 >= 0:
start = s0
stop = aa_seq.find('X', start)
if stop < 0:
cds = 'nostop'
elif (stop - start + 1) * 3 <= 0.8 * pred[12]:
cds = 'premature stop:{0:.2f}%'.format(
(stop - start + 1) * 300 / pred[12])
if cds == 'CDS':
if pred[11] == '+':
start, stop = s2 + start * 3 + frame, s2 + stop * 3 + 2 + frame
else:
start, stop = e2 - stop * 3 - 2 - frame, e2 - start * 3 - frame
break
else:
start, stop = s, e
if frame > 0:
cds = 'frameshift'
if pred[5] != op[0]:
op = [pred[5], 0, old_prediction.get(pred[5], [])]
old_tag = []
for k in xrange(op[1], len(op[2])):
opd = op[2][k]
if opd[2] < start:
op[1] = k + 1
elif opd[1] > stop:
break
elif opd[3] != pred[11]:
continue
ovl = min(opd[2], stop) - max(opd[1], start) + 1
if ovl >= 300 or ovl >= 0.6 * (
opd[2] - opd[1] + 1) or ovl >= 0.6 * (stop -
start + 1):
frame = min((opd[1] - start) % 3, (opd[2] - stop) % 3)
if frame == 0:
old_tag.append('{0}:{1}-{2}'.format(*opd))
fout.write(
'{0}\t{1}\tEToKi-ortho\t{2}\t{3}\t.\t{4}\t.\tID={5};{12}inference=ortholog group:{6},allele ID:{7},matched region:{8}-{9}{10}{11}\n'
.format(
pred[5],
'CDS' if cds == 'CDS' else 'pseudogene',
start,
stop,
pred[11],
'{0}_{1}_{2}'.format(prefix, gid, pid),
pred[0],
allele_id,
s,
e,
'' if pred[0] == pred[3] else
',structure variant group:' + pred[3],
'' if cds == 'CDS' else ';pseudogene=' + cds,
'' if len(old_tag) == 0 else 'locus_tag={0};'.format(
','.join(old_tag)),
))
allele_file.close()
return
def filt_per_group(data):
mat, ref, global_file = data
global_differences = dict(np.load(global_file))
nMat = mat.shape[0]
seqs = np.vstack([
np.vstack(mat.T[4]),
np.array(list(ref)).view(asc2int).astype(np.uint8)[np.newaxis, :]
])
seqs[np.in1d(seqs, [65, 67, 71, 84], invert=True).reshape(seqs.shape)] = 45
diff = compare_seq(
seqs, np.zeros(shape=[seqs.shape[0], seqs.shape[0], 2],
dtype=int)).astype(float)
incompatible, distances = {}, np.zeros(
shape=[seqs.shape[0], seqs.shape[0]], dtype=float)
for i1, m1 in enumerate(mat):
for i2 in xrange(i1 + 1, nMat):
m2 = mat[i2]
mut, aln = diff[i1, i2]
if aln > 0:
gd = global_differences.get(tuple(sorted([m1[1], m2[1]])),
(0.01, 4))
distances[i1, i2] = distances[i2, i1] = max(
0., 1 - (aln - mut) / aln / (1 - gd[0]))
difference = mut / aln / gd[0] / gd[1]
else:
distances[i1, i2] = distances[i2, i1] = 0.8
difference = 1.5
if difference > 1.:
incompatible[(i1, i2)] = 1
if len(incompatible) > 0:
groups = []
for j, m in enumerate(mat):
novel = 1
for g in groups:
if diff[g[0], j, 0] <= 0.6 * (
1.0 - params['clust_identity']) * diff[g[0], j, 1]:
g.append(j)
novel = 0
break
if novel:
groups.append([j])
group_tag = {gg: g[0] for g in groups for gg in g}
try:
tags = {
g[0]: mat[g[0]][4].tostring().decode('ascii')
for g in groups
}
except:
tags = {g[0]: mat[g[0]][4].tostring() for g in groups}
tags.update({'REF': ref})
ic2 = {}
for i1, i2 in incompatible:
t1, t2 = group_tag[i1], group_tag[i2]
if t1 != t2:
t1, t2 = str(t1), str(t2)
if t1 not in ic2: ic2[t1] = {}
if t2 not in ic2: ic2[t2] = {}
ic2[t1][t2] = ic2[t2][t1] = 1
incompatible = ic2
for ite in xrange(3):
try:
tmpFile = tempfile.NamedTemporaryFile(dir='.', delete=False)
for n, s in tags.items():
tmpFile.write('>X{0}\n{1}\n{2}'.format(
n, s, '\n' * ite).encode('utf-8'))
tmpFile.close()
cmd = params[params['orthology']].format(
tmpFile.name, **
params) if len(tags) < 500 else params['nj'].format(
tmpFile.name, **params)
phy_run = subprocess.Popen(shlex.split(cmd),
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
universal_newlines=True)
gene_phy = ete3.Tree(phy_run.communicate()[0].replace("'", ''))
break
except:
if ite == 2:
return mat
finally:
os.unlink(tmpFile.name)
for n in gene_phy.get_leaves():
if len(n.name):
n.name = n.name[1:]
node = gene_phy.get_midpoint_outgroup()
if node is not None:
gene_phy.set_outgroup(node)
for ite in xrange(3000):
gene_phy.ic, gene_phy.dist = {}, 0.
rdist = sum([c.dist for c in gene_phy.get_children()])
for c in gene_phy.get_children():
c.dist = rdist
for node in gene_phy.iter_descendants('postorder'):
if node.is_leaf():
node.ic = {
tuple(sorted([node.name, n2])): 1
for n2 in incompatible.get(node.name, {})
}
else:
node.ic = {}
for c in node.get_children():
for x in c.ic:
if x in node.ic:
node.ic.pop(x)
else:
node.ic[x] = 1
cut_node = max([[len(n.ic), n.dist, n]
for n in gene_phy.iter_descendants('postorder')],
key=lambda x: (x[0], x[1]))
if cut_node[0] > 0:
cut_node = cut_node[2]
prev_node = cut_node.up
cut_node.detach()
if 'REF' in cut_node.get_leaf_names():
gene_phy = cut_node
elif prev_node.is_root():
gene_phy = gene_phy.get_children()[0]
else:
prev_node.delete(preserve_branch_length=True)
tips = set(gene_phy.get_leaf_names())
for r1 in list(incompatible.keys()):
if r1 not in tips:
rr = incompatible.pop(r1, None)
for r2 in rr:
incompatible.get(r2, {}).pop(r1, None)
for r1 in list(incompatible.keys()):
if len(incompatible[r1]) == 0:
incompatible.pop(r1, None)
if len(incompatible) == 0:
break
logger(' Iteration {0}. Remains {1} tips.'.format(
ite + 1, len(gene_phy.get_leaf_names())))
else:
break
if len(gene_phy.get_leaf_names()) < len(tags):
groups = {str(g[0]): g for g in groups}
tips = sorted([
nn for n in gene_phy.get_leaf_names()
for nn in groups.get(n, [])
])
mat = mat[tips]
return mat
def do_polish(self, reference, reads, reassemble=False, onlySNP=False) :
if parameters.get('SNP', None) is not None :
return self.do_polish_with_SNPs(reference, parameters['SNP'])
else :
if parameters['mapper'] == 'minimap2' :
bams = self.__run_minimap(prefix, reference, reads )
elif parameters['mapper'] != 'bwa' :
bams = self.__run_bowtie(prefix, reference, reads )
else :
bams = self.__run_bwa(prefix, reference, reads )
sites = {}
for bam in bams :
if bam is not None :
depth = Popen('{samtools} depth -q 0 -Q 0 {bam}'.format(bam=bam, **parameters).split(), stdout=PIPE, universal_newlines=True)
for line in depth.stdout :
part = line.strip().split()
if len(part) > 2 and float(part[2]) > 0 :
sites[part[0]] = 1
sequence = readFasta(reference)
sequence = {n:s for n,s in sequence.items() if n in sites}
with open('{0}.mapping.reference.fasta'.format(prefix), 'w') as fout :
for n, s in sorted(sequence.items()) :
fout.write('>{0}\n{1}\n'.format(n, '\n'.join([ s[site:(site+100)] for site in xrange(0, len(s), 100)])))
bam_opt = ' '.join(['--bam {0}'.format(b) for b in bams if b is not None])
if reassemble :
pilon_cmd = '{pilon} --fix all,breaks --vcf --output {prefix}.mapping --genome {prefix}.mapping.reference.fasta {bam_opt}'.format(bam_opt=bam_opt, **parameters)
Popen( pilon_cmd.split(), stdout=PIPE, stderr=PIPE, universal_newlines=True).communicate()
else :
pilon_cmd = '{pilon} --fix all --vcf --output {prefix}.mapping --genome {prefix}.mapping.reference.fasta {bam_opt}'.format(bam_opt=bam_opt, **parameters)
Popen( pilon_cmd.split(), stdout=PIPE, stderr=PIPE, universal_newlines=True).communicate()
if not os.path.isfile('{0}.mapping.vcf'.format(prefix)) :
pilon_cmd = '{pilon} --fix snps,indels,gaps --vcf --output {prefix}.mapping --genome {prefix}.mapping.reference.fasta {bam_opt}'.format(bam_opt=bam_opt, **parameters)
Popen( pilon_cmd.split(), stdout=PIPE, stderr=PIPE, universal_newlines=True).communicate()
snps = []
with open('{0}.mapping.vcf'.format(prefix)) as fin, open('{0}.mapping.changes'.format(prefix), 'w') as fout :
for line in fin :
if line.startswith('#') : continue
part = line.strip().split('\t')
if part[-1] != '0/0':
try :
if (part[6] == 'PASS' or float(part[7][-4:]) >= 0.75) and re.match(r'^[ACGTN]+$', part[4]):
if (not onlySNP) or (len(part[3]) == 1 and len(part[4]) == 1 ) :
snps.append( [ part[0], int(part[1])-1, part[3], part[4] ] )
fout.write(line)
except :
pass
os.unlink('{0}.mapping.vcf'.format(prefix))
for n in sequence.keys() :
sequence[n] = list(sequence[n])
for n, site, ori, alt in reversed(snps) :
s = sequence[n]
end = site + len(ori)
s[site:end] = alt
logger('Observed and corrected {0} changes using PILON'.format(len(snps)))
with open('{0}.fasta'.format(prefix), 'w') as fout :
for n, s in sorted(sequence.items()) :
s = ''.join(s)
fout.write('>{0}\n{1}\n'.format(n, '\n'.join([ s[site:(site+100)] for site in xrange(0, len(s), 100)])))
return '{0}.fasta'.format(prefix)
def get_quality(self, reference, reads ) :
if parameters['mapper'] == 'minimap2' :
bams = self.__run_minimap(prefix, reference, reads, )
elif parameters['mapper'] != 'bwa' :
bams = self.__run_bowtie(prefix, reference, reads, )
else :
bams = self.__run_bwa(prefix, reference, reads, )
sequence = readFasta(reference)
for n, s in sequence.items() :
q = ['!'] * len(s)
sequence[n] = [s, q]
sites = { n:np.array([0 for ss in s[1] ]) for n, s in sequence.items() }
for bam in bams :
if bam is not None :
depth = Popen('{samtools} depth -q 0 -Q 0 {bam}'.format(bam=bam, **parameters).split(), stdout=PIPE, universal_newlines=True)
for line in depth.stdout :
part = line.strip().split()
if len(part) > 2 and float(part[2]) > 0 :
sites[part[0]][int(part[1]) - 1] += float(part[2])
sites = {n:[s.size, np.max([np.median(s), np.exp(np.mean(np.log(s + 0.5)))-0.5]), 0.] for n, s in sites.items()}
depth = np.array(list(sites.values()))
depth = depth[np.argsort(-depth.T[0])]
size = np.sum(depth.T[0])
acc = [0, 0]
for d in depth :
acc[0], acc[1] = acc[0] + d[0], acc[1] + d[0]*d[1]
if acc[0] *2 >= size :
break
ave_depth = acc[1]/acc[0]
exp_mut_depth = max(ave_depth * 0.2, 2.)
for n, s in sites.items() :
s[2] = s[1]/ave_depth
logger('Average read depth: {0}'.format(ave_depth))
sequence = {n:s for n, s in sequence.items() if sites[n][1]>0.}
with open('{0}.mapping.reference.fasta'.format(prefix), 'w') as fout :
for n, s in sorted(sequence.items()) :
fout.write('>{0}\n{1}\n'.format(n, '\n'.join([ s[0][site:(site+100)] for site in xrange(0, len(s[0]), 100)])))
bam_opt = ' '.join(['--bam {0}'.format(b) for b in bams if b is not None])
pilon_cmd = '{pilon} --fix all,breaks --vcf --output {prefix}.mapping --genome {prefix}.mapping.reference.fasta {bam_opt}'.format(bam_opt=bam_opt, **parameters)
Popen( pilon_cmd.split(), stdout=PIPE, universal_newlines=True ).communicate()
if not os.path.isfile('{0}.mapping.vcf'.format(prefix)) :
pilon_cmd = '{pilon} --fix snps,indels,gaps,breaks --vcf --output {prefix}.mapping --genome {prefix}.mapping.reference.fasta {bam_opt}'.format(bam_opt=bam_opt, **parameters)
Popen( pilon_cmd.split(), stdout=PIPE, stderr=PIPE, universal_newlines=True).communicate()
cont_depth = [float(d) for d in parameters['cont_depth'].split(',')]
logger('Contigs with less than {0} depth will be removed from the assembly'.format(cont_depth[0]*ave_depth))
logger('Contigs with more than {0} depth will be treated as duplicates'.format(cont_depth[1]*ave_depth))
indels = []
with open('{0}.mapping.vcf'.format(prefix)) as fin, open('{0}.mapping.difference'.format(prefix), 'w') as fout :
for line in fin :
if line.startswith('#') : continue
part = line.strip().split('\t')
if sites[part[0]][2] < cont_depth[0] or sites[part[0]][2] >= cont_depth[1] :
continue
if part[-1] == '1/1':
if len(part[3]) > 1 :
indels.append([part[0], max(0, int(site)-1), int(site)-1+len(part[3])+2])
elif len(part[4]) > 1 and part[4] != '<DUP>' :
indels.append([part[0], max(0, int(site)-2), int(site)-1+len(part[3])+2])
try:
if part[-1] == '0/0' and len(part[3]) == 1 and len(part[4]) == 1 :
pp = part[7].split(';')
dp = float(pp[0][3:])
af = 100 - sorted([float(af) for af in pp[6][3:].split(',')])[-1]
if af <= 20 and dp >= 2 and dp * af/100. <= exp_mut_depth and (part[6] == 'PASS' or (part[6] == 'LowCov' and parameters['metagenome'])) :
site = int(part[1])-1
qual = chr(int(pp[4][3:])+33)
sequence[part[0]][1][site] = qual
else :
fout.write(line)
else :
fout.write(line)
except :
fout.write(line)
for n, s, e in indels :
sequence[n][1][s:e] = ['!'] * len(sequence[n][1][s:e])
if self.snps is not None :
for n, snvs in self.snps.items() :
for site, snv in snvs :
if snv.find('N') >= 0 : continue
if snv.startswith('+') :
s, e = site-4, site+3+len(snv)
else :
s, e = site-4, site+4
for k in xrange(s, e) :
sequence[n][1][k] = max(chr(40+33), sequence[n][1][k])
with open('{0}.result.fastq'.format(prefix), 'w') as fout :
p = prefix.rsplit('/', 1)[-1]
for n, (s, q) in sequence.items() :
if sites[n][2] >= cont_depth[0] :
fout.write( '@{0} {3} {4} {5}\n{1}\n+\n{2}\n'.format( p+'_'+n, s, ''.join(q), *sites[n] ) )
os.unlink( '{0}.mapping.vcf'.format(prefix) )
logger('Final result is written into {0}'.format('{0}.result.fastq'.format(prefix)))
return '{0}.result.fastq'.format(prefix)
