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 readMap(data) :
mTag, mFile = data
presences, absences, mutations = [], [], []
aligns = ['', 0, 0]
miss = -1
with uopen(mFile) as fin :
for line in fin :
if line.startswith('##') :
if line.startswith('## Reference: ') :
ref = line.split(' ')[-1]
elif line.startswith('## Query: ') :
qry = line.split(' ')[-1]
if ref == qry :
miss = -99999999
else :
break
with uopen(mFile) as fin :
for line in fin :
if line.startswith('#') : continue
part = line.strip().split('\t')
part[3:5] = [int(part[3]), int(part[4])]
if part[2] == 'misc_feature' :
if len(presences) == 0 or presences[-1][0] != part[0] or presences[-1][2] < part[3] :
presences.append([part[0], part[3], part[4]])
elif presences[-1][2] < part[4] :
presences[-1][2] = part[4]
elif part[2] == 'unsure' :
absences.append([part[0], part[3], part[4], miss])
elif part[2] == 'variation' :
alt = re.findall(r'replace="([^"]+)"', part[8])
ori = re.findall(r'origin="([^"]+)"', part[8])
if len(alt) and len(ori) :
mutations.append([mTag, part[0], part[3], ori[0], alt[0]])
return presences, absences, mutations
def readXFasta(fasta_file, core=0.8):
seqs = []
nameMap = {}
contLens, missingLens = [], []
with uopen(fasta_file) as fin, open(fasta_file + '.tmp', 'w') as fout:
for line in fin:
if line.startswith('>'):
name = line[1:].strip().split()[0]
seqName = name.split(':', 1)[0]
contName = name.split(':', 1)[-1]
if seqName in nameMap:
seqId = nameMap[seqName]
seqs[seqId] = [seqName, contName, []]
else:
seqId = nameMap[seqName] = len(seqs)
seqs.append([seqName, contName, []])
elif line.startswith('='):
for seq in seqs:
seq[2] = ''.join(seq[2]).upper()
contName = [seq[1] for seq in seqs if seq[2] != ''][0]
contLen = [len(seq[2]) for seq in seqs if seq[2] != ''][0]
for seq in seqs:
if seq[2] == '':
seq[2] = '-' * contLen
cLen, mLen = parseSNP(fout, seqs, core)
contLens.append(cLen)
missingLens.extend(mLen)
seqs = [[n, '', []] for n in nameMap]
else:
seqs[seqId][2].extend(line.strip().split())
if len(seqs[0][2]) > 0:
cLen, mLen = parseSNP(fout, seqs, core)
contLens.append(cLen)
missingLens.extend(mLen)
sys.stdout.write('## Constant_bases: {0} {1} {2} {3}\n'.format(
conservedSites[ord('A')], conservedSites[ord('C')],
conservedSites[ord('G')], conservedSites[ord('T')]))
for cLen in contLens:
sys.stdout.write('## Sequence_length: {0} {1}\n'.format(*cLen))
for mLen in missingLens:
sys.stdout.write('## Missing_region: {0} {1} {2}\n'.format(*mLen))
sys.stdout.write('#Seq\t#Site\t{0}\n'.format('\t'.join(
[n for n, i in sorted(nameMap.items(), key=lambda n: n[1])])))
with uopen(fasta_file + '.tmp') as fin:
for line in fin:
sys.stdout.write(line)
os.unlink(fasta_file + '.tmp')
return
def MLSTdb(args):
params = getParams(args)
database, refset, alleleFasta, refstrain, max_iden, min_iden, coverage, paralog, relaxEnd = params[
'database'], params['refset'], params['alleleFasta'], params[
'refstrain'], params['max_iden'], params['min_iden'], params[
'coverage'], params['paralog'], params['relaxEnd']
if os.path.isfile(alleleFasta):
alleles = readFasta(uopen(alleleFasta))
else:
alleles = readFasta(StringIO(alleleFasta))
alleles = [allele for allele in alleles \
if allele['value_id'].isdigit() and int(allele['value_id']) > 0 and allele['fieldname'].find('/') < 0]
refAlleles = ''
if refset is not None:
if refstrain:
if os.path.isfile(refstrain):
references = readFasta(uopen(refstrain))
else:
references = readFasta(StringIO(refstrain))
else:
loci, references = {}, []
for allele in alleles:
if allele['fieldname'] not in loci:
loci[allele['fieldname']] = 1
references.append(allele)
allele_text, refAlleles = buildReference(alleles, references, max_iden,
min_iden, coverage, paralog,
relaxEnd)
if refset:
with open(str(refset), 'w') as fout:
fout.write(refAlleles + '\n')
logger('A file of reference alleles has been generated: {0}'.format(
refset))
if database:
conversion = [[], []]
with open(database, 'w') as fout:
for allele in alleles:
conversion[0].append(get_md5(allele['value']))
conversion[1].append(
[allele['fieldname'],
int(allele['value_id'])])
conversion = pd.DataFrame(conversion[1], index=conversion[0])
conversion.to_csv(database, header=False)
logger('A lookup table of all alleles has been generated: {0}'.format(
database))
return allele_text, refAlleles
def xFasta2Matrix(prefix, fasta_file, core=0.95):
seqs = []
snp_data = []
nameMap = {}
with uopen(fasta_file) as fin:
for line in fin:
if line.startswith('>'):
name = line[1:].strip().split()[0]
seqName = name.split(':', 1)[0]
contName = name.split(':', 1)[-1]
if seqName in nameMap:
seqId = nameMap[seqName]
seqs[seqId] = [seqName, contName, []]
else:
seqId = nameMap[seqName] = len(seqs)
seqs.append([seqName, contName, []])
elif line.startswith('='):
if fillMissingSeq(seqs, len(snp_data)):
snp_data.append(
parse_snps(prefix, len(snp_data), seqs, core))
seqs = [[n]
for n, i in sorted(nameMap.items(), key=lambda x: x[1])
]
else:
seqs[seqId][2].extend(line.strip().split())
if fillMissingSeq(seqs, len(snp_data)):
snp_data.append(parse_snps(prefix, len(snp_data), seqs, core))
const_sites = np.sum([snp[2] for snp in snp_data], axis=0)
names = [n for n, i in sorted(nameMap.items(), key=lambda x: x[1])]
with uopen(prefix + '.matrix.gz', 'w') as fout:
fout.write('## Constant_bases: ' + ' '.join(const_sites.astype(str)) +
'\n')
for snp in snp_data:
fout.write('## Sequence_length: {0} {1}\n'.format(*snp[:2]))
for snp in snp_data:
for s, e in snp[3]:
fout.write('## Missing_region: {0} {1} {2}\n'.format(
snp[0], s, e))
fout.write('#seq\t#site\t' + '\t'.join(names) + '\n')
for snp in snp_data:
d = np.load(snp[4])
sites, sv = d['sites'], d['snps']
for s in sites:
fout.write('{0}\t{1}\t{2}\n'.format(
snp[0], s[0], '\t'.join(sv[s[1]].astype(str))))
os.unlink(snp[4])
return prefix + '.matrix.gz'
def phylo(args) :
args = add_args(args)
global pool
pool = Pool(args.n_proc)
if 'matrix' in args.tasks :
assert os.path.isfile( args.alignment )
seq = readXFasta( args.alignment )
names, sites, snps, seqLens, missing = parse_snps(seq, args.core)
args.snp = write_matrix(args.prefix+'.matrix.gz', names, sites, snps, seqLens, missing)
if len(names) < 4 :
raise ValueError('Taxa too few.')
snp_list = sorted([[info[0], int(math.ceil(info[2])), list(line), info[1]] for line, info in snps.items() ])
elif 'phylogeny' in args.tasks or 'ancestral' in args.tasks or 'ancestral_proportion' in args.tasks :
assert os.path.isfile( args.snp )
names, sites, snps, seqLens, missing = read_matrix(args.snp)
if len(names) < 4 :
raise ValueError('Taxa too few.')
snp_list = sorted([[info[0], int(math.ceil(info[2])), list(line), info[1]] for line, info in snps.items() ])
# build tree
if 'phylogeny' in args.tasks :
phy, weights, asc = write_phylip(args.prefix+'.tre', names, snp_list)
if phy != '' :
args.tree = run_raxml(args.prefix +'.tre', phy, weights, asc, 'CAT', args.n_proc)
else :
args.tree = args.prefix + '.tre'
with open(args.tree, 'w') as fout :
fout.write('({0}:0.0);'.format(':0.0,'.join(names)))
args.tree = get_root(args.prefix, args.tree)
elif 'ancestral' in args.tasks or 'ancestral_proportion' in args.tasks :
tree = Tree(args.tree, format=1)
# map snp
if 'ancestral' in args.tasks :
final_tree, node_names, states = infer_ancestral(args.tree, names, snp_list, sites, infer='viterbi')
states = np.array(states)
final_tree.write(format=1, outfile=args.prefix + '.labelled.nwk')
write_states(args.prefix+'.ancestral_states.gz', node_names, states, sites, seqLens, missing)
elif 'mutation' in args.tasks :
final_tree = Tree(args.tree, format=1)
node_names, states, sites = read_states(args.ancestral)
if 'ancestral_proportion' in args.tasks :
final_tree, node_names, states = infer_ancestral(args.tree, names, snp_list, sites, infer='margin')
final_tree.write(format=1, outfile=args.prefix + '.labelled.nwk')
write_ancestral_proportion(args.prefix+'.ancestral_proportion.gz', node_names, states, sites, seqLens, missing)
if 'mutation' in args.tasks :
mutations = get_mut(final_tree, node_names, states, sites)
with uopen(args.prefix + '.mutations.gz', 'w') as fout :
for sl in seqLens :
fout.write('## Sequence_length: {0} {1}\n'.format(*sl))
for ms in missing :
fout.write('## Missing_region: {0} {1} {2}\n'.format(*ms))
fout.write('#Node\t#Seq\t#Site\t#Homoplasy\t#Mutation\n')
for mut in mutations :
fout.write('\t'.join([str(m) for m in mut]) + '\n')
def readXFasta(fasta_file) :
seqs = [[]]
nameMap = {}
with uopen(fasta_file) as fin :
for line in fin :
if line.startswith('>') :
name = line[1:].strip().split()[0]
seqName = name.split(':', 1)[0]
contName = name.split(':', 1)[-1]
if seqName in nameMap :
seqId = nameMap[seqName]
seqs[-1][seqId] = [seqName, contName, []]
else :
seqId = nameMap[seqName] = len(seqs[-1])
seqs[-1].append([seqName, contName, []])
elif line.startswith('=') :
seqs.append([[seqName, str(len(seqs)), []] for seqName, contName, _ in seqs[0] ])
else :
seqs[-1][seqId][2].extend(line.strip().split())
res = []
for blocks in seqs:
seqLen = 0
for block in blocks :
block[2] = ''.join(block[2])
seqLen = max(seqLen, len(block[2]))
for block in blocks :
if len(block[2]) < seqLen :
block[2] += '-' * (seqLen - len(block[2]))
if seqLen > 0 :
res.append(blocks)
return res
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 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 loadMatrix(fname):
sequences, snps = {}, {}
with uopen(fname) as fin:
for line in fin:
if line.startswith('##'):
part = line.strip().split()
if line.startswith('## Sequence_length'):
sequences[part[2]] = np.zeros(int(part[3]), dtype=np.int8)
elif line.startswith('## Missing_region'):
sequences[part[2]][int(part[3]) - 1:int(part[4])] = -1
else:
headers = line.strip().split('\t')
break
matrix = pd.read_csv(fin, header=None, sep='\t').values
encode = {'A': 1, 'C': 2, 'G': 4, 'T': 8}
matrix.T[4] = list(
map(lambda d: encode.get(d[0], -9999) + encode.get(d[-1], -9999),
matrix.T[4].tolist()))
matrix = matrix[matrix.T[4] > 0]
matrix.T[2] -= 1
for m in matrix:
sequences[m[1]][m[2]] |= m[4]
if (m[1], m[2]) not in snps:
snps[(m[1], m[2])] = {}
if m[4] not in snps[(m[1], m[2])]:
snps[(m[1], m[2])][m[4]] = []
snps[(m[1], m[2])][m[4]].append(m[0])
return sequences, snps
def iter_readGFF(fname):
seq, cds = {}, {}
names = {}
with uopen(fname) as fin:
sequenceMode = False
for line in fin:
if line.startswith('#'):
continue
elif line.startswith('>'):
sequenceMode = True
name = line[1:].strip().split()[0]
assert name not in seq, logger(
'Error: duplicated sequence name {0}'.format(name))
seq[name] = [fname, []]
elif sequenceMode:
seq[name][1].extend(line.strip().split())
else:
part = line.strip().split('\t')
if len(part) > 2:
name = re.findall(r'locus_tag=([^;]+)', part[8])
if len(name) == 0:
parent = re.findall(r'Parent=([^;]+)', part[8])
if len(parent) and parent[0] in names:
name = names[parent[0]]
if len(name) == 0:
name = re.findall(r'Name=([^;]+)', part[8])
if len(name) == 0:
name = re.findall(r'ID=([^;]+)', part[8])
if part[2] == 'CDS':
assert len(name) > 0, logger(
'Error: CDS has no name. {0}'.format(line))
# source_file, seqName, Start, End, Direction, hash, Sequences
cds[name[0]] = [
fname, part[0],
int(part[3]),
int(part[4]), part[6], 0, ''
]
else:
ids = re.findall(r'ID=([^;]+)', part[8])
if len(ids):
names[ids[0]] = name
for n in seq:
seq[n][1] = ''.join(seq[n][1]).upper()
for n in cds:
c = cds[n]
try:
c[6] = seq[c[1]][1][(c[2] - 1):c[3]]
if c[4] == '-':
c[6] = rc(c[6])
if not checkCDS(n, c[6]):
c[6] = ''
else:
c[5] = int(hashlib.sha1(c[6].encode('utf-8')).hexdigest(), 16)
except:
c[6] = ''
return seq, cds
def prepReference(prefix, ref_tag, reference, aligner, pilercr, trf, **args) :
def mask_tandem(fasta_file) :
cmd = '{0} {1} 2 4 7 80 10 60 2000 -d -h -ngs'.format(trf, fasta_file)
trf_run = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE, universal_newlines=True)
region = []
for line in iter(trf_run.stdout.readline, r'') :
if line[0] == '@' :
cont_name = line[1:].strip().split()[0]
else :
part = line.split(' ',2)[:2]
region.append([cont_name, int(part[0])-2, int(part[1])+2])
return region
def mask_crispr(fasta_file, prefix) :
cmd = '{0} -in {1} -out {2}.crispr'.format(pilercr, fasta_file, prefix)
subprocess.Popen(cmd.split(), stderr=subprocess.PIPE).communicate()
summary_trigger = 0
region = []
with open('{0}.crispr'.format(prefix)) as fin :
for line in fin :
if line.startswith('SUMMARY BY POSITION') :
summary_trigger = 1
elif summary_trigger :
if line[0] == '>' :
cont_name = line[1:].strip().split()[0]
elif len(line) > 10 and line.strip()[0] in '0123456789' :
part = line[24:].strip().split()
region.append([cont_name, int(part[0]), int(part[0]) + int(part[1]) -1])
os.unlink('{0}.crispr'.format(prefix))
return region
# prepare reference
if reference :
if not isinstance(aligner, list) :
subprocess.Popen('{0} -k15 -w5 -d {2}.mmi {1}'.format(aligner, reference, prefix).split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()
else :
subprocess.Popen('{0} -cR01 {2}.mmi {1}'.format(aligner[0], reference, prefix).split()).communicate()
import tempfile
with tempfile.NamedTemporaryFile(dir='.') as tf :
seq, _ = readFastq(reference)
tf_fas = '{0}.fasta'.format(tf.name)
with open(tf_fas, 'wt') as fout:
for n, s in seq.items() :
fout.write('>{0}\n{1}\n'.format(n, s))
#tf_fas = '{0}.fasta'.format(tf.name)
#if reference.upper().endswith('GZ') :
# subprocess.Popen('{0} -cd {1} > {2}'.format(externals['pigz'], reference, tf_fas), shell=True).communicate()
#else :
# subprocess.Popen('cp {1} {2}'.format(externals['pigz'], reference, tf_fas), shell=True).communicate()
repeats = mask_tandem(tf_fas) + mask_crispr(tf_fas, tf.name)
os.unlink(tf_fas)
alignments = alignAgainst([prefix +'.' + ref_tag.rsplit('.', 1)[0] + '.0', aligner, prefix + '.mmi', [ref_tag, reference], [ref_tag, reference]])
with uopen(alignments[1], 'a') as fout :
for r in repeats :
fout.write('{0}\trefMapper\tunsure\t{1}\t{2}\t.\t+\t.\t/inference="repetitive_regions"\n'.format(
r[0], r[1], r[2],
))
return alignments
def write_states(fname, names, states, sites, seqLens, missing) :
with uopen(fname, 'w') as fout :
for sl in seqLens :
fout.write('## Sequence_length: {0} {1}\n'.format(*sl))
for ms in missing :
fout.write('## Missing_region: {0} {1} {2}\n'.format(*ms))
fout.write('#Seq\t#Site\t' + '\t'.join(names) + '\n')
for site in sites :
fout.write('{0}\t{1}\t{2}\n'.format(site[0], site[1], '\t'.join(states[site[2]]) ))
def read_matrix(fname) :
sites, snps = [], {}
invariant = []
seqLens, missing = [], []
with uopen(fname) as fin :
for line_id, line in enumerate(fin) :
if line.startswith('##'):
if line.startswith('## Constant_bases') :
part = line[2:].strip().split()
invariant = zip(['A', 'C', 'G', 'T'], [float(v) for v in part[1:]])
elif line.startswith('## Sequence_length:') :
part = line[2:].strip().split()
seqLens.append([part[1], int(part[2])])
elif line.startswith('## Missing_region:') :
part = line[2:].strip().split()
missing.append([part[1], int(part[2]), int(part[3])])
elif line.startswith('#') :
part = np.array(line.strip().split('\t'))
cols = np.where((1 - np.char.startswith(part, '#')).astype(bool))[0]
w_cols = np.where(np.char.startswith(part, '#!W'))[0]
names = part[cols]
break
else :
part = np.array(line.strip().split('\t'))
cols = np.ones(part.shape, dtype=bool)
cols[:2] = False
w_cols = np.char.startswith(part, '#!W')
names = part[cols]
break
mat = pd.read_csv(fin, header=None, sep='\t', usecols=cols.tolist() + w_cols.tolist() + [0,1]).values
val = {'A':'A', 'C':'C', 'G':'G', 'T':'T', '-':'-', 'N':'-', '.':'.'}
validate = np.vectorize(lambda b: b if len(b) > 1 else val.get(b, '-'))
for p2 in mat :
part = validate(np.char.upper(p2[cols].astype(str)))
b_key = tuple(part)
w = np.multiply.reduce(p2[w_cols].astype(float)) if w_cols.size else 1.
if b_key in snps :
snps[b_key][2] += w
else :
types = dict(zip(*np.unique(part, return_index=True)))
types.pop('-', None)
snps[b_key] = [len(snps), len(types)-1, w]
if snps[b_key][1] > 0 :
sites.append([ p2[0], int(p2[1]), snps[b_key][0] ])
for inv in invariant :
b_key = tuple([inv[0]] * len(names))
if b_key not in snps :
snps[b_key] = [len(snps), 0, float(inv[1])]
else :
snps[b_key][2] += float(inv[1])
return names, sites, snps, seqLens, missing
def readRec(fname):
rec = {}
with uopen(fname) as fin:
for line in fin:
part = line.strip().split('\t')
key = tuple([part[0], part[1]])
if key not in rec:
rec[key] = []
rec[key].append([int(part[2]), int(part[3])])
return rec
def write_ancestral_proportion(fname, names, states, sites, seqLens, missing) :
with uopen(fname, 'w') as fout :
for sl in seqLens :
fout.write('## Sequence_length: {0} {1}\n'.format(*sl))
for ms in missing :
fout.write('## Missing_region: {0} {1} {2}\n'.format(*ms))
fout.write('#Seq\t#Site\t#Type:Proportion\n')
for c, p, i in sites :
tag, state = states[i]
for n, ss in zip(names, state) :
fout.write( '{0}\t{1}\t{2}\t{3}\n'.format(c, p, n, '\t'.join([ '{0}:{1:.5f}'.format(t, s) for t, s in zip(tag, ss)]) ))
def RecHMM(args) :
args = parse_arg(args)
global pool, verbose
pool = Pool(args.n_proc)
verbose = not args.clean
model = recHMM(prefix=args.prefix, mode=args.task)
if not args.report or not args.model :
sequences, missing = [], []
with uopen(args.data) as fin :
for line in fin :
if line.startswith('##') :
if line.startswith('## Sequence_length:') :
part = line[2:].strip().split()
sequences.append([part[1], int(part[2])])
elif line.startswith('## Missing_region:') :
part = line[2:].strip().split()
missing.append([part[1], int(part[2]), int(part[3])])
else :
break
data = pd.read_csv(fin, sep='\t', dtype=str, header=None).values
branches, mutations = {}, []
seqLens = {seqName:[seqId, seqLen] for seqId, (seqName, seqLen) in enumerate(sequences)}
for d in data :
if re.findall(r'^[ACGTacgt]->[ACGTacgt]$', d[4]) :
if d[1] not in seqLens :
seqLens[d[1]] = [len(seqLens), int(d[2])]
if seqLens[d[1]][1] < int(d[2]) :
seqLens[d[1]][1] = int(d[2])
if d[0] not in branches :
branches[d[0]] = len(branches)
brId, seqId = branches[d[0]], seqLens[d[1]][0]
mutations.append([brId, seqId, int(d[2]), int(d[3])])
missing = np.array([ [seqLens.get(m[0], [-1])[0], m[1], m[2]] for m in missing ])
sequences = [ [n, i[1]] for n, i in sorted(seqLens.items(), key=lambda x:x[1][0])]
branches = np.array([ br for br, id in sorted(branches.items(), key=lambda x:x[1]) ])
mutations = np.array(mutations)
reorder = np.argsort(-np.bincount(mutations.T[0]))
branches = branches[reorder]
reorder = np.array([i1 for i1, i2 in sorted(enumerate(reorder), key=lambda x:x[1])])
mutations.T[0] = reorder[mutations.T[0]]
if args.model :
model.load(open(args.model, 'r'))
else :
#pass
model.fit(mutations, branches=branches, sequences=sequences, missing=missing, categories=args.categories, init=args.init, cool_down=args.cool_down)
model.save(open(args.prefix + '.best.model.json', 'w'))
print('Best HMM model is saved in {0}'.format(args.prefix + '.best.model.json'))
model.report(args.bootstrap)
if not args.report :
model.predict(mutations, branches=branches, sequences=sequences, missing=missing, marginal=args.marginal, tree=args.tree)
def readFasta(fasta):
sequence = []
with uopen(fasta) as fin:
for line in fin:
if line.startswith('>'):
name = line[1:].strip().split()[0]
sequence.append([name, []])
elif len(line) > 0 and not line.startswith('#'):
sequence[-1][1].extend(line.strip().split())
for s in sequence:
s[1] = (''.join(s[1])).upper()
return sequence
def write_filtered_matrix(fname, names, sites, snps, masks, m_weight):
bases = {65: 0, 67: 0, 71: 0, 84: 0}
for snp in snps:
for n, c in zip(*np.unique(snp[2], return_counts=True)):
if n in bases:
bases[n] += c * snp[1]
name_map = {name: id for id, name in enumerate(names)}
sss = []
for site in sites:
if site[1] not in m_weight or not len(m_weight[site[1]]): continue
weight = np.mean(list(m_weight[site[1]].values()))
if site[3].size == 0:
snvs = np.frompyfunc(chr, 1, 1)(snps[site[2]][2])
else:
x = snps[site[2]][2]
if 45 in x:
if '-' not in site[3]:
site[3] = np.concatenate([site[3], ['-']])
x[x == 45] = np.where(site[3] == '-')[0][0]
snvs = site[3][x]
snv_x = []
p = np.zeros(snvs.shape, dtype=bool)
for m in masks.get(site[1], []):
pp = np.ones(snvs.shape, dtype=bool)
pp[[name_map[mm] for mm in m]] = False
p = (p | (~pp))
snv_x.append(np.copy(snvs))
snv_x[-1][pp] = '-'
snv_x.append(snvs)
snv_x[-1][p] = '-'
for snv in snv_x:
snv_type, snv_cnt = np.unique(snv, return_counts=True)
snv_type, snv_cnt = snv_type[
~np.in1d(snv_type, ['-', 'N', 'n']
)], snv_cnt[~np.in1d(snv_type, ['-', 'N', 'n'])]
if snv_type.size > 1:
for k, v in zip(snv_type, snv_cnt):
if k in bases:
bases[k] -= v * weight
sss.append(['\t'.join(snv.tolist()), weight, site[:2]])
with uopen(fname, 'w') as fout:
fout.write('## Constant_bases: ' + ' '.join([
str(int(inv[1] / names.size + 0.5) if inv[1] > 0 else 0.)
for inv in sorted(bases.items())
]) + '\n')
fout.write('#seq\t#site\t' + '\t'.join(names) + '\t#!W[RecFilter]\n')
for snv, weight, site in sss:
fout.write('{2}\t{3}\t{0}\t{1:.5f}\n'.format(snv, weight, *site))
return fname
def readFasta(fasta, filter=None) :
sequence = {}
with uopen(fasta) as fin :
for line in fin :
if line.startswith('>') :
name = line[1:].strip().split()[0]
if not filter or name in filter :
sequence[name] = []
elif len(line) > 0 and not line.startswith('#') and name in sequence :
sequence[name].extend(line.strip().split())
for s in sequence :
sequence[s] = (''.join(sequence[s])).upper()
return sequence
def read_states(fname) :
names, ss, sites = [], {}, []
with uopen(fname) as fin :
names = fin.readline().strip().split('\t')[2:]
for line in fin :
seq, site, snp_str = line.strip().split('\t', 2)
if snp_str not in ss :
ss[snp_str] = len(ss)
sites.append([seq, int(site), ss[snp_str]])
states = []
for s, id in sorted(ss.items(), key=lambda x:x[1]) :
states.append(s.split('\t'))
return names, np.array(states), sites
def evaluate(profile, cluster, stepwise, ave_gene_length=1000.) :
with uopen(profile) as fin :
logger('Loading profiles ...')
profile_header = fin.readline().strip().split('\t')
ST_col = np.where([p.find('#ST')>=0 for p in profile_header])[0].tolist()
if len(ST_col) <= 0 :
ST_col = [0]
cols = ST_col + np.where([not h.startswith('#') for h in profile_header])[0].tolist()
profile = pd.read_csv(fin, sep='\t', header=None, index_col=0, usecols=cols)
profile_names = profile.index.values
profile = profile.values
with uopen(cluster) as fin :
logger('Loading hierCC ...')
cluster_header = fin.readline().strip().split('\t')
cols = [0] + np.where([not h.startswith('#') for h in cluster_header])[0].tolist()
cluster = pd.read_csv(fin, sep='\t', header=None, index_col=0, usecols=cols)
cluster_names = cluster.index.values
cluster = cluster.values
s = np.arange(0, cluster.shape[1], stepwise)
cluster = cluster[:, s]
presence = np.in1d(cluster_names, profile_names)
cluster, cluster_names = cluster[presence], cluster_names[presence]
order = {n:id for id, n in enumerate(cluster_names)}
profile_order = np.array([ [id, order[n]] for id, n in enumerate(profile_names) if n in order ])
profile_order = profile_order[np.argsort(profile_order.T[1]), 0]
profile_names = profile_names[profile_order]
profile = profile[profile_order]
shannon = shannon_index(cluster)
similarity = get_similarity('adjusted_rand_score', cluster, stepwise)
silhouette = get_silhouette(profile, cluster, stepwise, ave_gene_length)
np.savez_compressed('evalHCC.npz', shannon=shannon, similarity=similarity, silhouette=silhouette)
logger('Done. Results saved in evalHCC.npz')
def readRecRegions(recFile, seqRange):
recBlocks = []
with uopen(recFile) as fin:
for line in fin:
part = line.strip().split()
if part[0] == 'Importation':
if part[2] not in seqRange: continue
acc = seqRange[part[2]][0]
recBlocks.append(
[part[1], acc + int(part[3]), acc + int(part[4])])
elif len(part) == 3:
try:
recBlocks.append([part[0], int(part[1]), int(part[2])])
except:
pass
return recBlocks
def write_matrix(fname, names, sites, snps, seqNames, missing) :
invariants = { snp[0]:[base, snp[2]] for base, snp in snps.items() if snp[1] == 0 and base[0] != '-' }
bases = {}
for inv in invariants.values() :
bases[inv[0]] = bases.get(inv[0], 0) + inv[1]
sv = {ss[0]:'\t'.join(s) for s, ss in snps.items()}
with uopen(fname, 'w') as fout :
fout.write('## Constant_bases: ' + ' '.join([str(inv[1]) for inv in sorted(bases.items())]) + '\n')
for n, l in seqNames :
fout.write('## Sequence_length: {0} {1}\n'.format(n, l))
for n, s, e in missing :
fout.write('## Missing_region: {0} {1} {2}\n'.format(n, s, e))
fout.write('#seq\t#site\t' + '\t'.join(names) + '\n')
for site in sites :
fout.write('{1}\t{2}\t{0}\n'.format(sv[site[2]], *site[:2]))
return fname
def write_filtered_matrix(fname, names, sites, snps, masks, m_weight):
bases = {'A': 0, 'C': 0, 'G': 0, 'T': 0}
for base, snp in snps.items():
for n, c in zip(*np.unique(base, return_counts=True)):
if n in bases:
bases[n] += c * snp[2]
sv = {ss[0]: s for s, ss in snps.items()}
name_map = {name: id for id, name in enumerate(names)}
sss = []
for site in sites:
if not len(m_weight[site[1]]): continue
weight = np.mean(list(m_weight[site[1]].values()))
snvs = np.array(sv[site[2]])
snv_x = []
p = np.zeros(snvs.shape, dtype=bool)
for m in masks.get(site[1], []):
pp = np.ones(snvs.shape, dtype=bool)
pp[[name_map[mm] for mm in m]] = False
p = (p | (~pp))
snv_x.append(np.copy(snvs))
snv_x[-1][pp] = '-'
snv_x.append(snvs)
snv_x[-1][p] = '-'
for snv in snv_x:
snv_type, snv_cnt = np.unique(snv, return_counts=True)
snv_type, snv_cnt = snv_type[
~np.in1d(snv_type, ['-', 'N', 'n']
)], snv_cnt[~np.in1d(snv_type, ['-', 'N', 'n'])]
if snv_type.size > 1:
for k, v in zip(snv_type, snv_cnt):
if k in bases:
bases[k] -= v * weight
sss.append(['\t'.join(snv.tolist()), weight, site[:2]])
with uopen(fname, 'w') as fout:
fout.write('## Constant_bases: ' + ' '.join([
str(int(inv[1] / names.size + 0.5) if inv[1] > 0 else 0.)
for inv in sorted(bases.items())
]) + '\n')
fout.write('#seq\t#site\t' + '\t'.join(names) + '\t#!W[RecFilter]\n')
for snv, weight, site in sss:
fout.write('{2}\t{3}\t{0}\t{1:.5f}\n'.format(snv, weight, *site))
return fname
def read_states(fname):
names, ss, sites = [], {}, []
with uopen(fname) as fin:
for line in fin:
if line.startswith('##'):
continue
else:
names = line.strip().split('\t')[2:]
break
for line in fin:
seq, site, snp_str = line.strip().split('\t', 2)
if snp_str not in ss:
ss[snp_str] = len(ss)
sites.append([seq, int(site), ss[snp_str]])
states = []
for s, id in sorted(ss.items(), key=lambda x: x[1]):
states.append(np.array(s.split('\t')).view(asc2int))
return names, np.array(states), sites
def readAncestral(fname, nSample) :
sites = []
with uopen(fname) as fin :
for line in fin :
if line.startswith('##') :
pass
elif line.startswith('#') :
header = line.strip().split('\t')
nodes = np.array([not h.startswith('#') for h in header], dtype=bool)
nodeNames = np.array(header)[nodes]
mat = pd.read_csv(fin, sep='\t', header=None, dtype=str).values
break
mat = mat[np.in1d(mat.T[nodes][0], ['A', 'C', 'G', 'T'])]
conv = np.bincount([ 67, 71, 71, 84, 84, 84])
data = conv[mat[:, nodes].astype(bytes).view(np.int8)]
for id, (cont, site) in enumerate(mat[:, :2]) :
sites.append([(cont, int(site)), ] + [1.]*nSample)
return np.array(sites), nodeNames, np.ascontiguousarray(data.T, dtype=np.int8)
def readMutations(mutationFile):
accLength = 0
seqRange = {}
missingBlocks = []
with uopen(mutationFile) as fin:
for line in fin:
if line.startswith('##'):
if line.startswith('## Sequence_length:'):
part = line.strip().split()
seqRange[part[2]] = [accLength, accLength + int(part[3])]
accLength += int(part[3])
elif line.startswith('## Missing_region:'):
part = line.strip().split()
acc = seqRange[part[2]][0]
s, e = int(part[3]) + acc, int(part[4]) + acc
if [s - 1, e] == seqRange[part[2]]:
diff = e - s + 1
seqRange.pop(part[2])
for n in seqRange:
if seqRange[n][0] >= e: seqRange[n][0] -= diff
if seqRange[n][1] >= e: seqRange[n][1] -= diff
for blk in missingBlocks:
if blk[0] >= e: blk[0] -= diff
if blk[1] >= e: blk[1] -= diff
else:
if e - s + 1 >= 500:
missingBlocks.append([s, e])
elif line.startswith('#'):
mat = pd.read_csv(fin, sep='\t', header=None, dtype=str).values
break
mat = mat[np.vectorize(lambda x: len(x))(mat.T[4]) == 4]
sites = np.vstack([
np.vectorize(lambda x: seqRange.get(x, [-1])[0])(mat.T[1]),
mat.T[2].astype(int)
])
mat = mat[sites[0] >= 0]
sites = np.sum(sites[:, sites[0] >= 0], 0)
mutBlocks = list(zip(mat.T[0], sites))
return seqRange, missingBlocks, mutBlocks
def readAncestral(fname,):
sites = []
data = []
conv = np.bincount([65, 67, 67, 71, 71, 71, 84, 84, 84, 84]) - 1
sys.stderr.write('Start reading Matrix: \n')
with uopen(fname) as fin:
for line in fin:
if line.startswith('##'):
pass
elif line.startswith('#'):
header = line.strip().split('\t')
nodes = np.array([not h.startswith('#') for h in header], dtype=bool)
nodeNames = np.array(header)[nodes]
break
for i, mat in enumerate(pd.read_csv(fin, sep='\t', header=None, dtype=str, chunksize=20000)):
sys.stderr.write('Reading Matrix - Line: {0} \r'.format(i * 20000))
mat = mat.values
mat = mat[np.in1d(mat[:, np.where(nodes)[0][0]], ['A', 'C', 'G', 'T'])]
data.append(conv[np.vectorize(ord)(mat[:, nodes])])
for id, (cont, site) in enumerate(mat[:, :2]):
sites.append([(cont, int(site)), 1. ])
sys.stderr.write('Read Matrix DONE. Total SNP sites: {0} \n'.format(len(sites)))
return np.array(sites), nodeNames, np.ascontiguousarray(np.vstack(data).T, dtype=np.uint8)
def getMatrix(prefix, reference, alignments, core, matrixOut, alignmentOut) :
refSeq, refQual = readFastq(reference)
coreSites = { n:np.zeros(len(refSeq[n]), dtype=int) for n in refSeq }
matSites = { n:np.zeros(len(refSeq[n]), dtype=int) for n in refSeq }
alnId = { aln[0]:id for id, aln in enumerate(alignments) }
res = pool.map(readMap, alignments)
matrix = {}
for presences, absences, mutations in res :
for mut in mutations :
j = alnId[mut[0]]
site = tuple(mut[1:3])
if site not in matrix :
matrix[site] = [[], []]
matSites[mut[1]][mut[2]-1] = mut[2]
if len(mut[4]) == 1 :
if len(matrix[site][0]) == 0 :
matrix[site][0] = ['-' for id in alnId]
matrix[site][0][j] = mut[4]
else :
if len(matrix[site][1]) == 0 :
matrix[site][1] = ['-' for id in alnId]
matrix[site][1][j] = mut[4]
for (mTag, mFile), (presences, absences, mutations) in zip(alignments, res) :
j = alnId[mTag]
for n, s, e in presences :
coreSites[n][s-1:e] +=1
mutations = matSites[n][s-1:e]
for kk in mutations[mutations > 0] :
k = (n, kk)
if len(matrix[k][0]) and matrix[k][0][j] == '-' :
matrix[k][0][j] = '.'
if len(matrix[k][1]) and matrix[k][1][j] == '-' :
matrix[k][1][j] = '.'
for n, s, e, m in absences :
coreSites[n][s-1:e] -=1
mutations = matSites[n][s-1:e]
for kk in mutations[mutations > 0] :
k = (n, kk)
if len(matrix[k][0]) and matrix[k][0][j] == '.' :
matrix[k][0][j] = '-'
if len(matrix[k][1]) and matrix[k][1][j] == '.' :
matrix[k][1][j] = '-'
pres = np.unique(np.concatenate(list(coreSites.values())), return_counts=True)
pres = [pres[0][pres[0] > 0], pres[1][pres[0] > 0]]
coreNum = len(alignments) * core
for p, n in zip(*pres) :
sys.stderr.write('#{2} {0} {1}\n'.format(p, n, '' if p > coreNum else '#'))
missings = []
coreBases = {'A':0, 'C':0, 'G':0, 'T':0}
for n in sorted(coreSites) :
sites = coreSites[n]
for site, num in enumerate(sites) :
cSite = (n, site+1)
if num < coreNum and cSite in matrix and len(matrix[cSite][1]) > 0 :
num = np.sum(matrix[cSite][1] != '-')
matrix[cSite][0] = []
if num < coreNum :
matrix.pop(cSite, None)
if len(missings) == 0 or missings[-1][0] != n or missings[-1][2] + 1 < cSite[1] :
missings.append([n, cSite[1], cSite[1]])
else :
missings[-1][2] = cSite[1]
else :
b = refSeq[n][cSite[1]-1]
if cSite in matrix and len(matrix[cSite][0]) :
matrix[cSite][0] = [ (b if s == '.' else s) for s in matrix[cSite][0]]
else :
coreBases[b] = coreBases.get(b, 0) + 1
outputs = {}
if matrixOut :
outputs['matrix'] = prefix + '.matrix.gz'
with uopen(prefix + '.matrix.gz', 'w') as fout :
fout.write('## Constant_bases: {A} {C} {G} {T}\n'.format(**coreBases))
for n in refSeq :
fout.write('## Sequence_length: {0} {1}\n'.format(n, len(refSeq[n])))
for region in missings :
fout.write('## Missing_region: {0} {1} {2}\n'.format(*region))
fout.write('\t'.join(['#Seq', '#Site'] + [ mTag for mTag, mFile in alignments ]) + '\n')
for site in sorted(matrix) :
bases = matrix[site]
if len(bases[0]) :
fout.write('{0}\t{1}\t{2}\n'.format(site[0], site[1], '\t'.join(bases[0])))
if len(bases[1]) :
fout.write('{0}\t{1}\t{2}\n'.format(site[0], site[1], '\t'.join(bases[1])))
if alignmentOut :
outputs['alignment'] = prefix + '.fasta.gz'
sequences = []
for (mTag, mFile), (presences, absences, mutations) in zip(alignments, res) :
j = alnId[mTag]
seq = { n:['-']*len(s) for n, s in refSeq.items() } if j > 0 else { n:list(s) for n, s in refSeq.items() }
if j :
for n, s, e in presences :
seq[n][s-1:e] = refSeq[n][s-1:e]
for n, s, e, c in absences :
seq[n][s-1:e] = '-' * (e-s+1)
for site in matrix :
bases = matrix[site]
if len(bases[0]) :
seq[site[0]][site[1]-1] = bases[0][j]
sequences.append(seq)
with uopen(prefix + '.fasta.gz', 'w') as fout :
for id, n in enumerate(sorted(refSeq)) :
if id :
fout.write('=\n')
for (mTag, mFile), seq in zip(alignments, sequences) :
fout.write('>{0}:{1}\n{2}\n'.format(mTag, n, ''.join(seq[n])))
return outputs
