def make_single_genes(seq_lists, wd='src'):
seqs = fasta.fasta_read(seq_lists[0], generator=False)
for i in xrange(len(seqs)):
start = True
gene_list = ''
for faa in seq_lists:
title = os.path.basename(faa)
seqs = fasta.fasta_read(faa, generator=False)
if start:
start = False
with open(join(wd, 'sampling/accs%d.txt' % i), 'a') as s:
s.write(title + '\t' + seqs[i].name[1:seqs[i].name.find(' ')] + '\n')
gene_list += '>' + title + '\n'
gene_list += seqs[i].seq
gene_list += '\n'
yield (i, gene_list)
def run(n1, n2, n1_nam='genome1', n2_nam='genome2', k=19, mp=''):
'''Return MUMi value for 2 genomes'''
# seq generators
if not mp:
mp = 'mummer'
n1_s = fasta.fasta_read(n1)
n2_s = fasta.fasta_read(n2)
n2_l = 0
n1_l = 0
# concatenate files, beacause mumi cannot process multiple contigs
with tempfile.NamedTemporaryFile(delete=True) as f1:
with tempfile.NamedTemporaryFile(delete=True) as f2:
f1.write('>temp_seq_1\n')
for seq in n1_s:
f1.write(seq.seq)
f2.write('>temp_seq_2\n')
for seq in n2_s:
f2.write(seq.seq)
cmd = [mp, '-mum', '-b', '-c', '-l', '%d' % k, f1.name, f2.name]
# run MUMmer
output = sp.Popen(cmd, stdout=sp.PIPE, stderr=sp.PIPE)
o, e = output.communicate()
# get genome lengths
for line in e.splitlines():
if not n1_l:
if f1.name in line and 'length' in line:
n1_l = int(line[line.rfind(' ') + 1:])
if not n2_l:
if f2.name in line and 'length' in line:
n2_l = int(line[line.rfind(' ') + 1:])
# get MUMi
m = '%.5f' % give_mumi.get(from_text=o, l1=n1_l, l2=n2_l)
return '\t'.join((n1_nam, n2_nam, m))
def run_new(start='', d='blast_data', cov=65, wd='', **kw):
num_threads = kw.pop('num_threads', 7)
dbs = [str(d + '/' + f[:-4]) for f in os.listdir(d) if f.endswith('.phr')]
ref_genes = fasta.fasta_read(start, generator=False)
matches = {}
if not os.path.isdir(os.path.join(wd, 'ref_dbs')):
os.mkdir(os.path.join(wd, 'ref_dbs'))
# segment dbs into blocks (size = num_threads) - blocks don't have to be full
# process blocks
# refine
# repeat until all blocks completed
blocks = []
for i in xrange(0, len(dbs), num_threads):
blocks.append(dbs[i:i + num_threads])
for block in blocks:
args = zip(range(len(block)), block)
to_remove = []
print len(ref_genes)
for i, res in enumerate(
comps(
arg_list=args,
cov=cov,
ref=ref_genes,
cores=num_threads)):
for r in res:
if r[1] is None:
to_remove.append(r[0])
else:
try:
matches[r[0]][block[i]] = r[1]
except KeyError:
matches[r[0]] = {block[i]: r[1]}
# remove 0 score proteins from master list
for r in set(to_remove):
print matches.pop(r)[dbs[5]].index, 'Removed'
ref_genes = [matches[g][dbs[5]] for g in matches]
for d in dbs:
with open(os.path.join(wd, 'ref_dbs', os.path.basename(d)), 'w') as s:
for g in matches:
s.write(matches[g][d].fasta)
s.write('\n')
def compare(database, file_path=None, from_text=None, from_list=None, cov=65, remove=[], **kw):
removed = 0
# implemented for multi blast
prot = _faa_dict.faa(database)
if not from_list:
g = fasta.fasta_read(file_path=file_path, from_text=from_text, generator=False)
else:
g = from_list
print len(g)
ref = []
# create blast query
with open('src/tools/blast/query.fasta', 'w') as q:
q.write('\n'.join(gene.fasta for gene in g))
# run (multi) blast search
aln = local_blast.run(db_path=database, mr=1, make_db=False,
r_a=True, outfmt='details', join_hsps=True, **kw)
for index, gene in enumerate(g):
try:
# grab top alignment (position 0) % similarity
a = aln[index][0]
sim = a.psim
except IndexError:
# if no record, move onto the next gene
for j in xrange(len(remove)):
del remove[j][index - removed]
removed += 1
continue
if sim < cov:
# if psim lower than cut off value, move onto next gene
for j in xrange(len(remove)):
del remove[j][index - removed]
removed += 1
continue
else:
print index, gene.name[1:gene.name.find(' ')], a.h_acc, sim
# matched lists
ref.append(fasta.sequence(a.h_def, prot[a.h_acc]))
# keep track of genes to keep from previous queries
#indices.append(index)
remove.append(ref)
return remove
def fasta_chunks(fname, chunk_size=5000):
""" Split fasta into chunks and save (free up ram during run). For
continuity, a list of contig_ids and chunk numbers are pickled for
back-checking at the end of the process with checkout.py. """
fas = fasta_read(fname)
chunk_num = math.ceil(len(fas) / chunk_size)
# Iterate over chunk numbers
for i in range(chunk_num):
i += 1
if len(fas) > chunk_size:
# Take first 5k elements
chunk = dict(list(fas.items())[:chunk_size])
fas = dict(list(fas.items())[chunk_size:])
else:
# Else take all elements
chunk = fas
fasta_write(chunk, "temp/chunk_%s.fas" % i)
log("%s written to /temp" % i)
return chunk_num
def make_gene_list(seq_lists, size=10, repeats=1000, wd='src'):
'''
Generator Function to create samples
seq_lists - sequence of file paths to conserved gene lists
size - size of sample
repeats - number of samples to take
'''
# TODO: add check to see if samples dir is empty
for rep in xrange(repeats):
gene_list = ''
start = True
max_l = 0
for faa in seq_lists:
title = os.path.basename(faa)
seqs = fasta.fasta_read(faa, generator=False)
if not start:
# should always be true - if not, something has gone
# wrong somewhere
assert len(seqs) == max_l
else:
max_l = len(seqs)
if start:
start = False
# get some random indices
indices = [i for i in np.random.choice(range(len(seqs)),
size,
replace=False)]
for i in indices:
# write sample genes to file
seq = seqs[i]
#print(seq.name)
with open(join(wd, 'sampling/accs%d.txt' % rep), 'a') as s:
s.write(title + '\t' + seq.name[1:seq.name.find(' ')] + '\n')
gene_list += '>' + title + '\n'
gene_list += ''.join(seqs[i].seq for i in indices)
gene_list += '\n'
yield (rep, gene_list)