def shogun_bugbase(input, output, img_database_folder):
verify_make_dir(output)
utree_outf = os.path.join(output, 'taxa_counts.txt')
# Indexing for emblalmer
if not os.path.isfile(utree_outf):
utree_indx = os.path.join(img_database_folder, 'img.genes.ctr')
with open(os.path.join(img_database_folder, 'img_map.pkl'),
'rb') as inf:
gg2img_oid = pickle.load(inf)
basenames = [
os.path.basename(filename)[:-4] for filename in os.listdir(input)
if filename.endswith('.fna')
]
for basename in basenames:
fna_file = os.path.join(input, basename + '.fna')
tsv_outf = os.path.join(output, basename + '.utree.tsv')
if not os.path.isfile(tsv_outf):
print(utree_search(utree_indx, fna_file, tsv_outf))
else:
print(
"Found the output file \"%s\". Skipping the alignment phase for this file."
% tsv_outf)
counts = []
for basename in basenames:
lcas = []
utree_tsv = os.path.join(output, basename + '.utree.tsv')
with open(utree_tsv) as inf:
tsv_parser = csv.reader(inf, delimiter='\t')
for line in tsv_parser:
if line[1]:
taxon = line[1].replace('; ', ';')
if taxon in gg2img_oid:
lcas.append(gg2img_oid[taxon])
counts.append(Counter(filter(None, lcas)))
df = pd.DataFrame(counts, index=basenames).fillna(0).astype(int).T
df.to_csv(utree_outf, sep='\t', index_label='#OTU ID')
else:
print("Found the output file \"%s\". Skipping all steps." % utree_outf)
def shogun_utree_capitalist(input, output, utree_indx, reference_fasta,
reference_map, extract_ncbi_tid, threads):
verify_make_dir(output)
basenames = [
os.path.basename(filename)[:-4] for filename in os.listdir(input)
if filename.endswith('.fna')
]
for basename in basenames:
fna_file = os.path.join(input, basename + '.fna')
tsv_outf = os.path.join(output, basename + '.utree.tsv')
if not os.path.isfile(tsv_outf):
print(utree_search(utree_indx, fna_file, tsv_outf))
else:
print(
"Found the output file \"%s\". Skipping the alignment phase for this file."
% tsv_outf)
embalmer_outf = os.path.join(output, 'embalmer_out.txt')
# Indexing for emblalmer
if not os.path.isfile(embalmer_outf):
lca_maps = defaultdict(lambda: defaultdict(list))
for basename in basenames:
utree_tsv = os.path.join(output, basename + '.utree.tsv')
with open(utree_tsv) as inf:
tsv_parser = csv.reader(inf, delimiter='\t')
for line in tsv_parser:
if line[1]:
lca_maps[';'.join(
line[1].split('; '))][basename].append(line[0])
fna_faidx = {}
for basename in basenames:
fna_faidx[basename] = pyfaidx.Fasta(
os.path.join(input, basename + '.fna'))
dict_reference_map = defaultdict(list)
with open(reference_map) as inf:
tsv_in = csv.reader(inf, delimiter='\t')
for line in tsv_in:
dict_reference_map[';'.join(line[1].split('; '))].append(
line[0])
# reverse the dict to feed into embalmer
references_faidx = pyfaidx.Fasta(reference_fasta)
tmpdir = tempfile.mkdtemp()
print(tmpdir)
with open(embalmer_outf, 'w') as embalmer_cat:
for species in lca_maps.keys():
queries_fna_filename = os.path.join(tmpdir, 'queries.fna')
references_fna_filename = os.path.join(tmpdir, 'reference.fna')
output_filename = os.path.join(tmpdir, 'output.txt')
with open(queries_fna_filename, 'w') as queries_fna:
for basename in lca_maps[species].keys():
for header in lca_maps[species][basename]:
record = fna_faidx[basename][header][:]
queries_fna.write(
'>filename|%s|%s\n%s\n' %
(basename, record.name, record.seq))
with open(references_fna_filename, 'w') as references_fna:
for i in dict_reference_map[species]:
record = references_faidx[i][:]
references_fna.write('>%s\n%s\n' %
(record.name, record.seq))
print(
embalmer_align(queries_fna_filename,
references_fna_filename, output_filename))
with open(output_filename) as embalmer_out:
for line in embalmer_out:
embalmer_cat.write(line)
os.remove(queries_fna_filename)
os.remove(references_fna_filename)
os.remove(output_filename)
os.rmdir(tmpdir)
else:
print(
"Found the output file \"%s\". Skipping the strain alignment phase for this file."
% embalmer_outf)
# Convert the results from embalmer into CSV
sparse_ncbi_dict = defaultdict(dict)
begin, end = extract_ncbi_tid.split(',')
# build query by NCBI_TID DataFrame
with open(embalmer_outf) as embalmer_cat:
embalmer_csv = csv.reader(embalmer_cat, delimiter='\t')
for line in embalmer_csv:
# line[0] = qname, line[1] = rname, line[2] = %match
ncbi_tid = np.int(find_between(line[1], begin, end))
sparse_ncbi_dict[line[0]][ncbi_tid] = np.float(line[2])
df = pd.DataFrame.from_dict(sparse_ncbi_dict)
df.to_csv(os.path.join(output, 'strain_alignments.csv'))
def shogun_utree_lca(input, output, utree_indx, threads, confidence, support,
mincount):
verify_make_dir(output)
basenames = [
os.path.basename(filename)[:-4] for filename in os.listdir(input)
if filename.endswith('.fna')
]
for basename in basenames:
fna_file = os.path.join(input, basename + '.fna')
tsv_outf = os.path.join(output, basename + '.utree.tsv')
if not os.path.isfile(tsv_outf):
print(utree_search(utree_indx, fna_file, tsv_outf))
else:
print(
"Found the output file \"%s\". Skipping the alignment phase for this file."
% tsv_outf)
counts = []
utree_outf = os.path.join(output, 'taxon_counts.txt')
# Tabulating
print("Tabulating and filtering hits...")
# print a row of "-" for every 10 samples
if len(basenames) >= 100:
for i in range(floor(len(basenames) / 10)):
sys.stdout.write('-')
sys.stdout.write('\n')
sys.stdout.flush()
if not os.path.isfile(utree_outf):
n_fail_confidence_only = 0
n_fail_support_only = 0
n_fail_both = 0
n = 0
n_pass = 0
for i, basename in enumerate(basenames):
if len(basenames) >= 100:
if (i + 1) % 10 == 0:
sys.stdout.write('.')
sys.stdout.flush()
lcas = [] # list of tuples [redistribute, confidence, support]
utree_tsv = os.path.join(output, basename + '.utree.tsv')
with open(utree_tsv) as inf:
tsv_parser = csv.reader(inf, delimiter='\t')
for line in tsv_parser:
if line[1]:
taxonomy = line[1]
is_confident = float(line[2]) >= confidence
is_supported = int(line[3]) >= support
n += 1
if not is_confident and not is_supported:
n_fail_both += 1
elif not is_confident:
n_fail_confidence_only += 1
elif not is_supported:
n_fail_support_only += 1
else:
n_pass += 1
lcas.append(taxonomy)
counts.append(Counter(lcas))
print(
'%d total assignments\n%d failed confidence only\n%d failed support_only\n%d failed both\n%d remaining'
% (n, n_fail_confidence_only, n_fail_support_only, n_fail_both,
n_pass))
sys.stdout.write('\n')
sys.stdout.flush()
df = pd.DataFrame(counts, index=basenames)
# filter by mincount
df[df < mincount] = 0
# drop spaces in column
df.columns = [colname.replace('; ', ';') for colname in df.columns]
# drop trailing t__ in redistribute
df.columns = [re.sub(';t__$', '', colname) for colname in df.columns]
df.T.to_csv(os.path.join(output, 'taxon_counts.csv'),
index_label='Taxon',
na_rep='0',
sep='\t')