def shogun_bt2_lca(input, output, bt2_indx, extract_ncbi_tid, depth, threads, annotate_lineage, run_lca):
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_inf = os.path.join(input, basename + '.fna')
sam_outf = os.path.join(output, basename + '.sam')
if os.path.isfile(sam_outf):
print("Found the samfile \"%s\". Skipping the alignment phase for this file." % sam_outf)
else:
print(bowtie2_align(fna_inf, sam_outf, bt2_indx, num_threads=threads))
if run_lca:
tree = NCBITree()
rank_name = list(tree.lineage_ranks.keys())[depth-1]
if not rank_name:
raise ValueError('Depth must be between 0 and 7, it was %d' % depth)
begin, end = extract_ncbi_tid.split(',')
counts = []
for basename in basenames:
sam_file = os.path.join(output, basename + '.sam')
lca_map = {}
for qname, rname in yield_alignments_from_sam_inf(sam_file):
ncbi_tid = int(find_between(rname, begin, end))
if qname in lca_map:
current_ncbi_tid = lca_map[qname]
if current_ncbi_tid:
if current_ncbi_tid != ncbi_tid:
lca_map[qname] = tree.lowest_common_ancestor(ncbi_tid, current_ncbi_tid)
else:
lca_map[qname] = ncbi_tid
if annotate_lineage:
lca_map = valmap(lambda x: tree.green_genes_lineage(x, depth=depth), lca_map)
taxon_counts = Counter(filter(None, lca_map.values()))
else:
lca_map = valfilter(lambda x: tree.get_rank_from_taxon_id(x) == rank_name, lca_map)
taxon_counts = Counter(filter(None, lca_map.values()))
counts.append(taxon_counts)
df = pd.DataFrame(counts, index=basenames)
df.T.to_csv(os.path.join(output, 'taxon_counts.csv'))
def shogun_bt2_align(infile, outfile, database):
print(bowtie2_align(infile, outfile, database))
def shogun_bt2_capitalist(input, output, bt2_indx, reference_fasta, reference_map, extract_ncbi_tid, depth, threads):
verify_make_dir(output)
fna_files = [os.path.join(input, filename) for filename in os.listdir(input) if filename.endswith('.fna')]
for fna_file in fna_files:
sam_outf = os.path.join(output, '.'.join(str(os.path.basename(fna_file)).split('.')[:-1]) + '.sam')
print(bowtie2_align(fna_file, sam_outf, bt2_indx, num_threads=threads))
tree = NCBITree()
begin, end = extract_ncbi_tid.split(',')
sam_files = [os.path.join(output, filename) for filename in os.listdir(output) if filename.endswith('.sam')]
lca_maps = {}
for sam_file in sam_files:
lca_map = {}
for qname, rname in yield_alignments_from_sam_inf(sam_file):
ncbi_tid = int(find_between(rname, begin, end))
if qname in lca_map:
current_ncbi_tid = lca_map[qname]
if current_ncbi_tid:
if current_ncbi_tid != ncbi_tid:
lca_map[qname] = tree.lowest_common_ancestor(ncbi_tid, current_ncbi_tid)
else:
lca_map[qname] = ncbi_tid
lca_map = valmap(lambda x: tree.green_genes_lineage(x, depth=depth), lca_map)
# filter out null values
lca_maps['.'.join(os.path.basename(sam_file).split('.')[:-1])] = reverse_collision_dict(lca_map)
for basename in lca_maps.keys():
lca_maps[basename] = valmap(lambda val: (basename, val), lca_maps[basename])
lca_map_2 = defaultdict(list)
for basename in lca_maps.keys():
for key, val in lca_maps[basename].items():
if key:
lca_map_2[key].append(val)
fna_faidx = {}
for fna_file in fna_files:
fna_faidx[os.path.basename(fna_file)[:-4]] = pyfaidx.Fasta(fna_file)
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()
with open(os.path.join(output, 'embalmer_out.txt'), 'w') as embalmer_cat:
for key in lca_map_2.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, headers in lca_map_2[key]:
for header in headers:
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[key]:
record = references_faidx[i][:]
references_fna.write('>%s\n%s\n' % (record.name, record.seq))
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)
sparse_ncbi_dict = defaultdict(dict)
# build query by NCBI_TID DataFrame
with open(os.path.join(output, 'embalmer_out.txt')) 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'))