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'))
