def main():
args = parser.parse_args()
# Check that input filenames exist.
check_files_exist([args.study_fasta, args.ref_msa, args.tree])
# If intermediate output directory set then create and output there.
# Otherwise make a temporary directory for the intermediate files.
if args.intermediate:
make_output_dir(args.intermediate)
place_seqs_pipeline(study_fasta=args.study_fasta,
ref_msa=args.ref_msa,
tree=args.tree,
out_tree=args.out_tree,
threads=args.threads,
papara_output=args.papara_output,
out_dir=args.intermediate,
chunk_size=args.chunk_size,
print_cmds=args.print_cmds)
else:
with TemporaryDirectory() as temp_dir:
place_seqs_pipeline(study_fasta=args.study_fasta,
ref_msa=args.ref_msa,
tree=args.tree,
out_tree=args.out_tree,
threads=args.threads,
papara_output=args.papara_output,
out_dir=temp_dir,
chunk_size=args.chunk_size,
print_cmds=args.print_cmds)
def main():
args = parser.parse_args()
# If intermediate output directory set then create and output there.
# Otherwise make a temporary directory for the intermediate files.
if args.intermediate:
make_output_dir(args.intermediate)
place_seqs_pipeline(study_fasta=args.study_fasta,
ref_dir=args.ref_dir,
out_tree=args.out_tree,
threads=args.processes,
out_dir=args.intermediate,
chunk_size=args.chunk_size,
print_cmds=args.print_cmds)
else:
with TemporaryDirectory() as temp_dir:
place_seqs_pipeline(study_fasta=args.study_fasta,
ref_dir=args.ref_dir,
out_tree=args.out_tree,
threads=args.processes,
out_dir=temp_dir,
chunk_size=args.chunk_size,
print_cmds=args.print_cmds)
def test_run_place_seqs_pipeline(self):
'''Basic test of full place seqs pipeline. As for EPA-NG, exact
matches to a treefile are not checked since slight differences
are expected depending on different versions.'''
with TemporaryDirectory() as temp_dir:
tmp_tree = path.join(temp_dir, "out.tre")
place_seqs_pipeline(study_fasta=test_study_seqs,
ref_dir=test_ref_dir,
out_tree=tmp_tree,
threads=1,
out_dir=temp_dir,
chunk_size=5000,
print_cmds=False)
def full_pipeline(study_fasta, input_table, output_folder, threads, ref_msa,
tree, hmm, in_traits, custom_trait_tables, marker_gene_table,
pathway_map, no_pathways, regroup_map, no_regroup,
stratified, alignment_tool, max_nsti, min_reads, min_samples,
hsp_method, calculate_NSTI, confidence, seed, no_gap_fill,
per_sequence_contrib, no_descrip, verbose):
'''Function that contains wrapper commands for full PICRUSt2 pipeline.
Descriptions of all of these input arguments/options are given in the
picrust2_pipeline.py script.'''
# Check that input files exist.
check_files_exist([study_fasta, input_table])
if path.exists(output_folder):
sys.exit("Stopping - output directory " + output_folder +
" already exists.")
# Make output folder.
make_output_dir(output_folder)
out_tree = path.join(output_folder, "out.tre")
if custom_trait_tables is None:
# Check that specified functional categories are allowed.
FUNC_TRAIT_OPTIONS = ['COG', 'EC', 'KO', 'PFAM', 'TIGRFAM']
funcs = in_traits.split(",")
for func in funcs:
if func not in FUNC_TRAIT_OPTIONS:
sys.exit("Error - specified category " + func +
" is not one of "
"the default categories.")
# Add EC to this set if pathways are to be predicted.
if "EC" not in funcs and not no_pathways:
funcs.append("EC")
rxn_func = "EC"
func_tables = default_tables
else:
no_descrip = True
funcs = []
func_tables = {}
table_i = 0
for custom in custom_trait_tables.split(","):
func_id = path.splitext(path.basename(custom))[0]
funcs.append(func_id)
func_tables[func_id] = custom
if table_i == 0:
rxn_func = func_id
table_i += 1
# Append marker as well, since this also needs to be run.
funcs.append("marker")
func_tables["marker"] = marker_gene_table
# Methods for discrete trait prediction with CI enabled.
discrete_set = set(['emp_prob', 'mp'])
if confidence and hsp_method in discrete_set:
ci_setting = True
else:
ci_setting = False
gap_fill_opt = not no_gap_fill
if verbose:
print("Placing sequences onto reference tree.")
# Define folders for intermediate files.
intermediate_dir = path.join(output_folder, "intermediate")
place_seqs_intermediate = path.join(intermediate_dir, "place_seqs")
make_output_dir(intermediate_dir)
make_output_dir(place_seqs_intermediate)
place_seqs_pipeline(study_fasta=study_fasta,
ref_msa=ref_msa,
tree=tree,
hmm=hmm,
out_tree=out_tree,
alignment_tool=alignment_tool,
threads=threads,
out_dir=place_seqs_intermediate,
chunk_size=5000,
print_cmds=verbose)
if verbose:
print("Finished placing sequences on output tree: " + out_tree)
# Get predictions for all specified functions and keep track of outfiles.
predicted_funcs = {}
for func in funcs:
count_outfile = hsp_pipeline_steps(func=func,
calculate_NSTI=calculate_NSTI,
out_tree=out_tree,
func_table_in=func_tables[func],
hsp_method=hsp_method,
ci_setting=ci_setting,
threads=threads,
seed=seed,
output_folder=output_folder,
verbose=verbose)
# Keep track of output file name for next step of pipeline.
predicted_funcs[func] = count_outfile
marker_infile = predicted_funcs["marker"]
# Inititalize dictionary of function names to output filenames to return.
func_output = {}
# Each value will be a list of 2 elements corresponding to the unstratified
# and stratified tables respectively (stratified will be None of not calculated).
# Loop over each function again and run metagenome pipeline.
for func in funcs:
if func == "marker":
continue
if verbose:
print("Running metagenome pipeline for " + func)
func_infile = predicted_funcs[func]
func_output_dir = path.join(output_folder, func + "_metagenome_out")
func_map = None
if func in default_map:
func_map = default_map[func]
func_strat_out, func_unstrat_out = metagenome_pipeline_steps(
input_table=input_table,
func_infile=func_infile,
marker_infile=marker_infile,
func_output_dir=func_output_dir,
no_descrip=no_descrip,
max_nsti=max_nsti,
min_reads=min_reads,
min_samples=min_samples,
stratified=stratified,
threads=threads,
func_map=func_map,
verbose=verbose)
if stratified:
func_output[func] = func_strat_out
else:
func_output[func] = func_unstrat_out
pathway_outfiles = None
# Infer pathway abundances and coverages unless --no_pathways set.
if not no_pathways:
pathways_intermediate = path.join(intermediate_dir, "pathways")
make_output_dir(pathways_intermediate)
if verbose:
print("Inferring pathways from predicted " + rxn_func)
predicted_rxn = func_output[rxn_func]
# Set regrouping mapfile to be empty if no_regroup set.
if no_regroup:
regroup_map = None
unstrat_abun, unstrat_cov, strat_abun, strat_cov = run_minpath_pipeline(
inputfile=predicted_rxn,
mapfile=pathway_map,
regroup_mapfile=regroup_map,
proc=threads,
out_dir=pathways_intermediate,
gap_fill=gap_fill_opt,
per_sequence_contrib=per_sequence_contrib,
print_cmds=verbose)
pathways_out = path.join(output_folder, "pathways_out")
unstrat_abun.index.name = 'pathway'
unstrat_cov.index.name = 'pathway'
unstrat_abun.reset_index(inplace=True)
unstrat_cov.reset_index(inplace=True)
pathway_outfiles = {}
if not no_descrip:
unstrat_abun = add_descrip_col(inputfile=unstrat_abun,
mapfile=default_map["METACYC"],
in_df=True)
if not no_descrip:
unstrat_cov = add_descrip_col(inputfile=unstrat_cov,
mapfile=default_map["METACYC"],
in_df=True)
if verbose:
print("Writing predicted pathway abundances and coverages to " +
pathways_out)
make_output_dir(pathways_out)
unstrat_abun_outfile = path.join(pathways_out, "path_abun_unstrat.tsv")
unstrat_cov_outfile = path.join(pathways_out, "path_cov_unstrat.tsv")
unstrat_abun.to_csv(path_or_buf=unstrat_abun_outfile,
sep="\t",
index=False)
unstrat_cov.to_csv(path_or_buf=unstrat_cov_outfile,
sep="\t",
index=False)
pathway_outfiles["unstrat_abun"] = unstrat_abun_outfile
pathway_outfiles["unstrat_cov"] = unstrat_cov_outfile
strat_abun_outfile = None
strat_cov_outfile = None
# Write stratified output only if something besides None was returned.
if strat_abun is not None:
if not no_descrip:
strat_abun = add_descrip_col(inputfile=strat_abun,
mapfile=default_map["METACYC"],
in_df=True)
strat_abun_outfile = path.join(pathways_out, "path_abun_strat.tsv")
strat_abun.to_csv(path_or_buf=strat_abun_outfile,
sep="\t",
index=False)
if strat_cov is not None:
if not no_descrip:
strat_cov = add_descrip_col(inputfile=strat_cov,
mapfile=default_map["METACYC"],
in_df=True)
strat_cov_outfile = path.join(pathways_out, "path_cov_strat.tsv")
strat_cov.to_csv(path_or_buf=strat_cov_outfile,
sep="\t",
index=False)
pathway_outfiles["strat_abun"] = strat_abun_outfile
pathway_outfiles["strat_cov"] = strat_cov_outfile
return (func_output, pathway_outfiles)
def main():
args = parser.parse_args()
# Get start time.
start_time = time.time()
# Check that input files exist.
check_files_exist([args.study_fasta, args.input])
# Make output folder.
make_output_dir(args.output)
out_tree = path.join(args.output, "out.tre")
if args.custom_trait_tables is None:
# Check that specified functional categories are allowed.
FUNC_TRAIT_OPTIONS = ['COG', 'EC', 'KO', 'PFAM', 'TIGRFAM']
funcs = args.in_traits.split(",")
for func in funcs:
if func not in FUNC_TRAIT_OPTIONS:
sys.exit("Error - specified category " + func +
" is not one of "
"the default categories.")
# Add EC to this set if pathways are to be predicted.
if "EC" not in funcs and not args.no_pathways:
funcs.append("EC")
rxn_func = "EC"
func_tables = default_tables
else:
funcs = []
func_tables = {}
table_i = 0
for custom in args.custom_trait_tables.split(","):
func_id = path.splitext(path.basename(custom))[0]
funcs.append(func_id)
func_tables[func_id] = custom
if table_i == 0:
rxn_func = func_id
table_i += 1
# Append marker as well, since this also needs to be run.
funcs.append("marker")
func_tables["marker"] = args.marker_gene_table
# Methods for discrete trait prediction with CI enabled.
discrete_set = set(['emp_prob', 'mp'])
if args.confidence and args.hsp_method in discrete_set:
ci_setting = True
else:
ci_setting = False
gap_fill_opt = not args.no_gap_fill
with TemporaryDirectory() as temp_dir:
print("Placing sequences onto reference tree.")
place_seqs_pipeline(study_fasta=args.study_fasta,
ref_msa=args.ref_msa,
tree=args.tree,
out_tree=out_tree,
threads=args.threads,
papara_output=None,
out_dir=temp_dir,
chunk_size=5000,
print_cmds=args.print_cmds)
print("Finished placing sequences on output tree: " + out_tree)
# Get predictions for all specified functions and keep track of outfiles.
predicted_funcs = {}
for func in funcs:
# Only output NSTI in 16S table.
nsti_setting = False
if func == "marker" and args.calculate_NSTI:
nsti_setting = True
print("Running hidden-state prediction for " + func)
hsp_table, ci_table = castor_hsp_workflow(
tree_path=out_tree,
trait_table_path=func_tables[func],
hsp_method=args.hsp_method,
calc_nsti=nsti_setting,
calc_ci=ci_setting,
check_input=False,
num_proc=args.threads,
ran_seed=args.seed)
count_outfile = path.join(args.output, func + "_predicted.tsv")
# Add "_nsti" to filename if output.
if nsti_setting:
count_outfile = path.join(args.output,
func + "_nsti_predicted.tsv")
# Keep track of output file name for next step of pipeline.
predicted_funcs[func] = count_outfile
print("Writing out predicted gene family abundances to " +
count_outfile)
hsp_table.to_csv(path_or_buf=count_outfile,
index_label="sequence",
sep="\t")
# Output the CI file as well if option set.
if ci_setting:
ci_outfile = path.join(args.output, func + "_predicted_ci.tsv")
print("Writing out predicted gene family CIs to " + ci_outfile)
ci_table.to_csv(path_or_buf=ci_outfile,
index_label="sequence",
sep="\t")
marker_infile = predicted_funcs["marker"]
# Loop over each function again and run metagenome pipeline.
for func in funcs:
if func == "marker":
continue
func_infile = predicted_funcs[func]
func_output_dir = path.join(args.output, func + "_metagenome_out")
print("Running metagenome pipeline for " + func)
# Infer metagenome abundances per-sample.
with TemporaryDirectory() as temp_dir:
# Pass arguments to key function and get predicted functions
# stratified and unstratified by genomes.
strat_pred, unstrat_pred = run_metagenome_pipeline(
input_biom=args.input,
function=func_infile,
marker=marker_infile,
out_dir=func_output_dir,
max_nsti=args.max_nsti,
min_reads=args.min_reads,
min_samples=args.min_samples,
strat_out=args.stratified,
proc=args.threads,
output_normfile=True)
print("Writing metagenome output files for " + func + " to: " +
func_output_dir)
# Generate output table filepaths and write out pandas dataframe.
unstrat_outfile = path.join(func_output_dir,
"pred_metagenome_unstrat.tsv")
unstrat_pred.index.name = "function"
unstrat_pred.reset_index(inplace=True)
if args.custom_trait_tables is None:
unstrat_pred = add_descrip_col(inputfile=unstrat_pred,
mapfile=default_map[func],
in_df=True)
unstrat_pred.to_csv(path_or_buf=unstrat_outfile,
sep="\t",
index=False)
# Write out stratified table only if that option was specified.
if args.stratified:
strat_outfile = path.join(func_output_dir,
"pred_metagenome_strat.tsv")
strat_pred.reset_index(inplace=True)
if args.custom_trait_tables is None:
strat_pred = add_descrip_col(inputfile=strat_pred,
mapfile=default_map[func],
in_df=True)
strat_pred.to_csv(path_or_buf=strat_outfile,
sep="\t",
index=False)
# Infer pathway abundances and coverages unless --no_pathways set.
if not args.no_pathways:
if args.stratified:
in_metagenome = path.join(args.output,
rxn_func + "_metagenome_out",
"pred_metagenome_strat.tsv")
else:
in_metagenome = path.join(args.output,
rxn_func + "_metagenome_out",
"pred_metagenome_unstrat.tsv")
print("Inferring MetaCyc pathways from predicted functions in this "
"file: " + in_metagenome)
with TemporaryDirectory() as temp_dir:
unstrat_abun, unstrat_cov, strat_abun, strat_cov = run_minpath_pipeline(
inputfile=in_metagenome,
mapfile=default_pathway_map,
regroup_mapfile=default_regroup_map,
proc=args.threads,
out_dir=temp_dir,
gap_fill=gap_fill_opt,
per_sequence_contrib=args.per_sequence_contrib,
print_cmds=args.print_cmds)
pathways_out = path.join(args.output, "pathways_out")
make_output_dir(pathways_out)
print("Writing predicted pathway abundances and coverages to " +
pathways_out)
# Write output files.
unstrat_abun_outfile = path.join(pathways_out, "path_abun_unstrat.tsv")
unstrat_abun.reset_index(inplace=True)
if args.custom_trait_tables is None:
unstrat_abun = add_descrip_col(inputfile=unstrat_abun,
mapfile=default_map["METACYC"],
in_df=True)
unstrat_abun.to_csv(path_or_buf=unstrat_abun_outfile,
sep="\t",
index=False)
unstrat_cov_outfile = path.join(pathways_out, "path_cov_unstrat.tsv")
unstrat_cov.reset_index(inplace=True)
if args.custom_trait_tables is None:
unstrat_cov = add_descrip_col(inputfile=unstrat_cov,
mapfile=default_map["METACYC"],
in_df=True)
unstrat_cov.to_csv(path_or_buf=unstrat_cov_outfile,
sep="\t",
index=False)
# Write stratified output only if something besides None was returned.
if strat_abun is not None:
strat_abun_outfile = path.join(pathways_out, "path_abun_strat.tsv")
if args.custom_trait_tables is None:
strat_abun = add_descrip_col(inputfile=strat_abun,
mapfile=default_map["METACYC"],
in_df=True)
strat_abun.to_csv(path_or_buf=strat_abun_outfile,
sep="\t",
index=False)
if strat_cov is not None:
strat_cov_outfile = path.join(pathways_out, "path_cov_strat.tsv")
if args.custom_trait_tables is None:
strat_cov = add_descrip_col(inputfile=strat_cov,
mapfile=default_map["METACYC"],
in_df=True)
strat_cov.to_csv(path_or_buf=strat_cov_outfile,
sep="\t",
index=False)
# Print out elapsed time.
elapsed_time = time.time() - start_time
print("Completed PICRUSt2 pipeline in " + "%.2f" % elapsed_time +
" seconds.")