def run_main(parser, args):
args = parser.parse_args()
verbose = True
if args.ont:
args.expected_error_rate = 0.15
args.filters = ["LOW_GT_CONF"]
args.model = "kmer_count"
logger.debug("Setting expected error rate to %s (--ont)" %
args.expected_error_rate)
logger.debug(
"Removing LOW_PERCENT_COVERAGE filter (increases sensitivity - in particular for ONT data)")
if args.min_variant_conf is None:
args.min_variant_conf = 100
cp = CoverageParser(
sample=args.sample,
panel_file_paths=[args.probe_set],
seq=args.seq,
ctx=args.ctx,
kmer=args.kmer,
force=args.force,
verbose=verbose,
tmp_dir=args.tmp,
skeleton_dir=args.skeleton_dir,
threads=args.threads,
memory=args.memory,
mccortex31_path=args.mccortex31_path)
cp.run()
if args.expected_depth is None:
args.expected_depth = cp.estimate_depth()
base_json = {args.sample: {}}
base_json[args.sample]["probe_set"] = args.probe_set
if args.seq:
base_json[args.sample]["files"] = args.seq
else:
base_json[args.sample]["files"] = args.ctx
base_json[args.sample]["kmer"] = args.kmer
base_json[args.sample]["version"] = __version__
gt = Genotyper(
sample=args.sample,
expected_error_rate=args.expected_error_rate,
expected_depths=[
args.expected_depth],
variant_covgs=cp.variant_covgs,
gene_presence_covgs=cp.covgs["presence"],
base_json=base_json,
contamination_depths=[],
ignore_filtered=args.ignore_filtered,
filters=args.filters,
model=args.model,
report_all_calls=args.report_all_calls,
variant_confidence_threshold=args.min_variant_conf,
sequence_confidence_threshold=args.min_gene_conf,
min_gene_percent_covg_threshold=args.min_gene_percent_covg_threshold)
gt.run()
if not args.keep_tmp:
cp.remove_temporary_files()
return gt.out_json
def run(parser, args):
logger.info(
f"Start runnning mykrobe predict. Command line: {' '.join(sys.argv)}")
base_json = {args.sample: {}}
ref_data = ref_data_from_args(args)
if (args.species == "custom" and ref_data["var_to_res_json"] is None
and ref_data["lineage_json"] is None):
logger.info(
"Forcing --report_all_calls because species is 'custom' and options --custom_variant_to_resistance_json,--custom_lineage_json were not used"
)
args.report_all_calls = True
logger.info(
f"Running mykrobe predict using species {args.species}, and panel version {ref_data['version']}"
)
if args.tmp is None:
args.tmp = tempfile.mkdtemp() + "/"
if args.ont:
args.expected_error_rate = ONT_E_RATE
logger.info(
f"Set expected error rate to {args.expected_error_rate} because --ont flag was used"
)
args.ploidy = ONT_PLOIDY
logger.info(f"Set ploidy to {args.ploidy} because --ont flag used")
# Run Cortex
cp = CoverageParser(
sample=args.sample,
panel_file_paths=ref_data["fasta_files"],
seq=args.seq,
kmer=ref_data["kmer"],
force=args.force,
threads=args.threads,
verbose=False,
tmp_dir=args.tmp,
skeleton_dir=args.skeleton_dir,
memory=args.memory,
)
cp.run()
logger.debug("CoverageParser complete")
if ref_data["species_phylo_group"] is None:
phylogenetics = {}
depths = [cp.estimate_depth()]
else:
phylogenetics, depths = detect_species_and_get_depths(
cp, ref_data["hierarchy_json"], ref_data["species_phylo_group"])
# Genotype
variant_calls_dict = {}
sequence_calls_dict = {}
lineage_calls_dict = {}
lineage_predict_dict = {}
if args.force and len(depths) == 0:
depths = [cp.estimate_depth()]
gt = None
if len(depths) > 0 or args.force:
# Running the genotyper changes the contents of cp.covgs["presence"].
# The changes can cause later second run (if it happens) of the
# genotytper to crash.
# Store the original cp.covgs["presence"] so we can use it again later.
original_covgs_presence = copy.deepcopy(cp.covgs["presence"])
gt = Genotyper(
sample=args.sample,
expected_depths=depths,
expected_error_rate=args.expected_error_rate,
variant_covgs=cp.variant_covgs,
gene_presence_covgs=cp.covgs["presence"],
base_json=base_json,
contamination_depths=[],
report_all_calls=True,
ignore_filtered=True,
filters=args.filters,
variant_confidence_threshold=args.min_variant_conf,
sequence_confidence_threshold=args.min_gene_conf,
model=args.model,
kmer_size=ref_data["kmer"],
min_proportion_expected_depth=args.min_proportion_expected_depth,
ploidy=args.ploidy,
lineage_variants=ref_data["lineage_dict"],
)
gt.run()
(
kmer_count_error_rate,
incorrect_kmer_to_pc_cov,
) = gt.estimate_kmer_count_error_rate_and_incorrect_kmer_to_percent_cov(
)
logger.debug("Estimated error rate for kmer count model: " +
str(round(100 * kmer_count_error_rate, 2)) + "%")
if args.guess_sequence_method and kmer_count_error_rate > 0.001:
logger.warning(
"Guess sequence method is on, and we've guessed ONT")
# this is duplicated from the if args.ont statement above
args.expected_error_rate = ONT_E_RATE
logger.info(
f"Set expected error rate to {args.expected_error_rate}")
args.ploidy = ONT_PLOIDY
logger.info(f"Set ploidy to {args.ploidy}")
# conf_percent_cutoff == 100 means that we want to keep all variant calls,
# in which case there is no need to run the simulations
if args.conf_percent_cutoff < 100:
logger.debug("Expected depth: " + str(depths[0]))
conf_thresholder = ConfThresholder(
kmer_count_error_rate,
depths[0],
ref_data["kmer"],
incorrect_kmer_to_pc_cov,
)
time_start = time.time()
conf_threshold = conf_thresholder.get_conf_threshold(
percent_to_keep=args.conf_percent_cutoff)
time_end = time.time()
time_to_sim = time_end - time_start
logger.debug("Simulation time: " + str(time_to_sim))
logger.debug("Confidence cutoff (using percent cutoff " +
str(args.conf_percent_cutoff) + "%): " +
str(conf_threshold))
cp.covgs["presence"] = copy.deepcopy(original_covgs_presence)
gt = Genotyper(
sample=args.sample,
expected_depths=depths,
expected_error_rate=kmer_count_error_rate,
variant_covgs=cp.variant_covgs,
gene_presence_covgs=cp.covgs["presence"],
base_json=base_json,
contamination_depths=[],
report_all_calls=True,
ignore_filtered=True,
filters=args.filters,
variant_confidence_threshold=conf_threshold,
sequence_confidence_threshold=args.min_gene_conf,
model=args.model,
kmer_size=ref_data["kmer"],
min_proportion_expected_depth=args.
min_proportion_expected_depth,
ploidy=args.ploidy,
lineage_variants=ref_data["lineage_dict"],
)
gt.run()
variant_calls_dict = gt.variant_calls_dict
sequence_calls_dict = gt.sequence_calls_dict
lineage_predict_dict, lineage_calls_dict = gt.predict_lineage()
else:
depths = [cp.estimate_depth()]
mykrobe_predictor_susceptibility_result = MykrobePredictorSusceptibilityResult(
)
if (gt is not None and (max(depths) > args.min_depth or args.force)
and ref_data["var_to_res_json"] is not None):
predictor = BasePredictor(
variant_calls=gt.variant_calls,
called_genes=gt.sequence_calls_dict,
base_json=base_json[args.sample],
depth_threshold=args.min_depth,
ignore_filtered=True,
ignore_minor_calls=args.ignore_minor_calls,
variant_to_resistance_json_fp=ref_data["var_to_res_json"],
)
mykrobe_predictor_susceptibility_result = predictor.run()
logger.info("Progress: finished making AMR predictions")
base_json[args.sample] = {
"susceptibility":
list(mykrobe_predictor_susceptibility_result.to_dict().values())[0],
"phylogenetics": {}
if phylogenetics == {} else list(phylogenetics.to_dict().values())[0],
"variant_calls":
variant_calls_dict,
"sequence_calls":
sequence_calls_dict,
"lineage_calls":
lineage_calls_dict,
"kmer":
ref_data["kmer"],
"probe_sets":
ref_data["fasta_files"],
"files":
args.seq,
"version": {
"mykrobe-predictor": predictor_version,
"mykrobe-atlas": atlas_version,
"panel": ref_data["version"],
},
"genotype_model":
args.model,
}
if len(lineage_predict_dict) > 0:
base_json[
args.sample]["phylogenetics"]["lineage"] = lineage_predict_dict
if not args.keep_tmp:
cp.remove_temporary_files()
logger.info("Progress: writing output")
fix_X_amino_acid_variants(base_json[args.sample])
write_outputs(args, base_json)
logger.info("Progress: finished")
def run(parser, args):
base_json = {args.sample: {}}
args = parser.parse_args()
hierarchy_json_file = None
if args.panel is not None:
variant_to_resistance_json_fp = None
if args.species == "tb" and args.panel == "bradley-2015":
TB_PANELS = [
"data/panels/tb-species-170421.fasta.gz",
"data/panels/tb-bradley-probe-set-jan-2019.fasta.gz",
]
elif args.species == "tb" and args.panel == "walker-2015":
TB_PANELS = [
"data/panels/tb-species-170421.fasta.gz",
"data/panels/tb-walker-probe-set-jan-2019.fasta.gz",
]
elif args.species == "tb" and args.panel == "201901":
TB_PANELS = [
"data/panels/tb-species-170421.fasta.gz",
"data/panels/tb-hunt-probe-set-jan-03-2019.fasta.gz",
]
data_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), "../data/predict/tb/"))
variant_to_resistance_json_fp = os.path.join(
data_dir, "variant_to_resistance_drug-jan-03-2019.json")
elif args.species == "tb" and args.panel == "atlas":
TB_PANELS = [
"data/panels/tb-species-170421.fasta.gz",
"data/panels/tb-walker-probe-set-jan-2019.fasta.gz",
"data/panels/tb-k21-probe-set-feb-09-2017.fasta.gz",
]
elif args.panel == "custom":
if not args.custom_probe_set_path:
raise ValueError("Custom panel requires custom_probe_set_path")
TB_PANELS = [
args.custom_probe_set_path,
"data/panels/tb-species-170421.fasta.gz",
]
variant_to_resistance_json_fp = args.custom_variant_to_resistance_json
Predictor = None
if not args.species:
panels = TB_PANELS + GN_PANELS + STAPH_PANELS
elif args.species == "staph":
panels = STAPH_PANELS
Predictor = StaphPredictor
args.kmer = 15 # Forced
variant_to_resistance_json_fp = None
elif args.species == "tb":
panels = TB_PANELS
hierarchy_json_file = "data/phylo/mtbc_hierarchy.json"
Predictor = TBPredictor
logger.info("Running AMR prediction with panels %s" % ", ".join(panels))
version = {}
version["mykrobe-predictor"] = predictor_version
version["mykrobe-atlas"] = atlas_version
# Get real paths for panels
panels = [
os.path.realpath(os.path.join(os.path.dirname(__file__), "..", f))
for f in panels
]
if hierarchy_json_file is not None:
hierarchy_json_file = os.path.realpath(
os.path.join(os.path.dirname(__file__), "..", hierarchy_json_file))
# Run Cortex
cp = CoverageParser(
sample=args.sample,
panel_file_paths=panels,
seq=args.seq,
kmer=args.kmer,
force=args.force,
threads=1,
verbose=False,
tmp_dir=args.tmp,
skeleton_dir=args.skeleton_dir,
mccortex31_path=args.mccortex31_path,
)
cp.run()
logger.debug("CoverageParser complete")
# Detect species
species_predictor = AMRSpeciesPredictor(
phylo_group_covgs=cp.covgs.get("complex",
cp.covgs.get("phylo_group", {})),
sub_complex_covgs=cp.covgs.get("sub-complex", {}),
species_covgs=cp.covgs["species"],
lineage_covgs=cp.covgs.get("sub-species", {}),
hierarchy_json_file=hierarchy_json_file,
)
phylogenetics = species_predictor.run()
# ## AMR prediction
depths = []
if species_predictor.is_saureus_present():
depths = [
species_predictor.out_json["phylogenetics"]["phylo_group"]
["Staphaureus"]["median_depth"]
]
elif species_predictor.is_mtbc_present():
depths = [
species_predictor.out_json["phylogenetics"]["phylo_group"]
["Mycobacterium_tuberculosis_complex"]["median_depth"]
]
# pprint (species_predictor.out_json["phylogenetics"]["species"])
# Genotype
q = args.quiet
args.quiet = True
variant_calls_dict = {}
sequence_calls_dict = {}
if args.force and not depths:
depths = [1]
gt = None
if depths or args.force:
gt = Genotyper(
sample=args.sample,
expected_depths=depths,
expected_error_rate=args.expected_error_rate,
variant_covgs=cp.variant_covgs,
gene_presence_covgs=cp.covgs["presence"],
base_json=base_json,
contamination_depths=[],
report_all_calls=True,
ignore_filtered=True,
filters=args.filters,
variant_confidence_threshold=args.min_variant_conf,
sequence_confidence_threshold=args.min_gene_conf,
model=args.model,
kmer_size=args.kmer,
min_proportion_expected_depth=args.min_proportion_expected_depth,
ploidy=args.ploidy,
)
gt.run()
kmer_count_error_rate, incorrect_kmer_to_pc_cov = (
gt.
estimate_kmer_count_error_rate_and_incorrect_kmer_to_percent_cov())
logger.info("Estimated error rate for kmer count model: " +
str(round(100 * kmer_count_error_rate, 2)) + "%")
if args.guess_sequence_method and kmer_count_error_rate > 0.001:
logger.warning(
"Guess sequence method is on, and we've guessed ONT")
args.ont = True
if args.ont:
args.expected_error_rate = 0.15
args.ploidy = "haploid"
args.ignore_minor_calls = True
logger.warning("Setting ploidy to haploid")
logger.warning("Setting ignore_minor_calls to True")
logger.warning("Setting expected error rate to %s (--ont)" %
args.expected_error_rate)
args.model = "kmer_count"
# If the user didn't specify the conf_percent_cutoff, then set it
# depending on whether or not the --ont flag was used
if args.conf_percent_cutoff == -1:
args.conf_percent_cutoff = 90 if args.ont else 100
# conf_percent_cutoff == 100 means that we want to keep all variant calls,
# in which case there is no need to run the simulations
if args.conf_percent_cutoff < 100:
logger.info("Expected depth: " + str(depths[0]))
conf_thresholder = ConfThresholder(kmer_count_error_rate,
depths[0], args.kmer,
incorrect_kmer_to_pc_cov)
time_start = time.time()
conf_threshold = conf_thresholder.get_conf_threshold(
percent_to_keep=args.conf_percent_cutoff)
time_end = time.time()
time_to_sim = time_end - time_start
logger.info("Simulation time: " + str(time_to_sim))
logger.info("Confidence cutoff (using percent cutoff " +
str(args.conf_percent_cutoff) + "%): " +
str(conf_threshold))
gt = Genotyper(
sample=args.sample,
expected_depths=depths,
expected_error_rate=kmer_count_error_rate,
# expected_error_rate=args.expected_error_rate,
variant_covgs=cp.variant_covgs,
gene_presence_covgs=cp.covgs["presence"],
base_json=base_json,
contamination_depths=[],
report_all_calls=True,
ignore_filtered=True,
filters=args.filters,
variant_confidence_threshold=conf_threshold,
sequence_confidence_threshold=args.min_gene_conf,
model=args.model,
kmer_size=args.kmer,
min_proportion_expected_depth=args.
min_proportion_expected_depth,
ploidy=args.ploidy,
)
gt.run()
variant_calls_dict = gt.variant_calls_dict
sequence_calls_dict = gt.sequence_calls_dict
else:
depths = [cp.estimate_depth()]
args.quiet = q
mykrobe_predictor_susceptibility_result = MykrobePredictorSusceptibilityResult(
)
if gt is not None and (max(depths) > args.min_depth or args.force):
predictor = Predictor(
variant_calls=gt.variant_calls,
called_genes=gt.sequence_calls_dict,
base_json=base_json[args.sample],
depth_threshold=args.min_depth,
ignore_filtered=True,
ignore_minor_calls=args.ignore_minor_calls,
variant_to_resistance_json_fp=variant_to_resistance_json_fp,
)
mykrobe_predictor_susceptibility_result = predictor.run()
base_json[args.sample] = MykrobePredictorResult(
susceptibility=mykrobe_predictor_susceptibility_result,
phylogenetics=phylogenetics,
variant_calls=variant_calls_dict,
sequence_calls=sequence_calls_dict,
probe_sets=panels,
files=args.seq,
kmer=args.kmer,
version=version,
model=args.model,
).to_dict()
if not args.keep_tmp:
cp.remove_temporary_files()
# write to file is specified by user, otherwise send to stdout
if args.output_format == "csv":
output = json_to_csv(base_json)
else:
## Verbose json output requires --report_all_calls
if not args.report_all_calls:
del base_json[args.sample]["variant_calls"]
del base_json[args.sample]["sequence_calls"]
output = json.dumps(base_json, indent=4)
if args.output:
with open(args.output, "w") as outfile:
outfile.write(output)
else:
print(output)
def run_main(parser, args):
args = parser.parse_args()
verbose = True
if args.ont:
args.expected_error_rate = ONT_E_RATE
logger.debug("Setting expected error rate to %s (--ont)" %
args.expected_error_rate)
if args.min_variant_conf is None:
args.min_variant_conf = 100
if args.tmp is None:
args.tmp = tempfile.mkdtemp() + "/"
cp = CoverageParser(
sample=args.sample,
panel_file_paths=[args.probe_set],
seq=args.seq,
ctx=args.ctx,
kmer=args.kmer,
force=args.force,
verbose=verbose,
tmp_dir=args.tmp,
skeleton_dir=args.skeleton_dir,
threads=args.threads,
memory=args.memory,
)
cp.run()
if args.expected_depth is None:
args.expected_depth = cp.estimate_depth()
base_json = {args.sample: {}}
base_json[args.sample]["probe_set"] = args.probe_set
if args.seq:
base_json[args.sample]["files"] = args.seq
else:
base_json[args.sample]["files"] = args.ctx
base_json[args.sample]["kmer"] = args.kmer
base_json[args.sample]["version"] = __version__
if args.lineage is None:
lineage_dict = None
else:
lineage_dict = load_json(args.lineage)
gt = Genotyper(
sample=args.sample,
expected_error_rate=args.expected_error_rate,
expected_depths=[args.expected_depth],
variant_covgs=cp.variant_covgs,
gene_presence_covgs=cp.covgs["presence"],
base_json=base_json,
contamination_depths=[],
ignore_filtered=args.ignore_filtered,
filters=args.filters,
model=args.model,
report_all_calls=args.report_all_calls,
variant_confidence_threshold=args.min_variant_conf,
sequence_confidence_threshold=args.min_gene_conf,
min_gene_percent_covg_threshold=args.min_gene_percent_covg_threshold,
kmer_size=args.kmer,
min_proportion_expected_depth=args.min_proportion_expected_depth,
ploidy=args.ploidy,
lineage_variants=lineage_dict,
)
gt.run()
if args.output:
with open(args.output, "w") as outfile:
json.dump(gt.out_json, outfile, indent=4)
if not args.keep_tmp:
cp.remove_temporary_files()
return gt.out_json
def run(parser, args):
base_json = {args.sample: {}}
args = parser.parse_args()
hierarchy_json_file = None
variant_to_resistance_json_fp: Optional[PathLike] = None
species = Species(args.species)
if species is not Species.TB and args.panel != "custom":
args.panel = "default"
panels = Panel.from_species_and_name(species, args.panel)
if species is Species.TB and panels.name is TbPanel.NEJM_WALKER:
data_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), "../data/predict/tb/"))
variant_to_resistance_json_fp = os.path.join(
data_dir, "variant_to_resistance_drug-jan-03-2019.json")
if panels.name in (TbPanel.CUSTOM, StaphPanel.CUSTOM):
if not args.custom_probe_set_path:
raise ValueError("Custom panel requires custom_probe_set_path")
if not os.path.exists(args.custom_probe_set_path):
raise FileNotFoundError(
f"Custom probe path {args.custom_probe_set_path} does not exist!"
)
panels.add_path(args.custom_probe_set_path)
if not os.path.exists(args.custom_variant_to_resistance_json):
raise FileNotFoundError(
("Custom variant to resistance json "
f"{args.custom_variant_to_resistance_json} does not exist!"))
variant_to_resistance_json_fp = args.custom_variant_to_resistance_json
if species is Species.STAPH:
Predictor = StaphPredictor
args.kmer = 15 # Forced
elif species is Species.TB:
hierarchy_json_file = "data/phylo/mtbc_hierarchy.json"
Predictor = TBPredictor
else:
raise ValueError(f"Unrecognised species {species}")
logger.info("Running AMR prediction with panels %s" %
", ".join(panels.paths))
version = dict()
version["mykrobe-predictor"] = predictor_version
version["mykrobe-atlas"] = atlas_version
# Get real paths for panels
panels = [
os.path.realpath(os.path.join(os.path.dirname(__file__), "..", f))
for f in panels.paths
]
if hierarchy_json_file is not None:
hierarchy_json_file = os.path.realpath(
os.path.join(os.path.dirname(__file__), "..", hierarchy_json_file))
# Run Cortex
cp = CoverageParser(
sample=args.sample,
panel_file_paths=panels,
seq=args.seq,
kmer=args.kmer,
force=args.force,
threads=1,
verbose=False,
tmp_dir=args.tmp,
skeleton_dir=args.skeleton_dir,
)
cp.run()
logger.debug("CoverageParser complete")
# Detect species
species_predictor = AMRSpeciesPredictor(
phylo_group_covgs=cp.covgs.get("complex",
cp.covgs.get("phylo_group", {})),
sub_complex_covgs=cp.covgs.get("sub-complex", {}),
species_covgs=cp.covgs["species"],
lineage_covgs=cp.covgs.get("sub-species", {}),
hierarchy_json_file=hierarchy_json_file,
)
phylogenetics = species_predictor.run()
# ## AMR prediction
depths = []
if species_predictor.is_saureus_present():
depths = [
species_predictor.out_json["phylogenetics"]["phylo_group"]
["Staphaureus"]["median_depth"]
]
elif species_predictor.is_mtbc_present():
depths = [
species_predictor.out_json["phylogenetics"]["phylo_group"]
["Mycobacterium_tuberculosis_complex"]["median_depth"]
]
# pprint (species_predictor.out_json["phylogenetics"]["species"])
# Genotype
q = args.quiet
args.quiet = True
variant_calls_dict = {}
sequence_calls_dict = {}
if args.force and not depths:
depths = [1]
gt = None
if depths or args.force:
gt = Genotyper(
sample=args.sample,
expected_depths=depths,
expected_error_rate=args.expected_error_rate,
variant_covgs=cp.variant_covgs,
gene_presence_covgs=cp.covgs["presence"],
base_json=base_json,
contamination_depths=[],
report_all_calls=True,
ignore_filtered=True,
filters=args.filters,
variant_confidence_threshold=args.min_variant_conf,
sequence_confidence_threshold=args.min_gene_conf,
model=args.model,
kmer_size=args.kmer,
min_proportion_expected_depth=args.min_proportion_expected_depth,
ploidy=args.ploidy,
)
gt.run()
(
kmer_count_error_rate,
incorrect_kmer_to_pc_cov,
) = gt.estimate_kmer_count_error_rate_and_incorrect_kmer_to_percent_cov(
)
logger.info("Estimated error rate for kmer count model: " +
str(round(100 * kmer_count_error_rate, 2)) + "%")
if args.guess_sequence_method and kmer_count_error_rate > 0.001:
logger.warning(
"Guess sequence method is on, and we've guessed ONT")
args.ont = True
if args.ont:
args.expected_error_rate = 0.15
args.ploidy = "haploid"
args.ignore_minor_calls = True
logger.warning("Setting ploidy to haploid")
logger.warning("Setting ignore_minor_calls to True")
logger.warning("Setting expected error rate to %s (--ont)" %
args.expected_error_rate)
args.model = "kmer_count"
# If the user didn't specify the conf_percent_cutoff, then set it
# depending on whether or not the --ont flag was used
if args.conf_percent_cutoff == -1:
args.conf_percent_cutoff = 90 if args.ont else 100
# conf_percent_cutoff == 100 means that we want to keep all variant calls,
# in which case there is no need to run the simulations
if args.conf_percent_cutoff < 100:
logger.info("Expected depth: " + str(depths[0]))
conf_thresholder = ConfThresholder(kmer_count_error_rate,
depths[0], args.kmer,
incorrect_kmer_to_pc_cov)
time_start = time.time()
conf_threshold = conf_thresholder.get_conf_threshold(
percent_to_keep=args.conf_percent_cutoff)
time_end = time.time()
time_to_sim = time_end - time_start
logger.info("Simulation time: " + str(time_to_sim))
logger.info("Confidence cutoff (using percent cutoff " +
str(args.conf_percent_cutoff) + "%): " +
str(conf_threshold))
gt = Genotyper(
sample=args.sample,
expected_depths=depths,
expected_error_rate=kmer_count_error_rate,
variant_covgs=cp.variant_covgs,
gene_presence_covgs=cp.covgs["presence"],
base_json=base_json,
contamination_depths=[],
report_all_calls=True,
ignore_filtered=True,
filters=args.filters,
variant_confidence_threshold=conf_threshold,
sequence_confidence_threshold=args.min_gene_conf,
model=args.model,
kmer_size=args.kmer,
min_proportion_expected_depth=args.
min_proportion_expected_depth,
ploidy=args.ploidy,
)
gt.run()
variant_calls_dict = gt.variant_calls_dict
sequence_calls_dict = gt.sequence_calls_dict
else:
depths = [cp.estimate_depth()]
args.quiet = q
mykrobe_predictor_susceptibility_result = MykrobePredictorSusceptibilityResult(
)
if gt is not None and (max(depths) > args.min_depth or args.force):
predictor = Predictor(
variant_calls=gt.variant_calls,
called_genes=gt.sequence_calls_dict,
base_json=base_json[args.sample],
depth_threshold=args.min_depth,
ignore_filtered=True,
ignore_minor_calls=args.ignore_minor_calls,
variant_to_resistance_json_fp=variant_to_resistance_json_fp,
)
mykrobe_predictor_susceptibility_result = predictor.run()
base_json[args.sample] = MykrobePredictorResult(
susceptibility=mykrobe_predictor_susceptibility_result,
phylogenetics=phylogenetics,
variant_calls=variant_calls_dict,
sequence_calls=sequence_calls_dict,
probe_sets=panels,
files=args.seq,
kmer=args.kmer,
version=version,
model=args.model,
).to_dict()
if not args.keep_tmp:
cp.remove_temporary_files()
outputs = {}
if args.output_format in ["csv", "json_and_csv"]:
outputs["csv"] = json_to_csv(base_json)
if args.output_format in ["json", "json_and_csv"]:
# Verbose json output requires --report_all_calls
if not args.report_all_calls:
del base_json[args.sample]["variant_calls"]
del base_json[args.sample]["sequence_calls"]
outputs["json"] = json.dumps(base_json, indent=4)
if len(outputs) == 0:
raise ValueError(
(f"Output format must be one of: csv,json,json_and_csv. Got "
f"'{args.output_format}'"))
for output_type, output in outputs.items():
# write to file is specified by user, otherwise send to stdout
if args.output:
if args.output_format == "json_and_csv":
outfile = args.output + "." + output_type
else:
outfile = args.output
with open(outfile, "w") as f:
f.write(output)
else:
print(output)
def run(parser, args):
base_json = {args.sample: {}}
args = parser.parse_args()
hierarchy_json_file = None
if args.panel is not None:
if args.panel == "bradley-2015":
TB_PANELS = [
"data/panels/tb-species-170421.fasta.gz",
"data/panels/tb-bradley-probe-set-feb-09-2017.fasta.gz"
]
elif args.panel == "walker-2015":
TB_PANELS = [
"data/panels/tb-species-170421.fasta.gz",
"data/panels/tb-walker-probe-set-feb-09-2017.fasta.gz"
]
elif args.panel == "custom":
if not args.custom_probe_set_path:
raise ValueError("Custom panel requires custom_probe_set_path")
TB_PANELS = [
args.custom_probe_set_path,
"data/panels/tb-species-170421.fasta.gz"
]
Predictor = None
if not args.species:
panels = TB_PANELS + GN_PANELS + STAPH_PANELS
elif args.species == "staph":
panels = STAPH_PANELS
Predictor = StaphPredictor
args.kmer = 15 # Forced
elif args.species == "tb":
panels = TB_PANELS
hierarchy_json_file = "data/phylo/mtbc_hierarchy.json"
Predictor = TBPredictor
logger.info("Running AMR prediction with panels %s" % ", ".join(panels))
version = {}
version["mykrobe-predictor"] = predictor_version
version["mykrobe-atlas"] = atlas_version
# Get real paths for panels
panels = [
os.path.realpath(os.path.join(os.path.dirname(__file__), "..", f))
for f in panels
]
if hierarchy_json_file is not None:
hierarchy_json_file = os.path.realpath(
os.path.join(os.path.dirname(__file__), "..", hierarchy_json_file))
if args.ont:
args.expected_error_rate = 0.15
logger.debug("Setting expected error rate to %s (--ont)" %
args.expected_error_rate)
args.filters = ["LOW_GT_CONF"]
args.model = "kmer_count"
# Run Cortex
cp = CoverageParser(sample=args.sample,
panel_file_paths=panels,
seq=args.seq,
kmer=args.kmer,
force=args.force,
threads=1,
verbose=False,
tmp_dir=args.tmp,
skeleton_dir=args.skeleton_dir,
mccortex31_path=args.mccortex31_path)
cp.run()
logger.debug('CoverageParser complete')
# Detect species
species_predictor = AMRSpeciesPredictor(
phylo_group_covgs=cp.covgs.get("complex",
cp.covgs.get("phylo_group", {})),
sub_complex_covgs=cp.covgs.get("sub-complex", {}),
species_covgs=cp.covgs["species"],
lineage_covgs=cp.covgs.get("sub-species", {}),
hierarchy_json_file=hierarchy_json_file)
phylogenetics = species_predictor.run()
# ## AMR prediction
depths = []
if species_predictor.is_saureus_present():
depths = [
species_predictor.out_json["phylogenetics"]["phylo_group"]
["Staphaureus"]["median_depth"]
]
elif species_predictor.is_mtbc_present():
depths = [
species_predictor.out_json["phylogenetics"]["phylo_group"]
["Mycobacterium_tuberculosis_complex"]["median_depth"]
]
# pprint (species_predictor.out_json["phylogenetics"]["species"])
# Genotype
q = args.quiet
args.quiet = True
variant_calls_dict = {}
sequence_calls_dict = {}
if args.force and not depths:
depths = [1]
gt = None
if depths or args.force:
gt = Genotyper(sample=args.sample,
expected_depths=depths,
expected_error_rate=args.expected_error_rate,
variant_covgs=cp.variant_covgs,
gene_presence_covgs=cp.covgs["presence"],
base_json=base_json,
contamination_depths=[],
report_all_calls=True,
ignore_filtered=True,
filters=args.filters,
variant_confidence_threshold=args.min_variant_conf,
sequence_confidence_threshold=args.min_gene_conf,
model=args.model)
gt.run()
variant_calls_dict = gt.variant_calls_dict
sequence_calls_dict = gt.sequence_calls_dict
else:
depths = [cp.estimate_depth()]
args.quiet = q
mykrobe_predictor_susceptibility_result = MykrobePredictorSusceptibilityResult(
)
if gt is not None and (max(depths) > args.min_depth or args.force):
predictor = Predictor(variant_calls=gt.variant_calls,
called_genes=gt.sequence_calls_dict,
base_json=base_json[args.sample],
depth_threshold=args.min_depth,
ignore_filtered=True,
ignore_minor_calls=args.ont,
variant_to_resistance_json_fp=args.
custom_variant_to_resistance_json)
mykrobe_predictor_susceptibility_result = predictor.run()
base_json[args.sample] = MykrobePredictorResult(
susceptibility=mykrobe_predictor_susceptibility_result,
phylogenetics=phylogenetics,
variant_calls=variant_calls_dict,
sequence_calls=sequence_calls_dict,
probe_sets=panels,
files=args.seq,
kmer=args.kmer,
version=version,
model=args.model).to_dict()
if not args.keep_tmp:
cp.remove_temporary_files()
# write to file is specified by user, otherwise send to stdout
if args.output:
with open(args.output, 'w') as outfile:
json.dump(base_json, outfile, indent=4)
else:
print(json.dumps(base_json, indent=4))