def find_ks_domains(fasta: str) -> Dict[str, List[HMMResult]]:
""" Analyse KS domains & PKS/NRPS protein domain composition to detect NRPS/PKS types
Arguments:
fasta: a group of features in fasta format
Returns:
a dictionary mapping feature name to a list of KS domain results for that feature
"""
opts = ["--cut_tc"]
ks_file = path.get_full_path(__file__, "data", "ksdomains.hmm")
lengths = utils.get_hmm_lengths(ks_file)
domains = subprocessing.run_hmmscan(ks_file, fasta, opts)
refine_hmmscan_results(domains, lengths, neighbour_mode=True)
raise NotImplementedError("no return value used from refine_hmmscan_results")
def scan_for_functions(cds_features: List[CDSFeature], database: str,
hmmscan_opts: Optional[List[str]] = None) -> Dict[str, HMMResult]:
""" Finds possible classifications for the provided genes.
Arguments:
cds_features: a list of CDSFeatures to classify
database: the path to the database to check
hmmscan_opts: a list of extra options to provide to hmmscan
Returns:
a dictionary mapping CDS name to a list of HMMResult instances of
classifications
"""
search_fasta = fasta.get_fasta_from_features(cds_features)
results = subprocessing.run_hmmscan(database, search_fasta, hmmscan_opts)
hmm_lengths = utils.get_hmm_lengths(database)
hmm_results = refine_hmmscan_results(results, hmm_lengths)
best_hits = {} # type: Dict[str, HMMResult]
for cds in cds_features:
cds_name = cds.get_name()
hits = hmm_results.get(cds_name)
if not hits:
continue
best_hits[cds_name] = hits[0]
return best_hits
def test_combined(self):
results = refinement.refine_hmmscan_results(self.results,
self.hmm_lengths)
assert len(results) == 1
assert len(results[self.gene_id]) == 1
best = results[self.gene_id][0]
assert best.hit_id == "SMCOG1048:sensor_histidine_kinase"
assert best.evalue == 3.6e-13
assert best.bitscore == 43.5
assert best.query_start == 91
assert best.query_end == 390
def find_ab_motifs(fasta: str) -> Dict[str, List[HMMResult]]:
""" Analyse for abMotifs
Arguments:
fasta: a group of features in fasta format
Returns:
a dictionary mapping feature name to a list of motif results for that feature
"""
opts = ["-E", "0.25"]
motif_file = path.get_full_path(__file__, "data", "abmotifs.hmm")
abmotif_results = subprocessing.run_hmmscan(motif_file, fasta, opts)
lengths = utils.get_hmm_lengths(motif_file)
return refine_hmmscan_results(abmotif_results, lengths, neighbour_mode=True)
def run_t2pks_hmmscan(cluster: Cluster) -> Dict[str, List[HMMResult]]:
""" Runs hmmscan for type II PKS proteins on coding sequences in cluster
Arguments:
cluster: Cluster on which the type II PKS hmmscan shall be run
Returns:
a dictionary of key: cds and value: list of HMMResults, for hmmscan results of the cluster
"""
cluster_fasta = fasta.get_fasta_from_features(cluster.cds_children)
hmm_file = path.get_full_path(__file__, "data", "t2pks.hmm")
hmm_results = subprocessing.run_hmmscan(hmm_file,
cluster_fasta,
opts=['--cut_tc'])
hmm_lengths = get_hmm_lengths(hmm_file)
return refine_hmmscan_results(hmm_results, hmm_lengths)
def find_domains(fasta: str, record: Record) -> Dict[str, List[HMMResult]]:
""" Analyse for C/A/PCP/E/KS/AT/ATd/DH/KR/ER/ACP/TE/TD/COM/Docking/MT/CAL domains
Arguments:
fasta: a group of features in fasta format
record: the Record that contains all the features
Returns:
a dictionary mapping feature name to a list of domain results for that feature
"""
opts = ["--cut_tc"]
nrpspks_file = path.get_full_path(__file__, "data", "nrpspksdomains.hmm")
nrpspksdomain_results = subprocessing.run_hmmscan(nrpspks_file, fasta, opts)
lengths = utils.get_hmm_lengths(nrpspks_file)
domains = refine_hmmscan_results(nrpspksdomain_results, lengths, neighbour_mode=True)
return filter_nonterminal_docking_domains(record, domains)
def run_t2pks_hmmscan(
cds_features: Iterable[CDSFeature]) -> Dict[str, List[HMMResult]]:
""" Runs hmmscan for type II PKS proteins on the given CDSFeatures
Arguments:
cluster: Protocluster on which to run the type II PKS hmmscan
Returns:
a dictionary of key: cds and value: list of HMMResults, for hmmscan results of the cluster
"""
cluster_fasta = fasta.get_fasta_from_features(cds_features)
hmm_file = path.get_full_path(__file__, "data", "t2pks.hmm")
hmm_results = subprocessing.run_hmmscan(hmm_file,
cluster_fasta,
opts=['--cut_tc'])
hmm_lengths = get_hmm_lengths(hmm_file)
return refine_hmmscan_results(hmm_results, hmm_lengths)
def classify_genes(
cds_features: List[CDSFeature]) -> Dict[str, List[HMMResult]]:
""" Finds possible classifications for the provided genes.
Arguments:
cds_features: a list of CDSFeatures to classify
Returns:
a dictionary mapping CDS name to a list of HMMResult instances of
classifications
"""
smcogs_fasta = fasta.get_fasta_from_features(cds_features)
smcogs_opts = ["-E", "1E-6"]
hmm_file = path.get_full_path(__file__, "data", "smcogs.hmm")
smcogs_results = subprocessing.run_hmmscan(hmm_file, smcogs_fasta,
smcogs_opts)
hmm_lengths = utils.get_hmm_lengths(hmm_file)
return refine_hmmscan_results(smcogs_results, hmm_lengths)
def run_starter_unit_blastp(
cluster: Cluster,
cds_hmm_hits: Dict[str,
List[HMMResult]]) -> Dict[str, List[HMMResult]]:
""" Runs blastp on starter unit coding sequences in given cluster
Arguments:
cluster: Cluster on which the blastp shall be run
cds_hmm_hits: HMMResults by cds from type II PKS hmmscan
Returns:
None if no starter unit cds are present otherwise a dictionary of key: cds and value: list of HMMresults, for blastp results of the cluster
"""
starter_unit_cds = {}
for cds, hmm_hits in cds_hmm_hits.items():
starter_unit_hit_ids = [
hit.hit_id for hit in hmm_hits
if hit.hit_id in ['KSIII', 'AT', 'AMID', 'LIG']
]
if starter_unit_hit_ids:
starter_unit_cds[cluster.parent_record.get_cds_by_name(
cds)] = starter_unit_hit_ids
if starter_unit_cds:
blastp_results = []
blastp_fasta_files = set()
for cds, starter_unit_hit_ids in starter_unit_cds.items():
query_sequence = fasta.get_fasta_from_features([cds])
for hit_id in starter_unit_hit_ids:
blast_database = path.get_full_path(__file__, 'data', hit_id)
blastp_results.extend(
subprocessing.run_blastp(blast_database, query_sequence))
blastp_fasta_files.add(
path.get_full_path(__file__, 'data', hit_id + '.fasta'))
fasta_lengths = {}
for fasta_file in blastp_fasta_files:
fasta_lengths.update(get_fasta_lengths(fasta_file))
return refine_hmmscan_results(blastp_results, fasta_lengths)
return {}
def run_starter_unit_blastp(
cds_hmm_hits: Dict[CDSFeature,
List[HMMResult]]) -> Dict[str, List[HMMResult]]:
""" Runs blastp on starter unit coding sequences in given cluster
Arguments:
cds_hmm_hits: HMMResults by cds from type II PKS hmmscan
Returns:
a dictionary mapping CDS name to a list of HMMresults
"""
blastp_results = []
blastp_fasta_files = set()
for cds, hmm_hits in cds_hmm_hits.items():
query_sequence = fasta.get_fasta_from_features([cds])
for hit in hmm_hits:
if hit.hit_id not in ['KSIII', 'AT', 'AMID', 'LIG']:
continue
blast_database = path.get_full_path(__file__, 'data', hit.hit_id)
blastp_results.extend(
subprocessing.run_blastp(blast_database, query_sequence))
blastp_fasta_files.add(
path.get_full_path(__file__, 'data', hit.hit_id + '.fasta'))
if not blastp_results:
return {}
fasta_lengths = {}
for fasta_file in blastp_fasta_files:
fasta_lengths.update(get_fasta_lengths(fasta_file))
results = refine_hmmscan_results(blastp_results, fasta_lengths)
for hits in results.values():
for i, hit in enumerate(hits):
if not hit.hit_id.endswith("-CoA"):
hits[i] = HMMResult(hit.hit_id + "-CoA", hit.query_start,
hit.query_end, hit.evalue, hit.bitscore)
return results
