def run_both_dirs(self, expected, seq):
def reverse_location(location, length):
return location._flip(length)
record = DummyRecord(seq=seq)
assert expected == [feat.location for feat in find_all_orfs(record)]
record.seq = record.seq.reverse_complement()
expected = [reverse_location(loc, len(seq)) for loc in expected[::-1]]
assert expected == [feat.location for feat in find_all_orfs(record)]
def test_multi_start_single_stop(self):
seq = "ATGNNNATG" + "N" * 60 + "TAG"
expected = [
FeatureLocation(ExactPosition(0), ExactPosition(72), strand=1)
]
assert expected == [
feat.location for feat in find_all_orfs(DummyRecord(seq=seq))
]
seq = str(DummyRecord(seq=seq).seq.reverse_complement())
expected[0].strand = -1
assert expected == [
feat.location for feat in find_all_orfs(DummyRecord(seq=seq))
]
def specific_analysis(record: secmet.Record) -> ThioResults:
""" Runs thiopeptide prediction over all cluster features and any extra ORFs
that are found not overlapping with existing features
"""
results = ThioResults(record.id)
for cluster in record.get_protoclusters():
if cluster.product != "thiopeptide":
continue
# Find candidate ORFs that are not yet annotated
new_orfs = all_orfs.find_all_orfs(record, cluster)
thio_features = list(cluster.cds_children) + new_orfs
domains = get_detected_domains(cluster)
thio_type = predict_type_from_cluster(domains)
amidation = predict_amidation(domains)
for thio_feature in thio_features:
result_vec = run_thiopred(thio_feature, thio_type, domains)
if result_vec is None:
continue
if amidation:
result_vec.amidation = True
new_feature = result_vec_to_feature(thio_feature, result_vec)
if thio_feature in new_orfs:
results.cds_features[cluster.get_protocluster_number()].append(thio_feature)
results.motifs.append(new_feature)
results.clusters_with_motifs.add(cluster)
logging.debug("Thiopeptides marked %d motifs", len(results.motifs))
return results
def specific_analysis(record: Record) -> LassoResults:
""" Runs the full lassopeptide analysis over the given record
Arguments:
record: the Record instance to analyse
Returns:
A populated LassoResults object
"""
results = LassoResults(record.id)
motif_count = 0
for cluster in record.get_clusters():
if cluster.product != 'lassopeptide':
continue
precursor_candidates = list(cluster.cds_children)
# Find candidate ORFs that are not yet annotated
extra_orfs = all_orfs.find_all_orfs(record, cluster)
precursor_candidates.extend(extra_orfs)
for candidate in precursor_candidates:
motif = run_lassopred(record, cluster, candidate)
if motif is None:
continue
results.motifs_by_locus[candidate.get_name()].append(motif)
motif_count += 1
results.clusters[cluster.get_cluster_number()].add(candidate.get_name())
# track new CDSFeatures if found with all_orfs
if candidate.region is None:
results.new_cds_features.add(candidate)
logging.debug("Lassopeptide module marked %d motifs", motif_count)
return results
def specific_analysis(record: secmet.Record,
options: ConfigType) -> SactiResults:
""" Analyse each sactipeptide cluster and find precursors within it.
If an unannotated ORF would contain the precursor, it will be annotated.
Arguments:
record: the Record to analyse
Returns:
a SactiResults instance holding all found precursors and new ORFs
"""
results = SactiResults(record.id)
new_feature_hits = 0
motif_count = 0
counter = 0
for cluster in record.get_protoclusters():
if cluster.product != 'sactipeptide':
continue
# Find candidate ORFs that are not yet annotated
new_orfs = all_orfs.find_all_orfs(record, cluster)
hmm_results = run_non_biosynthetic_phmms(
fasta.get_fasta_from_features(new_orfs))
annotate_orfs(new_orfs, hmm_results)
# Get all CDS features to evaluate for RiPP-likeness
candidates = list(cluster.cds_children) + new_orfs
domains = get_detected_domains(cluster)
# Evaluate each candidate precursor peptide
for candidate in candidates:
motif = run_sactipred(cluster, candidate, domains)
if motif is None:
continue
results.motifs_by_locus[candidate.get_name()].append(motif)
motif_count += 1
results.clusters[cluster.get_protocluster_number()].add(
candidate.get_name())
# track new CDSFeatures if found with all_orfs
if candidate.region is None:
results.new_cds_features.add(candidate)
new_feature_hits += 1
# Analyze the cluster with RREfinder
counter += 1
name = '%s_%s_%s' % (record.id, cluster.product, counter)
RRE_main(cluster, results, name, options)
if not motif_count:
logging.debug("Found no sactipeptide motifs")
else:
verb = "is" if new_feature_hits == 1 else "are"
logging.debug(
"Found %d sactipeptide motif(s) in %d feature(s), %d of which %s new",
motif_count, len(results.motifs_by_locus), new_feature_hits, verb)
return results
def run_specific_analysis(record: Record,
options: ConfigType) -> LanthiResults:
""" Runs the full lanthipeptide analysis over the given record
Arguments:
record: the Record instance to analyse
Returns:
A populated LanthiResults object
"""
results = LanthiResults(record.id)
counter = 0
for cluster in record.get_protoclusters():
if cluster.product != 'lanthipeptide':
continue
# find core biosynthetic enzyme locations
core_domain_names = {
'Lant_dehydr_N', 'Lant_dehydr_C', 'DUF4135', 'Pkinase'
}
core_genes = []
for gene in cluster.cds_children:
if not gene.sec_met:
continue
# We seem to hit Lant_dehydr_C on some O-Methyltranferases that also hit PCMT
if 'PCMT' in gene.sec_met.domain_ids:
continue
if core_domain_names.intersection(set(gene.sec_met.domain_ids)):
core_genes.append(gene)
precursor_candidates = find_lan_a_features(cluster)
# Find candidate ORFs that are not yet annotated
extra_orfs = all_orfs.find_all_orfs(record, cluster)
for orf in extra_orfs:
if len(orf.translation) < 80:
precursor_candidates.append(orf)
for gene in core_genes:
neighbours = find_neighbours_in_range(gene, precursor_candidates)
if not neighbours:
continue
run_lanthi_on_genes(record, gene, cluster, neighbours, results)
# Analyze the cluster with RREfinder
counter += 1
name = '%s_%s_%s' % (record.id, cluster.product, counter)
RRE_main(cluster, results, name, options)
logging.debug("Lanthipeptide module marked %d motifs",
sum(map(len, results.motifs_by_locus)))
return results
def run_specific_analysis(record: Record) -> LanthiResults:
""" Runs the full lanthipeptide analysis over the given record
Arguments:
record: the Record instance to analyse
Returns:
A populated LanthiResults object
"""
results = LanthiResults(record.id)
for cluster in record.get_clusters():
if 'lanthipeptide' not in cluster.products:
continue
# find core biosynthetic enzyme locations
core_domain_names = {
'Lant_dehyd_N', 'Lant_dehyd_C', 'DUF4135', 'Pkinase'
}
core_genes = []
for gene in cluster.cds_children:
if not gene.sec_met:
continue
if core_domain_names.intersection(set(gene.sec_met.domain_ids)):
core_genes.append(gene)
precursor_candidates = find_lan_a_features(cluster)
# Find candidate ORFs that are not yet annotated
extra_orfs = all_orfs.find_all_orfs(record, cluster)
for orf in extra_orfs:
if len(orf.translation) < 80:
precursor_candidates.append(orf)
for gene in core_genes:
neighbours = find_neighbours_in_range(gene, precursor_candidates)
run_lanthi_on_genes(record, gene, neighbours, results)
logging.debug("Lanthipeptide module marked %d motifs",
sum(map(len, results.motifs_by_locus)))
return results
