def create_cluster_borders(anchor: str, clusters: List[ClusterPrediction],
record: Record) -> List[ClusterBorder]:
""" Create the predicted ClusterBorders """
if not clusters:
return []
borders = []
for i, cluster in enumerate(clusters):
# cluster borders returned by hmmdetect are based on CDS features
# in contrast, cluster borders returned by cassis are based on gene features
# --> hmmdetect derived clusters have exact loctions, like the CDSs have
# --> cassis derived clusters may have fuzzy locations, like the genes have
left_name = cluster.start.gene
right_name = cluster.end.gene
left = None
right = None
for gene in record.get_genes():
if gene.get_name() == left_name:
left = gene
if gene.get_name() == right_name:
right = gene
if left and right:
break
new_feature = SeqFeature(FeatureLocation(left.location.start,
right.location.end),
type="cluster_border")
new_feature.qualifiers = {
"aStool": ["cassis"],
"anchor": [anchor],
"abundance": [cluster.start.abundance + cluster.end.abundance],
"motif_score":
["{:.1e}".format(cluster.start.score + cluster.end.score)],
"gene_left": [cluster.start.gene],
"promoter_left": [cluster.start.promoter],
"abundance_left": [cluster.start.abundance],
"motif_left": [cluster.start.pairing_string],
"motif_score_left": ["{:.1e}".format(cluster.start.score)],
"gene_right": [cluster.end.gene],
"promoter_right": [cluster.end.promoter],
"abundance_right": [cluster.end.abundance],
"motif_right": [cluster.end.pairing_string],
"motif_score_right": ["{:.1e}".format(cluster.end.score)],
"genes": [cluster.genes],
"promoters": [cluster.promoters],
}
if i == 0:
new_feature.qualifiers["note"] = [
"best prediction (most abundant) for anchor gene {}".format(
anchor)
]
else:
new_feature.qualifiers["note"] = [
"alternative prediction ({}) for anchor gene {}".format(
i, anchor)
]
new_feature = ClusterBorder.from_biopython(new_feature)
borders.append(new_feature)
return borders
def detect(record: Record, options: ConfigType) -> CassisResults:
"""Use core genes (anchor genes) from hmmdetect as seeds to detect gene clusters"""
logging.info("Detecting gene clusters using CASSIS")
results = CassisResults(record.id)
# get core genes from hmmdetect --> necessary CASSIS input, aka "anchor genes"
anchor_gene_names = get_anchor_gene_names(record)
logging.info("Record has %d anchor genes", len(anchor_gene_names))
if not anchor_gene_names:
return results
# filter all genes in record for neighbouring genes with overlapping annotations
genes = record.get_genes()
logging.info("Record has %d features of type 'gene'", len(genes))
if not genes:
return results
candidate_genes, ignored_genes = ignore_overlapping(list(genes))
# compute promoter sequences/regions --> necessary for motif prediction (MEME and FIMO input)
try:
# why these values? see "Wolf et al (2015): CASSIS and SMIPS ..."
upstream_tss = 1000 # nucleotides upstream TSS
downstream_tss = 50 # nucleotides downstream TSS
promoters = get_promoters(record, candidate_genes, upstream_tss,
downstream_tss)
results.promoters = promoters
write_promoters_to_file(options.output_dir, record.name, promoters)
except DuplicatePromoterError:
logging.error(
"CASSIS discovered an error while working on the promoter sequences, skipping CASSIS analysis"
)
return results
if not promoters:
logging.debug(
"CASSIS found zero promoter regions, skipping CASSIS analysis")
return results
elif len(promoters) < 3:
logging.debug(
"Sequence %r yields less than 3 promoter regions, skipping CASSIS analysis",
record.name)
return results
elif len(promoters) < 40:
logging.debug("Sequence %r yields only %d promoter regions",
record.name, len(promoters))
logging.debug(
"Cluster detection on small sequences may lead to incomplete cluster predictions"
)
predicted_borders = []
cluster_predictions = {} # {anchor gene: cluster predictions}
for i, anchor in enumerate(anchor_gene_names):
logging.debug("Detecting cluster around anchor gene %r (%d of %d)",
anchor, i + 1, len(anchor_gene_names))
# get cluster predictions sorted by border abundance
# (most abundant --> "best" prediction)
predictions = get_predictions_for_anchor(anchor, promoters, record,
ignored_genes, options)
if predictions:
cluster_predictions[anchor] = predictions
predicted_borders.extend(
create_cluster_borders(anchor, predictions, record))
logging.debug("Cleaning up MEME and FIMO output directories")
cleanup_outdir(anchor_gene_names, cluster_predictions, options)
results.borders = predicted_borders
return results
