def run(self):
features = []
for feature in iter_domains(open(self.params['location'])):
feature.sequence_index = get_id_index(self.target.msa.ids, feature.sequence_id)
if feature.sequence_index is None:
continue
feature.mapping = map_region_to_msa(feature.region, self.target.msa.msa_positions[feature.sequence_index])
if feature.mapping is None:
continue
features.append(feature)
self.target.add_features(features)
def from_pfam_match_etree(cls, match_element, msa, sequence_index, offset, sequence_id=None):
loc = match_element.find('location')
start = int(loc.attrib['start']) - 1
region = Region(start, int(loc.attrib['end']) - start)
mapping = map_region_to_msa(region, msa.msa_positions[sequence_index], offset)
if not mapping:
return None
hmm_start = int(loc.attrib['hmm_start']) - 1
return cls(sequence_index=sequence_index,
sequence_id=sequence_id,
source=match_element.attrib['type'],
name=match_element.attrib['id'],
region=region,
mapping=mapping,
accession=match_element.attrib['accession'],
hmm_region=Region(hmm_start, int(loc.attrib['hmm_end']) - hmm_start),
score=float(loc.attrib['bitscore']),
evalue=float(loc.attrib['evalue']))
def run(self):
f = open(self.params['location'])
prediction = DisopredPrediction.from_file(f)
f.close()
if not prediction:
return
sequence_index = self.coord.sequence
offset = prediction.sequence.find(self.target.unaligned[sequence_index])
if offset < 0:
return
msa_positions = self.target.msa_positions[sequence_index]
sequence_id = self.target.ids[sequence_index]
features = []
for region in prediction.regions:
mapping = map_region_to_msa(region, msa_positions, offset)
if not mapping:
continue
features.append(SequenceFeature(sequence_index, sequence_id, 'disopred', 'natively disordered', region, mapping))
self.target.features.add_features(features)
