def parse_reference_output(self):
"""Reads and processes DIAMOND tabular output of the first DIAMOND
search.
Note: this function finds query sequences similar to reference
proteins. Since a query sequence may have more than one areas of
similarity (for instance, in fusion proteins of two subunits or
in multi-domain proteins), it will try to find as many such areas
as possible.
DIAMOND hits are filtered by two parameters: length of alignment
and amino acid identity %.
This function does not return anything. Instead, it populates
'reads' dictionary with AnnotatedRead objects.
"""
tsvfile = os.path.join(
self.options.get_project_dir(self.sample.sample_id),
self.sample.sample_id + '_' + self.end + '_' + self.options.ref_output_name
)
current_sequence_read_id = ''
hit_list = DiamondHitList(current_sequence_read_id)
# TODO: cleanup identity_cutoff = self.config.get_identity_cutoff(self.collection)
length_cutoff = self.config.get_length_cutoff(self.collection)
print('Length cutoff:', length_cutoff)
with open(tsvfile, 'r', newline='') as infile:
tsvin = csv.reader(infile, delimiter='\t')
for row in tsvin:
hit = DiamondHit()
(row[0], _) = parse_fastq_seqid(row[0])
hit.create_hit(row)
# filtering by length
if hit.length < length_cutoff:
continue # go to next hit
if hit.query_id != current_sequence_read_id:
# when new query ID reached, process collected hits,
# then start over with new query identifier
# filtering: remove overlapping hits
hit_list.filter_list(self.config.get_overlap_cutoff(self.collection))
# if any hits left, assign function to hits and populate reads dictionary
hit_list.annotate_hits(self.ref_data)
hit_list.filter_list_by_identity(self.ref_data)
if hit_list.hits_number != 0:
read = AnnotatedRead(current_sequence_read_id)
read.hit_list = hit_list
self.reads[current_sequence_read_id] = read
# start over
current_sequence_read_id = hit.query_id
hit_list = DiamondHitList(current_sequence_read_id)
hit_list.add_hit(hit)
# when EOF reached, process collected hits
hit_list.filter_list(self.config.get_overlap_cutoff(self.collection))
hit_list.annotate_hits(self.ref_data)
hit_list.filter_list_by_identity(self.ref_data)
if hit_list.hits_number != 0:
read = AnnotatedRead(current_sequence_read_id)
read.hit_list = hit_list
self.reads[current_sequence_read_id] = read
def parse_reference_output(self):
"""Reads and processes DIAMOND tabular output of the preselection
DIAMOND search.
Note: this function finds query sequences similar to reference
proteins. Since a query sequence may have more than one areas of
similarity (for instance, in fusion proteins of two subunits or
in multi-domain proteins), it will try to find as many such areas
as possible.
DIAMOND hits are filtered by two parameters: length of alignment
and amino acid identity %, which are defined in program config ini.
"""
tsvfile = os.path.join(self.assembly_dir,
'all_contigs_' + self.project.options.ref_output_name)
current_id = ''
hit_list = DiamondHitList(current_id)
identity_cutoff = self.project.config.get_identity_cutoff(
self.project.options.get_collection())
length_cutoff = self.project.config.get_length_cutoff(
self.project.options.get_collection())
print('Parse reference output: Identity cutoff: ',
identity_cutoff,
', Length cutoff: ',
length_cutoff)
with open(tsvfile, 'r', newline='') as infile:
tsvin = csv.reader(infile, delimiter='\t')
for row in tsvin:
hit = DiamondHit()
hit.create_hit(row)
# filtering by identity and length
if hit.identity < identity_cutoff:
continue # skip this line
if hit.length < length_cutoff:
continue # skip this line
if hit.query_id != current_id:
# filter list for overlapping hits
hit_list.filter_list(self.project.config.get_overlap_cutoff(
self.project.options.get_collection()))
if hit_list.hits_number != 0:
# annotate_hits
hit_list.annotate_hits(self.project.ref_data)
function_id, contig_id, _ = parse_gene_id(current_id)
self.assembly.contigs[function_id][contig_id].\
genes[current_id].hit_list = hit_list
current_id = hit.query_id
hit_list = DiamondHitList(current_id)
hit_list.add_hit(hit)
hit_list.filter_list(
self.project.config.get_overlap_cutoff(self.project.options.get_collection()))
if hit_list.hits_number != 0:
# annotate_hits
hit_list.annotate_hits(self.project.ref_data)
function_id, contig_id, _ = parse_gene_id(current_id)
self.assembly.contigs[function_id][contig_id].genes[current_id].hit_list = \
hit_list