def get_fancy_results_dict(self, max_per_query = 10, defline_white_space_mask = None):
b6 = b6lib.B6Source(self.output)
input_fasta = u.SequenceSource(self.input)
target_db = u.SequenceSource(self.target)
query_counts = {}
fancy_results_dict = {}
while next(b6):
if b6.entry.query_id not in query_counts:
query_counts[b6.entry.query_id] = 1
if query_counts[b6.entry.query_id] - 1 == max_per_query:
continue
else:
query_counts[b6.entry.query_id] += 1
if b6.entry.query_id not in fancy_results_dict:
fancy_results_dict[b6.entry.query_id] = []
query_seq = input_fasta.get_seq_by_read_id(b6.entry.query_id).replace('-', '')
target_seq = target_db.get_seq_by_read_id(b6.entry.subject_id)
if defline_white_space_mask:
b6.entry = remove_white_space_mask_from_B6_entry(b6.entry, defline_white_space_mask)
# parts that were aligned during the search are being aligned to each other to generate
# hsp_match data to include into results
query_aligned, target_aligned = nw_align(query_seq[int(b6.entry.q_start) - 1:int(b6.entry.q_end)],\
target_seq[int(b6.entry.s_start) - 1:int(b6.entry.s_end)])
query_aligned, target_aligned = query_aligned.upper(), target_aligned.upper()
coverage = (b6.entry.q_end - (b6.entry.q_start - 1)) * 100.0 / b6.entry.q_len
hsp_match = ''.join(['|' if query_aligned[i] == target_aligned[i] else ' ' for i in range(0, len(query_aligned))])
entry = copy.deepcopy(b6.entry)
entry.coverage = coverage
entry.hsp_query = query_aligned
entry.hsp_subject = target_aligned
entry.hsp_match = hsp_match
entry = remove_white_space_mask_from_B6_entry(entry)
fancy_results_dict[entry.query_id].append(entry)
return fancy_results_dict
def get_results_dict(self, mismatches = None, gaps = None, min_identity = None, max_identity = None, penalty_for_terminal_gaps = True):
results_dict = {}
b6 = b6lib.B6Source(self.output)
ids_with_hits = set()
while b6.next():
if b6.entry.query_id == b6.entry.subject_id:
continue
if penalty_for_terminal_gaps:
# following correction is to take secret gaps into consideration.
# because we are working with reads that are supposed to be almost the
# same length, we want query and target to be aligned 100%. sometimes it
# is not the case, and mismatches are being calculated by the aligned
# part of query or target. for instance if query is this:
#
# ATCGATCG
#
# and target is this:
#
# TATCGATCG
#
# the alignment discards the T at the beginning and gives 0 mismatches.
# here we introduce those gaps back:
additional_gaps = 0
if b6.entry.q_start != 1 or b6.entry.s_start != 1:
additional_gaps += (b6.entry.q_start - 1) if b6.entry.q_start > b6.entry.s_start else (b6.entry.s_start - 1)
if additional_gaps != b6.entry.q_len or b6.entry.s_end != b6.entry.s_len:
additional_gaps += (b6.entry.q_len - b6.entry.q_end) if (b6.entry.q_len - b6.entry.q_end) > (b6.entry.s_len - b6.entry.s_end) else (b6.entry.s_len - b6.entry.s_end)
identity_penalty = additional_gaps * 100.0 / (b6.entry.q_len + additional_gaps)
if identity_penalty:
b6.entry.gaps += additional_gaps
b6.entry.identity -= identity_penalty
# done correcting the hit. carry on.
if max_identity is not None:
if round(b6.entry.identity, 1) >= round(max_identity, 1):
continue
if min_identity is not None:
if round(b6.entry.identity, 1) < round(min_identity, 1):
continue
if mismatches is not None:
if b6.entry.mismatches != mismatches:
continue
if gaps is not None:
if b6.entry.gaps != gaps:
continue
# if it made here, we are interested in this one, after all.
if b6.entry.query_id not in ids_with_hits:
results_dict[b6.entry.query_id] = set()
ids_with_hits.add(b6.entry.query_id)
results_dict[b6.entry.query_id].add(b6.entry.subject_id)
b6.close()
return results_dict