def annotate_blast_hits(blast_results_xml, gb_records, annotation_type, qualifiers_dict=None):
from Bio.Blast import NCBIXML
from Bio.SeqFeature import SeqFeature, FeatureLocation
from krpy import krbioio
blast_results_handle = open(blast_results_xml)
blast_records = NCBIXML.parse(blast_results_handle)
gb_records_dict = gb_records
if isinstance(gb_records_dict, basestring):
gb_records_dict = krbioio.read_sequence_file(
file_path=gb_records,
file_format='gb',
ret_type='dict',
key='gi')
for blast_record in blast_records:
for alignment in blast_record.alignments:
# alignment_id = (alignment.title.split("|")[1]).split(" ")[0]
alignment_id = alignment.title.split(' ')[1]
if gb_records_dict.has_key(str(alignment_id)):
gb_record = gb_records_dict[str(alignment_id)]
for hsp in alignment.hsps:
f_start = hsp.sbjct_start-1
f_end = hsp.sbjct_end
f_strand = 1
if hsp.frame[1] < 0:
f_strand = -1
default_qualifiers = {'query_start':hsp.query_start, 'query_end':hsp.query_end, 'label':blast_record.query}
if qualifiers_dict:
default_qualifiers = dict(default_qualifiers.items() + qualifiers_dict.items())
alignment_feature = SeqFeature(FeatureLocation(f_start, f_end), strand=f_strand, type=annotation_type, qualifiers=default_qualifiers)
gb_record.features.append(alignment_feature)
gb_records_dict[str(alignment_id)] = gb_record
blast_results_handle.close()
return gb_records_dict
input_file = None
output_file = None
min_taxa = 0
args = parser.parse_args()
if args.input_file:
input_file = args.input_file
if args.output_file:
output_file = args.output_file
if args.min_taxa:
min_taxa = args.min_taxa
if input_file and output_file and min_taxa:
records = krbioio.read_sequence_file(input_file, 'gb', ret_type='list')
# print('Found', len(records), 'in', input_file)
excluded_qualifiers = ['translation', 'db_xref', 'exception',
'rpt_unit_seq', 'gene_synonym', 'rpt_type', 'satellite', 'transl_table',
'replace', 'rpt_unit_range', 'protein_id', 'codon_recognized',
'EC_number', 'function', 'estimated_length', 'mobile_element_type',
'codon_start', 'transl_except', 'number', 'standard_name', 'allele',
'inference']
feature_dict = dict()
taxa_dict = dict()
for record in records:
txid = krncbi.get_ncbi_tax_id_for_record(record)
def merge_record_features(gb_records, annotation_type_to_merge, annotation_type_merged, merged_label, qualifiers_dict=None):
from Bio.SeqFeature import SeqFeature, FeatureLocation
from krpy import krbioio
from krpy import krother
gb_records_dict = gb_records
if isinstance(gb_records_dict, basestring):
gb_records_dict = krbioio.read_sequence_file(
file_path=gb_records,
file_format='gb',
ret_type='dict',
key='gi')
for gb_record in gb_records_dict.values():
merged_features = []
features = []
for feature in gb_record.features:
if feature.type == annotation_type_to_merge:
features.append(feature)
features.sort(key=lambda x: x.location.start, reverse=False)
prev_range = [-1,-1]
start = -1
# q_start = -1
# q_end = -1
strand = None
for feature in features:
if prev_range[0] == -1:
start = int(feature.location.nofuzzy_start)
prev_range = [int(feature.location.nofuzzy_start),int(feature.location.nofuzzy_end)]
# q_start_temp = feature.qualifiers['query_start']
# q_end_temp = feature.qualifiers['query_end']
# q_start = None
# if not isinstance(q_start_temp, int):
# q_start = int(feature.qualifiers['query_start'][0])
# else:
# q_start = int(q_start_temp)
# q_end = None
# if not isinstance(q_end_temp, int):
# q_end = int(feature.qualifiers['query_start'][0])
# else:
# q_end = int(q_end_temp)
if feature.strand:
strand = int(feature.strand)
if not krother.in_range(int(feature.location.nofuzzy_start),prev_range[0],prev_range[1],100):
merged_features.append([start, prev_range[1], strand])
# q_start_temp = feature.qualifiers['query_start']
# q_end_temp = feature.qualifiers['query_end']
# q_start = None
# if not isinstance(q_start_temp, int):
# q_start = int(feature.qualifiers['query_start'][0])
# else:
# q_start = int(q_start_temp)
# q_end = None
# if not isinstance(q_end_temp, int):
# q_end = int(feature.qualifiers['query_start'][0])
# else:
# q_end = int(q_end_temp)
start = int(feature.location.nofuzzy_start)
prev_range = [int(feature.location.nofuzzy_start),max(prev_range[1],int(feature.location.nofuzzy_end))]
# q_start_temp = feature.qualifiers['query_start']
# q_end_temp = feature.qualifiers['query_end']
# q_start = None
# if not isinstance(q_start_temp, int):
# q_start = int(feature.qualifiers['query_start'][0])
# else:
# q_start = int(q_start_temp)
# q_end = None
# if not isinstance(q_end_temp, int):
# q_end = int(feature.qualifiers['query_start'][0])
# else:
# q_end = int(q_end_temp)
if feature.strand:
strand = int(feature.strand)
if len(features) > 0:
merged_features.append([start, prev_range[1], strand])
for merged_feature in merged_features:
# default_qualifiers = {'query_start':merged_feature[2], 'query_end':merged_feature[3], 'label':merged_label}
# if qualifiers_dict:
# default_qualifiers = dict(default_qualifiers.items() + qualifiers_dict.items())
# alignment_feature = SeqFeature(FeatureLocation(merged_feature[0], merged_feature[1]), strand=merged_feature[2], type=annotation_type_merged)
alignment_feature = SeqFeature(FeatureLocation(merged_feature[0], merged_feature[1]), strand=1, type=annotation_type_merged)
gb_record.features.append(alignment_feature)
gb_records_dict[gb_record.annotations['gi']] = gb_record
return gb_records_dict
