def annotate_consensus_contig(reference_path,
min_score_ratios,
min_word_sizes,
contig_name,
clonotype_name,
seq,
quals,
read_count=None,
umi_count=None,
info_dict=None,
primers=None,
use_features=None):
""" Given a sequence and some auxiliary info, return a populated AnnotatedContig """
contig = vdj_annot.AnnotatedContig(contig_name,
seq,
quals=quals,
clonotype=clonotype_name,
read_count=read_count,
umi_count=umi_count,
info_dict=info_dict,
filtered=True,
high_confidence=True)
res = vdj_annot.setup_feature_aligners(reference_path,
min_score_ratios,
min_word_sizes,
use_features=use_features)
feature_types, feature_aligners, feature_filters = res
contig.annotations = contig.annotate_features(feature_types,
feature_aligners,
feature_filters)
if primers:
primer_aligner, primer_filter = vdj_annot.setup_primer_aligner(
primers, VDJ_ANNOTATION_MIN_SCORE_RATIO)
contig.primer_annotations = contig.annotate_features_by_group(
primer_aligner, alignment_filter=primer_filter)
contig.unannotated_intervals = contig.get_unannotated_intervals()
contig.annotate_cdr3()
return contig
def main(args, outs):
if args.vdj_reference_path is None:
outs.chunked_annotations = None
return
chunk_contigs = []
barcodes_in_chunk = set(args.barcodes)
# Set of barcodes that were called as cells
if args.cell_barcodes:
cell_barcodes_set = set(vdj_utils.load_cell_barcodes_json(args.cell_barcodes))
else:
cell_barcodes_set = set()
# Setup feature reference sequences
res = vdj_annot.setup_feature_aligners(args.vdj_reference_path,
args.min_score_ratios,
args.min_word_sizes)
feature_types, feature_aligners, feature_filters = res
# Setup primer reference sequnces
if args.primers:
primer_aligner, primer_filter = vdj_annot.setup_primer_aligner(args.primers,
vdj_constants.VDJ_ANNOTATION_MIN_SCORE_RATIO)
read_counts = {}
umi_counts = {}
if args.contig_summary and os.path.isfile(args.contig_summary):
contig_summary = pd.read_csv(args.contig_summary, header=0, index_col=None, sep='\t')
for _, row in contig_summary.iterrows():
read_counts[row.contig_name] = int(row.num_reads)
umi_counts[row.contig_name] = int(row.num_umis)
if args.filter_summary:
try:
filter_summary = vdj_utils.load_contig_summary_table(open(args.filter_summary))
except EmptyDataError:
filter_summary = None
else:
filter_summary = None
if not args.contigs_fastq is None:
fq_iter = tk_fasta.read_generator_fastq(open(args.contigs_fastq), paired_end=False)
for header, contig_sequence in cr_utils.get_fasta_iter(open(args.contigs)):
if args.contigs_fastq is None:
contig_quals = None
else:
header_fq, contig_sequence_fq, contig_quals = fq_iter.next()
assert(contig_sequence_fq == contig_sequence)
assert(header_fq == header)
barcode = vdj_utils.get_barcode_from_contig_name(header)
contig_name = header.split(' ')[0]
# Only annotate barcodes assigned to this chunk and contigs with enough read support
if barcode in barcodes_in_chunk:
if filter_summary is not None:
filtered = vdj_utils.is_contig_filtered(filter_summary, contig_name)
else:
filtered = True
contig = vdj_annot.AnnotatedContig(contig_name,
contig_sequence,
quals=contig_quals,
barcode=barcode,
is_cell=barcode in cell_barcodes_set,
filtered=filtered,
read_count=read_counts.get(contig_name),
umi_count=umi_counts.get(contig_name),
)
contig.annotations = contig.annotate_features(feature_types,
feature_aligners,
feature_filters)
if args.primers:
contig.primer_annotations = contig.annotate_features_by_group(primer_aligner,
alignment_filter=primer_filter)
contig.annotate_cdr3()
chunk_contigs.append(contig)
cPickle.dump(chunk_contigs, open(outs.chunked_annotations, 'wb'),
protocol=cPickle.HIGHEST_PROTOCOL)