def calc_reconstruction(self, exons, blocks, size=None): """Calcuates reconstruction given list of alignment blocks and exons""" bases_reconstructed = intspan.intersect(exons, blocks) if size is None: size = intspan.cardinality_multi(exons) fraction_reconstructed = float(bases_reconstructed)/float(size) return bases_reconstructed, fraction_reconstructed
def process_single_model(self, matches, gene2txt, contig_coverage=None):
model = matches[0].model
matches_by_transcript, matches_by_gene, gene2transcript = self.group_matches(matches)
results = []
for gene in gene2transcript.keys():
matches_gene = matches_by_gene[gene]
all_txts = self.get_all_transcripts(gene, model, gene2txt)
if not all_txts:
continue
collapsed_exons = self.collapse_txts(all_txts)
collapsed_blocks = self.collapse_align_blocks([match.align for match in matches_gene])
exons_size = intspan.cardinality_multi(collapsed_exons)
bases_reconstructed, fraction_reconstructed = self.calc_reconstruction(collapsed_exons, collapsed_blocks, size=exons_size)
contigs = ','.join([match.align.query for match in matches_gene])
if contig_coverage:
nreads, nbases = self.calc_coverage([match.align.query for match in matches_gene], contig_coverage)
else:
nreads, nbases = '-', '-'
results.append({'feature': 'gene',
'model': model,
'gene': gene,
'strand': all_txts[0].strand,
'coord': '%s:%s-%s' % (all_txts[0].chrom, collapsed_exons[0][0], collapsed_exons[-1][1]),
'feature_size': exons_size,
'bases_reconstructed': bases_reconstructed,
'reconstruction': "%.3f" % (fraction_reconstructed),
'contigs': contigs,
'num_contigs': len(matches_gene),
'align_blocks': self.blocks_as_string(collapsed_blocks),
'exons': self.blocks_as_string(collapsed_exons),
'num_reads': str(nreads),
'bases_reads': str(nbases),
#'depth': "%.3f" % (float(nbases) / float(exon_len)),
})
transcripts = gene2transcript[gene].keys()
for transcript in sorted(transcripts):
matches_txt = matches_by_transcript[transcript]
txt = matches_txt[0].txt
transcript_len = txt.length
#best_match
best_match = None
for match in matches_txt:
if best_match is None or match.coverage > best_match.coverage:
best_match = match
#reconstruction
collapsed_blocks = self.collapse_align_blocks([match.align for match in matches_txt])
exons_size = intspan.cardinality_multi(txt.exons)
bases_reconstructed, fraction_reconstructed = self.calc_reconstruction(txt.exons, collapsed_blocks, size=exons_size)
contigs = ','.join([match.align.query for match in matches_txt])
if contig_coverage:
nreads, nbases = self.calc_coverage([match.align.query for match in matches_txt], contig_coverage)
else:
nreads, nbases = '-', '-'
results.append({'feature': 'transcript',
'model': txt.model,
'transcript': txt.name,
'gene': txt.alias,
'strand': txt.strand,
'coord': '%s:%s-%s' % (txt.chrom, int(txt.txStart) + 1, txt.txEnd),
'feature_size': txt.length,
'bases_reconstructed': bases_reconstructed,
'reconstruction': "%.3f" % (fraction_reconstructed),
'contigs': contigs,
'num_contigs': len(matches_txt),
'best_contig': best_match.align.query,
'best_contig_reconstruction': "%.3f" % (best_match.coverage),
'align_blocks': self.blocks_as_string(collapsed_blocks),
'exons': self.blocks_as_string(txt.exons),
'num_reads': str(nreads),
'bases_reads': str(nbases),
#'depth': "%.3f" % (float(nbases) / float(txt.length)),
})
return results