def parseGene(pickle_filename, event): """ Parse a pickled gene. """ if not os.path.isfile(pickle_filename): raise Exception, "Error: no filename %s" %(pickle_filename) gff_genes = gff_utils.load_indexed_gff_file(pickle_filename) if gff_genes == None: raise Exception, "Error: could not load genes from %s" \ %(pickle_filename) exon_starts = [] exon_ends = [] mRNAs = [] chrom = None mRNA_ids = [] for gene_id, gene_info in gff_genes.iteritems(): if event == gene_id: gene_obj = gene_info['gene_object'] gene_hierarchy = gene_info['hierarchy'] tx_start, tx_end = gff_utils.get_inclusive_txn_bounds(\ gene_hierarchy[gene_id]) chrom = gene_obj.chrom for mRNA_id, mRNA_info in gene_hierarchy[gene_id]['mRNAs'].iteritems(): mRNA = [] mRNA_ids.append(mRNA_id) for exon_id, exon_info in gene_hierarchy[gene_id]['mRNAs']\ [mRNA_id]['exons'].\ iteritems(): exon_rec = gene_hierarchy[gene_id]['mRNAs']\ [mRNA_id]['exons'][exon_id]['record'] strand = exon_rec.strand exon_starts.append(exon_rec.start) exon_ends.append(exon_rec.end) mRNA.append(sorted([exon_rec.start, exon_rec.end])) mRNAs.append(mRNA) break mRNAs.sort(key=len) return tx_start, tx_end, exon_starts, exon_ends, gene_obj, \ mRNAs, strand, chrom, mRNA_ids
def parseGene(pickle_filename, event): """ Parse a pickled gene. """ if not os.path.isfile(pickle_filename): raise Exception, "Error: no filename %s" % (pickle_filename) gff_genes = gff_utils.load_indexed_gff_file(pickle_filename) if gff_genes == None: raise Exception, "Error: could not load genes from %s" \ %(pickle_filename) exon_starts = [] exon_ends = [] mRNAs = [] chrom = None for gene_id, gene_info in gff_genes.iteritems(): if event == gene_id: gene_obj = gene_info['gene_object'] gene_hierarchy = gene_info['hierarchy'] tx_start, tx_end = gff_utils.get_inclusive_txn_bounds(\ gene_hierarchy[gene_id]) chrom = gene_obj.chrom for mRNA_id, mRNA_info in gene_hierarchy[gene_id][ 'mRNAs'].iteritems(): mRNA = [] for exon_id, exon_info in gene_hierarchy[gene_id]['mRNAs']\ [mRNA_id]['exons'].\ iteritems(): exon_rec = gene_hierarchy[gene_id]['mRNAs']\ [mRNA_id]['exons'][exon_id]['record'] strand = exon_rec.strand exon_starts.append(exon_rec.start) exon_ends.append(exon_rec.end) mRNA.append(sorted([exon_rec.start, exon_rec.end])) mRNAs.append(mRNA) break mRNAs.sort(key=len) return tx_start, tx_end, exon_starts, exon_ends, gene_obj, \ mRNAs, strand, chrom
def compute_gene_psi(gene_ids, gff_index_filename, bam_filename, output_dir, read_len, overhang_len, paired_end=None, event_type=None, verbose=True): """ Run Psi at the Gene-level (for multi-isoform inference.) Arguments: - Set of gene IDs corresponding to gene IDs from the GFF - Indexed GFF filename describing the genes - BAM filename with the reads (must be sorted and indexed) - Output directory - Optional: Run in paired-end mode. Gives mean and standard deviation of fragment length distribution. """ misc_utils.make_dir(output_dir) if not os.path.exists(gff_index_filename): print "Error: No GFF %s" %(gff_index_filename) return num_genes = len(gene_ids) print "Computing Psi for %d genes..." %(num_genes) print " - " + ", ".join(gene_ids) print " - GFF filename: %s" %(gff_index_filename) print " - BAM: %s" %(bam_filename) print " - Outputting to: %s" %(output_dir) if paired_end: print " - Paired-end mode: ", paired_end settings = Settings.get() settings_params = Settings.get_sampler_params() burn_in = settings_params["burn_in"] lag = settings_params["lag"] num_iters = settings_params["num_iters"] num_chains = settings_params["num_chains"] min_event_reads = Settings.get_min_event_reads() strand_rule = Settings.get_strand_param() mean_frag_len = None frag_variance = None if paired_end: mean_frag_len = int(paired_end[0]) frag_variance = power(int(paired_end[1]), 2) # Load the genes from the GFF gff_genes = gff_utils.load_indexed_gff_file(gff_index_filename) # If given a template for the SAM file, use it template = None if settings and "sam_template" in settings: template = settings["sam_template"] if "filter_reads" not in settings: filter_reads = True else: filter_reads = settings["filter_reads"] # Load the BAM file upfront bamfile = sam_utils.load_bam_reads(bam_filename, template=template) # Check if we're in compressed mode compressed_mode = misc_utils.is_compressed_index(gff_index_filename) for gene_id, gene_info in gff_genes.iteritems(): lookup_id = gene_id # Skip genes that we were not asked to run on if lookup_id not in gene_ids: continue gene_obj = gene_info['gene_object'] gene_hierarchy = gene_info['hierarchy'] # Sanity check: if the isoforms are all shorter than the read, # skip the event if all(map(lambda l: l < read_len, gene_obj.iso_lens)): print "All isoforms of %s shorter than %d, so skipping" \ %(gene_id, read_len) continue # Find the most inclusive transcription start and end sites # for each gene tx_start, tx_end = \ gff_utils.get_inclusive_txn_bounds(gene_info['hierarchy'][gene_id]) # Fetch reads aligning to the gene boundaries gene_reads = \ sam_utils.fetch_bam_reads_in_gene(bamfile, gene_obj.chrom, tx_start, tx_end, gene_obj) # Parse reads: checking strandedness and pairing # reads in case of paired-end data reads, num_raw_reads = \ sam_utils.sam_parse_reads(gene_reads, paired_end=paired_end, strand_rule=strand_rule, target_strand=gene_obj.strand, given_read_len=read_len) # Skip gene if none of the reads align to gene boundaries if filter_reads: if num_raw_reads < min_event_reads: print "Only %d reads in gene, skipping (needed >= %d reads)" \ %(num_raw_reads, min_event_reads) continue else: print "%d raw reads in event" %(num_raw_reads) num_isoforms = len(gene_obj.isoforms) hyperparameters = ones(num_isoforms) ## ## Run the sampler ## # Create the sampler with the right parameters depending on whether # this is a paired-end or single-end data set. if paired_end: # Sampler parameters for paired-end mode sampler_params = \ miso.get_paired_end_sampler_params(num_isoforms, mean_frag_len, frag_variance, read_len, overhang_len=overhang_len) sampler = miso.MISOSampler(sampler_params, paired_end=True, log_dir=output_dir) else: # Sampler parameters for single-end mode sampler_params = miso.get_single_end_sampler_params(num_isoforms, read_len, overhang_len) sampler = miso.MISOSampler(sampler_params, paired_end=False, log_dir=output_dir) # Make directory for chromosome -- if given an event type, put # the gene in the event type directory if event_type != None: chrom_dir = os.path.join(output_dir, event_type, gene_obj.chrom) else: chrom_dir = os.path.join(output_dir, gene_obj.chrom) try: os.makedirs(chrom_dir) except OSError: pass # Pick .miso output filename based on the pickle filename miso_basename = os.path.basename(gff_index_filename) if not miso_basename.endswith(".pickle"): print "Error: Invalid index file %s" %(gff_index_filename) sys.exit(1) miso_basename = miso_basename.replace(".pickle", "") output_filename = os.path.join(chrom_dir, "%s" %(miso_basename)) sampler.run_sampler(num_iters, reads, gene_obj, hyperparameters, sampler_params, output_filename, num_chains=num_chains, burn_in=burn_in, lag=lag)
def compute_gene_psi(gene_ids, gff_index_filename, bam_filename, output_dir, read_len, overhang_len, paired_end=None, event_type=None, verbose=True): """ Run Psi at the Gene-level (for multi-isoform inference.) Arguments: - Set of gene IDs corresponding to gene IDs from the GFF - Indexed GFF filename describing the genes - BAM filename with the reads (must be sorted and indexed) - Output directory - Optional: Run in paired-end mode. Gives mean and standard deviation of fragment length distribution. """ misc_utils.make_dir(output_dir) if not os.path.exists(gff_index_filename): print "Error: No GFF %s" %(gff_index_filename) return num_genes = len(gene_ids) print "Computing Psi for %d genes..." %(num_genes) print " - " + ", ".join(gene_ids) print " - GFF filename: %s" %(gff_index_filename) print " - BAM: %s" %(bam_filename) print " - Outputting to: %s" %(output_dir) if paired_end: print " - Paired-end mode: ", paired_end settings = Settings.get() settings_params = Settings.get_sampler_params() burn_in = settings_params["burn_in"] lag = settings_params["lag"] num_iters = settings_params["num_iters"] num_chains = settings_params["num_chains"] min_event_reads = Settings.get_min_event_reads() strand_rule = Settings.get_strand_param() mean_frag_len = None frag_variance = None if paired_end: mean_frag_len = int(paired_end[0]) frag_variance = power(int(paired_end[1]), 2) # Load the genes from the GFF gff_genes = gff_utils.load_indexed_gff_file(gff_index_filename) # If given a template for the SAM file, use it template = None if settings and "sam_template" in settings: template = settings["sam_template"] if "filter_reads" not in settings: filter_reads = True else: filter_reads = settings["filter_reads"] # Load the BAM file upfront bamfile = sam_utils.load_bam_reads(bam_filename, template=template) # Check if we're in compressed mode compressed_mode = misc_utils.is_compressed_index(gff_index_filename) for gene_id, gene_info in gff_genes.iteritems(): lookup_id = gene_id # Skip genes that we were not asked to run on if lookup_id not in gene_ids: continue gene_obj = gene_info['gene_object'] gene_hierarchy = gene_info['hierarchy'] # Sanity check: if the isoforms are all shorter than the read, # skip the event if all(map(lambda l: l < read_len, gene_obj.iso_lens)): print "All isoforms of %s shorter than %d, so skipping" \ %(gene_id, read_len) continue # Find the most inclusive transcription start and end sites # for each gene tx_start, tx_end = \ gff_utils.get_inclusive_txn_bounds(gene_info['hierarchy'][gene_id]) # Fetch reads aligning to the gene boundaries gene_reads = \ sam_utils.fetch_bam_reads_in_gene(bamfile, gene_obj.chrom, tx_start, tx_end, gene_obj) # Parse reads: checking strandedness and pairing # reads in case of paired-end data reads, num_raw_reads = \ sam_utils.sam_parse_reads(gene_reads, paired_end=paired_end, strand_rule=strand_rule, target_strand=gene_obj.strand) # Skip gene if none of the reads align to gene boundaries if filter_reads: if num_raw_reads < min_event_reads: print "Only %d reads in gene, skipping (needed >= %d reads)" \ %(num_raw_reads, min_event_reads) continue else: print "%d raw reads in event" %(num_raw_reads) num_isoforms = len(gene_obj.isoforms) hyperparameters = ones(num_isoforms) ## ## Run the sampler ## # Create the sampler with the right parameters depending on whether # this is a paired-end or single-end data set. if paired_end: # Sampler parameters for paired-end mode sampler_params = \ miso.get_paired_end_sampler_params(num_isoforms, mean_frag_len, frag_variance, read_len, overhang_len=overhang_len) sampler = miso.MISOSampler(sampler_params, paired_end=True, log_dir=output_dir) else: # Sampler parameters for single-end mode sampler_params = miso.get_single_end_sampler_params(num_isoforms, read_len, overhang_len) sampler = miso.MISOSampler(sampler_params, paired_end=False, log_dir=output_dir) # Make directory for chromosome -- if given an event type, put # the gene in the event type directory if event_type != None: chrom_dir = os.path.join(output_dir, event_type, gene_obj.chrom) else: chrom_dir = os.path.join(output_dir, gene_obj.chrom) try: os.makedirs(chrom_dir) except OSError: pass # Pick .miso output filename based on the pickle filename miso_basename = os.path.basename(gff_index_filename) if not miso_basename.endswith(".pickle"): print "Error: Invalid index file %s" %(gff_index_filename) sys.exit(1) miso_basename = miso_basename.replace(".pickle", "") output_filename = os.path.join(chrom_dir, "%s" %(miso_basename)) sampler.run_sampler(num_iters, reads, gene_obj, hyperparameters, sampler_params, output_filename, num_chains=num_chains, burn_in=burn_in, lag=lag)