def setUp(self): ref_seq = Seq.Seq('GA' * 9 + 'GC' * 6 + 'GT' * 15) query_seq = 'AA' * 4 + 'GA' * 5 + 'AC' + 'GC' * 5 + 'GT' * 15 aln_string = '>seq1\n{0}'.format(query_seq) self.aln = Alignment(helpers.parse_fasta(aln_string).values(), reference_sequence=ref_seq) self.mutation_patterns = [mut_pattern.GA, mut_pattern.GM]
def setUp(self): aln_string = """ >seq1 GTCAGTCAGTCAGTCACCCC >seq2 GTCAGTCAGTCAGTCACCCC >seq3 ATCAATCAGTCAATCACCCC """ self.seqs = helpers.parse_fasta(aln_string) self.aln = Alignment(self.seqs.values())
def setUp(self): aln_string = """ >seq1 GGTGACGCT >seq2 AGTAACGCT >seq3 GGTAACACT """ ref_seq = Seq.Seq('GGTGACGCT') self.aln = Alignment(helpers.parse_fasta(aln_string).values(), reference_sequence=ref_seq)
def setUp(self): ref_seq = helpers.parse_fasta(""" >all GGGGGGGGGTGTGTGTGT""") self.aln = Alignment(helpers.parse_fasta(""" >seq1 GGGGGGGGGTGTGTGTGT >seq2 AGAGAGAGGTGTGTGTGT >seq3 GGGGGGGGGTATATATAT """).values(), reference_sequence=ref_seq['all'])
def setUp(self): aln_string = """ >seq1 GTCAGTCAGTCAGTCA GTCAGTCAGTCAGTCA >seq2 GTCAGTCAGTCAGTCA GTCAGTCAGTCAGTCA >seq3 ATCAATCAGTCAATCG ATCAATCAGTCAATCG""" self.seqs = helpers.parse_fasta_list(aln_string) self.aln = Alignment(self.seqs)
def split(args): hm_col_reader = csv.DictReader(args.columns) hm_columns = map(lambda x: int(x['column']), hm_col_reader) hm_columns = list(set(hm_columns)) seq_records = SeqIO.parse(args.alignment, 'fasta') aln = Alignment(seq_records) aln.split_hypermuts(hm_columns=hm_columns) fn_base = path.join(args.out_dir, args.prefix) hm_pos_handle = open(fn_base + '.pos.fasta', 'w') hm_neg_handle = open(fn_base + '.neg.fasta', 'w') AlignIO.write(aln.hm_pos_aln, hm_pos_handle, 'fasta') AlignIO.write(aln.hm_neg_aln, hm_neg_handle, 'fasta') for handle in [args.alignment, args.columns, hm_pos_handle, hm_neg_handle]: handle.close()
def analyze(args): import logging logging.captureWarnings(True) # Fetch sequence records and analysis patterns seq_records = SeqIO.to_dict(SeqIO.parse(args.alignment, 'fasta')) patterns = [mut_pattern.patterns[p] for p in args.patterns] pattern_names = [p.name for p in patterns] prefix = path.join(args.out_dir, args.prefix) analysis_settings = dict(rpr_cutoff=args.rpr_cutoff, significance_level=args.significance_level, quants=args.quants, pos_quants_only=args.pos_quants_only, caller=args.caller, prior=args.prior, cdfs=args.cdfs, quadr_maxiter=args.quadr_maxiter, optim_maxiter=args.optim_maxiter) # Need to think about how best to fork things here; for instance, might make sense to let the user specify # the initial clusters for whatever reason... However, specifying the reference sequences shouldn't make # any sense there if args.reference_sequences: reference_sequences = SeqIO.to_dict( SeqIO.parse(args.reference_sequences, 'fasta')) else: reference_sequences = None # This lets the cluster map be optional, so that this script can be used # for naive hm filtering/analysis cluster_map = load_cluster_map( args.cluster_map, cluster_col=args.cluster_col) if args.cluster_map else None alignments = AlignmentSet(seq_records, cluster_map, consensus_threshold=args.consensus_threshold, reference_sequences=reference_sequences) # Create the analysis generator analysis = alignments.multiple_context_analysis(patterns, **analysis_settings) if args.cluster_threshold: for hm_it in range(args.cluster_iterations - 1): print " ..On hm/cluster iteration", hm_it # Grab the HM columns from the most recent analysis and split out the pos sites hm_columns = [] for result in analysis: hm_columns += result['call']['mut_columns'] hm_neg_aln = Alignment( seq_records.values()).split_hypermuts(hm_columns).hm_neg_aln # Cluster with the specified settings clustering = alnclst.Clustering(hm_neg_aln, args.cluster_threshold, args.consensus_threshold) clustering = clustering.recenter(args.recentering_iterations) clustering.merge_small_clusters(args.min_per_cluster) cluster_map = parse_clusters(clustering.mapping_iterator(), cluster_key=0, sequence_key=1) # Create the Alignment set clustered_alignment = AlignmentSet( seq_records, cluster_map, consensus_threshold=args.consensus_threshold) analysis = clustered_alignment.multiple_context_analysis( patterns, **analysis_settings) # write out the final clusters clusterout_handle = file(prefix + '.clst.csv', 'w') clustering.write(clusterout_handle) if args.interactive: local = copy.copy(locals()) import hyperfreq local.update( dict(hyperfreq=hyperfreq, Alignment=Alignment, AlignmentSet=AlignmentSet, mut_pattern=mut_pattern, write_analysis=write_analysis)) code.interact(local=local) # Write the final analysis to file write_analysis(analysis, prefix, pattern_names, args.quants, args.cdfs, call_only=args.call_only) if args.write_references: write_reference_seqs(alignments, prefix) # Closing files args.alignment.close() if args.cluster_map: args.cluster_map.close()