def test001(self): """reverse translate""" N = 1000 aas = "ACDEFGHIKLMNPQRSTVWY" for i in range(N): prot = "".join([random.choice(aas) for xi in range(100)]) gene = translate.reverseTranslate(prot) newprot = translate.translate(gene) self.assertTrue(prot == newprot)
def test001(self): """reverse translate""" N = 1000 aas = 'ACDEFGHIKLMNPQRSTVWY' for i in range(N): prot = ''.join([random.choice(aas) for xi in range(100)]) gene = translate.reverseTranslate(prot) newprot = translate.translate(gene) self.assertTrue(prot == newprot)
fname = os.path.expanduser(seq) if os.path.isfile(fname): (headers, seq_list) = biofile.readFASTA(fname) seqs = zip(headers, seq_list) info_outs.write("# Read {0:d} sequences from {1}\n".format( len(seqs), fname)) else: seqs = [("command-line input", seq.upper())] info_outs.write("# Read sequence L={:d} from standard input\n".format( len(seqs[0][1]))) # If reverse-translation is desired, do it. if options.reverse_translate: new_seqs = [] for (h, s) in seqs: rev_trans_seq = translate.reverseTranslate(s) new_seqs.append((h, rev_trans_seq)) seqs = new_seqs # Obtain gene sequence using only optimal codons info_outs.write("# Using optimal codons for {}\n".format(options.species)) opt_codons = cai.getOptimalCodons(options.species) relad_dict = cai.getRelativeAdaptivenessValues(options.species) cai_fxn = cai.getCAIFunction(options.species) # DAD: cai.getCAI takes only log-transformed relative adaptiveness values. # Here, knowing that some values are == 0, add half of the minimum nonzero value. # Should be using some sort of better estimator! min_relad_value = 0.5 * min([v for v in relad_dict.values() if v > 0.0]) for k in relad_dict.keys(): if relad_dict[k] <= 0.0: relad_dict[k] = min_relad_value
# Read sequences from a FASTA file? fname = os.path.expanduser(seq) if os.path.isfile(fname): (headers, seq_list) = biofile.readFASTA(fname) seqs = zip(headers,seq_list) info_outs.write("# Read {0:d} sequences from {1}\n".format(len(seqs), fname)) else: seqs = [("command-line input",seq.upper())] info_outs.write("# Read sequence L={:d} from standard input\n".format(len(seqs[0][1]))) # If reverse-translation is desired, do it. if options.reverse_translate: new_seqs = [] for (h,s) in seqs: rev_trans_seq = translate.reverseTranslate(s) new_seqs.append((h,rev_trans_seq)) seqs = new_seqs # Obtain gene sequence using only optimal codons info_outs.write("# Using optimal codons for {}\n".format(options.species)) opt_codons = cai.getOptimalCodons(options.species) relad_dict = cai.getRelativeAdaptivenessValues(options.species) cai_fxn = cai.getCAIFunction(options.species) # DAD: cai.getCAI takes only log-transformed relative adaptiveness values. # Here, knowing that some values are == 0, add half of the minimum nonzero value. # Should be using some sort of better estimator! min_relad_value = 0.5 * min([v for v in relad_dict.values() if v>0.0]) for k in relad_dict.keys(): if relad_dict[k] <= 0.0: relad_dict[k] = min_relad_value