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
