gc = translate.geneticCode(rna=False)
codons = {}
opt_codon_dict = dict([(gc[c], c) for c in opt_codons])
opt_codon_dict['W'] = 'TGG'
opt_codon_dict['M'] = 'ATG'
opt_headers = []
opt_seqs = []
# optimize the codon sequences
for (id, seq) in seqs:
orig_codons = [c for c in translate.codons(seq)]
prot_seq = translate.translate(seq)
if not prot_seq is None:
for aa in translate.AAs():
codons[aa] = [
c for c in translate.getCodonsForAA(aa, rna=False)
if relad_dict[c] >= options.min_rel_adapt
]
opt_seq = ''
for (aai, aa) in enumerate(prot_seq):
#opt_seq += opt_codon_dict[aa] #random.choice(codons[aa])
codons_to_choose_from = codons[aa]
# If avoiding codons and we have a choice, eliminate the avoided codon.
if options.avoid_sequence and len(
codons_to_choose_from) > 1:
try:
codons_to_choose_from.remove(orig_codons[aai])
except ValueError: # codon to be avoided not among codon choices anyway
pass
opt_seq += random.choice(codons_to_choose_from)
assert translate.translate(opt_seq) == prot_seq
def test_run(self):
# Here we are trying to test whether ln odds X/Y + ln odds Y/Z = ln odds X/Z.
# Assign reference codons
# Build a dictionary where each codon gets its reference.
random.seed(111)
gc = translate.geneticCode(rna=False)
reference_codon_dict1 = {}
reference_codon_dict2 = {}
for codon in translate.AADNACodons():
aa = gc[codon]
aa_codons = translate.getCodonsForAA(aa, rna=False)
# Sort in alphabetical order by reverse.
aa_codons.sort(key=lambda x: x[::-1])
reference_codon_dict1[codon] = aa_codons[0]
reference_codon_dict2[codon] = aa_codons[-1]
reference_codon_dict1['GCA'] = 'GCC'
reference_codon_dict2['GCA'] = 'GCT'
# Focus on alanine: GCN. Check if GCA->GCC + GCC->GCT = GCA->GCT
#reference_codon_dict[]
species = ['x','y']
prots = {"dmel":'MNKYGMVGVCLLAALGALLLEVTASPSS--AA-SSKVDPSQLGGLSAQFLPPEYRNTNVSIEDIKRIYREKCKKVNGADNATFYEEIERAAAKMSTCISGVVNLTALQEEMDVARPNGDLDTVFSKYCLKAPEAEACVKEFNDKAQHCLTPEEKRHQETVTRIGASVLGFACSRGGDQIALFIAEQGPECLEANKEAISNCLNQSFHQYIPKDGQVPDLMSRPELLFSPTHCVDLQRFEACVIHHLEQCTQITTANIVQSVFRFVKNETDCQAWMQARANEKPILMAASSNNTAPGLA-YS--LAGTL-----LGATILLILPMNKYGMVGVCLLAALGALLLEVTASPSS--AA-SSKVDPSQLGGLSAQFLPPEYRNTNVSIEDIKRIYREKCKKVNGADNATFYEEIERAAAKMSTCISGVVNLTALQEEMDVARPNGDLDTVFSKYCLKAPEAEACVKEFNDKAQHCLTPEEKRHQETVTRIGASVLGFACSRGGDQIALFIAEQGPECLEANKEAISNCLNQSFHQYIPKDGQVPDLMSRPELLFSPTHCVDLQRFEACVIHHLEQCTQITTANIVQSVFRFVKNETDCQAWMQARANEKPILMAASSNNTAPGLA-YS--LAGTL-----LGATILLILP',
#"dere":'MNKYGIVGVCLLAALGALLLEVTADS-----A-SPKLDPSQLGGLSAQFLPPEYRNTNVSIDDMKRIYREKCKKVNGADNATFYAEIERAAAKMSNCLNGVVNLTALQEEMDVAKPNGDLDTVFSKYCQKAPEAVACVKEFNEKAQHCLTAEEKRHQETVTRIGASVLGFACSRGGDQIALFIAEQGPECLEANKEAISNCLNQSFHQYIPKDGQVPDLMSRPELLFSPTHCVDLQRFESCVIHHLEQCTQITTANIVQSVFKFVKNETDCQSWMQARANEKPILLAASSNNTATGLA-YS--LAGPL-----LGATLLLMRP',
#"dana":'MHKYTLMGLCLMAALGAVLLEVNASPAG--VAIPTKLDPSQLGGLSAQFLPPEYRNTNVTVDDLKRLYREKCKKVTGADNSSFYEEIERAAAKMSNCISGVANLTAIQEEMEQAKPQGELDTVFHKYCQKAPEAEACVKEFNTKMQVCLTAEEKRHQETIARIGASLLGFACSRGGDQIALFVAEQGPECLDANKEAIANCLNQSFHNYIPKDGQVPDLMSAPELLFSPTHCVDLQRFESCVLHHLEQCSEITPANIVQSIFKFVKNETDCQAYMTARANEKPILMAAAGNSTGGGATGLTSHFGSLLAGIFASGLVLILNRY',
#"dyak":'MNKYGMVGVCLLAALGALLLEVTASPSSTGSA-STKLDPSQLGGLSAQFLPPEYRNTNVSIEDVKRIYREKCKKVNGADNATFYEEIERAAAKMSTCISGVVNLTALQEEMDVAKPNGDLDMVFSKYCQKAPQAEACVKEFNAKAQHCLTAEEKRHQETVTRIGASVLGFACSHGGDQI-------GPECLEANKEAISNCLNQSFHQYIPKDGQVPDLMSRPELLFSPTHCVDLQRFEACVVHHLEQCTQITTANIVQSVFRFVKNETDCQAWMQARANEKPILLAASGNNTATGLA-YS--LAGPL-----LGATMLLMRP'}
"dyak":'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXQHCLTAEEKRHQETVTRIGASVLGFACSHGGDQI-------GPECLEANKEAISNCLNQSFHQYIPKDGQVPDLMSRPELLFSPTHCVDLQRFEACVVHHLEQCTQITTANIVQSVFRFVKNETDCQAWMQARANEKPILLAASGNNTATGLA-YS--LAGPL-----LGATMLLMRPMNKYGMVGVCLLAALGALLLEVTASPSS--AA-SSKVDPSQLGGLSAQFLPPEYRNTNVSIEDIKRIYREKCKKVNGADNATFYEEIERAAAKMSTCISGVVNLTALQEEMDVARPNGDLDTVFSKYCLKAPEAEACVKEFNDKAQHCLTPEEKRHQETVTRXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX-XX--XXXXX-----XXXXXXXXXX'}
genes = {"dmel":"ATGAACAAGTACGGGATGGTCGGCGTCTGCCTACTGGCTGCTCTGGGCGCTCTGCTCCTGGAGGTCACTGCCTCTCCTTCGTCCGCCGCCTCGTCTAAGGTGGATCCTAGCCAACTTGGCGGACTTTCAGCTCAGTTCTTGCCACCCGAGTACCGCAACACGAACGTTAGCATCGAGGATATAAAAAGAATATATCGTGAAAAATGCAAGAAGGTAAATGGAGCGGACAACGCAACCTTCTACGAAGAAATCGAGCGGGCGGCAGCCAAGATGAGCACCTGCATCAGCGGGGTGGTCAATCTGACGGCTCTGCAGGAGGAGATGGATGTGGCGAGGCCGAACGGCGACTTGGACACCGTGTTTAGCAAATACTGTCTCAAGGCACCGGAGGCAGAGGCCTGCGTCAAGGAGTTCAACGACAAGGCGCAGCATTGCTTGACCCCCGAGGAGAAGCGCCACCAGGAGACGGTTACCCGAATTGGAGCGTCCGTTTTGGGATTCGCCTGTTCGCGTGGCGGCGATCAGATTGCCCTCTTCATTGCCGAGCAGGGACCCGAGTGCCTGGAGGCCAACAAGGAAGCCATTAGCAATTGCCTCAATCAATCCTTTCATCAGTACATTCCCAAGGATGGCCAAGTTCCGGACCTGATGAGCCGCCCAGAGCTCCTTTTCTCACCCACCCACTGCGTGGACCTGCAGCGCTTCGAGGCCTGTGTCATCCATCATTTGGAGCAGTGCACGCAGATCACCACCGCTAATATCGTTCAGTCCGTCTTCCGTTTCGTGAAGAACGAGACCGACTGCCAGGCTTGGATGCAGGCACGTGCGAACGAGAAGCCCATTCTGATGGCCGCCTCCAGCAACAACACAGCCCCTGGACTCGCCTACTCCCTGGCCGGCACTCTTTTGGGCGCCACAATACTCCTGATACTCCCCTGAATGAACAAGTACGGGATGGTCGGCGTCTGCCTACTGGCTGCTCTGGGCGCTCTGCTCCTGGAGGTCACTGCCTCTCCTTCGTCCGCCGCCTCGTCTAAGGTGGATCCTAGCCAACTTGGCGGACTTTCAGCTCAGTTCTTGCCACCCGAGTACCGCAACACGAACGTTAGCATCGAGGATATAAAAAGAATATATCGTGAAAAATGCAAGAAGGTAAATGGAGCGGACAACGCAACCTTCTACGAAGAAATCGAGCGGGCGGCAGCCAAGATGAGCACCTGCATCAGCGGGGTGGTCAATCTGACGGCTCTGCAGGAGGAGATGGATGTGGCGAGGCCGAACGGCGACTTGGACACCGTGTTTAGCAAATACTGTCTCAAGGCACCGGAGGCAGAGGCCTGCGTCAAGGAGTTCAACGACAAGGCGCAGCATTGCTTGACCCCCGAGGAGAAGCGCCACCAGGAGACGGTTACCCGAATTGGAGCGTCCGTTTTGGGATTCGCCTGTTCGCGTGGCGGCGATCAGATTGCCCTCTTCATTGCCGAGCAGGGACCCGAGTGCCTGGAGGCCAACAAGGAAGCCATTAGCAATTGCCTCAATCAATCCTTTCATCAGTACATTCCCAAGGATGGCCAAGTTCCGGACCTGATGAGCCGCCCAGAGCTCCTTTTCTCACCCACCCACTGCGTGGACCTGCAGCGCTTCGAGGCCTGTGTCATCCATCATTTGGAGCAGTGCACGCAGATCACCACCGCTAATATCGTTCAGTCCGTCTTCCGTTTCGTGAAGAACGAGACCGACTGCCAGGCTTGGATGCAGGCACGTGCGAACGAGAAGCCCATTCTGATGGCCGCCTCCAGCAACAACACAGCCCCTGGACTCGCCTACTCCCTGGCCGGCACTCTTTTGGGCGCCACAATACTCCTGATACTCCCCTGA",
"dyak":"ATGAACAAGTACGGGATGGTTGGCGTTTGCCTACTGGCTGCTCTGGGCGCTCTGCTCCTGGAGGTCACCGCCTCTCCTTCGTCCACCGGCTCGGCGAGTACCAAGCTGGATCCCAGCCAGCTAGGCGGACTTTCGGCCCAGTTCTTACCGCCCGAGTACCGCAACACGAACGTTAGCATCGAGGACGTTAAAAGAATATATCGTGAAAAATGCAAGAAGGTTAATGGAGCGGACAACGCGACCTTCTACGAGGAAATCGAGCGGGCGGCCGCGAAGATGAGCACCTGCATCAGCGGAGTGGTCAACCTGACGGCTCTGCAGGAGGAGATGGATGTGGCCAAGCCGAACGGCGACCTGGACATGGTGTTTAGCAAGTACTGCCAGAAGGCACCGCAGGCGGAGGCCTGTGTCAAGGAGTTCAACGCCAAGGCCCAGCATTGCTTGACCGCCGAGGAGAAGCGCCACCAGGAGACGGTCACCCGCATTGGAGCGTCCGTTCTGGGCTTCGCCTGCTCGCATGGTGGCGATCAGATTGGACCCGAGTGCCTGGAGGCCAACAAGGAGGCCATAAGCAATTGCCTCAACCAATCCTTCCATCAGTACATTCCCAAGGATGGCCAAGTTCCGGACCTGATGAGCCGCCCAGAACTCCTGTTCTCGCCCACCCACTGCGTGGACCTGCAGCGCTTCGAGGCGTGTGTCGTCCATCATTTGGAACAGTGCACCCAGATCACAACCGCCAACATCGTTCAGTCCGTCTTCCGCTTCGTGAAGAACGAGACCGACTGCCAGGCTTGGATGCAGGCTCGTGCCAACGAGAAGCCCATCCTGCTGGCCGCCTCCGGCAACAATACAGCCACTGGACTCGCCTACTCTCTGGCCGGCCCTCTCTTGGGCGCCACAATGCTCCTGATGCGCCCCTGAATGAACAAGTACGGGATGGTTGGCGTTTGCCTACTGGCTGCTCTGGGCGCTCTGCTCCTGGAGGTCACCGCCTCTCCTTCGTCCACCGGCTCGGCGAGTACCAAGCTGGATCCCAGCCAGCTAGGCGGACTTTCGGCCCAGTTCTTACCGCCCGAGTACCGCAACACGAACGTTAGCATCGAGGACGTTAAAAGAATATATCGTGAAAAATGCAAGAAGGTTAATGGAGCGGACAACGCGACCTTCTACGAGGAAATCGAGCGGGCGGCCGCGAAGATGAGCACCTGCATCAGCGGAGTGGTCAACCTGACGGCTCTGCAGGAGGAGATGGATGTGGCCAAGCCGAACGGCGACCTGGACATGGTGTTTAGCAAGTACTGCCAGAAGGCACCGCAGGCGGAGGCCTGTGTCAAGGAGTTCAACGCCAAGGCCCAGCATTGCTTGACCGCCGAGGAGAAGCGCCACCAGGAGACGGTCACCCGCATTGGAGCGTCCGTTCTGGGCTTCGCCTGCTCGCATGGTGGCGATCAGATTGGACCCGAGTGCCTGGAGGCCAACAAGGAGGCCATAAGCAATTGCCTCAACCAATCCTTCCATCAGTACATTCCCAAGGATGGCCAAGTTCCGGACCTGATGAGCCGCCCAGAACTCCTGTTCTCGCCCACCCACTGCGTGGACCTGCAGCGCTTCGAGGCGTGTGTCGTCCATCATTTGGAACAGTGCACCCAGATCACAACCGCCAACATCGTTCAGTCCGTCTTCCGCTTCGTGAAGAACGAGACCGACTGCCAGGCTTGGATGCAGGCTCGTGCCAACGAGAAGCCCATCCTGCTGGCCGCCTCCGGCAACAATACAGCCACTGGACTCGCCTACTCTCTGGCCGGCCCTCTCTTGGGCGCCACAATGCTCCTGATGCGCCCCTGA"}
#algenes = dict([(s,muscle.alignGeneFromProtein(genes[s], prots[s])) for s in species])
species = ['x','y']
prot1 = randomProtein(1000)
prot2 = mutProtein(prot1)
prots = {"x":prot1, "y":prot2}
algenes = dict([(s,translate.randomReverseTranslate(prots[s],bad_codon='---')) for s in species])
#print algenes
gene_codon_tables1 = cai.getAkashi2x2TablesForORFRefCodon(cai.conservedAA, reference_codon_dict1, algenes['x'], prots['x'], [algenes['y']], [prots['y']], pseudocount=0, n_terminal_start=0)
gene_codon_tables2 = cai.getAkashi2x2TablesForORFRefCodon(cai.conservedAA, reference_codon_dict2, algenes['x'], prots['x'], [algenes['y']], [prots['y']], pseudocount=0, n_terminal_start=0)
#print gene_codon_tables1
eps = 1e-6
#for aa in translate.degenerateAAs():
# for codon in translate.getCodonsForAA(aa):
for codon in ['GCA']:
ref_codon1 = reference_codon_dict1[codon]
ref_codon2 = reference_codon_dict2[codon]
self.assertTrue(gc[codon] == gc[ref_codon1])
self.assertTrue(gc[codon] == gc[ref_codon2])
# codon to ref_codon1
mh_res1 = stats.MantelHaenszelOddsRatioVariance(gene_codon_tables1[codon])
sc_1_to_r1 = -mh_res1.ln_odds_ratio
# ref_codon1 to ref_codon2
mh_res2 = stats.MantelHaenszelOddsRatioVariance(gene_codon_tables2[ref_codon1])
sc_r1_to_r2 = -mh_res2.ln_odds_ratio
# codon to ref_codon2
mh_res3 = stats.MantelHaenszelOddsRatioVariance(gene_codon_tables2[codon])
sc_1_to_r2 = -mh_res3.ln_odds_ratio
# prediction from additivity
pred_sc_1_to_r2 = sc_1_to_r1 + sc_r1_to_r2
#print "{0}->{1} = {2}".format(codon, ref_codon1, sc_1_to_r1)
#print "{0}->{1} = {2}".format(ref_codon1, ref_codon2, sc_r1_to_r2)
#print "{0}->{1} = {2}".format(codon, ref_codon2, sc_1_to_r2)
#, ref_codon2, sc_1_to_r1, sc_r1_to_r2, sc_1_to_r2, pred_sc_1_to_r2
self.assertTrue(abs(sc_1_to_r2-pred_sc_1_to_r2) < eps)
data_outs = util.OutStreams()
# Start up output
if not options.out_fname is None:
outf = file(options.out_fname, 'w')
data_outs.addStream(outf)
else:
data_outs.addStream(sys.stdout)
formatFxn = biofile.getIDFunction(options.format)
cdna_dict = biofile.readFASTADict(in_fname, formatFxn)
calc = Calculator()
calc.initializeFromSequences(cdna_dict.values(), options.pseudocount)
syn_dict = calc.getCodonSYNScores()
syn_opt_codons = []
for aa in translate.degenerateAAs():
codons = translate.getCodonsForAA(aa, rna=False)
best_syn_codon = sorted([(syn_dict[c],c) for c in codons])[-1][1]
syn_opt_codons.append(best_syn_codon)
data_outs.write("# Read {0}\n#{1:d} sequences, {2:d} codons, {3:d} nucleotides\n".format(in_fname, len(cdna_dict.keys()), int(sum(calc.codon_freq.values())), int(sum(calc.nucleotide_freq.values()))))
data_outs.write("# syn_scores = {0!s}\n".format(syn_dict))
data_outs.write("# SYN opt codons = {0!s}\n".format(sorted(syn_opt_codons)))
data_outs.write("{0!s}".format(calc))
if not options.score_dict_fname is None:
pickle.dump(syn_dict, file(options.score_dict_fname,'w'))
if not options.score_fname is None:
outf = file(options.score_fname, 'w')
outf.write("orf\tsyn\n")
orfs = cdna_dict.keys()
n_written = 0
def test_run(self):
# Here we are trying to test whether ln odds X/Y + ln odds Y/Z = ln odds X/Z.
# Assign reference codons
# Build a dictionary where each codon gets its reference.
random.seed(111)
gc = translate.geneticCode(rna=False)
reference_codon_dict1 = {}
reference_codon_dict2 = {}
for codon in translate.AADNACodons():
aa = gc[codon]
aa_codons = translate.getCodonsForAA(aa, rna=False)
# Sort in alphabetical order by reverse.
aa_codons.sort(key=lambda x: x[::-1])
reference_codon_dict1[codon] = aa_codons[0]
reference_codon_dict2[codon] = aa_codons[-1]
reference_codon_dict1['GCA'] = 'GCC'
reference_codon_dict2['GCA'] = 'GCT'
# Focus on alanine: GCN. Check if GCA->GCC + GCC->GCT = GCA->GCT
#reference_codon_dict[]
species = ['x', 'y']
prots = {
"dmel":
'MNKYGMVGVCLLAALGALLLEVTASPSS--AA-SSKVDPSQLGGLSAQFLPPEYRNTNVSIEDIKRIYREKCKKVNGADNATFYEEIERAAAKMSTCISGVVNLTALQEEMDVARPNGDLDTVFSKYCLKAPEAEACVKEFNDKAQHCLTPEEKRHQETVTRIGASVLGFACSRGGDQIALFIAEQGPECLEANKEAISNCLNQSFHQYIPKDGQVPDLMSRPELLFSPTHCVDLQRFEACVIHHLEQCTQITTANIVQSVFRFVKNETDCQAWMQARANEKPILMAASSNNTAPGLA-YS--LAGTL-----LGATILLILPMNKYGMVGVCLLAALGALLLEVTASPSS--AA-SSKVDPSQLGGLSAQFLPPEYRNTNVSIEDIKRIYREKCKKVNGADNATFYEEIERAAAKMSTCISGVVNLTALQEEMDVARPNGDLDTVFSKYCLKAPEAEACVKEFNDKAQHCLTPEEKRHQETVTRIGASVLGFACSRGGDQIALFIAEQGPECLEANKEAISNCLNQSFHQYIPKDGQVPDLMSRPELLFSPTHCVDLQRFEACVIHHLEQCTQITTANIVQSVFRFVKNETDCQAWMQARANEKPILMAASSNNTAPGLA-YS--LAGTL-----LGATILLILP',
#"dere":'MNKYGIVGVCLLAALGALLLEVTADS-----A-SPKLDPSQLGGLSAQFLPPEYRNTNVSIDDMKRIYREKCKKVNGADNATFYAEIERAAAKMSNCLNGVVNLTALQEEMDVAKPNGDLDTVFSKYCQKAPEAVACVKEFNEKAQHCLTAEEKRHQETVTRIGASVLGFACSRGGDQIALFIAEQGPECLEANKEAISNCLNQSFHQYIPKDGQVPDLMSRPELLFSPTHCVDLQRFESCVIHHLEQCTQITTANIVQSVFKFVKNETDCQSWMQARANEKPILLAASSNNTATGLA-YS--LAGPL-----LGATLLLMRP',
#"dana":'MHKYTLMGLCLMAALGAVLLEVNASPAG--VAIPTKLDPSQLGGLSAQFLPPEYRNTNVTVDDLKRLYREKCKKVTGADNSSFYEEIERAAAKMSNCISGVANLTAIQEEMEQAKPQGELDTVFHKYCQKAPEAEACVKEFNTKMQVCLTAEEKRHQETIARIGASLLGFACSRGGDQIALFVAEQGPECLDANKEAIANCLNQSFHNYIPKDGQVPDLMSAPELLFSPTHCVDLQRFESCVLHHLEQCSEITPANIVQSIFKFVKNETDCQAYMTARANEKPILMAAAGNSTGGGATGLTSHFGSLLAGIFASGLVLILNRY',
#"dyak":'MNKYGMVGVCLLAALGALLLEVTASPSSTGSA-STKLDPSQLGGLSAQFLPPEYRNTNVSIEDVKRIYREKCKKVNGADNATFYEEIERAAAKMSTCISGVVNLTALQEEMDVAKPNGDLDMVFSKYCQKAPQAEACVKEFNAKAQHCLTAEEKRHQETVTRIGASVLGFACSHGGDQI-------GPECLEANKEAISNCLNQSFHQYIPKDGQVPDLMSRPELLFSPTHCVDLQRFEACVVHHLEQCTQITTANIVQSVFRFVKNETDCQAWMQARANEKPILLAASGNNTATGLA-YS--LAGPL-----LGATMLLMRP'}
"dyak":
'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXQHCLTAEEKRHQETVTRIGASVLGFACSHGGDQI-------GPECLEANKEAISNCLNQSFHQYIPKDGQVPDLMSRPELLFSPTHCVDLQRFEACVVHHLEQCTQITTANIVQSVFRFVKNETDCQAWMQARANEKPILLAASGNNTATGLA-YS--LAGPL-----LGATMLLMRPMNKYGMVGVCLLAALGALLLEVTASPSS--AA-SSKVDPSQLGGLSAQFLPPEYRNTNVSIEDIKRIYREKCKKVNGADNATFYEEIERAAAKMSTCISGVVNLTALQEEMDVARPNGDLDTVFSKYCLKAPEAEACVKEFNDKAQHCLTPEEKRHQETVTRXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX-XX--XXXXX-----XXXXXXXXXX'
}
genes = {
"dmel":
"ATGAACAAGTACGGGATGGTCGGCGTCTGCCTACTGGCTGCTCTGGGCGCTCTGCTCCTGGAGGTCACTGCCTCTCCTTCGTCCGCCGCCTCGTCTAAGGTGGATCCTAGCCAACTTGGCGGACTTTCAGCTCAGTTCTTGCCACCCGAGTACCGCAACACGAACGTTAGCATCGAGGATATAAAAAGAATATATCGTGAAAAATGCAAGAAGGTAAATGGAGCGGACAACGCAACCTTCTACGAAGAAATCGAGCGGGCGGCAGCCAAGATGAGCACCTGCATCAGCGGGGTGGTCAATCTGACGGCTCTGCAGGAGGAGATGGATGTGGCGAGGCCGAACGGCGACTTGGACACCGTGTTTAGCAAATACTGTCTCAAGGCACCGGAGGCAGAGGCCTGCGTCAAGGAGTTCAACGACAAGGCGCAGCATTGCTTGACCCCCGAGGAGAAGCGCCACCAGGAGACGGTTACCCGAATTGGAGCGTCCGTTTTGGGATTCGCCTGTTCGCGTGGCGGCGATCAGATTGCCCTCTTCATTGCCGAGCAGGGACCCGAGTGCCTGGAGGCCAACAAGGAAGCCATTAGCAATTGCCTCAATCAATCCTTTCATCAGTACATTCCCAAGGATGGCCAAGTTCCGGACCTGATGAGCCGCCCAGAGCTCCTTTTCTCACCCACCCACTGCGTGGACCTGCAGCGCTTCGAGGCCTGTGTCATCCATCATTTGGAGCAGTGCACGCAGATCACCACCGCTAATATCGTTCAGTCCGTCTTCCGTTTCGTGAAGAACGAGACCGACTGCCAGGCTTGGATGCAGGCACGTGCGAACGAGAAGCCCATTCTGATGGCCGCCTCCAGCAACAACACAGCCCCTGGACTCGCCTACTCCCTGGCCGGCACTCTTTTGGGCGCCACAATACTCCTGATACTCCCCTGAATGAACAAGTACGGGATGGTCGGCGTCTGCCTACTGGCTGCTCTGGGCGCTCTGCTCCTGGAGGTCACTGCCTCTCCTTCGTCCGCCGCCTCGTCTAAGGTGGATCCTAGCCAACTTGGCGGACTTTCAGCTCAGTTCTTGCCACCCGAGTACCGCAACACGAACGTTAGCATCGAGGATATAAAAAGAATATATCGTGAAAAATGCAAGAAGGTAAATGGAGCGGACAACGCAACCTTCTACGAAGAAATCGAGCGGGCGGCAGCCAAGATGAGCACCTGCATCAGCGGGGTGGTCAATCTGACGGCTCTGCAGGAGGAGATGGATGTGGCGAGGCCGAACGGCGACTTGGACACCGTGTTTAGCAAATACTGTCTCAAGGCACCGGAGGCAGAGGCCTGCGTCAAGGAGTTCAACGACAAGGCGCAGCATTGCTTGACCCCCGAGGAGAAGCGCCACCAGGAGACGGTTACCCGAATTGGAGCGTCCGTTTTGGGATTCGCCTGTTCGCGTGGCGGCGATCAGATTGCCCTCTTCATTGCCGAGCAGGGACCCGAGTGCCTGGAGGCCAACAAGGAAGCCATTAGCAATTGCCTCAATCAATCCTTTCATCAGTACATTCCCAAGGATGGCCAAGTTCCGGACCTGATGAGCCGCCCAGAGCTCCTTTTCTCACCCACCCACTGCGTGGACCTGCAGCGCTTCGAGGCCTGTGTCATCCATCATTTGGAGCAGTGCACGCAGATCACCACCGCTAATATCGTTCAGTCCGTCTTCCGTTTCGTGAAGAACGAGACCGACTGCCAGGCTTGGATGCAGGCACGTGCGAACGAGAAGCCCATTCTGATGGCCGCCTCCAGCAACAACACAGCCCCTGGACTCGCCTACTCCCTGGCCGGCACTCTTTTGGGCGCCACAATACTCCTGATACTCCCCTGA",
"dyak":
"ATGAACAAGTACGGGATGGTTGGCGTTTGCCTACTGGCTGCTCTGGGCGCTCTGCTCCTGGAGGTCACCGCCTCTCCTTCGTCCACCGGCTCGGCGAGTACCAAGCTGGATCCCAGCCAGCTAGGCGGACTTTCGGCCCAGTTCTTACCGCCCGAGTACCGCAACACGAACGTTAGCATCGAGGACGTTAAAAGAATATATCGTGAAAAATGCAAGAAGGTTAATGGAGCGGACAACGCGACCTTCTACGAGGAAATCGAGCGGGCGGCCGCGAAGATGAGCACCTGCATCAGCGGAGTGGTCAACCTGACGGCTCTGCAGGAGGAGATGGATGTGGCCAAGCCGAACGGCGACCTGGACATGGTGTTTAGCAAGTACTGCCAGAAGGCACCGCAGGCGGAGGCCTGTGTCAAGGAGTTCAACGCCAAGGCCCAGCATTGCTTGACCGCCGAGGAGAAGCGCCACCAGGAGACGGTCACCCGCATTGGAGCGTCCGTTCTGGGCTTCGCCTGCTCGCATGGTGGCGATCAGATTGGACCCGAGTGCCTGGAGGCCAACAAGGAGGCCATAAGCAATTGCCTCAACCAATCCTTCCATCAGTACATTCCCAAGGATGGCCAAGTTCCGGACCTGATGAGCCGCCCAGAACTCCTGTTCTCGCCCACCCACTGCGTGGACCTGCAGCGCTTCGAGGCGTGTGTCGTCCATCATTTGGAACAGTGCACCCAGATCACAACCGCCAACATCGTTCAGTCCGTCTTCCGCTTCGTGAAGAACGAGACCGACTGCCAGGCTTGGATGCAGGCTCGTGCCAACGAGAAGCCCATCCTGCTGGCCGCCTCCGGCAACAATACAGCCACTGGACTCGCCTACTCTCTGGCCGGCCCTCTCTTGGGCGCCACAATGCTCCTGATGCGCCCCTGAATGAACAAGTACGGGATGGTTGGCGTTTGCCTACTGGCTGCTCTGGGCGCTCTGCTCCTGGAGGTCACCGCCTCTCCTTCGTCCACCGGCTCGGCGAGTACCAAGCTGGATCCCAGCCAGCTAGGCGGACTTTCGGCCCAGTTCTTACCGCCCGAGTACCGCAACACGAACGTTAGCATCGAGGACGTTAAAAGAATATATCGTGAAAAATGCAAGAAGGTTAATGGAGCGGACAACGCGACCTTCTACGAGGAAATCGAGCGGGCGGCCGCGAAGATGAGCACCTGCATCAGCGGAGTGGTCAACCTGACGGCTCTGCAGGAGGAGATGGATGTGGCCAAGCCGAACGGCGACCTGGACATGGTGTTTAGCAAGTACTGCCAGAAGGCACCGCAGGCGGAGGCCTGTGTCAAGGAGTTCAACGCCAAGGCCCAGCATTGCTTGACCGCCGAGGAGAAGCGCCACCAGGAGACGGTCACCCGCATTGGAGCGTCCGTTCTGGGCTTCGCCTGCTCGCATGGTGGCGATCAGATTGGACCCGAGTGCCTGGAGGCCAACAAGGAGGCCATAAGCAATTGCCTCAACCAATCCTTCCATCAGTACATTCCCAAGGATGGCCAAGTTCCGGACCTGATGAGCCGCCCAGAACTCCTGTTCTCGCCCACCCACTGCGTGGACCTGCAGCGCTTCGAGGCGTGTGTCGTCCATCATTTGGAACAGTGCACCCAGATCACAACCGCCAACATCGTTCAGTCCGTCTTCCGCTTCGTGAAGAACGAGACCGACTGCCAGGCTTGGATGCAGGCTCGTGCCAACGAGAAGCCCATCCTGCTGGCCGCCTCCGGCAACAATACAGCCACTGGACTCGCCTACTCTCTGGCCGGCCCTCTCTTGGGCGCCACAATGCTCCTGATGCGCCCCTGA"
}
#algenes = dict([(s,muscle.alignGeneFromProtein(genes[s], prots[s])) for s in species])
species = ['x', 'y']
prot1 = randomProtein(1000)
prot2 = mutProtein(prot1)
prots = {"x": prot1, "y": prot2}
algenes = dict([(s,
translate.randomReverseTranslate(prots[s],
bad_codon='---'))
for s in species])
#print algenes
gene_codon_tables1 = cai.getAkashi2x2TablesForORFRefCodon(
cai.conservedAA,
reference_codon_dict1,
algenes['x'],
prots['x'], [algenes['y']], [prots['y']],
pseudocount=0,
n_terminal_start=0)
gene_codon_tables2 = cai.getAkashi2x2TablesForORFRefCodon(
cai.conservedAA,
reference_codon_dict2,
algenes['x'],
prots['x'], [algenes['y']], [prots['y']],
pseudocount=0,
n_terminal_start=0)
#print gene_codon_tables1
eps = 1e-6
#for aa in translate.degenerateAAs():
# for codon in translate.getCodonsForAA(aa):
for codon in ['GCA']:
ref_codon1 = reference_codon_dict1[codon]
ref_codon2 = reference_codon_dict2[codon]
self.assertTrue(gc[codon] == gc[ref_codon1])
self.assertTrue(gc[codon] == gc[ref_codon2])
# codon to ref_codon1
mh_res1 = stats.MantelHaenszelOddsRatioVariance(
gene_codon_tables1[codon])
sc_1_to_r1 = -mh_res1.ln_odds_ratio
# ref_codon1 to ref_codon2
mh_res2 = stats.MantelHaenszelOddsRatioVariance(
gene_codon_tables2[ref_codon1])
sc_r1_to_r2 = -mh_res2.ln_odds_ratio
# codon to ref_codon2
mh_res3 = stats.MantelHaenszelOddsRatioVariance(
gene_codon_tables2[codon])
sc_1_to_r2 = -mh_res3.ln_odds_ratio
# prediction from additivity
pred_sc_1_to_r2 = sc_1_to_r1 + sc_r1_to_r2
#print "{0}->{1} = {2}".format(codon, ref_codon1, sc_1_to_r1)
#print "{0}->{1} = {2}".format(ref_codon1, ref_codon2, sc_r1_to_r2)
#print "{0}->{1} = {2}".format(codon, ref_codon2, sc_1_to_r2)
#, ref_codon2, sc_1_to_r1, sc_r1_to_r2, sc_1_to_r2, pred_sc_1_to_r2
self.assertTrue(abs(sc_1_to_r2 - pred_sc_1_to_r2) < eps)
info_outs.write("# Optimizing sequences...\n")
gc = translate.geneticCode(rna=False)
codons = {}
opt_codon_dict = dict([(gc[c],c) for c in opt_codons])
opt_codon_dict['W'] = 'TGG'
opt_codon_dict['M'] = 'ATG'
opt_headers = []
opt_seqs = []
# optimize the codon sequences
for (id, seq) in seqs:
orig_codons = [c for c in translate.codons(seq)]
prot_seq = translate.translate(seq)
if not prot_seq is None:
for aa in translate.AAs():
codons[aa] = [c for c in translate.getCodonsForAA(aa, rna=False) if relad_dict[c] >= options.min_rel_adapt]
opt_seq = ''
for (aai, aa) in enumerate(prot_seq):
#opt_seq += opt_codon_dict[aa] #random.choice(codons[aa])
codons_to_choose_from = codons[aa]
# If avoiding codons and we have a choice, eliminate the avoided codon.
if options.avoid_sequence and len(codons_to_choose_from)>1:
try:
codons_to_choose_from.remove(orig_codons[aai])
except ValueError: # codon to be avoided not among codon choices anyway
pass
opt_seq += random.choice(codons_to_choose_from)
assert translate.translate(opt_seq) == prot_seq
header_line = "{0} Fop = {1:.4f}, CAI = {2:.4f}, GC = {3:.2f}".format(id, cai.getFop(opt_seq, opt_codons), cai_fxn(opt_seq), cai.getGC(opt_seq))
info_outs.write("# Optimized {}\n".format(header_line))
opt_headers.append(header_line)
