#! /usr/bin/env python """A collection of pre-defined models. These are provided for convenience so that users do not need to keep reconstructing the standard models. We encorage users to think about the assumptions in these models and consider if their problem could benefit from a user defined model. Note that models that do not traditionally deal with gaps are implemented with gap recoding that will convert gaps to Ns, and model gaps set to False.""" #The models are constructed in a strait forward manner with no attempt to condense #this file using functions etc. to allow each model to serve as an example for users #wishing to construct their own models import numpy from cogent.evolve import substitution_model, predicate __author__ = "Matthew Wakefield" __copyright__ = "Copyright 2007-2009, The Cogent Project" __credits__ = ["Matthew Wakefield", "Peter Maxwell", "Gavin Huttley"] __license__ = "GPL" __version__ = "1.4.1" __maintainer__ = "Matthew Wakefield" __email__ = "matthew.wakefield@wehi.edu.au" __status__ = "Production" nucleotide_models = ['JC69','F81','HKY85', 'GTR'] codon_models = ['CNFGTR', 'CNFHKY', 'MG94HKY', 'MG94GTR', 'GY94', 'H04G', 'H04GK', 'H04GGK'] protein_models = [ 'DSO78', 'AH96', 'AH96_mtmammals', 'JTT92', 'WG01'] # Nucleotide Models def JC69(**kw): """Jukes and Cantor's 1969 model""" return substitution_model.Nucleotide( equal_motif_probs = True, do_scaling = True, model_gaps = False, recode_gaps = True, name = 'JC69', **kw) def F81(**kw): """Felsenstein's 1981 model""" return substitution_model.Nucleotide( motif_probs = None, do_scaling = True, model_gaps = False, recode_gaps = True, name = 'F81', **kw) # Substitution model rate matrix predicates MotifChange = predicate.MotifChange _gtr_preds = [MotifChange(x,y) for x,y in ['AC', 'AG', 'AT', 'CG', 'CT']] _kappa = (~MotifChange('R','Y')).aliased('kappa') _omega = predicate.replacement.aliased('omega') _cg = predicate.MotifChange('CG').aliased('G') _cg_k = (_cg & _kappa).aliased('G.K') def HKY85(**kw): """Hasegawa, Kishino and Yanamo 1985 model""" return substitution_model.Nucleotide( motif_probs = None, do_scaling = True, model_gaps = False, recode_gaps = True, name = 'HKY85', predicates = { 'kappa' : 'transition', }, **kw) def GTR(**kw): """General Time Reversible nucleotide substitution model.""" MotifChange = predicate.MotifChange preds = [MotifChange(x,y) for x,y in ['AC', 'AG', 'AT', 'CG', 'CT']] return substitution_model.Nucleotide( motif_probs = None, do_scaling = True, model_gaps = False, recode_gaps = True, name = 'GTR', predicates = _gtr_preds, **kw) # Codon Models def CNFGTR(**kw): """Conditional nucleotide frequency codon substitution model, GTR variant (with params analagous to the nucleotide GTR model). See Yap, Lindsay, Easteal and Huttley, Mol Biol Evol, In press.""" return substitution_model.Codon( motif_probs = None, do_scaling = True, model_gaps = False, recode_gaps = True, name = 'CNFGTR', predicates = _gtr_preds+[_omega], mprob_model='conditional', **kw) def CNFHKY(**kw): """Conditional nucleotide frequency codon substitution model, HKY variant (with kappa, the ratio of transitions to transversions) See Yap, Lindsay, Easteal and Huttley, Mol Biol Evol, In press.""" return substitution_model.Codon( motif_probs = None, do_scaling = True, model_gaps = False, recode_gaps = True, name = 'CNFHKY', predicates = [_kappa, _omega], mprob_model='conditional', **kw) def MG94HKY(**kw): """Muse and Gaut 1994 codon substitution model, HKY variant (with kappa, the ratio of transitions to transversions) see, Muse and Gaut, 1994, Mol Biol Evol, 11, 715-24""" return substitution_model.Codon( motif_probs = None, do_scaling = True, model_gaps = False, recode_gaps = True, name = 'MG94', predicates = [_kappa, _omega], mprob_model='monomer', **kw) def MG94GTR(**kw): """Muse and Gaut 1994 codon substitution model, GTR variant (with params analagous to the nucleotide GTR model) see, Muse and Gaut, 1994, Mol Biol Evol, 11, 715-24""" return substitution_model.Codon( motif_probs = None, do_scaling = True, model_gaps = False, recode_gaps = True, name = 'MG94', predicates = _gtr_preds+[_omega], mprob_model='monomer', **kw) def GY94(**kw): """Goldman and Yang 1994 codon substitution model. see, N Goldman and Z Yang, Mol. Biol. Evol., 11(5):725-36, 1994.""" return Y98(**kw) def Y98(**kw): """Yang's 1998 substitution model, a derivative of the GY94. see Z Yang. Mol. Biol. Evol., 15(5):568-73, 1998""" return substitution_model.Codon( motif_probs = None, do_scaling = True, model_gaps = False, recode_gaps = True, name = 'Y98', predicates = { 'kappa' : 'transition', 'omega' : 'replacement', }, mprob_model = 'tuple', **kw) def H04G(**kw): """Huttley 2004 CpG substitution model. Includes a term for substitutions to or from CpG's. see, GA Huttley. Mol Biol Evol, 21(9):1760-8""" return substitution_model.Codon( motif_probs = None, do_scaling = True, model_gaps = False, recode_gaps = True, name = 'H04G', predicates = [_cg, _kappa, _omega], mprob_model = 'tuple', **kw) def H04GK(**kw): """Huttley 2004 CpG substitution model. Includes a term for transition substitutions to or from CpG's. see, GA Huttley. Mol Biol Evol, 21(9):1760-8""" return substitution_model.Codon( motif_probs = None, do_scaling = True, model_gaps = False, recode_gaps = True, name = 'H04GK', predicates = [_cg_k, _kappa, _omega], mprob_model = 'tuple', **kw) def H04GGK(**kw): """Huttley 2004 CpG substitution model. Includes a general term for substitutions to or from CpG's and an adjustment for CpG transitions. see, GA Huttley. Mol Biol Evol, 21(9):1760-8""" return substitution_model.Codon( motif_probs = None, do_scaling = True, model_gaps = False, recode_gaps = True, name = 'H04GGK', predicates = [_cg, _cg_k, _kappa, _omega], mprob_model = 'tuple', **kw) # Protein Models # Empirical Protein Models DSO78_matrix = numpy.array( [[ 0.00000000e+00, 3.60000000e+01, 1.20000000e+02, 1.98000000e+02, 1.80000000e+01, 2.40000000e+02, 2.30000000e+01, 6.50000000e+01, 2.60000000e+01, 4.10000000e+01, 7.20000000e+01, 9.80000000e+01, 2.50000000e+02, 8.90000000e+01, 2.70000000e+01, 4.09000000e+02, 3.71000000e+02, 2.08000000e+02, 0.00000000e+00, 2.40000000e+01], [ 3.60000000e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.10000000e+01, 2.80000000e+01, 4.40000000e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.90000000e+01, 0.00000000e+00, 2.30000000e+01, 1.61000000e+02, 1.60000000e+01, 4.90000000e+01, 0.00000000e+00, 9.60000000e+01], [ 1.20000000e+02, 0.00000000e+00, 0.00000000e+00, 1.15300000e+03, 0.00000000e+00, 1.25000000e+02, 8.60000000e+01, 2.40000000e+01, 7.10000000e+01, 0.00000000e+00, 0.00000000e+00, 9.05000000e+02, 1.30000000e+01, 1.34000000e+02, 0.00000000e+00, 9.50000000e+01, 6.60000000e+01, 1.80000000e+01, 0.00000000e+00, 0.00000000e+00], [ 1.98000000e+02, 0.00000000e+00, 1.15300000e+03, 0.00000000e+00, 0.00000000e+00, 8.10000000e+01, 4.30000000e+01, 6.10000000e+01, 8.30000000e+01, 1.10000000e+01, 3.00000000e+01, 1.48000000e+02, 5.10000000e+01, 7.16000000e+02, 1.00000000e+00, 7.90000000e+01, 3.40000000e+01, 3.70000000e+01, 0.00000000e+00, 2.20000000e+01], [ 1.80000000e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.50000000e+01, 4.80000000e+01, 1.96000000e+02, 0.00000000e+00, 1.57000000e+02, 9.20000000e+01, 1.40000000e+01, 1.10000000e+01, 0.00000000e+00, 1.40000000e+01, 4.60000000e+01, 1.30000000e+01, 1.20000000e+01, 7.60000000e+01, 6.98000000e+02], [ 2.40000000e+02, 1.10000000e+01, 1.25000000e+02, 8.10000000e+01, 1.50000000e+01, 0.00000000e+00, 1.00000000e+01, 0.00000000e+00, 2.70000000e+01, 7.00000000e+00, 1.70000000e+01, 1.39000000e+02, 3.40000000e+01, 2.80000000e+01, 9.00000000e+00, 2.34000000e+02, 3.00000000e+01, 5.40000000e+01, 0.00000000e+00, 0.00000000e+00], [ 2.30000000e+01, 2.80000000e+01, 8.60000000e+01, 4.30000000e+01, 4.80000000e+01, 1.00000000e+01, 0.00000000e+00, 7.00000000e+00, 2.60000000e+01, 4.40000000e+01, 0.00000000e+00, 5.35000000e+02, 9.40000000e+01, 6.06000000e+02, 2.40000000e+02, 3.50000000e+01, 2.20000000e+01, 4.40000000e+01, 2.70000000e+01, 1.27000000e+02], [ 6.50000000e+01, 4.40000000e+01, 2.40000000e+01, 6.10000000e+01, 1.96000000e+02, 0.00000000e+00, 7.00000000e+00, 0.00000000e+00, 4.60000000e+01, 2.57000000e+02, 3.36000000e+02, 7.70000000e+01, 1.20000000e+01, 1.80000000e+01, 6.40000000e+01, 2.40000000e+01, 1.92000000e+02, 8.89000000e+02, 0.00000000e+00, 3.70000000e+01], [ 2.60000000e+01, 0.00000000e+00, 7.10000000e+01, 8.30000000e+01, 0.00000000e+00, 2.70000000e+01, 2.60000000e+01, 4.60000000e+01, 0.00000000e+00, 1.80000000e+01, 2.43000000e+02, 3.18000000e+02, 3.30000000e+01, 1.53000000e+02, 4.64000000e+02, 9.60000000e+01, 1.36000000e+02, 1.00000000e+01, 0.00000000e+00, 1.30000000e+01], [ 4.10000000e+01, 0.00000000e+00, 0.00000000e+00, 1.10000000e+01, 1.57000000e+02, 7.00000000e+00, 4.40000000e+01, 2.57000000e+02, 1.80000000e+01, 0.00000000e+00, 5.27000000e+02, 3.40000000e+01, 3.20000000e+01, 7.30000000e+01, 1.50000000e+01, 1.70000000e+01, 3.30000000e+01, 1.75000000e+02, 4.60000000e+01, 2.80000000e+01], [ 7.20000000e+01, 0.00000000e+00, 0.00000000e+00, 3.00000000e+01, 9.20000000e+01, 1.70000000e+01, 0.00000000e+00, 3.36000000e+02, 2.43000000e+02, 5.27000000e+02, 0.00000000e+00, 1.00000000e+00, 1.70000000e+01, 1.14000000e+02, 9.00000000e+01, 6.20000000e+01, 1.04000000e+02, 2.58000000e+02, 0.00000000e+00, 0.00000000e+00], [ 9.80000000e+01, 0.00000000e+00, 9.05000000e+02, 1.48000000e+02, 1.40000000e+01, 1.39000000e+02, 5.35000000e+02, 7.70000000e+01, 3.18000000e+02, 3.40000000e+01, 1.00000000e+00, 0.00000000e+00, 4.20000000e+01, 1.03000000e+02, 3.20000000e+01, 4.95000000e+02, 2.29000000e+02, 1.50000000e+01, 2.30000000e+01, 9.50000000e+01], [ 2.50000000e+02, 1.90000000e+01, 1.30000000e+01, 5.10000000e+01, 1.10000000e+01, 3.40000000e+01, 9.40000000e+01, 1.20000000e+01, 3.30000000e+01, 3.20000000e+01, 1.70000000e+01, 4.20000000e+01, 0.00000000e+00, 1.53000000e+02, 1.03000000e+02, 2.45000000e+02, 7.80000000e+01, 4.80000000e+01, 0.00000000e+00, 0.00000000e+00], [ 8.90000000e+01, 0.00000000e+00, 1.34000000e+02, 7.16000000e+02, 0.00000000e+00, 2.80000000e+01, 6.06000000e+02, 1.80000000e+01, 1.53000000e+02, 7.30000000e+01, 1.14000000e+02, 1.03000000e+02, 1.53000000e+02, 0.00000000e+00, 2.46000000e+02, 5.60000000e+01, 5.30000000e+01, 3.50000000e+01, 0.00000000e+00, 0.00000000e+00], [ 2.70000000e+01, 2.30000000e+01, 0.00000000e+00, 1.00000000e+00, 1.40000000e+01, 9.00000000e+00, 2.40000000e+02, 6.40000000e+01, 4.64000000e+02, 1.50000000e+01, 9.00000000e+01, 3.20000000e+01, 1.03000000e+02, 2.46000000e+02, 0.00000000e+00, 1.54000000e+02, 2.60000000e+01, 2.40000000e+01, 2.01000000e+02, 8.00000000e+00], [ 4.09000000e+02, 1.61000000e+02, 9.50000000e+01, 7.90000000e+01, 4.60000000e+01, 2.34000000e+02, 3.50000000e+01, 2.40000000e+01, 9.60000000e+01, 1.70000000e+01, 6.20000000e+01, 4.95000000e+02, 2.45000000e+02, 5.60000000e+01, 1.54000000e+02, 0.00000000e+00, 5.50000000e+02, 3.00000000e+01, 7.50000000e+01, 3.40000000e+01], [ 3.71000000e+02, 1.60000000e+01, 6.60000000e+01, 3.40000000e+01, 1.30000000e+01, 3.00000000e+01, 2.20000000e+01, 1.92000000e+02, 1.36000000e+02, 3.30000000e+01, 1.04000000e+02, 2.29000000e+02, 7.80000000e+01, 5.30000000e+01, 2.60000000e+01, 5.50000000e+02, 0.00000000e+00, 1.57000000e+02, 0.00000000e+00, 4.20000000e+01], [ 2.08000000e+02, 4.90000000e+01, 1.80000000e+01, 3.70000000e+01, 1.20000000e+01, 5.40000000e+01, 4.40000000e+01, 8.89000000e+02, 1.00000000e+01, 1.75000000e+02, 2.58000000e+02, 1.50000000e+01, 4.80000000e+01, 3.50000000e+01, 2.40000000e+01, 3.00000000e+01, 1.57000000e+02, 0.00000000e+00, 0.00000000e+00, 2.80000000e+01], [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 7.60000000e+01, 0.00000000e+00, 2.70000000e+01, 0.00000000e+00, 0.00000000e+00, 4.60000000e+01, 0.00000000e+00, 2.30000000e+01, 0.00000000e+00, 0.00000000e+00, 2.01000000e+02, 7.50000000e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 6.10000000e+01], [ 2.40000000e+01, 9.60000000e+01, 0.00000000e+00, 2.20000000e+01, 6.98000000e+02, 0.00000000e+00, 1.27000000e+02, 3.70000000e+01, 1.30000000e+01, 2.80000000e+01, 0.00000000e+00, 9.50000000e+01, 0.00000000e+00, 0.00000000e+00, 8.00000000e+00, 3.40000000e+01, 4.20000000e+01, 2.80000000e+01, 6.10000000e+01, 0.00000000e+00]]) DSO78_freqs = {'A': 0.087126912873087131, 'C': 0.033473966526033475, 'E': 0.04952995047004953, 'D': 0.046871953128046873, 'G': 0.088611911388088618, 'F': 0.039771960228039777, 'I': 0.036885963114036892, 'H': 0.033617966382033626, 'K': 0.08048191951808048, 'M': 0.014752985247014754, 'L': 0.085356914643085369, 'N': 0.040431959568040438, 'Q': 0.038254961745038257, 'P': 0.050679949320050689, 'S': 0.069576930423069588, 'R': 0.040903959096040908, 'T': 0.058541941458058543, 'W': 0.010493989506010494, 'V': 0.064717935282064723, 'Y': 0.029915970084029919} JTT92_matrix = numpy.array( [[ 0., 56., 81., 105., 15., 179., 27., 36., 35., 30., 54., 54., 194., 57., 58., 378., 475., 298., 9., 11.], [ 56., 0., 10., 5., 78., 59., 69., 17., 7., 23., 31., 34., 14., 9., 113., 223., 42., 62., 115., 209.], [ 81., 10., 0., 767., 4., 130., 112., 11., 26., 7., 15., 528., 15., 49., 16., 59., 38., 31., 4., 46.], [ 105., 5., 767., 0., 5., 119., 26., 12., 181., 9., 18., 58., 18., 323., 29., 30., 32., 45., 10., 7.], [ 15., 78., 4., 5., 0., 5., 40., 89., 4., 248., 43., 10., 17., 4., 5., 92., 12., 62., 53., 536.], [ 179., 59., 130., 119., 5., 0., 23., 6., 27., 6., 14., 81., 24., 26., 137., 201., 33., 47., 55., 8.], [ 27., 69., 112., 26., 40., 23., 0., 16., 45., 56., 33., 391., 115., 597., 328., 73., 46., 11., 8., 573.], [ 36., 17., 11., 12., 89., 6., 16., 0., 21., 229., 479., 47., 10., 9., 22., 40., 245., 961., 9., 32.], [ 35., 7., 26., 181., 4., 27., 45., 21., 0., 14., 65., 263., 21., 292., 646., 47., 103., 14., 10., 8.], [ 30., 23., 7., 9., 248., 6., 56., 229., 14., 0., 388., 12., 102., 72., 38., 59., 25., 180., 52., 24.], [ 54., 31., 15., 18., 43., 14., 33., 479., 65., 388., 0., 30., 16., 43., 44., 29., 226., 323., 24., 18.], [ 54., 34., 528., 58., 10., 81., 391., 47., 263., 12., 30., 0., 15., 86., 45., 503., 232., 16., 8., 70.], [ 194., 14., 15., 18., 17., 24., 115., 10., 21., 102., 16., 15., 0., 164., 74., 285., 118., 23., 6., 10.], [ 57., 9., 49., 323., 4., 26., 597., 9., 292., 72., 43., 86., 164., 0., 310., 53., 51., 20., 18., 24.], [ 58., 113., 16., 29., 5., 137., 328., 22., 646., 38., 44., 45., 74., 310., 0., 101., 64., 17., 126., 20.], [ 378., 223., 59., 30., 92., 201., 73., 40., 47., 59., 29., 503., 285., 53., 101., 0., 477., 38., 35., 63.], [ 475., 42., 38., 32., 12., 33., 46., 245., 103., 25., 226., 232., 118., 51., 64., 477., 0., 112., 12., 21.], [ 298., 62., 31., 45., 62., 47., 11., 961., 14., 180., 323., 16., 23., 20., 17., 38., 112., 0., 25., 16.], [ 9., 115., 4., 10., 53., 55., 8., 9., 10., 52., 24., 8., 6., 18., 126., 35., 12., 25., 0., 71.], [ 11., 209., 46., 7., 536., 8., 573., 32., 8., 24., 18., 70., 10., 24., 20., 63., 21., 16., 71., 0.]]) JTT92_freqs = {'A': 0.076747923252076758, 'C': 0.019802980197019805, 'E': 0.061829938170061841, 'D': 0.05154394845605155, 'G': 0.073151926848073159, 'F': 0.040125959874040135, 'I': 0.053760946239053767, 'H': 0.022943977056022944, 'K': 0.058675941324058678, 'M': 0.023825976174023829, 'L': 0.091903908096091905, 'N': 0.042644957355042652, 'Q': 0.040751959248040752, 'P': 0.050900949099050907, 'S': 0.068764931235068771, 'R': 0.051690948309051694, 'T': 0.058564941435058568, 'W': 0.014260985739014262, 'V': 0.066004933995066004, 'Y': 0.032101967898032102} AH96_matrix = numpy.array( [[ 0. , 59.93, 17.67, 9.77, 6.37, 120.71, 13.9 , 96.49, 8.36, 25.46, 141.88, 26.95, 54.31, 1.9 , 23.18, 387.86, 480.72, 195.06, 1.9 , 6.48], [ 59.93, 0. , 1.9 , 1.9 , 70.8 , 30.71, 141.49, 62.73, 1.9 , 25.65, 6.18, 58.94, 31.26, 75.24, 103.33, 277.05, 179.97, 1.9 , 33.6 , 254.77], [ 17.67, 1.9 , 0. , 583.55, 4.98, 56.77, 113.99, 4.34, 2.31, 1.9 , 1.9 , 794.38, 13.43, 55.28, 1.9 , 69.02, 28.01, 1.9 , 19.86, 21.21], [ 9.77, 1.9 , 583.55, 0. , 2.67, 28.28, 49.12, 3.31, 313.86, 1.9 , 1.9 , 63.05, 12.83, 313.56, 1.9 , 54.71, 14.82, 21.14, 1.9 , 13.12], [ 6.37, 70.8 , 4.98, 2.67, 0. , 1.9 , 48.16, 84.67, 6.44, 216.06, 90.82, 15.2 , 17.31, 19.11, 4.69, 64.29, 33.85, 6.35, 7.84, 465.58], [ 120.71, 30.71, 56.77, 28.28, 1.9 , 0. , 1.9 , 5.98, 22.73, 2.41, 1.9 , 53.3 , 1.9 , 6.75, 23.03, 125.93, 11.17, 2.53, 10.92, 3.21], [ 13.9 , 141.49, 113.99, 49.12, 48.16, 1.9 , 0. , 12.26, 127.67, 11.49, 11.97, 496.13, 60.97, 582.4 , 165.23, 77.46, 44.78, 1.9 , 7.08, 670.14], [ 96.49, 62.73, 4.34, 3.31, 84.67, 5.98, 12.26, 0. , 19.57, 329.09, 517.98, 27.1 , 20.63, 8.34, 1.9 , 47.7 , 368.43, 1222.94, 1.9 , 25.01], [ 8.36, 1.9 , 2.31, 313.86, 6.44, 22.73, 127.67, 19.57, 0. , 14.88, 91.37, 608.7 , 50.1 , 465.58, 141.4 , 105.79, 136.33, 1.9 , 24. , 51.17], [ 25.46, 25.65, 1.9 , 1.9 , 216.06, 2.41, 11.49, 329.09, 14.88, 0. , 537.53, 15.16, 40.1 , 39.7 , 15.58, 73.61, 126.4 , 91.67, 32.44, 44.15], [ 141.88, 6.18, 1.9 , 1.9 , 90.82, 1.9 , 11.97, 517.98, 91.37, 537.53, 0. , 65.41, 18.84, 47.37, 1.9 , 111.16, 528.17, 387.54, 21.71, 39.96], [ 26.95, 58.94, 794.38, 63.05, 15.2 , 53.3 , 496.13, 27.1 , 608.7 , 15.16, 65.41, 0. , 73.31, 173.56, 13.24, 494.39, 238.46, 1.9 , 10.68, 191.36], [ 54.31, 31.26, 13.43, 12.83, 17.31, 1.9 , 60.97, 20.63, 50.1 , 40.1 , 18.84, 73.31, 0. , 137.29, 23.64, 169.9 , 128.22, 8.23, 4.21, 16.21], [ 1.9 , 75.24, 55.28, 313.56, 19.11, 6.75, 582.4 , 8.34, 465.58, 39.7 , 47.37, 173.56, 137.29, 0. , 220.99, 54.11, 94.93, 19. , 1.9 , 38.82], [ 23.18, 103.33, 1.9 , 1.9 , 4.69, 23.03, 165.23, 1.9 , 141.4 , 15.58, 1.9 , 13.24, 23.64, 220.99, 0. , 6.04, 2.08, 7.64, 21.95, 1.9 ], [ 387.86, 277.05, 69.02, 54.71, 64.29, 125.93, 77.46, 47.7 , 105.79, 73.61, 111.16, 494.39, 169.9 , 54.11, 6.04, 0. , 597.21, 1.9 , 38.58, 64.92], [ 480.72, 179.97, 28.01, 14.82, 33.85, 11.17, 44.78, 368.43, 136.33, 126.4 , 528.17, 238.46, 128.22, 94.93, 2.08, 597.21, 0. , 204.54, 9.99, 38.73], [ 195.06, 1.9 , 1.9 , 21.14, 6.35, 2.53, 1.9 , 1222.94, 1.9 , 91.67, 387.54, 1.9 , 8.23, 19. , 7.64, 1.9 , 204.54, 0. , 5.37, 1.9 ], [ 1.9 , 33.6 , 19.86, 1.9 , 7.84, 10.92, 7.08, 1.9 , 24. , 32.44, 21.71, 10.68, 4.21, 1.9 , 21.95, 38.58, 9.99, 5.37, 0. , 26.25], [ 6.48, 254.77, 21.21, 13.12, 465.58, 3.21, 670.14, 25.01, 51.17, 44.15, 39.96, 191.36, 16.21, 38.82, 1.9 , 64.92, 38.73, 1.9 , 26.25, 0. ]]) AH96_freqs = { 'A': 0.071999999999999995, 'C': 0.0060000000000000001, 'E': 0.024, 'D': 0.019, 'G': 0.056000000000000001, 'F': 0.060999999999999999, 'I': 0.087999999999999995, 'H': 0.028000000000000001, 'K': 0.023, 'M': 0.053999999999999999, 'L': 0.16900000000000001, 'N': 0.039, 'Q': 0.025000000000000001, 'P': 0.053999999999999999, 'S': 0.071999999999999995, 'R': 0.019, 'T': 0.085999999999999993, 'W': 0.029000000000000001, 'V': 0.042999999999999997, 'Y': 0.033000000000000002} AH96_mtmammals_matrix = numpy.array( [[ 0.00000000e+00, 0.00000000e+00, 1.10000000e+01, 0.00000000e+00, 0.00000000e+00, 7.80000000e+01, 8.00000000e+00, 7.50000000e+01, 0.00000000e+00, 2.10000000e+01, 7.60000000e+01, 2.00000000e+00, 5.30000000e+01, 0.00000000e+00, 3.20000000e+01, 3.42000000e+02, 6.81000000e+02, 3.98000000e+02, 5.00000000e+00, 0.00000000e+00], [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 7.00000000e+00, 0.00000000e+00, 3.05000000e+02, 4.10000000e+01, 0.00000000e+00, 2.70000000e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.86000000e+02, 3.47000000e+02, 1.14000000e+02, 0.00000000e+00, 6.50000000e+01, 5.30000000e+02], [ 1.10000000e+01, 0.00000000e+00, 0.00000000e+00, 5.69000000e+02, 5.00000000e+00, 7.90000000e+01, 1.10000000e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 8.64000000e+02, 2.00000000e+00, 4.90000000e+01, 0.00000000e+00, 1.60000000e+01, 0.00000000e+00, 1.00000000e+01, 0.00000000e+00, 0.00000000e+00], [ 0.00000000e+00, 0.00000000e+00, 5.69000000e+02, 0.00000000e+00, 0.00000000e+00, 2.20000000e+01, 2.20000000e+01, 0.00000000e+00, 2.15000000e+02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.74000000e+02, 0.00000000e+00, 2.10000000e+01, 4.00000000e+00, 2.00000000e+01, 0.00000000e+00, 0.00000000e+00], [ 0.00000000e+00, 7.00000000e+00, 5.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.70000000e+01, 0.00000000e+00, 2.46000000e+02, 1.10000000e+01, 6.00000000e+00, 1.70000000e+01, 0.00000000e+00, 0.00000000e+00, 9.00000000e+01, 8.00000000e+00, 6.00000000e+00, 0.00000000e+00, 6.82000000e+02], [ 7.80000000e+01, 0.00000000e+00, 7.90000000e+01, 2.20000000e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 4.70000000e+01, 0.00000000e+00, 0.00000000e+00, 1.80000000e+01, 1.12000000e+02, 0.00000000e+00, 5.00000000e+00, 0.00000000e+00, 1.00000000e+00], [ 8.00000000e+00, 3.05000000e+02, 1.10000000e+01, 2.20000000e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.60000000e+01, 0.00000000e+00, 4.58000000e+02, 5.30000000e+01, 5.50000000e+02, 2.32000000e+02, 2.00000000e+01, 1.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.52500000e+03], [ 7.50000000e+01, 4.10000000e+01, 0.00000000e+00, 0.00000000e+00, 5.70000000e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 6.00000000e+00, 2.32000000e+02, 3.78000000e+02, 1.90000000e+01, 5.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.60000000e+02, 2.22000000e+03, 0.00000000e+00, 1.60000000e+01], [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.15000000e+02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 6.00000000e+00, 0.00000000e+00, 4.00000000e+00, 5.90000000e+01, 4.08000000e+02, 1.80000000e+01, 2.42000000e+02, 5.00000000e+01, 6.50000000e+01, 5.00000000e+01, 0.00000000e+00, 0.00000000e+00, 6.70000000e+01], [ 2.10000000e+01, 2.70000000e+01, 0.00000000e+00, 0.00000000e+00, 2.46000000e+02, 0.00000000e+00, 2.60000000e+01, 2.32000000e+02, 4.00000000e+00, 0.00000000e+00, 6.09000000e+02, 0.00000000e+00, 4.30000000e+01, 2.00000000e+01, 6.00000000e+00, 7.40000000e+01, 3.40000000e+01, 1.00000000e+02, 1.20000000e+01, 2.50000000e+01], [ 7.60000000e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.10000000e+01, 0.00000000e+00, 0.00000000e+00, 3.78000000e+02, 5.90000000e+01, 6.09000000e+02, 0.00000000e+00, 2.10000000e+01, 0.00000000e+00, 2.20000000e+01, 0.00000000e+00, 4.70000000e+01, 6.91000000e+02, 8.32000000e+02, 1.30000000e+01, 0.00000000e+00], [ 2.00000000e+00, 0.00000000e+00, 8.64000000e+02, 0.00000000e+00, 6.00000000e+00, 4.70000000e+01, 4.58000000e+02, 1.90000000e+01, 4.08000000e+02, 0.00000000e+00, 2.10000000e+01, 0.00000000e+00, 3.30000000e+01, 8.00000000e+00, 4.00000000e+00, 4.46000000e+02, 1.10000000e+02, 0.00000000e+00, 6.00000000e+00, 1.56000000e+02], [ 5.30000000e+01, 0.00000000e+00, 2.00000000e+00, 0.00000000e+00, 1.70000000e+01, 0.00000000e+00, 5.30000000e+01, 5.00000000e+00, 1.80000000e+01, 4.30000000e+01, 0.00000000e+00, 3.30000000e+01, 0.00000000e+00, 5.10000000e+01, 9.00000000e+00, 2.02000000e+02, 7.80000000e+01, 0.00000000e+00, 7.00000000e+00, 8.00000000e+00], [ 0.00000000e+00, 0.00000000e+00, 4.90000000e+01, 2.74000000e+02, 0.00000000e+00, 0.00000000e+00, 5.50000000e+02, 0.00000000e+00, 2.42000000e+02, 2.00000000e+01, 2.20000000e+01, 8.00000000e+00, 5.10000000e+01, 0.00000000e+00, 2.46000000e+02, 3.00000000e+01, 0.00000000e+00, 3.30000000e+01, 0.00000000e+00, 5.40000000e+01], [ 3.20000000e+01, 1.86000000e+02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.80000000e+01, 2.32000000e+02, 0.00000000e+00, 5.00000000e+01, 6.00000000e+00, 0.00000000e+00, 4.00000000e+00, 9.00000000e+00, 2.46000000e+02, 0.00000000e+00, 3.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.60000000e+01, 0.00000000e+00], [ 3.42000000e+02, 3.47000000e+02, 1.60000000e+01, 2.10000000e+01, 9.00000000e+01, 1.12000000e+02, 2.00000000e+01, 0.00000000e+00, 6.50000000e+01, 7.40000000e+01, 4.70000000e+01, 4.46000000e+02, 2.02000000e+02, 3.00000000e+01, 3.00000000e+00, 0.00000000e+00, 6.14000000e+02, 0.00000000e+00, 1.70000000e+01, 1.07000000e+02], [ 6.81000000e+02, 1.14000000e+02, 0.00000000e+00, 4.00000000e+00, 8.00000000e+00, 0.00000000e+00, 1.00000000e+00, 3.60000000e+02, 5.00000000e+01, 3.40000000e+01, 6.91000000e+02, 1.10000000e+02, 7.80000000e+01, 0.00000000e+00, 0.00000000e+00, 6.14000000e+02, 0.00000000e+00, 2.37000000e+02, 0.00000000e+00, 0.00000000e+00], [ 3.98000000e+02, 0.00000000e+00, 1.00000000e+01, 2.00000000e+01, 6.00000000e+00, 5.00000000e+00, 0.00000000e+00, 2.22000000e+03, 0.00000000e+00, 1.00000000e+02, 8.32000000e+02, 0.00000000e+00, 0.00000000e+00, 3.30000000e+01, 0.00000000e+00, 0.00000000e+00, 2.37000000e+02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [ 5.00000000e+00, 6.50000000e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.20000000e+01, 1.30000000e+01, 6.00000000e+00, 7.00000000e+00, 0.00000000e+00, 1.60000000e+01, 1.70000000e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.40000000e+01], [ 0.00000000e+00, 5.30000000e+02, 0.00000000e+00, 0.00000000e+00, 6.82000000e+02, 1.00000000e+00, 1.52500000e+03, 1.60000000e+01, 6.70000000e+01, 2.50000000e+01, 0.00000000e+00, 1.56000000e+02, 8.00000000e+00, 5.40000000e+01, 0.00000000e+00, 1.07000000e+02, 0.00000000e+00, 0.00000000e+00, 1.40000000e+01, 0.00000000e+00]]) AH96_mtmammals_freqs = { 'A': 0.069199999999999998, 'C': 0.0064999999999999997, 'E': 0.023599999999999999, 'D': 0.018599999999999998, 'G': 0.0557, 'F': 0.061100000000000002, 'I': 0.090499999999999997, 'H': 0.027699999999999999, 'K': 0.022100000000000002, 'M': 0.056099999999999997, 'L': 0.16750000000000001, 'N': 0.040000000000000001, 'Q': 0.023800000000000002, 'P': 0.053600000000000002, 'S': 0.072499999999999995, 'R': 0.0184, 'T': 0.086999999999999994, 'W': 0.0293, 'V': 0.042799999999999998, 'Y': 0.034000000000000002} WG01_matrix = numpy.array( [[ 0. , 1.02704 , 0.738998, 1.58285 , 0.210494, 1.41672 , 0.316954, 0.193335, 0.906265, 0.397915, 0.893496, 0.509848, 1.43855 , 0.908598, 0.551571, 3.37079 , 2.12111 , 2.00601 , 0.113133, 0.240735 ], [ 1.02704 , 0. , 0.0302949, 0.021352, 0.39802 , 0.306674, 0.248972, 0.170135, 0.0740339, 0.384287, 0.390482, 0.265256, 0.109404, 0.0988179, 0.528191, 1.40766 , 0.512984, 1.00214 , 0.71707 , 0.543833 ], [ 0.738998, 0.0302949, 0. , 6.17416 , 0.0467304, 0.865584, 0.930676, 0.039437, 0.479855, 0.0848047, 0.103754, 5.42942 , 0.423984, 0.616783, 0.147304, 1.07176 , 0.374866, 0.152335, 0.129767, 0.325711 ], [ 1.58285 , 0.021352, 6.17416 , 0. , 0.0811339, 0.567717, 0.570025, 0.127395, 2.58443 , 0.154263, 0.315124, 0.947198, 0.682355, 5.46947 , 0.439157, 0.704939, 0.822765, 0.588731, 0.156557, 0.196303 ], [ 0.210494, 0.39802 , 0.0467304, 0.0811339, 0. , 0.049931, 0.679371, 1.05947 , 0.088836, 2.11517 , 1.19063 , 0.0961621, 0.161444, 0.0999208, 0.102711, 0.545931, 0.171903, 0.649892, 1.52964 , 6.45428 ], [ 1.41672 , 0.306674, 0.865584, 0.567717, 0.049931, 0. , 0.24941 , 0.0304501, 0.373558, 0.0613037, 0.1741 , 1.12556 , 0.24357 , 0.330052, 0.584665, 1.34182 , 0.225833, 0.187247, 0.336983, 0.103604 ], [ 0.316954, 0.248972, 0.930676, 0.570025, 0.679371, 0.24941 , 0. , 0.13819 , 0.890432, 0.499462, 0.404141, 3.95629 , 0.696198, 4.29411 , 2.13715 , 0.740169, 0.473307, 0.118358, 0.262569, 3.87344 ], [ 0.193335, 0.170135, 0.039437, 0.127395, 1.05947 , 0.0304501, 0.13819 , 0. , 0.323832, 3.17097 , 4.25746 , 0.554236, 0.0999288, 0.113917, 0.186979, 0.31944 , 1.45816 , 7.8213 , 0.212483, 0.42017 ], [ 0.906265, 0.0740339, 0.479855, 2.58443 , 0.088836, 0.373558, 0.890432, 0.323832, 0. , 0.257555, 0.934276, 3.01201 , 0.556896, 3.8949 , 5.35142 , 0.96713 , 1.38698 , 0.305434, 0.137505, 0.133264 ], [ 0.397915, 0.384287, 0.0848047, 0.154263, 2.11517 , 0.0613037, 0.499462, 3.17097 , 0.257555, 0. , 4.85402 , 0.131528, 0.415844, 0.869489, 0.497671, 0.344739, 0.326622, 1.80034 , 0.665309, 0.398618 ], [ 0.893496, 0.390482, 0.103754, 0.315124, 1.19063 , 0.1741 , 0.404141, 4.25746 , 0.934276, 4.85402 , 0. , 0.198221, 0.171329, 1.54526 , 0.683162, 0.493905, 1.51612 , 2.05845 , 0.515706, 0.428437 ], [ 0.509848, 0.265256, 5.42942 , 0.947198, 0.0961621, 1.12556 , 3.95629 , 0.554236, 3.01201 , 0.131528, 0.198221, 0. , 0.195081, 1.54364 , 0.635346, 3.97423 , 2.03006 , 0.196246, 0.0719167, 1.086 ], [ 1.43855 , 0.109404, 0.423984, 0.682355, 0.161444, 0.24357 , 0.696198, 0.0999288, 0.556896, 0.415844, 0.171329, 0.195081, 0. , 0.933372, 0.679489, 1.61328 , 0.795384, 0.314887, 0.139405, 0.216046 ], [ 0.908598, 0.0988179, 0.616783, 5.46947 , 0.0999208, 0.330052, 4.29411 , 0.113917, 3.8949 , 0.869489, 1.54526 , 1.54364 , 0.933372, 0. , 3.0355 , 1.02887 , 0.857928, 0.301281, 0.215737, 0.22771 ], [ 0.551571, 0.528191, 0.147304, 0.439157, 0.102711, 0.584665, 2.13715 , 0.186979, 5.35142 , 0.497671, 0.683162, 0.635346, 0.679489, 3.0355 , 0. , 1.22419 , 0.554413, 0.251849, 1.16392 , 0.381533 ], [ 3.37079 , 1.40766 , 1.07176 , 0.704939, 0.545931, 1.34182 , 0.740169, 0.31944 , 0.96713 , 0.344739, 0.493905, 3.97423 , 1.61328 , 1.02887 , 1.22419 , 0. , 4.37802 , 0.232739, 0.523742, 0.786993 ], [ 2.12111 , 0.512984, 0.374866, 0.822765, 0.171903, 0.225833, 0.473307, 1.45816 , 1.38698 , 0.326622, 1.51612 , 2.03006 , 0.795384, 0.857928, 0.554413, 4.37802 , 0. , 1.38823 , 0.110864, 0.291148 ], [ 2.00601 , 1.00214 , 0.152335, 0.588731, 0.649892, 0.187247, 0.118358, 7.8213 , 0.305434, 1.80034 , 2.05845 , 0.196246, 0.314887, 0.301281, 0.251849, 0.232739, 1.38823 , 0. , 0.365369, 0.31473 ], [ 0.113133, 0.71707 , 0.129767, 0.156557, 1.52964 , 0.336983, 0.262569, 0.212483, 0.137505, 0.665309, 0.515706, 0.0719167, 0.139405, 0.215737, 1.16392 , 0.523742, 0.110864, 0.365369, 0. , 2.48539 ], [ 0.240735, 0.543833, 0.325711, 0.196303, 6.45428 , 0.103604, 3.87344 , 0.42017 , 0.133264, 0.398618, 0.428437, 1.086 , 0.216046, 0.22771 , 0.381533, 0.786993, 0.291148, 0.31473 , 2.48539 , 0. ]]) WG01_freqs = { 'A': 0.086627908662790867, 'C': 0.019307801930780195, 'E': 0.058058905805890577, 'D': 0.057045105704510574, 'G': 0.083251808325180837, 'F': 0.038431903843190382, 'I': 0.048466004846600491, 'H': 0.024431302443130246, 'K': 0.062028606202860624, 'M': 0.019502701950270197, 'L': 0.086209008620900862, 'N': 0.039089403908940397, 'Q': 0.036728103672810368, 'P': 0.045763104576310464, 'S': 0.069517906951790692, 'R': 0.043972004397200441, 'T': 0.061012706101270617, 'W': 0.014385901438590145, 'V': 0.070895607089560719, 'Y': 0.035274203527420354} def DSO78(**kw): """Dayhoff et al 1978 empirical protein model Dayhoff, MO, Schwartz RM, and Orcutt, BC. 1978 A model of evolutionary change in proteins. Pp. 345-352. Atlas of protein sequence and structure, Vol 5, Suppl. 3. National Biomedical Research Foundation, Washington D. C Matrix imported from PAML dayhoff.dat file""" sm = substitution_model.EmpiricalProteinMatrix( DSO78_matrix, DSO78_freqs, name='DSO78', **kw) return sm def JTT92(**kw): """Jones, Taylor and Thornton 1992 empirical protein model Jones DT, Taylor WR, Thornton JM. The rapid generation of mutation data matrices from protein sequences. Comput Appl Biosci. 1992 Jun;8(3):275-82. Matrix imported from PAML jones.dat file""" sm = substitution_model.EmpiricalProteinMatrix( JTT92_matrix, JTT92_freqs, name='JTT92', **kw) return sm def AH96(**kw): """Adachi and Hasegawa 1996 empirical model for mitochondrial proteins. Adachi J, Hasegawa M. Model of amino acid substitution in proteins encoded by mitochondrial DNA. J Mol Evol. 1996 Apr;42(4):459-68. Matrix imported from PAML mtREV24.dat file""" sm = substitution_model.EmpiricalProteinMatrix( AH96_matrix, AH96_freqs, name='AH96_mtREV24', **kw) return sm def mtREV(**kw): return AH96(**kw) def AH96_mtmammals(**kw): """Adachi and Hasegawa 1996 empirical model for mammalian mitochondrial proteins. Adachi J, Hasegawa M. Model of amino acid substitution in proteins encoded by mitochondrial DNA. J Mol Evol. 1996 Apr;42(4):459-68. Matrix imported from PAML mtmam.dat file""" sm = substitution_model.EmpiricalProteinMatrix( AH96_mtmammals_matrix, AH96_mtmammals_freqs, name='AH96_mtmammals', **kw) return sm def mtmam(**kw): return AH96_mtmammals(**kw) def WG01(**kw): """Whelan and Goldman 2001 empirical model for globular proteins. Whelan S, Goldman N. A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. Mol Biol Evol. 2001 May;18(5):691-9. Matrix imported from PAML wag.dat file""" sm = substitution_model.EmpiricalProteinMatrix( WG01_matrix, WG01_freqs, name='WG01', **kw) return sm