/usr/lib/python2.7/dist-packages/cogent/evolve/models.py is in python-cogent 1.9-9.
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"""A collection of pre-defined models. These are provided for convenience so that
users do not need to keep reconstructing the standard models. We encourage 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
from cogent.evolve.predicate import MotifChange, replacement
from cogent.evolve.solved_models import F81, HKY85, TN93
__author__ = "Matthew Wakefield"
__copyright__ = "Copyright 2007-2016, The Cogent Project"
__credits__ = ["Matthew Wakefield", "Peter Maxwell", "Gavin Huttley"]
__license__ = "GPL"
__version__ = "1.9"
__maintainer__ = "Matthew Wakefield"
__email__ = "wakefield@wehi.edu.au"
__status__ = "Production"
nucleotide_models = ['JC69','K80', 'F81','HKY85', 'TN93', 'GTR']
codon_models = ['CNFGTR', 'CNFHKY', 'MG94HKY', 'MG94GTR', 'GY94', 'H04G', 'H04GK', 'H04GGK']
protein_models = [ 'DSO78', 'AH96', 'AH96_mtmammals', 'JTT92', 'WG01']
# Substitution model rate matrix predicates
_gtr_preds = [MotifChange(x,y) for x,y in ['AC', 'AG', 'AT', 'CG', 'CT']]
_kappa = (~MotifChange('R','Y')).aliased('kappa')
_omega = replacement.aliased('omega')
_cg = MotifChange('CG').aliased('G')
_cg_k = (_cg & _kappa).aliased('G.K')
def K80(**kw):
"""Kimura 1980"""
return HKY85(equal_motif_probs=True, optimise_motif_probs=False, **kw)
def JC69(**kw):
"""Jukes and Cantor's 1969 model"""
return F81(equal_motif_probs=True, optimise_motif_probs=False, **kw)
def GTR(**kw):
"""General Time Reversible nucleotide substitution model."""
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
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