Applying a time-reversible codon model

We display the full set of codon models available.

from cogent3 import available_models

available_models("codon")

Specify a model using 'Abbreviation' (case sensitive).
Model Type	Abbreviation	Description
codon	CNFGTR	Conditional nucleotide frequency codon substitution model, GTR variant (with params analagous to the nucleotide GTR model). Yap, Lindsay, Easteal and Huttley, 2010, Mol Biol Evol 27: 726-734
codon	CNFHKY	Conditional nucleotide frequency codon substitution model, HKY variant (with kappa, the ratio of transitions to transversions) Yap, Lindsay, Easteal and Huttley, 2010, Mol Biol Evol 27: 726-734
codon	MG94HKY	Muse and Gaut 1994 codon substitution model, HKY variant (with kappa, the ratio of transitions to transversions) Muse and Gaut, 1994, Mol Biol Evol, 11, 715-24
codon	MG94GTR	Muse and Gaut 1994 codon substitution model, GTR variant (with params analagous to the nucleotide GTR model) Muse and Gaut, 1994, Mol Biol Evol, 11, 715-24
codon	GY94	Goldman and Yang 1994 codon substitution model. N Goldman and Z Yang, 1994, Mol Biol Evol, 11(5):725-36.
codon	Y98	Yang's 1998 substitution model, a derivative of the GY94. Z Yang, 1998, Mol Biol Evol, 15(5):568-73
codon	H04G	Huttley 2004 CpG substitution model. Includes a term for substitutions to or from CpG's. GA Huttley, 2004, Mol Biol Evol, 21(9):1760-8
codon	H04GK	Huttley 2004 CpG substitution model. Includes a term for transition substitutions to or from CpG's. GA Huttley, 2004, Mol Biol Evol, 21(9):1760-8
codon	H04GGK	Huttley 2004 CpG substitution model. Includes a general term for substitutions to or from CpG's and an adjustment for CpG transitions. GA Huttley, 2004, Mol Biol Evol, 21(9):1760-8
codon	GNC	General Nucleotide Codon, a non-reversible codon model. Kaehler, Yap, Huttley, 2017, Gen Biol Evol 9(1): 134–49

10 rows x 3 columns

Using the conditional nucleotide form codon model

The CNFGTR model (Yap et al) is the most robust of the time-reversible codon models available (Kaehler et al). By default, this model does not optimise the codon frequencies but uses the average estimated from the alignment. We configure the model to optimise the root motif probabilities.

from cogent3 import get_app

loader = get_app("load_aligned", format_name="fasta", moltype="dna")
aln = loader("data/primate_brca1.fasta")
model = get_app("model",
    "CNFGTR",
    tree="data/primate_brca1.tree",
    optimise_motif_probs=True,
)
result = model(aln)
result

CNFGTR
key	lnL	nfp	DLC	unique_Q
'CNFGTR'	-6739.3076	77	True	True

result.lf

CNFGTR

log-likelihood = -6739.3076

number of free parameters = 77

Global params
A/C	A/G	A/T	C/G	C/T	omega
1.07	3.94	0.79	1.95	4.23	0.76

Edge params
edge	parent	length
Galago	root	0.53
HowlerMon	root	0.14
Rhesus	edge.3	0.07
Orangutan	edge.2	0.02
Gorilla	edge.1	0.01
Human	edge.0	0.02
Chimpanzee	edge.0	0.01
edge.0	edge.1	0.00
edge.1	edge.2	0.01
edge.2	edge.3	0.04
edge.3	root	0.02

Motif params
AAA	AAC	AAG	AAT	ACA	ACC	ACG	ACT	AGA	AGC	AGG	AGT	ATA
0.05	0.02	0.03	0.05	0.02	0.01	0.00	0.03	0.02	0.03	0.01	0.04	0.02

continuation
ATC	ATG	ATT	CAA	CAC	CAG	CAT	CCA	CCC	CCG	CCT	CGA	CGC
0.01	0.01	0.02	0.02	0.01	0.02	0.02	0.02	0.00	0.00	0.02	0.00	0.00

continuation
CGG	CGT	CTA	CTC	CTG	CTT	GAA	GAC	GAG	GAT	GCA	GCC	GCG
0.00	0.01	0.01	0.01	0.01	0.01	0.07	0.01	0.03	0.03	0.02	0.01	0.00

continuation
GCT	GGA	GGC	GGG	GGT	GTA	GTC	GTG	GTT	TAC	TAT	TCA	TCC
0.01	0.02	0.01	0.01	0.01	0.02	0.01	0.01	0.02	0.00	0.02	0.02	0.01

continuation
TCG	TCT	TGC	TGG	TGT	TTA	TTC	TTG	TTT
0.00	0.02	0.00	0.00	0.02	0.02	0.01	0.01	0.01