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="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'	-6739.3067	77	True	True

result.lf

CNFGTR

log-likelihood = -6739.3067

number of free parameters = 77

Global params
A/C	A/G	A/T	C/G	C/T	omega
1.0656	3.9391	0.7851	1.9475	4.2265	0.7569

Edge params
edge	parent	length
Galago	root	0.5330
HowlerMon	root	0.1365
Rhesus	edge.3	0.0659
Orangutan	edge.2	0.0233
Gorilla	edge.1	0.0075
Human	edge.0	0.0182
Chimpanzee	edge.0	0.0085
edge.0	edge.1	0.0000
edge.1	edge.2	0.0101
edge.2	edge.3	0.0352
edge.3	root	0.0228

Motif params
AAA	AAC	AAG	AAT	ACA	ACC	ACG	ACT	AGA	AGC
0.0540	0.0242	0.0307	0.0543	0.0237	0.0063	0.0021	0.0297	0.0238	0.0280

continuation
AGG	AGT	ATA	ATC	ATG	ATT	CAA	CAC	CAG	CAT
0.0122	0.0405	0.0226	0.0071	0.0141	0.0203	0.0228	0.0063	0.0220	0.0237

continuation
CCA	CCC	CCG	CCT	CGA	CGC	CGG	CGT	CTA	CTC
0.0165	0.0043	0.0021	0.0239	0.0022	0.0012	0.0035	0.0058	0.0123	0.0065

continuation
CTG	CTT	GAA	GAC	GAG	GAT	GCA	GCC	GCG	GCT
0.0098	0.0105	0.0703	0.0112	0.0263	0.0310	0.0154	0.0083	0.0036	0.0145

continuation
GGA	GGC	GGG	GGT	GTA	GTC	GTG	GTT	TAC	TAT
0.0151	0.0072	0.0051	0.0139	0.0170	0.0077	0.0094	0.0210	0.0036	0.0171

continuation
TCA	TCC	TCG	TCT	TGC	TGG	TGT	TTA	TTC	TTG
0.0220	0.0083	0.0039	0.0214	0.0038	0.0033	0.0201	0.0222	0.0051	0.0107

continuation
TTT
0.0146