Applying GNC, a non-stationary codon model#

Note

These docs now use the new_type core objects via the following setting.

import os

# using new types without requiring an explicit argument
os.environ["COGENT3_NEW_TYPE"] = "1"

See Kaehler et al for the formal description of this model. Note that perform hypothesis testing using this model elsewhere.

We apply this to a sample alignment.

from cogent3 import get_app

loader = get_app("load_aligned", format="fasta", moltype="dna")
aln = loader("data/primate_brca1.fasta")

The model is specified using it’s abbreviation.

model = get_app("model", "GNC", tree="data/primate_brca1.tree")
result = model(aln)
result

GNC
key	lnL	nfp	DLC	unique_Q
'GNC'	-6713.2743	23	True	True

result.lf

log-likelihood = -6713.2743

number of free parameters = 23

Global params
A>C	A>G	A>T	C>A	C>G	C>T	G>A	G>C	G>T	T>A
0.8615	3.5374	0.9792	1.6668	2.2042	6.2567	7.9197	1.2253	0.8015	1.2911

continuation
T>C	omega
3.0724	0.8204

Motif params
AAA	AAC	AAG	AAT	ACA	ACC	ACG	ACT	AGA	AGC
0.0556	0.0235	0.0344	0.0556	0.0228	0.0046	0.0008	0.0289	0.0231	0.0286

continuation
AGG	AGT	ATA	ATC	ATG	ATT	CAA	CAC	CAG	CAT
0.0140	0.0381	0.0186	0.0070	0.0128	0.0192	0.0196	0.0052	0.0238	0.0221

continuation
CCA	CCC	CCG	CCT	CGA	CGC	CGG	CGT	CTA	CTC
0.0195	0.0062	0.0006	0.0263	0.0011	0.0009	0.0023	0.0032	0.0137	0.0078

continuation
CTG	CTT	GAA	GAC	GAG	GAT	GCA	GCC	GCG	GCT
0.0125	0.0105	0.0755	0.0105	0.0303	0.0315	0.0158	0.0096	0.0014	0.0137

continuation
GGA	GGC	GGG	GGT	GTA	GTC	GTG	GTT	TAC	TAT
0.0161	0.0090	0.0067	0.0133	0.0148	0.0070	0.0069	0.0213	0.0023	0.0101

continuation
TCA	TCC	TCG	TCT	TGC	TGG	TGT	TTA	TTC	TTG
0.0221	0.0082	0.0015	0.0251	0.0018	0.0040	0.0201	0.0212	0.0078	0.0108

continuation
TTT
0.0187

We can obtain the tree with branch lengths as ENS#

If this tree is written to newick (using the write() method), the lengths will now be ENS.

tree = result.tree
fig = tree.get_figure()
fig.scale_bar = "top right"
fig.show(width=500, height=500)