Applying GNC, a non-stationary codon model¶

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

We apply this to a sample alignment.

from cogent3.app import io, evo

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

The model is specified using it’s abbreviation.

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

GNC
key	lnL	nfp	DLC	unique_Q
'GNC'	-6707.1856	83	True	True

result.lf

log-likelihood = -6707.1856

number of free parameters = 83

Global params
A>C	A>G	A>T	C>A	C>G	C>T	G>A	G>C	G>T	T>A
0.8617	3.5387	0.9783	1.6707	2.2019	6.2622	7.8942	1.2213	0.7981	1.2836

continuation
T>C	omega
3.0613	0.8201

Motif params
AAA	AAC	AAG	AAT	ACA	ACC	ACG	ACT	AGA	AGC
0.0557	0.0228	0.0352	0.0548	0.0234	0.0032	0.0000	0.0320	0.0224	0.0285

continuation
AGG	AGT	ATA	ATC	ATG	ATT	CAA	CAC	CAG	CAT
0.0146	0.0379	0.0184	0.0074	0.0120	0.0181	0.0194	0.0053	0.0254	0.0236

continuation
CCA	CCC	CCG	CCT	CGA	CGC	CGG	CGT	CTA	CTC
0.0213	0.0065	0.0000	0.0280	0.0000	0.0011	0.0011	0.0021	0.0154	0.0073

continuation
CTG	CTT	GAA	GAC	GAG	GAT	GCA	GCC	GCG	GCT
0.0135	0.0107	0.0772	0.0088	0.0298	0.0318	0.0169	0.0107	0.0010	0.0130

continuation
GGA	GGC	GGG	GGT	GTA	GTC	GTG	GTT	TAC	TAT
0.0147	0.0099	0.0079	0.0112	0.0148	0.0064	0.0073	0.0207	0.0021	0.0086

continuation
TCA	TCC	TCG	TCT	TGC	TGG	TGT	TTA	TTC	TTG
0.0224	0.0074	0.0000	0.0275	0.0011	0.0043	0.0212	0.0198	0.0085	0.0102

continuation
TTT
0.0181

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)