Applying GNC, a non-stationary codon model

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_name="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

GNC

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	T>C	omega
0.86	3.54	0.98	1.67	2.20	6.26	7.92	1.23	0.80	1.29	3.07	0.82

Edge params

edge	parent	length
Galago	root	0.52
HowlerMon	root	0.13
Rhesus	edge.3	0.06
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
0.06	0.02	0.03	0.06	0.02	0.00	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	0.01	0.00	0.03	0.00	0.00

continuation

CGG	CGT	CTA	CTC	CTG	CTT	GAA	GAC	GAG	GAT	GCA	GCC	GCG
0.00	0.00	0.01	0.01	0.01	0.01	0.08	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	0.01	0.01	0.01	0.02	0.00	0.01	0.02	0.01

continuation

TCG	TCT	TGC	TGG	TGT	TTA	TTC	TTG	TTT
0.00	0.03	0.00	0.00	0.02	0.02	0.01	0.01	0.02

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)