natsel_timehet – a test of branch heterogeneity

We employ codon models to test whether the mode of natural selection affecting human and chimpanzee lineages is distinctive. This is done by specifying the edges of interest (Yang 1998). (Note I’m setting optimise_motif_probs=False to speed up execution of the examples, not because it’s a good idea!)

from cogent3.app import io, evo

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

hc_differ = evo.natsel_timehet("GNC",
                               tree="../data/primate_brca1.tree",
                               optimise_motif_probs=False,
                               tip1="Human", tip2="Chimpanzee")
result = hc_differ(aln)
result

Statistics

LR	df	pvalue
4.9248	1	0.0265

hypothesis	key	lnL	nfp	DLC	unique_Q
null	'GNC-null'	-6713.2733	23	True
alt	'GNC-alt'	-6710.8109	24	True

result.alt.lf

GNC-alt

log-likelihood = -6710.8109

number of free parameters = 24

Global params

A>C	A>G	A>T	C>A	C>G	C>T	G>A	G>C	G>T	T>A
0.8620	3.5361	0.9790	1.6698	2.2059	6.2630	7.9209	1.2265	0.8024	1.2882

T>C
3.0675

Edge params

edge	parent	length	omega
Galago	root	0.5237	0.7906
HowlerMon	root	0.1339	0.7906
Rhesus	edge.3	0.0640	0.7906
Orangutan	edge.2	0.0233	0.7906
Gorilla	edge.1	0.0075	0.7906
Human	edge.0	0.0182	2.6351
Chimpanzee	edge.0	0.0085	2.6351
edge.0	edge.1	0.0000	0.7906
edge.1	edge.2	0.0100	0.7906
edge.2	edge.3	0.0366	0.7906
edge.3	root	0.0238	0.7906

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

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

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

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

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

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

TTT
0.0187