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 |
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)