Building phylogenies#

Building A Phylogenetic Tree From Pairwise Distances#

Directly via alignment.quick_tree()#

Both the ArrayAlignment and Alignment classes support this.

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/primate_brca1.fasta", moltype="dna")
tree = aln.quick_tree(calc="TN93", show_progress=False)
tree = tree.balanced()  # purely for display
print(tree.ascii_art())
                    /-Rhesus
          /edge.1--|
         |         |          /-HowlerMon
         |          \edge.0--|
         |                    \-Galago
-root----|
         |--Orangutan
         |
         |          /-Chimpanzee
          \edge.2--|
                   |          /-Human
                    \edge.3--|
                              \-Gorilla

The quick_tree() method also supports non-parametric bootstrapping. The number of resampled alignments is specified using the bootstrap argument. In the following, trees are estimated from 100 resampled alignments and merged into a single consensus topology using a weighted consensus tree algorithm.

tree = aln.quick_tree(calc="TN93", bootstrap=100, show_progress=False)

Using the DistanceMatrix object#

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/primate_brca1.fasta", moltype="dna")
dists = aln.distance_matrix(calc="TN93")
tree = dists.quick_tree(show_progress=False)
tree = tree.balanced()  # purely for display
print(tree.ascii_art())
                    /-Rhesus
          /edge.1--|
         |         |          /-HowlerMon
         |          \edge.0--|
         |                    \-Galago
-root----|
         |--Orangutan
         |
         |          /-Chimpanzee
          \edge.2--|
                   |          /-Human
                    \edge.3--|
                              \-Gorilla

Explicitly via DistanceMatrix and cogent3.phylo.nj.nj()`#

from cogent3 import load_aligned_seqs
from cogent3.phylo import nj

aln = load_aligned_seqs("data/primate_brca1.fasta", moltype="dna")
dists = aln.distance_matrix(calc="TN93")
tree = nj.nj(dists, show_progress=False)
tree = tree.balanced()  # purely for display
print(tree.ascii_art())
                    /-Rhesus
          /edge.1--|
         |         |          /-HowlerMon
         |          \edge.0--|
         |                    \-Galago
-root----|
         |--Orangutan
         |
         |          /-Chimpanzee
          \edge.2--|
                   |          /-Human
                    \edge.3--|
                              \-Gorilla

Directly from a pairwise distance dict#

from cogent3.phylo import nj

dists = {("a", "b"): 2.7, ("c", "b"): 2.33, ("c", "a"): 0.73}
tree = nj.nj(dists, show_progress=False)
print(tree.ascii_art())
          /-b
         |
-root----|--a
         |
          \-c

By Least-squares#

We illustrate the phylogeny reconstruction by least-squares using the F81 substitution model. We use the advanced-stepwise addition algorithm to search tree space. Here a is the number of taxa to exhaustively evaluate all possible phylogenies for. Successive taxa are added to the top k trees (measured by the least-squares metric) and k trees are kept at each iteration.

from cogent3.phylo.least_squares import WLS
from cogent3.util.deserialise import deserialise_object

dists = deserialise_object("data/dists_for_phylo.json")
ls = WLS(dists)
stat, tree = ls.trex(a=5, k=5, show_progress=False)

Other optional arguments that can be passed to the trex method are: return_all, whether the k best trees at the final step are returned as a ScoredTreeCollection object; order, a series of tip names whose order defines the sequence in which tips will be added during tree building (this allows the user to randomise the input order).

By ML#

We illustrate the phylogeny reconstruction using maximum-likelihood using the F81 substitution model. We use the advanced-stepwise addition algorithm to search tree space.

from cogent3 import load_aligned_seqs
from cogent3.evolve.models import F81
from cogent3.phylo.maximum_likelihood import ML

aln = load_aligned_seqs("data/primate_brca1.fasta")
ml = ML(F81(), aln)