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.. jupyter-execute::
:hide-code:
import set_working_directory
********************
Building phylogenies
********************
Building A Phylogenetic Tree From Pairwise Distances
====================================================
Directly via ``alignment.quick_tree()``
=======================================
Both the ``ArrayAlignment`` and ``Alignment`` classes support this.
.. jupyter-execute::
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())
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.
.. jupyter-execute::
tree = aln.quick_tree(calc="TN93", bootstrap=100, show_progress=False)
Using the ``DistanceMatrix`` object
-----------------------------------
.. jupyter-execute::
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())
Explicitly via ``DistanceMatrix`` and ``cogent3.phylo.nj.nj()```
----------------------------------------------------------------
.. jupyter-execute::
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())
Directly from a pairwise distance ``dict``
------------------------------------------
.. jupyter-execute::
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())
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.
.. jupyter-execute::
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.
.. jupyter-execute::
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)
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