NJ tree

The Neighbour-Joining method uses genetic distances to build a phylogenetic tree. piqtree provides only piq_jc_distances for this. cogent3 includes many more methods. The results of either can be used to build a tree. For divergent sequences we will use Lake's paralinear measure as it accomodates divergent sequence compositions.

In [1]:

import cogent3

from piqtree import download_dataset

aln_path = download_dataset("example.phy.gz", dest_dir="data")
aln = cogent3.load_aligned_seqs(aln_path, moltype="dna", format_name="phylip")

import cogent3 from piqtree import download_dataset aln_path = download_dataset("example.phy.gz", dest_dir="data") aln = cogent3.load_aligned_seqs(aln_path, moltype="dna", format_name="phylip")

Getting a paralinear distance matrix¶

This can be obtained directly from the alignment object itself.

In [2]:

dists = aln.distance_matrix(calc="paralinear")
dists

dists = aln.distance_matrix(calc="paralinear") dists

Out[2]:

names	LngfishAu	LngfishSA	LngfishAf	Frog	Turtle	Sphenodon	Lizard	Crocodile	Bird	Human	Seal	Cow	Whale	Mouse	Rat	Platypus	Opossum
LngfishAu	0.0000	0.2974	0.2943	0.3090	0.3806	0.4356	0.4347	0.4429	0.4254	0.3956	0.3618	0.3885	0.3811	0.3950	0.3947	0.4123	0.3964
LngfishSA	0.2974	0.0000	0.2419	0.3630	0.4093	0.4380	0.4330	0.4362	0.4011	0.4291	0.4046	0.4214	0.4224	0.4116	0.4432	0.4184	0.3763
LngfishAf	0.2943	0.2419	0.0000	0.3619	0.4013	0.4595	0.4710	0.4734	0.4374	0.4558	0.4220	0.4275	0.4234	0.4324	0.4617	0.4332	0.3975
Frog	0.3090	0.3630	0.3619	0.0000	0.3704	0.4578	0.4300	0.4455	0.4133	0.4038	0.3788	0.3770	0.3922	0.4081	0.4110	0.3956	0.3751
Turtle	0.3806	0.4093	0.4013	0.3704	0.0000	0.3480	0.3542	0.3578	0.3225	0.3630	0.3425	0.3389	0.3429	0.3448	0.3649	0.3609	0.3473
Sphenodon	0.4356	0.4380	0.4595	0.4578	0.3480	0.0000	0.3845	0.4014	0.3759	0.4009	0.3991	0.3924	0.3944	0.3923	0.4210	0.4176	0.3793
Lizard	0.4347	0.4330	0.4710	0.4300	0.3542	0.3845	0.0000	0.4243	0.3799	0.4058	0.3947	0.4036	0.4209	0.3985	0.4337	0.4157	0.3886
Crocodile	0.4429	0.4362	0.4734	0.4455	0.3578	0.4014	0.4243	0.0000	0.3303	0.4406	0.4344	0.4318	0.4566	0.4733	0.4688	0.4349	0.4524
Bird	0.4254	0.4011	0.4374	0.4133	0.3225	0.3759	0.3799	0.3303	0.0000	0.3976	0.3835	0.3949	0.3931	0.4210	0.4166	0.3960	0.3802
Human	0.3956	0.4291	0.4558	0.4038	0.3630	0.4009	0.4058	0.4406	0.3976	0.0000	0.2138	0.2120	0.2246	0.2562	0.2618	0.3142	0.2899
Seal	0.3618	0.4046	0.4220	0.3788	0.3425	0.3991	0.3947	0.4344	0.3835	0.2138	0.0000	0.1616	0.1751	0.2093	0.2280	0.2663	0.2467
Cow	0.3885	0.4214	0.4275	0.3770	0.3389	0.3924	0.4036	0.4318	0.3949	0.2120	0.1616	0.0000	0.1529	0.2317	0.2354	0.2765	0.2434
Whale	0.3811	0.4224	0.4234	0.3922	0.3429	0.3944	0.4209	0.4566	0.3931	0.2246	0.1751	0.1529	0.0000	0.2357	0.2533	0.2723	0.2506
Mouse	0.3950	0.4116	0.4324	0.4081	0.3448	0.3923	0.3985	0.4733	0.4210	0.2562	0.2093	0.2317	0.2357	0.0000	0.1262	0.2887	0.2476
Rat	0.3947	0.4432	0.4617	0.4110	0.3649	0.4210	0.4337	0.4688	0.4166	0.2618	0.2280	0.2354	0.2533	0.1262	0.0000	0.3003	0.2767
Platypus	0.4123	0.4184	0.4332	0.3956	0.3609	0.4176	0.4157	0.4349	0.3960	0.3142	0.2663	0.2765	0.2723	0.2887	0.3003	0.0000	0.2428
Opossum	0.3964	0.3763	0.3975	0.3751	0.3473	0.3793	0.3886	0.4524	0.3802	0.2899	0.2467	0.2434	0.2506	0.2476	0.2767	0.2428	0.0000

Get help on the piq_nj_tree app.

In [3]:

cogent3.app_help("piq_nj_tree")

cogent3.app_help("piq_nj_tree")

Overview
--------
Construct a neighbour joining tree from a pairwise distance matrix.

Options for making the app
--------------------------
piq_nj_tree_app = get_app('piq_nj_tree', *args, **kwargs)

Parameters
----------
pairwise_distances : DistanceMatrix
    Pairwise distances to construct neighbour joining tree from.
allow_negative : bool, optional
    Whether to allow negative branch lengths in the output.
    Coerces to 0 if not allowed, by default False.

Returns
-------
PhyloNode
    The neighbour joining tree.

See Also
--------
jc_distances : construction of pairwise JC distance matrix from alignment.

Input type
----------
DistanceMatrix

Output type
-----------
PhyloNode

Make an app and apply it to the distance matrix.

In [4]:

nj = cogent3.get_app("piq_nj_tree")
tree = nj(dists)

nj = cogent3.get_app("piq_nj_tree") tree = nj(dists)

Warning Branch lengths can be negative in the piqtree NJ tree. This manifests as branches going backwards!

In [5]:

tree.get_figure().show()

tree.get_figure().show()

Note To write the tree to a file, use the write() method.

Combining the piqtree dist and nj apps¶

We can combine the piq_jc_distances and piq_nj_tree apps to build a tree from an alignment in one step.

In [6]:

jc = cogent3.get_app("piq_jc_distances")
nj = cogent3.get_app("piq_nj_tree")
app = jc + nj
tree = app(aln)
tree.get_figure().show()

jc = cogent3.get_app("piq_jc_distances") nj = cogent3.get_app("piq_nj_tree") app = jc + nj tree = app(aln) tree.get_figure().show()