Genetic distance calculation#

Fast pairwise distance estimation#

For a limited number of evolutionary models a fast implementation is available.

from cogent3 import available_distances

available_distances()

Specify a pairwise genetic distance calculator using 'Abbreviation' (case insensitive).
Abbreviation	Suitable for moltype
paralinear	dna, rna, protein
logdet	dna, rna, protein
jc69	dna, rna
tn93	dna, rna
hamming	dna, rna, protein, text, bytes
pdist	dna, rna, protein, text, bytes

6 rows x 2 columns

Computing genetic distances using the `Alignment` object#

Abbreviations listed from available_distances() can be used as values for the distance_matrix(calc=<abbreviation>).

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/primate_brca1.fasta", moltype="dna")
dists = aln.distance_matrix(calc="tn93", show_progress=False)
dists

names	Chimpanzee	Galago	Gorilla	HowlerMon	Human	Orangutan	Rhesus
Chimpanzee	0.0000	0.1921	0.0054	0.0704	0.0089	0.0140	0.0396
Galago	0.1921	0.0000	0.1923	0.2157	0.1965	0.1944	0.1962
Gorilla	0.0054	0.1923	0.0000	0.0700	0.0086	0.0137	0.0393
HowlerMon	0.0704	0.2157	0.0700	0.0000	0.0736	0.0719	0.0736
Human	0.0089	0.1965	0.0086	0.0736	0.0000	0.0173	0.0423
Orangutan	0.0140	0.1944	0.0137	0.0719	0.0173	0.0000	0.0411
Rhesus	0.0396	0.1962	0.0393	0.0736	0.0423	0.0411	0.0000

Using the distance calculator directly#

from cogent3 import get_distance_calculator, load_aligned_seqs

aln = load_aligned_seqs("data/primate_brca1.fasta")
dist_calc = get_distance_calculator("tn93", alignment=aln)
dist_calc

<cogent3.evolve.fast_distance.TN93Pair at 0x7f3c4453cc20>

dist_calc.run(show_progress=False)
dists = dist_calc.get_pairwise_distances()
dists

names	Chimpanzee	Galago	Gorilla	HowlerMon	Human	Orangutan	Rhesus
Chimpanzee	0.0000	0.1921	0.0054	0.0704	0.0089	0.0140	0.0396
Galago	0.1921	0.0000	0.1923	0.2157	0.1965	0.1944	0.1962
Gorilla	0.0054	0.1923	0.0000	0.0700	0.0086	0.0137	0.0393
HowlerMon	0.0704	0.2157	0.0700	0.0000	0.0736	0.0719	0.0736
Human	0.0089	0.1965	0.0086	0.0736	0.0000	0.0173	0.0423
Orangutan	0.0140	0.1944	0.0137	0.0719	0.0173	0.0000	0.0411
Rhesus	0.0396	0.1962	0.0393	0.0736	0.0423	0.0411	0.0000

The distance calculation object can provide more information. For instance, the standard errors.

dist_calc.stderr

Standard Error of Pairwise Distances
Seq1 \ Seq2	Galago	HowlerMon	Rhesus	Orangutan	Gorilla	Human	Chimpanzee
Galago	0	0.010274827058395896	0.009616307832648562	0.009535646532276787	0.009491382249540176	0.009615033091864917	0.009469268026590141
HowlerMon	0.010274827058395896	0	0.005411811712554772	0.0053348584951611175	0.005265612474694246	0.005406760238748984	0.005273572620183854
Rhesus	0.009616307832648562	0.005411811712554772	0	0.0039408549417865755	0.003852798161903095	0.004005045920100125	0.0038665597157698894
Orangutan	0.009535646532276787	0.0053348584951611175	0.0039408549417865755	0	0.0022291124743011375	0.0025151838791803655	0.0022606571679022955
Gorilla	0.009491382249540176	0.005265612474694246	0.003852798161903095	0.0022291124743011375	0	0.0017596919902326876	0.0013848543487237903
Human	0.009615033091864917	0.005406760238748984	0.004005045920100125	0.0025151838791803655	0.0017596919902326876	0	0.0017949285088691988
Chimpanzee	0.009469268026590141	0.005273572620183854	0.0038665597157698894	0.0022606571679022955	0.0013848543487237903	0.0017949285088691988	0

7 rows x 8 columns

Likelihood based pairwise distance estimation#

The standard cogent3 likelihood function can also be used to estimate distances. Because these require numerical optimisation they can be significantly slower than the fast estimation approach above.

The following will use the F81 nucleotide substitution model and perform numerical optimisation.

from cogent3 import get_model, load_aligned_seqs
from cogent3.evolve import distance

aln = load_aligned_seqs("data/primate_brca1.fasta", moltype="dna")
d = distance.EstimateDistances(aln, submodel=get_model("F81"))
d.run(show_progress=False)
dists = d.get_pairwise_distances()
dists

names	Chimpanzee	Galago	Gorilla	HowlerMon	Human	Orangutan	Rhesus
Chimpanzee	0.0000	0.1892	0.0054	0.0697	0.0089	0.0140	0.0395
Galago	0.1892	0.0000	0.1891	0.2112	0.1934	0.1915	0.1930
Gorilla	0.0054	0.1891	0.0000	0.0693	0.0086	0.0136	0.0391
HowlerMon	0.0697	0.2112	0.0693	0.0000	0.0729	0.0713	0.0729
Human	0.0089	0.1934	0.0086	0.0729	0.0000	0.0173	0.0421
Orangutan	0.0140	0.1915	0.0136	0.0713	0.0173	0.0000	0.0410
Rhesus	0.0395	0.1930	0.0391	0.0729	0.0421	0.0410	0.0000

Get the names of sequences with max pairwise distance#

Given a DistanceMatrix object, finding the sequences that have the maximum pairwise distance is achieved through the max_pair method.

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/primate_brca1.fasta", moltype="dna")
dists = aln.distance_matrix(calc="tn93", show_progress=False)
dists.max_pair()

('Galago', 'HowlerMon')

To find the maximum distance, index the DistanceMatrix with the result of max_pair.

dists[dists.max_pair()]

np.float64(0.2156879978632928)

Get the names of sequences with min pairwise distance#

Given a DistanceMatrix object, finding the sequences that have the minimum pairwise distance is achieved through the min_pair method.

Note

As the distance between a sequence and itself is zero, and this is not informative, min_pair will return the smallest distance not on the diagonal.

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/primate_brca1.fasta", moltype="dna")
dists = aln.distance_matrix(calc="tn93", show_progress=False)
dists.min_pair()

('Chimpanzee', 'Gorilla')

To find the minimum distance, index the DistanceMatrix with the result of min_pair.

dists[dists.min_pair()]

np.float64(0.005354100636467117)