evolution

Molecular sequence analysis for Python — fast pairwise distances and neighbour-joining trees for DNA, RNA, and protein sequences.

import fastphylo

aln  = fastphylo.read("sequences.fasta")          # FASTA, Stockholm, or Phylip
dm   = fastphylo.distance_matrix(aln)             # k2p for DNA, WAG for protein
tree = fastphylo.fnj(dm)
print(tree.to_newick())

Features

Reads FASTA, Stockholm, and Phylip files; format auto-detected from extension and content
DNA/RNA distances: Hamming, Jukes-Cantor, Kimura 2-parameter (default), Tamura-Nei 93 — computed by a fast C++ backend with SIMD (SSE2 / NEON)
Protein distances: maximum-likelihood estimation under WAG (default), LG, JTT, Dayhoff, BLOSUM62, VT, cpREV, MtREV, RtREV, HIVb, HIVw, DCMUT, JTT-DCMut, and PMB, using a Brent optimizer written in C++
Tree reconstruction: Neighbour-Joining, Fast NJ, and BioNJ (with branch lengths) via the FastPhylo library
Branch length estimation: fit_branch_lengths fits branch lengths to any tree topology by L1-minimisation against a distance matrix (requires scipy)
Distance matrix access: integer or taxon-name indexing, copy(), zeros() factory, NumPy and Phylip export
Multiple alignment: optional FAMSA integration for unaligned input
Pure-Python sequence types with Stockholm annotation support (organism, AC, description); edge-set Tree with to_newick() and merge()

Installation

pip install fastphylo

Optional extras:

pip install fastphylo[align]    # multiple-sequence alignment (pyfamsa)
pip install scipy               # branch length fitting (fit_branch_lengths)

Quick start

Aligned input → tree

import fastphylo

aln  = fastphylo.read("alignment.sto")            # Stockholm, FASTA, or Phylip
dm   = fastphylo.distance_matrix(aln, model="k2p")
tree = fastphylo.fnj(dm)
print(tree.to_newick())

Unaligned input → align → tree

import fastphylo

seqs = fastphylo.read("sequences.fasta")          # unaligned OK
aln  = fastphylo.align(seqs)                      # requires fastphylo[align]
dm   = fastphylo.distance_matrix(aln)
tree = fastphylo.fnj(dm)
print(tree.to_newick())

Protein sequences

import fastphylo

aln  = fastphylo.read("proteins.fasta")
dm   = fastphylo.distance_matrix(aln, model="LG")
tree = fastphylo.bionj(dm)                        # BioNJ with branch lengths
print(tree.to_newick())

Branch length fitting

NJ and FNJ return topology only (branch lengths are not computed). Use fit_branch_lengths to fit branch lengths to any tree topology by minimising the L1 deviation from the distance matrix:

import fastphylo

aln  = fastphylo.read("alignment.fasta")
dm   = fastphylo.distance_matrix(aln)
tree = fastphylo.fnj(dm)                          # fast topology, no lengths

tree = fastphylo.fit_branch_lengths(tree, dm)     # requires scipy
print(tree.to_newick())                           # now includes branch lengths

This solves a linear program: branch lengths are chosen to minimise sum |path_distance(i,j) − dm[i,j]| over all leaf pairs, subject to non-negative branch lengths.

Distance matrix access

dm = fastphylo.distance_matrix(aln)

# Integer or name-based indexing
d = dm[0, 1]
d = dm["human", "mouse"]

# Set elements
dm["human", "mouse"] = 0.15
dm["mouse", "human"] = 0.15

# Build a matrix manually
dm = fastphylo.DistanceMatrix.zeros(["human", "mouse", "rat"])
dm["human", "mouse"] = dm["mouse", "human"] = 0.15
dm["human", "rat"]   = dm["rat",   "human"] = 0.22
dm["mouse", "rat"]   = dm["rat",   "mouse"] = 0.08

# Copy, NumPy array, Phylip string
dm2  = dm.copy()
arr  = dm.to_numpy()
text = dm.to_phylip()

API overview

Function / class	Description
`fastphylo.read(path)`	Read FASTA / Stockholm / Phylip → `SequenceCollection` or `Alignment`
`fastphylo.align(seqs)`	Align with FAMSA → `Alignment`
`fastphylo.distance_matrix(aln, model=…)`	Compute pairwise distances → `DistanceMatrix`
`evolution.nj(dm)` / `fnj(dm)` / `bionj(dm)`	Tree reconstruction → `Tree`
`fastphylo.fit_branch_lengths(tree, dm)`	L1-optimal branch lengths for a given topology
`DistanceMatrix.zeros(names)`	Create an all-zero matrix with taxon names
`DistanceMatrix.copy()`	Deep copy
`DistanceMatrix.to_numpy()`	Export as NumPy array
`DistanceMatrix.to_phylip()`	Export as Phylip-format string
`Tree.to_newick()`	Newick string
`Tree.merge(other)`	Union of two edge-set trees

Distance models

Sequences	Model string	Notes
DNA / RNA	`"hamming"`	Raw mismatch count
DNA / RNA	`"jc"`	Jukes-Cantor
DNA / RNA	`"k2p"` (default)	Kimura 2-parameter
DNA / RNA	`"tn93"`	Tamura-Nei 93
Protein	`"WAG"` (default)	Whelan & Goldman
Protein	`"LG"`, `"JTT"`, `"Dayhoff"`, …	14 models total

RNA is handled transparently (U → T at the C++ boundary).

Requirements

Python ≥ 3.12
NumPy ≥ 1.24
A C compiler (for the bundled FastPhylo extension, built automatically by pip)

Optional:

pyfamsa ≥ 0.6.0 — multiple-sequence alignment (pip install fastphylo[align])
scipy — branch length fitting via fit_branch_lengths (pip install scipy)

License

GPLv3 — see FastPhylo for the upstream C++ library.

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Release history Release notifications | RSS feed

1.1.1

Aug 12, 2026

1.1.0

Aug 12, 2026

1.0.0

Jul 29, 2026

This release

0.2.1

Jul 24, 2026

0.1.0

May 19, 2026

fastphylo 0.2.1

evolution

Features

Installation

Quick start

Aligned input → tree

Unaligned input → align → tree

Protein sequences

Branch length fitting

Distance matrix access

API overview

Distance models

Requirements

License

Key dates

1 maintainer

License

Requires

Provides Extra

Classifiers

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