pynw 0.4.1

Rust-accelerated Needleman-Wunsch global sequence alignment for precomputed score matrices.

pynw

Rust-accelerated Needleman-Wunsch global sequence alignment for user-supplied similarity matrices. Python bindings are built with PyO3.

Align two ordered sequences given any precomputed pairwise similarity matrix. Unlike string-distance or bioinformatics libraries, which are designed around specific alphabets and scoring rules, pynw accepts whatever scores you provide: cosine similarity of embeddings, model outputs, or distance metrics.

Features

• Fast: Alignment runs in $O(nm)$ time; a 1000×1000 matrix takes <10 ms on modern CPUs.
• NumPy-first: Pass NumPy arrays directly, no conversion needed.
• Domain-agnostic: Operates on a precomputed similarity matrix; the scoring function is up to you.
• Asymmetric gaps: Penalize inserts and deletes independently.

Installation

Requires Python 3.10+ and NumPy 1.22+. Prebuilt wheels for Linux, macOS, and Windows are published on PyPI.

pip install pynw

On platforms without a prebuilt wheel, pip will build from the source distribution. This requires a Rust toolchain (1.85+).

Quick start

Using pynw involves three steps:

1. Build a similarity matrix. Compute pairwise scores between every element of your two sequences using any scoring function.
2. Run the alignment. Pass the matrix and a gap penalty to needleman_wunsch. The gap penalty controls when leaving an element unmatched is preferable to a low-scoring match.
3. Interpret the results. The returned edit operations tell you which elements were aligned, inserted, or deleted.

The example below aligns two word sequences. The similarity matrix is built from GloVe cosine similarities, letting semantically related words align even without an exact match:

import numpy as np
from pynw import needleman_wunsch, alignment_indices

source = np.array(
    ["clever", "sneaky", "fox", "leaped"]
)
target = np.array(
    ["sly", "fox", "jumped", "across"]
)

# Cosine similarity from GloVe (glove-wiki-gigaword-50)
similarity_matrix = np.array([
    # sly     fox     jumped  across
    [ 0.65,   0.25,   0.06,   0.20],  # clever
    [ 0.57,   0.06,  -0.14,  -0.05],  # sneaky
    [ 0.26,   1.00,   0.30,   0.41],  # fox
    [-0.00,   0.07,   0.77,   0.35],  # leaped
])

# Each gap deducts 0.5 from the total score; increase the penalty to force
# more alignments, decrease it to allow more gaps
score, editops = needleman_wunsch(similarity_matrix, gap_penalty=-0.5)
src_idx, tgt_idx = alignment_indices(editops)

# Reconstruct aligned sequences; masked positions are gaps
aligned_src = np.ma.array(source).take(src_idx).filled("-")
aligned_tgt = np.ma.array(target).take(tgt_idx).filled("-")

print(f"Score: {round(score, 2)}")
for s, t in zip(aligned_src, aligned_tgt):
    print(f"  {s:10s}  {t}")

# Score: 1.42
#   clever     sly       (semantic match)
#   sneaky     -         (deleted)
#   fox        fox       (exact match)
#   leaped     jumped    (semantic match)
#   -          across    (inserted)

User Guide

pynw exposes two alignment functions and a helper for interpreting results. Which function you need depends on whether you need just the score or the full alignment.

Score only: needleman_wunsch_score

Use needleman_wunsch_score when you only need the alignment score, for example when ranking or filtering many sequence pairs. It skips the traceback entirely, using O(m) memory instead of O(nm):

from pynw import needleman_wunsch_score

score = needleman_wunsch_score(similarity_matrix)

Score and alignment: needleman_wunsch

Use needleman_wunsch when you need to know how the sequences were aligned. It returns the optimal score along with an array of edit operations (editops). Each element in the editops array is one of three EditOp values:

• EditOp.Align: a source element is matched with a target element.
• EditOp.Delete: a source element is consumed with no matching target element (gap in target).
• EditOp.Insert: a target element is consumed with no matching source element (gap in source).

The editops array alone is enough for aggregate statistics:

from pynw import EditOp, needleman_wunsch

score, editops = needleman_wunsch(similarity_matrix)

n_aligned = np.sum(editops == EditOp.Align)
n_inserted = np.sum(editops == EditOp.Insert)
n_deleted = np.sum(editops == EditOp.Delete)

Reconstructing the alignment: alignment_indices

Use alignment_indices when you need to map alignment positions back to the original sequences. It converts the editops array into two masked index arrays: one for the source, one for the target. Each array has one entry per alignment position. Positions where the corresponding sequence has a gap are masked.

from pynw import alignment_indices

src_idx, tgt_idx = alignment_indices(editops)

Because the indices are masked arrays, take propagates the mask and filled substitutes a value at gap positions. This makes it easy to reconstruct aligned sequences with gap markers:

source = np.ma.array(["the", "quick", "fox"])
target = np.ma.array(["the", "slow", "red", "fox"])

aligned_src = source.take(src_idx).filled("-")
aligned_tgt = target.take(tgt_idx).filled("-")
# aligned_src: ['the', 'quick', '-',   'fox']
# aligned_tgt: ['the', 'slow',  'red', 'fox']

When iterating over a masked array, masked positions yield the np.ma.masked sentinel instead of an integer. This means you can iterate over editops and the index arrays together without checking masks explicitly. In the diff example below, s is masked at Insert positions and t is masked at Delete positions, so only the valid index is used in each branch:

for op, s, t in zip(editops, src_idx, tgt_idx):
    if op == EditOp.Align:
        print(f"  {source[s]}")
    elif op == EditOp.Delete:
        print(f"- {source[s]}")
    elif op == EditOp.Insert:
        print(f"+ {target[t]}")

Asymmetric gap penalties

By default, gap_penalty applies equally to insertions and deletions. To penalize them independently, pass insert_penalty and/or delete_penalty:

score, editops = needleman_wunsch(
    similarity_matrix,
    insert_penalty=-0.3,
    delete_penalty=-0.7,
)

When set, these override gap_penalty for the corresponding direction. This is useful when the cost of missing a source element differs from the cost of introducing a spurious target element.

Details

Precomputed similarity matrix

pynw takes a precomputed (n, m) similarity matrix rather than a scoring function. This means the entire alignment runs in compiled Rust code with no Python callbacks, and you can build scores with vectorized NumPy operations rather than element-wise Python loops.

The trade-off is that you must allocate the full matrix up front, which uses $O(nm)$ memory even when the scoring rule could be expressed more compactly.

Scoring

The total alignment score is the sum of similarity-matrix entries for matched positions and gap penalties for insertions/deletions. Gap penalties are typically negative. By default a single gap_penalty applies to both directions; set insert_penalty and/or delete_penalty to penalise them independently.

When multiple alignments achieve the same optimal score, pynw breaks ties deterministically: Align > Delete > Insert.

Edit-distance parameterizations

Needleman-Wunsch can reproduce common metrics with the right similarity-matrix values and gap penalty:

Metric	S[i,j] match	S[i,j] mismatch	gap_penalty	NW score equals
Levenshtein distance	0	-1	-1	-distance
Indel distance	0	-2	-1	-distance
LCS length	1	0	0	lcs_length
Hamming distance	0	-1	-(n+1)	-distance

For Hamming distance, strings must have equal length.

API

Full API documentation is available at chrisdeutsch.github.io/pynw. To browse locally:

pixi run docs          # generate static HTML in site/
pixi run docs-serve    # serve live docs in the browser

Development

This repository uses pixi for development:

pixi install
pixi run build            # build the Rust extension
pixi run test             # run deterministic tests
pixi run lint             # run all pre-commit checks (ruff, cargo fmt, prettier, markdownlint, taplo, actionlint)
pixi run check            # run all pre-push checks (cargo clippy, mypy)
pixi run docs             # generate API docs in site/
pixi run docs-serve       # serve API docs in the browser

Linting and formatting are managed by lefthook and run automatically as git hooks.

Related projects

• rapidfuzz: Highly optimized string distances (Levenshtein, Jaro-Winkler, etc.) with scoring, edit operations, and alignment. The better choice when you only need standard string metrics.
• sequence-align: Rust-accelerated Needleman-Wunsch and Hirschberg for token sequences with built-in match/mismatch scoring.
• scipy.optimize.linear_sum_assignment: Solves unconstrained bipartite matching ($O(N^3)$) where order does not matter.
• BioPython Bio.Align.PairwiseAligner: Needleman-Wunsch/Smith-Waterman for biological sequences with alphabet-based substitution matrices (built-in and custom).

License

MIT