pyseqalignment 0.1.2

pySeqAlign -- sequence alignment with Prolog-style distance functions and ILP learning

pySeqAlign

Sequence alignment library with Prolog-style distance functions and ILP-based rule learning.

pySeqAlign provides Smith-Waterman (local) and Needleman-Wunsch (global) sequence alignment algorithms with pluggable distance/scoring functions. Distance functions can be defined natively in Python, via Prolog predicates, or learned from data using Inductive Logic Programming (ILP). This enables alignment of structured logical atoms beyond simple character or amino acid sequences.

Install: the package is published on PyPI as pyseqalignment (the name pyseqalign was blocked by PyPI's name-similarity guard). The import package is pyseqalign:

pip install pyseqalignment

import pyseqalign

Features

• Smith-Waterman local alignment with k-best non-overlapping results
• Needleman-Wunsch global alignment
• Prolog-based distance functions via SWI-Prolog integration (optional)
• Substitution matrices -- BLOSUM (50, 60, 62, 70, 80, 90, 100) and PAM (50, 150, 200, 250) bundled; any NCBI-format matrix loadable from file or downloaded at runtime
• Nienhuys-Cheng distance for recursive structural comparison of logical atoms
• ILP learning of alignment rules and distance predicates:
  • Aleph backend -- classic ILP system (Srinivasan, 2001)
  • Popper backend -- modern ILP via learning from failures (Cropper & Morel, 2021)
• Pure Python core -- no C extension required (unlike the legacy version)
• Optional fast C++ aligner -- a pybind11 affine-gap Needleman-Wunsch kernel (pyseqalign.accel) for large all-pairs / iterative workloads; identical results to the Python aligner, ~100-270x faster

Installation

Prerequisites

• Python >= 3.10 (tested on 3.10 -- 3.14)
• SWI-Prolog (optional, for Prolog-based scoring and ILP learning)
• Clingo (optional, for the Popper ILP backend)

# macOS (Homebrew)
brew install swi-prolog clingo

# Ubuntu / Debian
sudo apt install swi-prolog gringo

Setting up with pyenv (recommended)

# Install a Python version
pyenv install 3.13.9

# Create a pyenv virtualenv
pyenv virtualenv 3.13.9 pyseqalign-env

# Activate it for this project directory
cd pySeqAlign
pyenv local pyseqalign-env

# Install all dependencies
pip install -r requirements.txt
pip install -e ".[learning,dev]"

# Register the Jupyter kernel so notebooks can use this environment
pip install ipykernel
python -m ipykernel install --user --name pyseqalign-env --display-name "Python (pySeqAlign)"

Setting up with venv (system Python)

cd pySeqAlign
python3 -m venv .venv
source .venv/bin/activate    # Linux / macOS
# .venv\Scripts\activate     # Windows

# Install all dependencies
pip install -r requirements.txt
pip install -e ".[learning,dev]"

# Register the Jupyter kernel
pip install ipykernel
python -m ipykernel install --user --name pyseqalign-env --display-name "Python (pySeqAlign)"

Optional dependencies

Install selectively if you don't need everything:

# Core only (no Prolog, no ILP)
pip install -e .

# Prolog-based distance functions (requires SWI-Prolog)
pip install -e ".[prolog]"

# Popper ILP backend's solver (Clingo). The Popper system itself is not on
# PyPI -- install it separately (see the note below).
pip install -e ".[popper]"

# Prolog + Clingo for the ILP backends (Popper installed separately)
pip install -e ".[learning]"

# Development tools (pytest, ruff, mypy)
pip install -e ".[dev]"

Using requirements.txt

A requirements.txt is provided listing all Python dependencies (core, Prolog, Popper, Jupyter, and development tools):

pip install -r requirements.txt

Note (Python 3.14): Popper currently uses pkg_resources, which was removed from setuptools>=81. If you are on Python 3.14, install setuptools<81 before Popper: pip install 'setuptools<81'.

Note: Popper is not available on PyPI, so it cannot be a declared dependency (PyPI forbids direct-URL deps). The popper/learning extras install its solver (Clingo); install the Popper system itself from the actively maintained GitHub repository (v4.2.0+):

pip install git+https://github.com/logic-and-learning-lab/Popper@main

Quick Start

from pyseqalign import SmithWaterman, NeedlemanWunsch
from pyseqalign.scoring import Blosum50

# Local alignment with BLOSUM50 scoring
sw = SmithWaterman(scoring=Blosum50(), gap_penalty=-8.0)
results = sw.align(seq1, seq2, k=4, min_score=2.0)

for alignment in results:
    print(f"Score: {alignment.score}")
    print(f"  Query:  {alignment.query}")
    print(f"  Target: {alignment.target}")

# Global alignment
nw = NeedlemanWunsch(scoring=Blosum50(), gap_penalty=-8.0)
result = nw.align(seq1, seq2)
print(f"Score: {result.score}")

Substitution Matrices

from pyseqalign.scoring import SubstitutionMatrix

# Load a bundled matrix by name
blosum62 = SubstitutionMatrix.from_bundled("BLOSUM62")
pam250   = SubstitutionMatrix.from_bundled("PAM250")

# Use with any aligner
sw = SmithWaterman(scoring=blosum62, gap_penalty=-8.0)

# Load from any NCBI-format file on disk
custom = SubstitutionMatrix.from_file("/path/to/my/MATRIX")

# Download directly from the NCBI FTP server at runtime
pam120 = SubstitutionMatrix.from_ncbi("PAM120")

# List all bundled matrices
print(SubstitutionMatrix.list_bundled())
# ['BLOSUM100', 'BLOSUM50', 'BLOSUM60', 'BLOSUM62', 'BLOSUM70',
#  'BLOSUM80', 'BLOSUM90', 'PAM150', 'PAM200', 'PAM250', 'PAM50']

Using Prolog Distance Functions

from pyseqalign.prolog import PrologEngine

engine = PrologEngine()
engine.consult("my_distances.pl")

sw = SmithWaterman(scoring=engine, gap_penalty=-8.0)
results = sw.align(seq1, seq2)

Learning Alignment Rules with ILP

from pyseqalign.learning import AlignmentTaskBuilder
from pyseqalign.learning.popper import PopperLearner

# Build an ILP task from labelled sequence pairs.
builder = AlignmentTaskBuilder()
builder.add_positive_pair([1, 2, 3], [1, 2, 4], label="similar")
builder.add_positive_pair([5, 6, 7], [5, 6, 8], label="similar")
builder.add_negative_pair([1, 2, 3], [10, 11, 12], label="similar")
builder.use_default_alignment_bias_popper()

task = builder.build()

# Learn rules with Popper (modern, recommended).
learner = PopperLearner(timeout=60)
result = learner.learn(task)
print(result.program_text)

Or using the classic Aleph backend:

from pyseqalign.learning.aleph import AlephLearner

builder = AlignmentTaskBuilder()
# ... add examples ...
builder.use_default_alignment_bias_aleph()
task = builder.build()

learner = AlephLearner(induce_mode="induce")
result = learner.learn(task)
print(result.program_text)

Project Structure

pySeqAlign/
├── src/
│   └── pyseqalign/
│       ├── core/              # Alignment algorithms (SW, NW)
│       ├── prolog/            # Prolog engine integration
│       │   └── knowledge/     # Prolog knowledge bases (.pl files)
│       ├── scoring/           # Scoring/distance functions
│       │   └── matrix_data/  # Bundled NCBI substitution matrices
│       ├── learning/          # ILP learning backends
│       │   ├── base.py        # Common types & protocol
│       │   ├── task_builder.py # Convert alignment data -> ILP format
│       │   ├── aleph.py       # Aleph backend (classic ILP)
│       │   ├── popper.py      # Popper backend (modern ILP)
│       │   └── aleph_files/   # Bundled Aleph Prolog source
│       └── utils/             # Helpers and data structures
├── tests/
├── examples/
└── docs/

ILP Backends

Popper (recommended)

Popper is a modern ILP system that learns from failures by combining ASP and Prolog. Advantages over Aleph:

• Learns optimal and recursive programs
• No metarules required
• Handles noise (MDL-based)
• Scales well (no grounding required)
• Native Python API

Aleph (classic)

Aleph is the classic ILP system by Ashwin Srinivasan, implementing Mode-Directed Inverse Entailment. The bundled aleph_swi_ak.pl is a SWI-Prolog compatible version ported from the legacy pySeqAlign codebase.

Supports multiple induction modes: induce, induce_max, induce_cover, induce_tree, induce_features, induce_constraints, induce_incremental, induce_theory.

Other Modern ILP Systems

For reference, other notable systems in the field include:

• PyILP -- Python interface wrapping Aleph and Metagol for teaching
• ILASP -- Answer Set Programming-based ILP
• Metagol -- Meta-Interpretive Learning
• DeepStochLog -- Neural-symbolic ILP combining logic and neural networks

Fast C++ aligner (optional)

The pure-Python aligners are fine for typical use. For heavy workloads (e.g. boosting that re-aligns thousands of sequence pairs per iteration), an optional C++ affine-gap Needleman-Wunsch kernel is provided. It is NOT built by default (the core stays pure Python); build it once with a C++ compiler + pybind11:

pip install pybind11
src/pyseqalign/cpp/build_cpp_aligner.sh          # or: PY=$(which python) src/.../build_cpp_aligner.sh

This compiles the extension into the pyseqalign package. Then:

from pyseqalign.accel import cpp_available, load
assert cpp_available()
cpp = load()                                  # the compiled module
al = cpp.CppAligner(num_ids, gap_open, gap_extend)
al.set_matrix(flat_score_matrix)              # (num_ids+1)^2 row-major scores
r = al.align(query_ids, target_ids)           # r.score, r.query, r.target, r.gap_opens, ...

The kernel implements the same 3-matrix (M/Ix/Iy) affine recurrence as a numeric aligner over a dense score matrix, so its results are interchangeable with a pure-Python affine Needleman-Wunsch (validated bit-for-bit). If the extension is not built, cpp_available() is False and load() raises a helpful error -- callers should fall back to the Python aligner.

Background

pySeqAlign is a modern, pure-Python reimplementation that revives the name of one of its own ancestors. It succeeds two legacy libraries behind the ILP 2006 / ICDM 2008 work: the original pyAlign (SWIG-wrapped C with YAP Prolog bindings for alignment) and the original pySeqAlign (which held the Aleph ILP framework for learning rules from alignment examples). This version is pure Python, with optional SWI-Prolog integration via Janus (the modern Python-Prolog bridge, replacing the older pyswip).

License

MIT License. See LICENSE for details.