kmer-learn 0.1.1

A modern Python toolkit for classical sequence machine learning

kmer-learn

A modern Python toolkit for classical sequence machine learning.

⚠ API stability: This package is in early development (v0.x). The public API is not yet stable — breaking changes may be introduced between minor versions until v1.0. Pin your dependency to an exact version (e.g. kmer-learn==0.1.0) if reproducibility matters.

Install

pip install kmer-learn

From source (requires a C compiler):

git clone https://github.com/synbioml/kmer-learn.git
cd kmer-learn
pip install -e .

Optional backends (strongly recommended for speed):

pip install rapidfuzz parasail   # 10-100x faster edit distances and alignments
pip install pyfastx              # fast FASTA parsing for examples

Features

Kernels

• GKMKernel family — gapped k-mer kernels from gkmSVM / LS-GKM (Ghandi et al. 2014; Lee 2016). C-backed, OpenMP-parallel, with full/estimated/truncated mismatch schemes, 6 post-transforms (RBF, poly, sigmoid, exponential, …), 5 positional weighting kernels (triangular, Epanechnikov, Gaussian, Laplacian, Cauchy), reverse-complement indexing, sliding-window scan, and 3D windowed tensors.
• DistanceKernel — turns any distance into a kernel via a post-transform.

Encoders (CSR output)

• SpectrumEncoder — plain k-mer counts via rolling hash (k ≤ 12).
• GappyEncoder — gappy k-mer counts with explicit masks ("*--*") or gap ranges (L=6, g_min=2, g_max=3).
• MismatchEncoder — mismatch-tolerant k-mer counts (Leslie, Eskin, Noble 2004).

All encoders support canonical_rc=True for reverse-complement collapsing.

Distances

• Hamming, Levenshtein (rapidfuzz backend + Python fallback).
• NeedlemanWunsch, SmithWaterman (parasail backend + Python fallback), with custom substitution matrices (NUC4.4, BLOSUM62, …).

Models

• DifferentialKmerScorer — Multinomial Naive Bayes on k-mer features, with auto-generated negatives via Shuffler/Chunker.
• KernelSVM — SVM with a precomputed kernel (works with GKMKernel and DistanceKernel).
• LinearSVM — Linear SVM on encoder features.
• KNNClassifier — k-Nearest Neighbors with a sequence distance.

Sequence perturbation

• KmerShuffler — k-mer-preserving shuffle via random Eulerian paths in the De Bruijn graph. Three endpoint modes (preserve / free / crop). Philox4×32-10 RNG, reproducible across n_jobs.
• Chunker — block-level perturbation: split into chunks of size [min, max], optionally reverse-complement each, shuffle, concatenate. Five residual-handling modes, two algorithms (random / backtrack).
• BaseBackgroundModel — ABC for custom background models.

Utilities

• kmer.utils — bit-packed k-mer helpers (kmer_to_code, code_to_kmer, reverse_complement, canonical_code).

Quick start

from kmer.kernels import GKMKernel
from kmer.models import KernelSVM

# Train a gkm-SVM
clf = KernelSVM(GKMKernel(L=10, k=6, d=3, kernel_type="truncated", use_rc=True), C=1.0)
clf.fit(positives + negatives, [1]*len(positives) + [0]*len(negatives))
preds = clf.predict(test_seqs)

from kmer.encoders import SpectrumEncoder
from kmer.models import DifferentialKmerScorer
from kmer.perturb import KmerShuffler

# Differential k-mer scoring with dinucleotide-shuffled background
scorer = DifferentialKmerScorer(
    featurizer=SpectrumEncoder(k=6, canonical_rc=True),
    background=KmerShuffler(k=2, seed=42),
)
scorer.fit(positives)
top_motifs = scorer.kmer_scores_.sort_values(ascending=False).head(20)

from kmer.distance import Levenshtein, DistanceKernel
from kmer.models import KNNClassifier

# KNN with edit distance
clf = KNNClassifier(Levenshtein(), n_neighbors=5)
clf.fit(train_seqs, y_train)

Examples (Vignettes)

The examples/ directory (top-level, next to kmer/) contains a series of cross-linked Jupyter notebooks. Each notebook starts with a vignette index linking to all others.

#	Notebook	Topic
01	01_basic_kernel_matrix.ipynb	GKMKernel: build, inspect, verify invariants
02	02_distance_metrics_and_kernels.ipynb	Distance metrics (Hamming, Levenshtein, NW, SW) + DistanceKernel (RBF, PSD, KernelSVM)
03	03_svc_with_kernel.ipynb	Train a gkm-SVM with KernelSVM
04	04_clustering_sequences.ipynb	Hierarchical clustering with kernel distances
05	05_score_long_sequence.ipynb	Sliding-window scan of a long sequence
06	06_weighted_kernel.ipynb	WGKMKernel positional weighting (centered motif)
07	07_transform_and_comparison.ipynb	All 3 schemes × 6 transforms, GKM vs WGKM
08	08_windowed_3d_tensors.ipynb	WindowedGKMKernel 3D output (line plot)
09	09_spectrum_encoder_and_differential.ipynb	SpectrumEncoder + DifferentialKmerScorer
10	10_gappy_encoder.ipynb	GappyEncoder with masks, gap ranges, RC collapse
11	11_mismatch_encoder.ipynb	MismatchEncoder and comparison to spectrum
12	12_shuffler_and_chunker.ipynb	KmerShuffler + Chunker for negative-set generation

Citation

An article describing this package is in preparation. Until it is published, please cite the package as:

Zinkevich A. kmer-learn: Classical machine learning primitives for nucleotide sequences. (in preparation).

For the mean time, if you use the package in your research, please cite the relevant foundational works listed below.

References

The package builds on the following foundational works:

• gkmSVM — Ghandi M, Lee D, Mohammad-Noori M, Beer MA. Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput Biol. 2014;10(7):e1003711.
• LS-GKM — Lee D. LS-GKM: a new gkm-SVM for large-scale datasets. Bioinformatics. 2016;32(14):2196–8.
• Mismatch kernel — Leslie CS, Eskin E, Cohen A, Weston J, Noble WS. Mismatch string kernels for discriminative protein classification. Bioinformatics. 2004;20 Suppl 1:i467–76.
• Needleman-Wunsch — Needleman SB, Wunsch CD. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol. 1970;48(3):443–53.
• Smith-Waterman — Smith TF, Waterman MS. Identification of common molecular subsequences. J Mol Biol. 1981;147(1):195–7.

Third-party libraries used as optional backends:

• rapidfuzz — fast Levenshtein and Hamming distances.
• parasail — SIMD-accelerated sequence alignment (Daily, 2016).
• scikit-learn — SVM, Naive Bayes, KNN.
• NumPy / SciPy — array and sparse-matrix infrastructure.