gkmsvm-lite
A pure-Python implementation of gapped k-mer SVMs (gkm-SVMs) for DNA sequence analysis. Supports classification (C-SVC) and regression (epsilon-SVR), all six kernel types, GPU acceleration via CuPy (NVIDIA) and MLX (Apple Silicon), and compatibility with models from LS-GKM, classic gkmSVM, and the R gkmSVM package.
Install
pip install gkmsvm-lite # CPU (NumPy + Numba)
pip install gkmsvm-lite[gpu] # NVIDIA GPU (+ CuPy)
pip install gkmsvm-lite[mlx] # Apple Silicon GPU (+ MLX)
For development installation from source, see CONTRIBUTING.md.
Quick start
from gkmsvm import load_lsgkm_model, one_hot_encode, ism, gkmexplain
# Load an existing model
model = load_lsgkm_model("model.txt.gz")
# Score a sequence
x = one_hot_encode("ACGTACGTACGTACGT")[None, ...] # [1, 4, 16]
score = model(x) # [1, 1]
# Variant effect prediction
ref = one_hot_encode("ACGTACGTACGTACGT")[None, ...]
alt = one_hot_encode("ACGTACGAACGTACGT")[None, ...]
delta = model(alt).item() - model(ref).item()
# Attribution
hyp = gkmexplain(model, x, mode=1) # [1, 4, 16] hypothetical importance
attr = gkmexplain(model, x, mode=0) # [1, 4, 16] importance (= mode 1 × input)
deltas = ism(model, x) # [1, 4, 16] mutation deltas
# GPU acceleration
model.cuda() # NVIDIA (~6x speedup)
model.mlx() # Apple Silicon (~2-6x speedup)
Command-line interface
gkmsvm predict -m model.npz -i sequences.fa -o scores.tsv --device cuda
gkmsvm train -p positive.fa -n negative.fa -o model.npz -l 11 -k 7 -d 3
gkmsvm explain -m model.npz -i sequences.fa -o attr.npz -s seqs.npz
gkmsvm ism -m model.npz -i sequences.fa -o ism.npz
gkmsvm score-variants -m model.npz --ref ref.fa --alt alt.fa -o deltas.tsv
gkmsvm to-deltasvm -m model.npz -o weights.txt
gkmsvm import -i model.txt.gz -f lsgkm -o model.npz
See docs/cli.md for full usage.
Train a model
from gkmsvm import train_gkmsvm, train_gkmsvr
# Classification
model = train_gkmsvm(pos_seqs, neg_seqs, l=11, k=7, d=3, C=1.0)
# Regression (e.g., chromatin accessibility)
model = train_gkmsvr(sequences, labels, l=11, k=7, d=3, epsilon=0.1)
model.save("my_model.npz")
Load models from any gkm-SVM package
from gkmsvm import (
load_lsgkm_model, # LS-GKM / kundajelab/lsgkm
load_classic_model, # C gkmSVM (LIBSVM-style format)
load_r_gkmsvm_model, # R gkmSVM (Ghandi et al. 2016)
load_deltasvm_model, # DeltaSVM k-mer weights
load_model, # auto-detect npz or LS-GKM text
)
See docs/models.md for format details, training options, and examples.
Kernel types
All six LS-GKM kernel types are supported:
-t |
Name | Description |
|---|---|---|
| 0 | gkm | Direct gapped k-mer count |
| 1 | gkm_estfull | Estimated full l-mer |
| 2 | gkm_esttrunc | Estimated truncated l-mer (LS-GKM default) |
| 3 | gkmrbf | RBF-transformed estimated |
| 4 | wgkm | Center-weighted gapped k-mer |
| 5 | wgkmrbf | Center-weighted RBF |
Documentation
| Guide | Contents |
|---|---|
| CLI reference | All gkmsvm subcommands, flags, and usage examples |
| Loading and training models | Importing from all formats, training SVC/SVR, solver selection, kernel types |
| Scoring and variant effects | Inference, batch scoring, VEP, GPU acceleration, memory management |
| Interpretation | GkmExplain, ISM, when to use each, why gradients don't work |
| Performance | Benchmarks, optimization details, tuning guidance |
| Design decisions | Architecture, kernel math, format specifications |
| Roadmap | Completed features and what's next |
Citation
If you use gkmsvm-lite, please cite the relevant methods papers:
Ghandi M, Lee D, Mohammad-Noori M, Beer MA. Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput Biol 10(7):e1003711 (2014).
Ghandi M, Mohammad-Noori M, Ghareghani N, Lee D, Garraway L, Beer MA. gkmSVM: an R package for gapped-kmer SVM. Bioinformatics 32(14):2205-2207 (2016).
Lee D. LS-GKM: a new gkm-SVM for large-scale datasets. Bioinformatics 32(14):2196-2198 (2016).
If you use GkmExplain:
Shrikumar A, Prakash E, Kundaje A. GkmExplain: fast and accurate interpretation of nonlinear gapped k-mer SVMs. Bioinformatics 35(14):i173-i182 (2019).
If you use DeltaSVM:
Lee D, Gorkin DU, Baker M, Strober BJ, Asoni AL, McCallion AS, Beer MA. A method to predict the impact of regulatory variants from DNA sequence. Nat Genet 47(8):955-961 (2015).
See CITATION.cff for machine-readable citation metadata.
License
MIT. LS-GKM C (Dongwon-Lee/lsgkm) is GPL v3 — gkmsvm-lite is a clean-room reimplementation.
1 maintainer
Metadata
1 maintainer
Release files for gkmsvm-lite 0.1.0
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