BioSieve

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BioSieve is a Python toolkit for preparing biological sequence datasets for machine learning.

It covers two main workflows:

  • Redundancy reduction — remove near-duplicate sequences before training using sequence, embedding, descriptor, or structural similarity
  • Leakage-aware splitting — partition datasets into train/val/test (or k-folds) with strategies that respect biological structure (homology clusters, sequence distance, groups, time)

Installation

BioSieve supports Python 3.11+.

pip install biosieve

Install optional extras as needed:

  • minhash for approximate Jaccard-based deduplication (minhash_jaccard strategy)
  • faiss for GPU-accelerated embedding similarity search (embedding_cosine strategy)
pip install 'biosieve[minhash]'
pip install 'biosieve[faiss]'

The mmseqs2 reducer and homology_aware splitter require the MMseqs2 binary to be available in PATH.

[!TIP] You can install MMSeqs2 easily with pixi: pixi global install -c bioconda -c conda-forge mmseqs2.

Quick Start

Redundancy reduction

Remove near-duplicate sequences using k-mer Jaccard similarity:

biosieve reduce \
  -i dataset.csv \
  -o dataset_nr.csv \
  --strategy kmer_jaccard \
  --mapping-output mapping.csv \
  --report-output report.json

Pass parameters via a YAML file:

biosieve reduce \
  -i dataset.csv \
  -o dataset_nr.csv \
  --strategy kmer_jaccard \
  --params params.yaml

# params.yaml
kmer_jaccard:
  threshold: 0.8
  k: 5

Override a single parameter inline without a file:

biosieve reduce -i dataset.csv -o out.csv --strategy kmer_jaccard --set kmer_jaccard.threshold=0.9

Dataset splitting

Split with a leakage-aware strategy:

biosieve split \
  -i dataset_nr.csv \
  -o splits/ \
  --strategy homology_aware \
  --params params.yaml

# params.yaml
homology_aware:
  mode: precomputed
  clusters_path: clusters.csv
  member_col: id
  cluster_col: cluster_id
  test_size: 0.2

Strategies

Redundancy reduction

Strategy Description Extra needed
exact Remove exact sequence duplicates —
identity_greedy Greedy reduction by sequence identity —
kmer_jaccard Greedy reduction by k-mer Jaccard similarity —
minhash_jaccard Approximate k-mer Jaccard via MinHash LSH (fast) biosieve[minhash]
embedding_cosine Cosine similarity on precomputed embeddings biosieve[faiss] (optional)
descriptor_euclidean Euclidean distance on numeric descriptor columns —
structural_distance Graph-based reduction on precomputed structural edges —
mmseqs2 Homology clustering via MMseqs2 mmseqs binary

Splitting

Strategy Description
random Random train/val/test split
stratified Stratified by a categorical label column
stratified_numeric Stratified by a numeric label column (binned)
group No group appears in more than one split
time Chronological split by a time column
cluster_aware Group split using a precomputed cluster column
distance_aware Test set selected as farthest points in embedding/descriptor space
homology_aware Group split derived from MMseqs2 clusters or precomputed clusters

All strategies also support k-fold variants (random_kfold, stratified_kfold, group_kfold, stratified_numeric_kfold, distance_aware_kfold).

Outputs

Every run produces consistent artefacts:

  • Reduced or split CSVs
  • Mapping CSV (removed_id, representative_id, cluster_id, score) for reduction runs
  • JSON report with strategy name, effective parameters, and reduction/split statistics

Learn More

License

MIT. See LICENSE.

Acknowledgements

Built on top of scikit-learn, polars, NumPy, and optionally datasketch, FAISS, and MMseqs2.

Developed by KREN AI Lab at Universidad de Magallanes, Chile.

Project links

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Credits

Author: KrenAI-Lab

GitHub Statistics

Data verified by PyPI on Aug 14, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
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Requires

Python <3.15, >=3.11

Provides Extra

faiss minhash

Tags

bioinformatics biology dataset-splitting deduplication machine-learning sequence-analysis

Classifiers

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