Leech¶

Learning Enhanced Electrical Classifiers from Hanopore signals

Leech classifies aminoacylation state and amino acid identity from Oxford Nanopore tRNA sequencing data. It extracts dwell time features from move tables (the BAM mv tag) and feeds them alongside raw signal and sequence context into a multi-branch neural network, giving it information that signal-only tools like Remora discard.

Install¶

uv add "leech[rust]"     # or: pip install "leech[rust]"

Released on PyPI as leech plus leech-core, the optional Rust accelerator pulled by the rust extra. See Installation for the from-source path and platform notes.

Workflow¶

graph LR
    A["leech data prepare"] --> B["leech model train"]
    B --> C["leech eval test"]
    C --> D["leech predict"]

1. Prepare -- extract signal, sequence, and dwell features from POD5 + BAM files
2. Train -- fit a multi-branch neural network on the extracted features
3. Test -- evaluate on held-out data (accuracy, AUC, confusion matrix)
4. Predict -- apply the model to new reads and write predictions to BAM

Documentation¶

Citation¶

If you use leech, please cite:

• This work (publication pending)
• Remora (underlying training framework)

License¶

MIT License -- see LICENSE for details.