leech

[!WARNING] leech is alpha quality and under active development. APIs, CLI flags, and output formats may change without notice, and bugs are expected. Validate results before relying on it for anything important.

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.

Installation

Requires Python 3.12+

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

The rust extra pulls leech-core, the compiled accelerator for data preparation and inference. leech runs without it — every accelerated path has a pure-Python fallback — so plain uv add leech is fine if no wheel matches your platform (wheels are built for manylinux x86_64 and aarch64).

To work on leech itself:

# Install uv if you don't have it
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone and install
git clone https://github.com/rnabioco/leech.git
cd leech
uv sync

Quick start

1. Prepare training data

uv run leech data prepare \
  --pod5 reads.pod5 \
  --bam alignments.bam \
  --output-dir chunks/ \
  --motif CCAGGC --motif-offset 2 \
  --label 1 --workers 8

2. Train a model

uv run leech model train \
  --train-data chunks/train.json \
  --val-data chunks/val.json \
  --model ConvLSTMDwell \
  --output-dir models/

3. Evaluate

uv run leech eval test \
  --model models/model_best.pt \
  --test-data chunks/test.json \
  --output metrics.json

4. Run inference

uv run leech predict \
  --model models/ \
  --pod5 new_reads.pod5 \
  --bam new_alignments.bam \
  --output predictions.bam

5. Bundle and deploy pairwise models

Package multiple pairwise models into a single file and run aggregated inference:

# Bundle all pairwise models
uv run leech model bundle \
  --model-dir results/models/pairwise/ \
  --output bundle.pt --version 1.0.0

# Inspect bundle contents
uv run leech model bundle-info --bundle bundle.pt

# Run all models (aggregated amino acid prediction)
uv run leech predict \
  --bundle bundle.pt --all \
  --pod5 reads.pod5 --bam alignments.bam \
  --output predictions.bam

CLI overview

Model architectures

29 architectures across 5 families, all supporting multi-channel signal input (signal_in_channels):

Sequence models (CTC-CRF)

Alongside the classifiers, leech.crf trains sequence models: a CTC-CRF over n_base ** state_len states whose Viterbi traceback emits one base per move, for reading a sequence out of raw signal rather than assigning it a label. The formulation is Oxford Nanopore's, introduced in bonito; the architecture is SeqTagger's published parameters (Genome Res 35:956).

from leech.crf import CrfEncoder, CtcCrfLoss, decode_batch, encoder_config_from_toml, load_config

cfg = encoder_config_from_toml(load_config())
model, criterion = CrfEncoder(cfg), CtcCrfLoss(cfg.n_base, cfg.state_len)
scores = model(signal)                        # (N, 1, chunk) -> (T, N, n_score)
sequences = decode_batch(scores, cfg.n_base, cfg.state_len)

Corpora are described by a manifest — one row per read naming the signal window and its target — so the vocabulary of a given assay stays with whatever produced it. See the CRF API reference.

Training features

• Loss functions: BCE, focal loss (for class imbalance), and cross-entropy
• Regularization: weight decay, gradient clipping, dropout
• LR scheduling: reduce-on-plateau, cosine annealing with warmup
• Data augmentation: mixup (signal jitter + random scaling)
• Mixed precision: FP16 training on CUDA; TF32 matmul on Ampere+
• Performance: torch.compile support, Rust-accelerated signal statistics (217x)
• Class balancing: automatic class weight computation
• Balance-groups sampling: equal contribution per source group per epoch
• K-fold cross-validation: stratified read-level k-fold splits
• Platt calibration: post-hoc Platt scaling for probability calibration
• Signal map refinement: Viterbi-based kmer level table refinement (matches Remora)
• Kmer residual features: expected level, signed/unsigned deviation from kmer table
• Multi-channel signal: 2-channel input (raw + kmer residual) for Residual model variants
• Aggregation: naive, confidence-weighted, and tournament pairwise aggregation
• Composable config: dataclass-based configuration shared between prep and inference
• TorchScript export: standalone model export for deployment without leech

Snakemake pipeline

For production workloads, leech includes a Snakemake pipeline supporting:

• Charged vs. uncharged classification
• Pairwise amino acid discrimination
• Grid search optimization
• Multi-architecture comparison
• HPC clusters (SLURM/LSF)

See pipeline/ for configuration and usage.

Development

uv sync --all-extras      # Install with dev tools
uv run pytest              # Run tests
uv run ruff check .        # Lint
uv run ruff format .       # Format
uv run ty check src/leech/ # Type check

Citation

If you use leech, please cite:

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

License

MIT License - see LICENSE for details.