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Dwell Time Features

Leech extends Remora by adding a third feature branch to the model architecture: per-base dwell time and signal level statistics computed from move tables. This page describes the feature set, how it integrates into the model, and how the convolutional architecture learns to use these features.

How leech extends Remora

Remora uses two input branches for base modification detection:

  1. Signal branch -- raw nanopore signal processed by Conv1d layers
  2. Sequence branch -- one-hot encoded k-mer context processed by Conv1d layers

These branches are concatenated and fed through a BiLSTM for classification. Leech adds a feature branch that provides per-base dwell time and signal level statistics as a third input. This is the novel contribution: by making dwell information explicitly available as structured features (rather than relying on the signal branch to implicitly learn timing patterns), the model gains direct access to translocation kinetics.

Feature components

Leech computes 9 feature channels for each base in the k-mer context window. These are stacked into a feature matrix of shape (9, kmer_len):

Channel Description Source
dwell Raw dwell time (signal samples per base) Move table
dwell_log Log-transformed dwell time Move table
dwell_mean Mean dwell in local window Move table
dwell_std Dwell standard deviation in local window Move table
dwell_ratio Ratio of base dwell to local mean Move table
level_mean Mean signal amplitude for this base Signal + moves
level_median Median signal amplitude for this base Signal + moves
level_std Signal amplitude standard deviation Signal + moves
level_range Signal amplitude range (max - min) Signal + moves

The dwell features capture translocation kinetics, while the level features capture signal amplitude statistics. Together, they provide a compact per-base summary that complements the raw signal.

Note

The dwell_ratio feature (base dwell / local mean dwell) normalizes for read-level variation in translocation speed, making the feature more comparable across reads.

Three-branch architecture

The ConvLSTMDwell model processes the three input types through parallel convolutional branches before merging them:

Signal (1 ch)  →  Conv1d stack  →  256 features
                                        ↘
Sequence (4 ch) → Conv1d stack  →  256 features  →  Concat (768)  →  BiLSTM  →  FC  →  Output
                                        ↗
Features (9 ch) →  Conv1d stack  →  256 features

Each branch applies three Conv1d layers (increasing channels: 4 → 16 → 256) with batch normalization and ReLU activation, followed by adaptive average pooling. The concatenated 768-dimensional representation feeds a bidirectional LSTM (768 → 96 × 2 directions), from which the center position is extracted and passed through fully connected layers (192 → 64 → output).

Architecture comparison

Component ConvLSTMDwell ConvLSTMBase Remora
Signal branch Conv1d (1→4→16→256) Conv1d (same) Conv1d
Sequence branch Conv1d (4→4→16→256) Conv1d (same) Conv1d
Feature branch Conv1d (9→4→16→256) None None
BiLSTM input 768 512 512
BiLSTM hidden 96 per direction 96 per direction varies
Center extraction Yes Yes Yes
Output FC 192→64→num_classes FC 192→64→num_classes FC→num_classes

The ConvLSTMBase model (without the feature branch) serves as a direct comparison to measure the impact of dwell features.

Feature learning

A natural question is whether explicit feature engineering (the 9 channels above) is necessary, or whether the model could learn equivalent representations from raw signal alone.

Convolutional receptive fields

Each Conv1d layer with kernel_size=3 contributes a receptive field of 3 bases. Three stacked layers yield an effective receptive field of 7 bases. This means the feature branch automatically learns to combine information across a 7-base window without explicit windowed statistics.

The pre-computed features (like dwell_mean and dwell_std over a local window) provide the model with summary statistics that would otherwise require the network to learn averaging operations. This gives the model a head start, particularly with limited training data.

Grid search for context windows

Leech includes a grid search tool to find the optimal signal and k-mer context window sizes for a given dataset:

Bash
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uv run leech model optimize \
    --train-data chunks/train.npz \
    --val-data chunks/val.npz \
    --context-grid 200,500,1000 \
    --output-dir grid_results/

This evaluates model performance across different context window sizes. In practice, the landscape is often flat (less than 2% difference between reasonable window sizes), confirming that the convolutional layers effectively adapt to the available context.

When features matter most

The feature branch has the largest impact when:

  • Training data is limited -- explicit features reduce what the model must learn from scratch
  • The classification task is subtle -- amino acid discrimination relies on small dwell differences that benefit from direct feature access
  • The region of interest is the CCA tail -- this is where aminoacylation produces the strongest dwell signal, and explicit features help the model focus there