CNNAMON
Convolutional Neural Network Analysis & Motif Discovery
A modular, interpretability-first framework for deep learning in genomics.
🧠 Overview
CNNAMON is a Python framework designed to bridge the gap between training high-performance
1D Convolutional Neural Networks (CNNs) on DNA sequences and understanding what they learn.
It provides an end-to-end ecosystem for:
- Dataset Preparation – Converting genomic intervals (BED3 + labels) to one-hot tensors
- Modeling – Building complex Keras models via simple JSON configuration files
- Explainability – Extracting learned motifs, clustering filters by activation profiles, assessing filter importance, and associating filters with prediction classes
⚡ Key Features
| Module | Functionality |
|---|---|
| 🧬 PrepareData | Extract sequences from FASTA/BED files. Supports random, chromosome, or custom splits, and reverse-complement augmentation. |
| 🏗 KerasBuilder | Define model architectures, optimizers, and callbacks using JSON for reproducible experiments. |
| 🎨 FilterVisualize | Extract learned motifs using Top-Activating, Consensus, or Significant (permutation-based) strategies. Export to MEME for TOMTOM validation. |
| 📉 FilterImportance | Rank filters by their contribution to model loss using perturbation analysis. |
| 🌳 FilterClustering | Group redundant or co-activated filters with hierarchical clustering and visualize relationships using circular dendrograms. |
| 🧪 Enrichment | Identify filters statistically enriched for prediction classes (e.g., Enhancer vs. Silencer). |
📦 Installation
We recommend installing CNNAMON in a fresh environment to manage dependencies (TensorFlow, BedTools).
# 1. Create environment
conda create -n cnnamon_env python=3.10
conda activate cnnamon_env
# 2. Install CNNAMON
pip install cnnamon
# 3. Install BedTools (required for sequence extraction)
conda install -c bioconda bedtools
🚀 Quick Start
Train a model and visualize motifs in four steps:
import cnnamon as cn
# 1. Prepare data
preparer = cn.utility.PrepareData(
intervalfile="peaks.bed",
genomefasta="hg38.fa",
outdir="data/",
split_segmentation="random"
)
train, test, val = preparer.run()
# 2. Train model (from JSON config)
model = cn.utility.KerasModelBuilder.from_json("model_config.json")
model.train(train['x'], train['y'], val['x'], val['y'])
# 3. Extract significant motifs
motifs = cn.CNN1D.FilterVisualize.significant_activating(
model,
data=test,
n_perturbations=1000,
q_value_cutoff=0.05,
n_cores=10
)
# 4. Plot sequence logos
motifs.to_motifs(savefig="learned_motifs.png")
📖 Documentation
Full documentation is available here:
👉 https://georgakilaslab.github.io/CNNAMON/
📚 Citation
If you use CNNAMON in your research, please cite:
(Add paper reference / DOI here when available)
Built by the Georgakilas Lab.
1 maintainer
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