Metadata-Version: 2.4
Name: cnnamon
Version: 0.2.1
Summary: CNNAMON is a modular Python framework for building, training, and interpreting 1D Convolutional Neural Networks (CNNs) for DNA sequence analysis. It integrates data preparation, model construction, and rich explainability tools in a unified and flexible system tailored for genomics research.
Author-email: Panagiotis Xiropotamos <panosxirop4@gmail.com>
Project-URL: Homepage, https://github.com/GeorgakilasLab/CNNAMON
Project-URL: Bug Tracker, https://github.com/GeorgakilasLab/CNNAMON/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: absl-py>=1.4.0
Requires-Dist: joblib>=1.2.0
Requires-Dist: logomaker>=0.8.0
Requires-Dist: matplotlib>=3.7.0
Requires-Dist: numpy>=1.24.0
Requires-Dist: pandas>=2.0.0
Requires-Dist: pycirclize>=1.0.0
Requires-Dist: scikit-learn>=1.2.0
Requires-Dist: scipy>=1.10.0
Requires-Dist: seaborn>=0.12.0
Requires-Dist: statsmodels>=0.14.0
Requires-Dist: tensorflow==2.15.0
Requires-Dist: keras==2.15.0
Requires-Dist: tqdm>=4.65.0
Requires-Dist: plotly>=5.15.0
Requires-Dist: kaleido>=0.2.1
Requires-Dist: protobuf>=3.20.0

CNNAMON Logo

# 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:

1. **Dataset Preparation** – Converting genomic intervals (BED3 + labels) to one-hot tensors  
2. **Modeling** – Building complex Keras models via simple JSON configuration files  
3. **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).

```bash
# 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:

```python
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.

