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ENNGene: an Easy Neural Network model building tool for Genomics.

Eliška Chalupová^1,2, Ondřej Vaculík^1,2, Jakub Poláček³

¹Faculty of Science, National Centre for Biomolecular Research, Masaryk University, Brno, Czechia.

|April 1, 2022

Summary

ENNGene is a new tool that simplifies building and training custom Deep Learning models for genomics research. It offers a user-friendly interface, enabling researchers to achieve state-of-the-art results without extensive computational expertise.

Keywords:

Convolutional Neural Network Deep Learning Evolutionary Conservation Score GUI RNA Secondary Structure Recurrent Neural Network

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Published on: December 7, 2021

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

The big data revolution in genomics necessitates advanced machine learning methods.
Deep Learning (DL) models like CNNs and RNNs are increasingly used in genomics.
Most genomics researchers lack the expertise to develop and train complex DL models.

Purpose of the Study:

To present ENNGene, an easy-to-use tool for building and training custom Deep Learning models in genomics.
To enable genomics researchers without a computational background to leverage DL for big data analysis.

Main Methods:

ENNGene provides a Graphical User Interface (GUI) for simplified model building.
Supports multiple input types (sequence, conservation, structure) and preprocessing.
Allows full customization of network architecture (layers, setup).
Automates model training, evaluation, and metric export (ROC, precision-recall curves, TensorBoard).
Integrates Integrated Gradients for result interpretation and visualization.

Main Results:

ENNGene successfully trained models on the RBP24 dataset, achieving state-of-the-art performance.
Improved performance for over half of the proteins by incorporating evolutionary conservation scores.
Demonstrated the tool's capability to fine-tune networks per protein for enhanced results.

Conclusions:

ENNGene makes advanced DL techniques accessible to a broader range of genomics researchers.
Facilitates deeper insights and information extraction from large genomic datasets.
Empowers researchers without computational science backgrounds to utilize DL effectively.