ModiDeC-RNA-modification-classifier

ModiDeC is a Personalized two input neural network that was designed to identify RNA modifications from direct RNA sequencing using RNA002 or RNA004 Oxford Nanopore technology (ONT) kits. In detail, ModiDeC combines LSTM and a newly designed inception-res-net block for the multi-classification process. In this GitHub repository, we offer the ModiDeC models and several user graphic interfaces to retrain from scratch the neural network to readapt ModiDeC to your specific problem.

Requirements and Installation

ModiDeC uses simple libraries such as NumPy and TensorFlow. It also uses the pre-compiled library "ont-remora" from ONT. Here below is a list of the libraries used for ModiDeC creation:

 python == 3.10.14
 TensorFlow == 2.15
 pyqt5 == 5.15.11
 matplotlib == 3.9.1
 numpy == 1.26.4 
 ont-remora == 3.2.0 

To install the Conda environment to run the ModiDeC GUI, we suggest running the two following command lines in the prompt of Linux or WSL.

 sudo apt install gcc -y
 conda env create -f /path_to_ModiDeC_folder/remora_TF2_env.yml

These two command lines will install C and C++ interpreters (needed for Remora), and it will create a new conda environment called "Remora_TF2", which contains all the libraries necessary to run ModiDeC.

IMPORTANT: The ont-remora library is a Linux-based library, which means that ModiDeC can be used in the Linx system or Windows with WSL.

General information ModiDeC GUI

ModiDeC GUI is divided in three sub-interfaces (see figure below), which each of them has a specific design. The ModiDeC GUI can be used in several ways, from retraining the neural network to directly analyzing an aligned sample using a pre-trained neural network. We decided to create the GUIs to give the opportunity to adapt and customize ModiDeC for specific problems.

the figure below shows a general overview of ModiDeC GUIs. The first thing that can be observed is that the GUIs are divided into ”ModiDeC data curation”, “ModiDeC training” and “ModiDeC analysis”.

in the tutorial folder it is possible to find detailed tutorials for each of the three GUI.

Epi2Me pipeline link

We also implemented ModiDeC in Epi2Me. Epi2Me links repositary can be found here below.

https://github.com/Nanopore-Hackathon/wf-modidec_data-curation

https://github.com/Nanopore-Hackathon/wf-modidec_training

https://github.com/Nanopore-Hackathon/wf-modidec_analysis

Collaboration

This work is a collaboration partnership with the group of Prof. Dr. Susanne Gerber, Uni Medical Center, Mainz. https://csg.uni-mainz.de/group-member/susanne-gerber/

Credit and Licence

This code is provided by Dr. Nicolo Alagna and the Computational Systems Genetics Group of the University Medical Center of Mainz. © 2024 All rights reserved.

This code is licensed for non-commercial academic use only. See LICENSE file.