# Deep Learning for Elucidating Modifications to RNA—Status and Challenges Ahead

## TL;DR

This paper reviews how deep learning is used to study RNA modifications and RNA-binding proteins, highlighting current methods and future challenges.

## Contribution

The paper provides a comprehensive overview of deep learning applications in RNA modification studies and outlines key areas for future research.

## Key findings

- Deep learning models can predict RNA modifications based on sequence and secondary structure features.
- Model training strategies, including selection of negative regions and data splitting, are critical for accurate predictions.
- The paper identifies four key areas for advancing the field of RNA modification analysis using deep learning.

## Abstract

RNA-binding proteins and chemical modifications to RNA play vital roles in the co- and post-transcriptional regulation of genes. In order to fully decipher their biological roles, it is an essential task to catalogue their precise target locations along with their preferred contexts and sequence-based determinants. Recently, deep learning approaches have significantly advanced in this field. These methods can predict the presence or absence of modification at specific genomic regions based on diverse features, particularly sequence and secondary structure, allowing us to decipher the highly non-linear sequence patterns and structures that underlie site preferences. This article provides an overview of how deep learning is being applied to this area, with a particular focus on the problem of mRNA-RBP binding, while also considering other types of chemical modification to RNA. It discusses how different types of model can handle sequence-based and/or secondary-structure-based inputs, the process of model training, including choice of negative regions and separating sets for testing and training, and offers recommendations for developing biologically relevant models. Finally, it highlights four key areas that are crucial for advancing the field.