Deep Learning for Elucidating Modifications to RNA-Status and Challenges Ahead.

Area of Science:

• Computational biology
• Molecular biology
• Genomics

Background:

• RNA-binding proteins (RBPs) and RNA modifications are crucial for gene regulation.
• Understanding their precise targets and sequence determinants is essential for deciphering biological roles.

Purpose of the Study:

• To provide an overview of deep learning applications in predicting RNA modification and RBP binding sites.
• To discuss model inputs, training strategies, and recommendations for biologically relevant model development.

Main Methods:

• Deep learning models are employed to predict RNA modification and RBP binding.
• Models utilize sequence and secondary structure features to identify binding sites.
• Training involves careful selection of negative regions and appropriate data splitting.

Main Results:

• Deep learning effectively deciphers non-linear sequence patterns and structures underlying RNA site preferences.
• The article reviews various model types for handling sequence and/or structure-based inputs.

Conclusions:

• Deep learning significantly advances the prediction of RNA-protein interactions and RNA modifications.
• Further research in key areas is crucial for advancing the field of RNA regulation studies.