Integrated Biochemical and Computational Methods for Deciphering RNA-Processing Codes.

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• RNA processing encompasses crucial steps like splicing and polyadenylation, essential for gene expression and RNA diversity.
• High-throughput sequencing technologies generate vast amounts of RNA data, necessitating advanced computational approaches.
• Machine learning and deep learning are increasingly used to interpret the complex 'RNA codes' that dictate RNA sequence-to-function relationships.

Purpose of the Study:

• To systematically review biochemical and computational methods for deciphering key RNA codes.
• To highlight experimental data generation, computational model features, and tools for studying RNA codes.
• To discuss challenges and propose improvements for computational RNA code analysis.

Main Methods:

• Systematic literature review of biochemical and computational methodologies.
• Analysis of experimental techniques for generating RNA processing data.
• Evaluation of machine learning and deep learning models for RNA code prediction.

Main Results:

• Comprehensive summary of methods for five major RNA codes: alternative splicing, alternative polyadenylation, RNA localization, RNA modifications, and RNA-binding protein (RBP) binding.
• Detailed overview of features, model architectures, and performance of existing computational tools.
• Identification of challenges in predictive modeling, including data integration and domain knowledge incorporation.

Conclusions:

• Accurate RNA codes are vital for predicting RNA products, understanding molecular mechanisms, and assessing disease variant effects.
• Continued development of computational tools, leveraging large language models and domain knowledge, is essential for advancing RNA code research.
• Improved computational approaches will enhance our ability to decipher RNA biology and its role in health and disease.