CRIS: A Centralized Resource for High-Quality RNA Structure and Interaction Data in the AI Era

Area of Science:

• Bioinformatics
• Computational Biology
• Molecular Biology

Background:

• RNA structure and function are crucial for biological processes.
• Existing RNA databases face challenges in data consistency, reproducibility, and accessibility.
• Advancements in experimental and computational methods highlight the need for standardized data integration.

Purpose of the Study:

• To introduce the Crosslinking-based RNA Interactomes and Structurome (CRIS) database as a unified platform.
• To address limitations in data reproducibility, quality control, and accessibility in RNA databases.
• To facilitate AI-driven applications and comparative analyses in RNA biology.

Main Methods:

• Harmonization of crosslinking-based sequencing data (e.g., PARIS, SHARC).
• Implementation of standardized workflows and built-in quality metrics.
• Development of user-friendly tools and visualization guides for data accessibility.

Main Results:

• CRIS provides a unified, rigorously curated repository of RNA structure and interaction data.
• The platform ensures reproducibility and transparency through structured workflows and quality metrics.
• CRIS enables robust comparative analyses and machine learning applications for RNA research.

Conclusions:

• CRIS establishes a new standard for RNA data management and accessibility.
• The database accelerates RNA biology research and drug development by bridging complex data with accessible tools.
• CRIS is designed to support the growing needs of AI-driven research in RNA science.