Abstract
The Crosslinking-based RNA Interactomes and Structurome (CRIS) database is a unified platform for RNA structure and interaction data, combining rigorously curated datasets, standardized workflows, and user-friendly tools. While advancements in experimental and computational methods expand our understanding of RNA structure and function, challenges in reproducibility, quality control, and data accessibility remain prevalent due to inconsistencies in data processing. As a result, many existing databases lack breadth in their data sources, are inconsistent in their processing pipelines, and limit accessibilities for end users. Designed for the AI-driven era, CRIS addresses critical gaps in reproducibility and accessibility by harmonizing crosslinking-based sequencing data (e.g., PARIS, SHARC) with built-in quality metrics, enabling robust comparative analyses and machine learning applications. Its structured workflow, built-in quality metrics, and detailed visualization guides ensure reproducibility and transparency while integrating seamlessly with existing experimental pipelines. By bridging complex RNA datasets with accessible computational tools, CRIS accelerates RNA biology and drug development, establishing a new standard for the field.