RAIN: RNA-protein Association and Interaction Networks.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Protein association networks are crucial for understanding cellular mechanisms.
• Non-coding RNAs (ncRNAs) are increasingly recognized for their regulatory roles.
• Integrating ncRNA interactions into existing protein networks presents challenges due to data heterogeneity.

Purpose of the Study:

• To present RAIN, a novel database for ncRNA-RNA and ncRNA-protein interactions.
• To integrate ncRNA interactions with the STRING protein-protein interaction database.
• To provide a confidence scoring system for inferred ncRNA associations.

Main Methods:

• Compiled ncRNA interaction data from curated examples, experimental data, predictions, and literature mining across four organisms.
• Integrated ncRNA interaction data with the STRING database.
• Developed an integrative scoring scheme to assign confidence scores to ncRNA interactions.

Main Results:

• RAIN successfully integrates diverse ncRNA interaction data.
• The RAIN database covers ncRNA-RNA and ncRNA-protein interactions for four organisms.
• RAIN's scoring scheme demonstrates superior performance in inferring ncRNA interactions compared to standalone microRNA-target predictions.

Conclusions:

• RAIN offers a valuable resource for studying ncRNA functions within cellular networks.
• The integrative approach and confidence scoring enhance the reliability of inferred ncRNA interactions.
• RAIN facilitates the exploration of ncRNA roles in biological processes through its web interface and downloadable data.