RNAct: Protein-RNA interaction predictions for model organisms with supporting experimental data.

Area of Science:

• Molecular biology
• Bioinformatics
• Genomics

Background:

• Protein-RNA interactions are crucial for cellular functions and are implicated in various diseases.
• While ~1400 human proteins are known RNA-binders, experimental data on their specific RNA targets is limited for most (~250 proteins).
• A significant gap exists in understanding the full scope of protein-RNA interactions.

Purpose of the Study:

• To computationally predict and compile a comprehensive database of protein-RNA interactions.
• To expand the knowledge of the protein-RNA interactome beyond experimentally validated data.
• To provide a resource for exploring global protein-RNA interaction networks in multiple species.

Main Methods:

• Utilized the catRAPID computational method for predicting protein-RNA interactions.
• Developed the RNAct database to store and organize predicted and known interaction data.
• Integrated experimental data with computational predictions to create a larger interactome resource.

Main Results:

• The RNAct database was populated with a large number of known and predicted protein-RNA interactions.
• The database provides genome-wide views of protein-RNA interactomes for human, mouse, and yeast.
• RNAct significantly expands the available data on protein-RNA interactions beyond current experimental limitations.

Conclusions:

• The RNAct database serves as a valuable resource for studying protein-RNA interactions.
• Computational predictions can effectively bridge the gap in experimentally derived interaction data.
• RNAct facilitates a deeper understanding of the protein-RNA interactome's role in biological processes and diseases.