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Expansion of the RNAStructuromeDB to include secondary structural data spanning the human protein-coding
Warren B Rouse1, Collin A O'Leary1, Nicholas J Booher2
1Roy J. Carver Department of Biophysics, Biochemistry and Molecular Biology, Iowa State University, Ames, IA, 50011, USA.
Scientific Reports
|August 25, 2022
Summary
This study enhances RNA secondary structure prediction for over 100,000 human protein-coding gene isoforms using the ScanFold tool and the RNAStructuromeDB. The findings offer more robust structure predictions and enable the discovery of novel, stable RNA motifs for gene regulation.
Area of Science:
- Molecular Biology
- Genomics
- Bioinformatics
Background:
- RNA secondary structures are crucial for gene regulation and overall biological function.
- Accurate prediction of RNA structures is essential for understanding these regulatory roles.
- Previous work established the RNAStructuromeDB for analyzing RNA structures.
Purpose of the Study:
- To expand RNA secondary structure prediction to high resolution across the human protein-coding transcriptome.
- To identify stable, ordered RNA motifs using the ScanFold tool.
- To provide a comprehensive, searchable database of these predictions.
Main Methods:
- High-resolution (single nucleotide) scanning of the human protein-coding transcriptome.
- Utilizing the ScanFold motif identification tool for modeling stable RNA structures.
- Uploading all data to the RNAStructuromeDB for accessibility and analysis.
Main Results:
- Generated robust RNA secondary structure predictions for over 100,000 human protein-coding gene isoforms.
- Identified numerous highly-ordered RNA motifs with potential for evolved stability.
- Demonstrated the utility of ScanFold in analyzing specific genes like MAT2A for known and novel structures.
Conclusions:
- The enhanced RNAStructuromeDB provides a valuable resource for studying RNA structure-function relationships.
- ScanFold effectively models stable RNA structures and aids in discovering functional motifs.
- This high-resolution data facilitates hypothesis generation for RNA-mediated gene regulation.
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