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Discovering common stem-loop motifs in unaligned RNA sequences.
J Gorodkin1, S L Stricklin, G D Stormo
1Department of Genetics and Ecology, The Institute of Biological Sciences, University of Aarhus, Building 540, Ny Munkegade, DK-8000 Aarhus C, Denmark.
Nucleic Acids Research
|May 23, 2001
Summary
This study introduces a novel computational method combining FOLDALIGN and COVE to accurately identify and model RNA stem-loop motifs in unaligned sequences. The approach successfully discovers RNA structures, aiding in understanding post-transcriptional gene regulation.
Area of Science:
- Computational Biology
- Molecular Biology
- Bioinformatics
Background:
- Post-transcriptional gene regulation relies on proteins binding mRNA motifs, which often involve conserved structures with variable sequences.
- Discovering RNA motifs is challenging due to sequence covariation, unlike DNA regulatory site identification.
Purpose of the Study:
- To develop and evaluate a combined computational method for discovering and modeling stem-loop RNA motifs in unaligned sequences.
- To address the difficulty of RNA motif discovery caused by positional covariation.
Main Methods:
- Combined use of FOLDALIGN and COVE for RNA structure prediction.
- Application of the method to unaligned sequences, including UTRs from coregulated genes.
- Evaluation on rRNA gene sections and randomized UTR sequences containing IRE-like elements.
Main Results:
- The combined method successfully identified stem-loop RNA motifs in both tested datasets.
- Accurate determination of RNA secondary structures, including loops and bulges, was achieved for rRNA genes.
- Quantitative evaluation showed close agreement with curated database structures (correlation coefficients up to 0.9).
Conclusions:
- The integrated FOLDALIGN and COVE approach is effective for discovering and modeling RNA stem-loop motifs in unaligned sequences.
- This method aids in understanding post-transcriptional gene regulation by identifying key RNA structures.
- A publicly available server, SLASH, has been developed to perform these stem-loop searches.