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Identification of clustered microRNAs using an ab initio prediction method
Alain Sewer1, Nicodème Paul, Pablo Landgraf
1Biozentrum, Universität Basel, Basel, Switzerland. alain.sewer@unibas.ch
BMC Bioinformatics
|November 9, 2005
Summary
Bioinformatics aids microRNA (miRNA) gene discovery by identifying novel mammalian precursor miRNAs (pre-miRNAs) within genomic clusters. This computational method predicts many new miRNAs, with significant experimental validation, advancing understanding of gene regulation.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- MicroRNAs (miRNAs) are small RNA molecules regulating gene expression via RNA interference.
- Discovering new miRNA genes is crucial for understanding this regulatory mechanism.
- Bioinformatics tools are valuable for guiding experimental miRNA discovery.
Purpose of the Study:
- To develop and apply a computational method for predicting novel mammalian microRNAs (miRNAs).
- To investigate the utility of miRNA clustering for novel miRNA gene discovery.
- To analyze species-specific organization of miRNA loci in mammals.
Main Methods:
- A computational approach focusing on genomic regions flanking known miRNAs to identify precursor miRNAs (pre-miRNAs).
- Analysis of human, mouse, and rat genomes separately, with cross-species comparisons for validation.
- Utilizing an ab initio prediction method to identify novel pre-miRNAs.
Main Results:
- The method predicted 50-100 novel pre-miRNAs per species.
- Approximately 30% of predicted novel miRNAs had experimental support.
- Predictions conserved across species showed a 60% validation rate, with some missed by other methods.
Conclusions:
- MiRNA gene discovery is enhanced by considering that miRNAs often occur in clusters.
- While miRNA content in clusters is conserved, their internal organization evolves differently across species.