Computational analysis and prediction of microRNA binding sites
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| Description: |
Enright, A (Sanger)
Wednesday 02 April 2008, 09:00-10:00 High Dimensional Statistics in Biology |
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| Created: | 2008-04-08 07:39 | ||
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| Collection: | Statistical Theory and Methods for Complex, High-Dimensional Data | ||
| Publisher: | Isaac Newton Institute | ||
| Copyright: | (Sanger) | ||
| Language: | eng (English) | ||
| Distribution: |
World
(downloadable)
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| Explicit content: | No | ||
| Aspect Ratio: | 4:3 | ||
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| Abstract: | MicroRNAs (miRNAs) are small 22 nucleotide RNA molecules that directly bind to the 3' Untranslated regions of protein-coding messenger RNAs. This binding event represses the target transcript rendering it unsuitable for protein production and causing its degradation. Many miRNAs have been found and a large-number of them have already been implicated in human disease and development. We have developed a number of computational approaches for predicting the target transcripts of miRNAs. One method (miRanda) is purely computational and uses a simple dynamic programming algorithm and a statistical model to identify significant binding sites. Our second approach (Sylamer) is an algorithm for scanning genome sequences for 7mer words and testing gene-expression data to identify gene sets which are significantly enriched or depleted in such 7mer words using Hypergeometric Statistics. This combined computational/experimental approach has worked extremely well for identifying candidate miRNA targets in B and T blood cells, developing Zebrafish embryos and in mouse mutants with deafness.
Related Links * http://www.sanger.ac.uk/Teams/Team101/ |
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