PREVALT: Prediction and Validation of Alternative Splicing in Plants

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Colorado State University is awarded a grant to develop machine learning methods for detecting alternative splicing in plants and to experimentally validate selected predictions. Alternative splicing has an important role in proteome diversity and gene regulation. Recent studies of large scale EST/cDNA datasets have revealed that the prevalence of alternative splicing in plants is much larger than expected, reaching around 30% of the genes, which is still significantly less than in human and mouse. This is primarily due to the much smaller amount of cDNA/EST data that is available in plants. Therefore we are likely far from the true extent of alternative splicing in plants. In human and mouse, several projects have made non-EST-based predictions of alternative splicing; none have been reported in plants to our knowledge. To fill this gap, the PIs will develop computational tools to predict novel alternative splicing events and the cis-elements involved in regulated alternative splicing. Alternative splicing in plants has different characteristics than in animals, and the proposed computational and experimental work will help elucidate the mechanistic basis for these differences. The initial focus will be in Arabidopsis, and the methods will be extended to rice and other plants for which genome and EST data are available. The end-results of the proposed research will be the creation of a web-accessible database of predicted and validated alternative splicing events and cis-elements; the software developed during the course of this project will be made available for researchers interested in predicting alternative splicing in other plant species.