PREVALT: Prediction and Validation of Alternative Splicing in Plants

科罗拉多州立大学获得了一笔拨款，用于开发机器学习方法，以检测植物中的选择性剪接，并通过实验验证选定的预测。选择性剪接在蛋白质组多样性和基因调控中具有重要作用。最近对大规模EST/cDNA数据集的研究表明，植物中选择性剪接的流行率比预期的要大得多，约占基因的30%，但仍明显低于人类和小鼠。这主要是由于植物中可用的cDNA/EST数据量少得多。因此，我们可能远远没有了解植物中选择性剪接的真实程度。在人类和小鼠中，一些项目已经对选择性剪接进行了非est的预测；据我们所知，在植物中没有报道。为了填补这一空白，pi将开发计算工具来预测新的选择性剪接事件和参与调节的选择性剪接的顺式元件。植物的选择性剪接具有不同于动物的特征，提出的计算和实验工作将有助于阐明这些差异的机制基础。最初的重点将是拟南芥，该方法将扩展到水稻和其他可获得基因组和EST数据的植物。拟议研究的最终结果将是创建一个可访问的网络数据库，预测和验证备选剪接事件和顺式元件；在这个项目过程中开发的软件将提供给对预测其他植物物种的选择性剪接感兴趣的研究人员。

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.