PREVALT: Prediction and Validation of Alternative Splicing in Plants

PREVALT：植物选择性剪接的预测和验证

• 批准号: 0743097
• 负责人: Asa Ben-Hur
• 金额: $ 108.66万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-03-01 至 2013-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0743097&HistoricalAwards=false
• 关键词: PREVALT; Prediction; Validation; Alternative; Splicing

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.

科罗拉多州立大学获得了一笔赠款，用于开发机器学习方法来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，达到约30%的基因，这仍然明显低于人类和小鼠。 这主要是由于植物中可获得的cDNA/EST数据量少得多。因此，我们很可能离植物中选择性剪接的真实程度还很远。 在人类和小鼠中，有几个项目已经对选择性剪接进行了非EST预测;据我们所知，没有一个在植物中报道过。 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选择性剪接的顺式元件。选择性剪接在植物中有不同的特点，比在动物中，和拟议的计算和实验工作将有助于阐明这些差异的机制基础。 最初的重点将放在拟南芥上，这些方法将扩展到水稻和其他基因组和EST数据可用的植物。 拟议研究的最终结果将是建立一个预测和验证的选择性剪接事件和顺式元件的网络访问数据库;在该项目过程中开发的软件将提供给有兴趣预测其他植物物种中的选择性剪接的研究人员。