Sequence and Structural Patterns in RNA Splicing

RNA 剪接中的序列和结构模式

• 批准号: 0515986
• 负责人: Michael Gribskov
• 金额: --
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0515986&HistoricalAwards=false
• 关键词: Sequence; Structural; Patterns; RNA; Splicing

Purdue University is awarded a grant to further develop the MAASE resource (a database of manually Annotated Alternative Splice Events). It is a unique repository of highly validated splicing information and will use machine-learning techniques to identify signals that explain the control of alternative splicing. Firstly, both profile-based probabilistic approaches and word-based enumerative approaches will be used to attempt to identify short, imperfectly conserved, sub-sequences that are correlated with alternative splicing. The MAASE resource allows generating datasets that comprise many distinct classes of alternative splicing so class specific effects can be identified. It is likely that these signals, if present, are weak and act in groups (as in eukaryotic promoters). Methods that allow multiple motifs to be combined will be used, and new methods developed. Because secondary and tertiary structure are known to be important for RNA function, and because conserved sequence motifs are weak and difficult to find, it appears highly likely that structural motifs could be involved in control of RNA splicing. Methods to identify conserved secondary or tertiary structures are not well developed. The PIs will use existing methods and develop novel methods that are capable of identifying the greatest common structure in a training set of sequences. Such shared structures are strong candidates for structural regulatory elements. The datasets produced and used in this study will be made publicly available for other research groups. This project also provides a strong vehicle for training students at the boundary of biology and computation.

普渡大学被授予赠款，以进一步开发MAASE资源（手动注释的替代剪接事件的数据库）。它是一个独特的高度有效的剪接信息库，并将使用机器学习技术来识别解释选择性剪接控制的信号。首先，将使用基于轮廓的概率方法和基于单词的枚举方法来尝试识别与选择性剪接相关的短的、不完全保守的子序列。MAASE资源允许生成包含许多不同类别的选择性剪接的数据集，因此可以识别类别特异性效应。很可能这些信号，如果存在的话，是弱的并且在群体中起作用（如在真核启动子中）。将使用允许多个图案组合的方法，并开发新的方法。由于已知二级和三级结构对RNA功能很重要，并且由于保守序列基序很弱且难以找到，因此结构基序极有可能参与RNA剪接的控制。鉴定保守的二级或三级结构的方法还没有得到很好的开发。PI将使用现有的方法，并开发新的方法，能够识别序列训练集中最常见的结构。这种共享结构是结构调节元件的强有力的候选者。本研究中制作和使用的数据集将向其他研究小组公开。该项目也为培养生物学和计算边界的学生提供了一个强大的工具。