Sequence and Structural Patterns in RNA Splicing

普渡大学被授予进一步开发MAASE资源(手动注释的另类剪接事件数据库)的赠款。它是一个经过高度验证的剪接信息的独特存储库，将使用机器学习技术来识别解释替代剪接控制的信号。首先，基于轮廓的概率方法和基于单词的枚举方法都将被用来试图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备选剪接的数据集，因此可以识别特定于类别的效果。这些信号如果存在，很可能是微弱的，并以群体形式发挥作用(如在真核生物启动子中)。将使用允许组合多个主题的方法，并开发新的方法。由于二级和三级结构对RNA的功能很重要，而且保守的序列基序很弱，很难找到，因此结构基序很有可能参与控制RNA的剪接。识别保守的二级或三级结构的方法还不是很成熟。PI将使用现有方法并开发能够识别训练序列集中的最大公共结构的新方法。这种共享结构是结构性监管要素的有力候选者。这项研究中产生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和计算领域的学生提供了一个强有力的工具。

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.