Sequence and Structural Patterns in RNA Splicing

普渡大学获得了一笔拨款，用于进一步开发MAASE资源（手动注释的可选拼接事件数据库）。它是高度验证的拼接信息的独特存储库，并将使用机器学习技术来识别解释替代拼接控制的信号。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备选剪接相关的短的、不完全保守的子序列。MAASE资源允许生成包含许多不同的可选拼接类的数据集，因此可以识别特定于类的效果。这些信号如果存在，很可能是微弱的，并且是成群作用的（如真核生物的启动子）。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较弱且难以发现，因此结构基序极有可能参与RNA剪接的控制。鉴定保存的二级或三级结构的方法还不发达。pi将使用现有方法并开发能够识别序列训练集中最大共同结构的新方法。这种共享结构是结构调控要素的有力候选。本研究中产生和使用的数据集将公开提供给其他研究小组。这个项目也提供了一个强大的工具来训练学生在生物学和计算的边界。

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.