III-CXT: Structure Comparison and Mining for RNA Genomics

由于预测正确的RNA结构和进行比对的难度很大，涉及大量的计算成本，因此很少有自动化的RNA基序发现方法。 这个项目将实现一个新的工具，使用算法有效的比对方法的基序发现。 该提案的第一个重点是基于RNA结构预测中常用的环模型的扩展。 一个扩展的模型实现了更好的效率，目前的算法，并允许生物学家注释保守的区域，并将这些纳入到过程中，从而获得更有意义的结果。 第二个重点是将比对算法应用于特征选择和基序发现。 这是RNA挖掘的重要步骤，从一组分子中选择一组重要的子结构。 该子集可以单独使用或与内核方法结合使用，以构建用于RNA分类和聚类的新工具。 这项工作将得到验证，可以通过培训研究生和本科生来推进跨学科数据挖掘和开发人力资源。 还将开发一门新的本科课程，并为高中学生选择教材。

Few methods exist for automated RNA motif discovery, due to the difficulty in predicting correct RNA structures and doing alignments where substantial computing costs are involved. This project will implement a new tool for motif discovery using algorithmically efficient alignment methods. The first thrust of the proposal is based on an extension of the loop model commonly used in RNA structure prediction. An extended model achieves better efficiency that current algorithms and allows a biologist to annotate conserved regions and incorporate these into the process, thereby obtaining more meaningful results. The second thrust applies the alignment algorithms to feature selection and motif discovery. This is an essential step in RNA mining, choosing a set of significant substructure from a set of molecules. The subset can be used alone or in combination with kernel methods to build new tools for RNA classification and clustering. The work will be validated and can advance interdisciplinary data mining and develop human resources by training graduate and undergraduate students. A new undergraduate course will also be developed, and selected materials also used for high school students.