Jason Wang, PhD
Vivian Bellofatto, PhD

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 than 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 substructures 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 as well as bioinformatics and computational genomics.

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This material is based upon work supported by the National Science Foundation under Grant No. IIS-0707571 (2007-2010). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. This support is greatly appreciated.