Computational Intelligence and Applications in Bioinformatics

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Structure prediction of bio-molecules such as RNA and proteins is an important topic in Computational Biology. The structure is often determining the function of the molecule and can also be used to infer genetic origins (phylogeny). This research program focuses primarily on the following two objectives:1) To develop improved algorithms for structure prediction of bio-molecules, particularly RNA. This is an important topic as more and more functional RNAs are discovered. 2) To develop improved visualization techniques, particularly for RNA visualization. For objective 1) the starting point will be the algorithm RnaPredict, developed in my lab and based on evolutionary computation. RnaPredict uses a number of advanced operators and representations. I plan to further evaluate, improve, and extend RnaPredict, particularly in the area of modeling non-canonical base pairs and the prediction of pseudoknots. The starting point for objective 2) will be jViz.Rna, a visualization tool for RNA secondary structure which has a number of benefits over existing tools, including: wide support of input and output formats, ability to produce high quality graphics for reproduction, interactive output for further manipulation by the end user, ability to display pseudoknots in a classical structure plot, and the ability to overlay two different structures for comparison. jViz.Rna also produces quantitative and qualitative comparisons between two structures. One particular challenge of RNA visualization is the the handling of pseudoknots. Most existing tools either do not model them in a traditional structure plot or do a relatively poor job. I plan to extend jViz.Rna to incorporate various topological tools to display more classes of pseudoknots in a planar way. This will also have potential benefits for the visualization community at large.