Algorithm design and analysis, and software tool development, in bioinformatics and computational biology

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Algorithm design and analysis plays a central role in computer science. Bioinformatics and computational biology are emerging interdisciplinary areas of study that require expertise from computational sciences and biological sciences. On one hand, biology has become a high-throughput science where its applications are desperate for advanced computing techniques to digest the huge amount of biological data to provide further understandings leading to new biological discoveries. On the other hand, the practice of algorithm design and analysis on the computational problems formulated out of the biological applications helps improve our understanding of the nature of computation, and perhaps triggers new revolutions in computer science. The proposed research has exactly this two-fold significance, where we focus on two biological applications: protein Nuclear Magnetic Resonance (NMR) spectroscopy and whole genome phylogenetic analysis. Protein NMR spectroscopy is one of the two key technologies that determine protein three dimensional structure at the atomic resolution, where in order to obtain accurate structure a nearly complete resonance sequential assignment is a prerequisite. Our research focus in this project is the automated spectral data analysis to guarantee nearly complete resonance sequential assignments. Upon success, we would be able to transform the currently time-consuming and expensive technology into a high-throughput one. The impacts of phylogenetic analysis using whole genomes are that individual genes might suggest controversial evolutionary pathways and that whole genomes are believed to contain the richest evolutionary information. The key thing in this line of research is to represent whole genomes in a way that the phylogenetic analysis can be efficiently and effectively done. We have recently proposed some new representations, and with them, we are interested in the phylogenetic analysis on avian flu viruses to picture their global migration and evolutionary patterns. The expected results will help in fast identification of emerging avian flu virus and fast determination of vaccination strategies.