Algorithm design and analysis, web-database and web-server development in bioinformatics research driven by large volume data

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Algorithm design and analysis plays a central role in computer science. Bioinformatics and computational omics 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 understandings of the nature of computation which could perhaps trigger new revolutions in computer science. The proposed research, which is driven by large volumes of biological data, has exactly this two-fold significance. The target applications in our proposed research are genome-wide linkage and association studies (GWLAS) using single nucleotide polymorphism (SNP) genotype data, and whole proteome and whole metabolome identification using mass spectral data. On the former, our goal is to build the dense SNP haplotype map to provide finer and deterministic haplotype allelic information for reviving haplotype-based GWLAS. Upon success, we would be able to pass through the seeming bottleneck of the genotype-based GWLAS, particularly to make contributions to the cattle genomic selection programs. On the latter, we aim at designing novel algorithms with proven performance to automate the peptide identification and the metabolite identification for the most effective use of mass spectral data. Upon success, we would be able to use the whole proteome profile for whole genome annotation to detect new genes not expected by current gene prediction tools; we would also be able to use the whole metabolome quantitative profiling to pinpoint effective biomarkers to assist disease diagnosis. Our research results, including curated data, analytical results, and designed algorithms, will be developed into web-databases and web-servers to serve the community. We have started several subtopics in the proposed research, and foreseen a number of successes.