"Bioinformatics Algorithm Design and Analysis, and Web-Service Development"

算法设计和分析在计算机科学中起着核心作用。生物信息学和计算组学是新兴的跨学科研究领域，需要计算科学和生物科学的专业知识。一方面，生物学已经成为一门高通量的科学，其应用程序迫切需要先进的计算技术来消化海量的生物数据，以提供进一步的见解，从而导致新的生物学发现。另一方面，对生物应用中形成的计算问题进行算法设计和分析的实践有助于提高我们对计算本质的理解，这可能会引发计算突破。提出的跨学科研究正是具有这种双重意义。在我们提出的研究中，目标应用是基于单核苷酸多态(SNP)基因数据的基因组学应用，以及基于质谱学数据的蛋白质组学和代谢组学应用。在前者，我们的主要目标是构建密集的SNP单倍型图谱，为基于单倍型的全基因组连锁和关联研究(GWAS)的复兴提供更精细和确定的单倍型等位基因信息；在后者，我们的目标是设计具有成熟性能的新算法，以实现多肽和代谢物鉴定的自动化，从而最有效地利用质谱学数据。如果成功，我们将能够面对基于基因型的GWAS的瓶颈，特别是对牛基因组选择计划做出贡献；我们将能够使用全基因组注释的整个蛋白质组图谱来检测当前基因预测工具没有预料到的新基因；我们还将能够使用整个代谢组定量图谱来精确定位有效的生物标记物来辅助疾病诊断。我们的研究成果，包括精选数据、分析结果和设计的算法，将扩展到为社区提供网络服务。我们已经开始了拟议研究的几个分专题，并已预见到一些成功。这项拟议的研究还将为加拿大的生物信息学跨学科研究培养高素质的人员。

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 volume of biological data to provide further insights leading to new biological discoveries. On the other hand, the practice of algorithm design and analysis on the computational problems formulated from biological applications helps improve our understandings of the nature of computation which can potentially trigger computational breakthroughs. The proposed interdisciplinary research has exactly this two-fold significance. The target applications in our proposed research are genomics applications based on single nucleotide polymorphism (SNP) genotype data, and proteomics and metabolomics applications based on mass spectral data. In the former, our main goal is to build the dense SNP haplotype map to provide finer and deterministic haplotype allelic information for reviving haplotype-based genome-wide linkage and association studies (GWAS); in the latter, we aim to design novel algorithms with proven performance, to automate the peptide and metabolite identification for the most effective use of mass spectral data. If successful, we will be able to confront the bottleneck of genotype-based GWAS, particularly to make contributions to the cattle genomic selection programs; we will be able to use the whole proteome profile for whole genome annotation to detect new genes not expected by current gene prediction tools; we will 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 extended into web-services for the community. We have started several subtopics of the proposed research, and have foreseen a number of successes. The proposed research will also train highly qualified personnel for Canada's bioinformatics interdisciplinary research.