"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，需要来自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，其应用迫切需要先进的计算技术来消化大量的生物数据，以提供进一步的见解，从而导致新的生物学发现。另一方面，算法设计和分析从生物学应用中制定的计算问题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。所提出的跨学科研究正是具有这种双重意义。我们计划研究的目标应用是基于单核苷酸多态性（SNP）基因型数据的基因组学应用，以及基于质谱数据的蛋白质组学和代谢组学应用。在前者中，我们的主要目标是建立密集的单倍型图谱，为恢复基于单倍型的全基因组连锁与关联研究（GWAS）提供更精细和确定的单倍型等位基因信息；在后者中，我们的目标是设计具有成熟性能的新算法，以最有效地利用质谱数据自动化肽和代谢物鉴定。如果成功，我们将能够克服基于基因型的GWAS的瓶颈，特别是对牛基因组选择计划做出贡献；我们将能够使用全蛋白质组谱进行全基因组注释，以检测当前基因预测工具未预料到的新基因；我们还将能够使用整个代谢组定量分析来确定有效的生物标志物，以协助疾病诊断。我们的研究成果，包括整理的数据、分析结果和设计的算法，将扩展到社区的网络服务中。我们已经开始了拟议研究的几个子主题，并预见了一些成功。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人才。

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.