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

“生物信息学算法设计与分析以及网络服务开发”

• 批准号: 249633-2012
• 负责人: Lin, Guohui
• 金额: $ 2.04万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=508235
• 关键词: Bioinformatics; Algorithm; Design; Analysis; Web

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.

算法设计和分析在计算机科学中起着核心作用。生物信息学和计算法学是新兴的研究领域，需要计算科学和生物科学的专业知识。一方面，生物学已成为一门高通量科学，其应用迫切希望采用先进的计算技术来消化大量的生物学数据，以提供进一步的见解，从而导致新的生物学发现。另一方面，对从生物应用提出的计算问题的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，这可能会触发计算突破。拟议的跨学科研究完全具有这两种意义。我们提出的研究中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，我们的主要目标是构建密集的SNP单倍型图，以提供更精细，确定性的单倍型等位基因信息，以恢复基于单倍型的基于单倍型的基因组链接与协会研究（GWAS）；在后者中，我们旨在设计具有良好性能的新型算法，以使肽和代谢物识别自动化，以最有效地使用质谱数据。如果成功，我们将能够面对基于基因型的GWA的瓶颈，尤其是为牛基因组选择计划做出贡献；我们将能够将整个蛋白质组谱用于整个基因组注释来检测当前基因预测工具所期望的新基因。我们还将能够使用整个代谢组定量分析来查明有效的生物标志物以帮助疾病诊断。我们的研究结果，包括精选的数据，分析结果和设计算法，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，并预见了许多成功。拟议的研究还将培训高素质的人员进行加拿大的生物信息学跨学科研究。