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

大数据驱动的生物信息学研究中的算法设计与分析、网络数据库和网络服务器开发

• 批准号: 249633-2011
• 负责人: Lin, Guohui
• 金额: $ 1.75万
• 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: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=479263
• 关键词: Algorithm; design; analysis; web; database

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.

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，以提供进一步的理解，从而导致新的生物学发现。另一方面，对生物应用中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，这可能会引发计算机科学的新革命。这项由大量生物学数据驱动的研究具有双重意义。在我们提出的研究中的目标应用是全基因组连锁和关联研究（GWLAS）使用单核苷酸多态性（SNP）基因型数据，和全蛋白质组和全代谢组鉴定使用质谱数据。在前者，我们的目标是建立密集的SNP单倍型图谱，为恢复基于单倍型的GWLAS提供更精细和确定性的单倍型等位基因信息。一旦成功，我们将能够通过基于基因型的GWLAS的瓶颈，特别是为牛基因组选择计划做出贡献。在后者，我们的目标是设计新的算法与性能证明自动化的肽鉴定和代谢物鉴定的质谱数据的最有效的使用。一旦成功，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期的新基因;我们还将能够使用全代谢组定量分析来确定有效的生物标志物，以辅助疾病诊断。我们的研究成果，包括策划的数据，分析结果和设计的算法，将被开发成网络数据库和网络服务器，以服务于社区。我们已经在拟议的研究中开始了几个子课题，并预见了一些成功。