Cloud-computing MapReduce toSearch for Post-Translationally Modified Peptides

云计算 MapReduce 搜索翻译后修饰的肽

• 批准号: 8002844
• 负责人: ERIK J NILSSON
• 金额: $ 17.95万
• 依托单位: INSILICOS
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-20 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8002844
• 关键词: Address; Biological; Cells; Computational Technique; Computer software; Computers; Data; Data Set; Discipline; Elements; Equipment; Face; Health; Human; Intention; Investigation; Life; Mass Spectrum Analysis; Molecular; Peptides; Performance; Post-Translational Protein Processing; Proteomics; Qualifying; Research; Research Infrastructure; Research Personnel; Resources; Running; Sampling; Source; Techniques; Time; base; computer cluster; computing resources; cost; experience; high throughput analysis; improved; insight; instrument; open source; operation; public health relevance; research study; routine practice

DESCRIPTION (provided by applicant): A enhancement to the popular peptide search engine X!Tandem is proposed, to allow it to run on new super-scalable MapReduce computer clusters. This will allow much faster and less-expensive operation, allowing proteomics researchers to routinely search for post-translational modifications (PTMs). Proteomics has led to many important advances in biological understanding. Yet, many valuable data sets are not searched for PTMs, simply because the computer power necessary to conduct the searches is not available. With this project, we plan to substantially reduce the computational cost of proteomics experiments, via a peptide search engine operating on highly-scalable computer clusters. The research team is well-qualified to undertake this research, having extensive, direct experience in all the scientific disciplines and specific software elements necessary. The research team includes experts in proteomics, mass spectrometry, peptide search, cloud computing, and MapReduce. PUBLIC HEALTH RELEVANCE: High-throughput analysis of post-translational modifications is increasingly pivotal for understanding the molecular function and dynamics of living cells. This proposal thus addresses key opportunities for applying proteomics to human health research.

描述（由申请人提供）：增强流行的肽搜索引擎X！Tandem被提出，允许它在新的超级可扩展的MapReduce计算机集群上运行。这将允许更快和更便宜的操作，允许蛋白质组学研究人员常规搜索翻译后修饰（PTM）。蛋白质组学在生物学理解方面取得了许多重要进展。然而，许多有价值的数据集没有搜索PTM，仅仅是因为进行搜索所需的计算机能力不可用。通过这个项目，我们计划通过在高度可扩展的计算机集群上运行的肽搜索引擎，大幅降低蛋白质组学实验的计算成本。该研究团队完全有资格进行这项研究，在所有科学学科和必要的特定软件元素方面拥有广泛的直接经验。研究团队包括蛋白质组学、质谱、肽搜索、云计算和MapReduce方面的专家。 公共卫生关系：翻译后修饰的高通量分析对于理解活细胞的分子功能和动力学越来越重要。因此，这项建议涉及将蛋白质组学应用于人类健康研究的关键机会。