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）：提出了对流行肽搜索引擎X！串联的增强，以使其可以在新的超级尺度MapReduce计算机簇上运行。这将允许更快且价格较低的操作，从而使蛋白质组学研究人员常规搜索翻译后修改（PTMS）。蛋白质组学导致了生物学理解的许多重要进步。但是，许多有价值的数据集没有被搜索PTM，仅仅是因为进行搜索所需的计算机功率是不可用的。在这个项目中，我们计划通过在高度可观的计算机簇上运行的肽搜索引擎大大降低蛋白质组学实验的计算成本。研究团队合格地进行了这项研究，在所有必要的科学学科和特定的软件元素中都具有丰富的直接经验。研究团队包括蛋白质组学，质谱，肽搜索，云计算和MAPREDUCE的专家。 公共卫生相关性：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而言，越来越重要。因此，该提案解决了将蛋白质组学应用于人类健康研究的关键机会。