GRID COMPUTING & ANALYSIS FOR MULTI SCALE BIOMEDICAL APPLICATIONS

网格计算

• 批准号: 7955223
• 负责人: PETER William ARZBERGER
• 金额: $ 19.16万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2010-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7955223
• 关键词: Academia; Biological; Biology; Biomedical Computing; Biomedical Research; Code; Communities; Computer Retrieval of Information on Scientific Projects Database; Computer software; Data; Data Analyses; Data Set; Databases; Deposition; Development; Electron Microscope; Environment; Funding; Grant; Industry; Institution; Magnetic Resonance Imaging; Micro Array Data; Process; Proteins; Research; Research Personnel; Research Project Grants; Resolution; Resources; Running; Scientist; Security; Services; Source; Techniques; Technology; United States National Institutes of Health; biomedical informatics; cluster computing; experience; experimental analysis; instrument; programs; research and development; research study; simulation

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Biology and biomedical research is very data-rich with community databases (e.g. Protein Data Bank) at several different scales. Furthermore, data are being generated at increasingly faster rates via high through-put technologies, such as micro array data, high-resolution electron microscopes, and high-resolution magnetic resonance imaging scanners. The sequence of steps needed to complete biological and biomedical research or analysis of experimental data spans from taking raw data on a specialized instrument to depositing annotated data in community databases. The complete workflow requires a scientist to bring together data from disparate resources, compare experiment with simulation, analyze data, visualize it, and then repeat some portions of this cycle. The emerging IT support paradigm for this merges grid services, workflows and data technologies. Grid computing is rapidly gaining acceptance as a routine way to transparently access computational power and datasets for biological and biomedical informatics applications. These techniques are being employed in academia as well as in industry. However, enabling biomedical codes to run in such an environment requires so-called "grid-enabling" of such codes and the associated data repositories. This process of bringing software programs, databases and instruments together to then facilitate a workflow generally involves development of a layer of software. We refer to this grid-enabling step as the "wrapping" of codes and databases. The effort in this Core Technological Research and Development project focuses on examining the grid computing requirements of several exemplary biomedical research project activities, and defining and implementing standard interfaces with appropriate security and access logging by leveraging emerging grid practice and experience. Once grid-enabled, such codes can more easily be combined together to create biomedical analysis pipelines, or workflows.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 生物学和生物医学研究在几个不同的尺度上具有社区数据库（例如蛋白质数据库）非常丰富的数据。此外，通过较高的横向技术，例如微阵列数据，高分辨率电子显微镜和高分辨率的磁共振成像扫描仪，以越来越快的速度生成数据。 完成生物学和生物医学研究所需的一系列步骤或实验数据分析从在专用仪器上获取原始数据到将带注释的数据存储在社区数据库中所需的步骤。 完整的工作流程要求科学家从不同资源中汇总出数据，将实验与仿真，分析数据，可视化数据，然后重复本周期的某些部分。 新兴的IT支持此合并网格服务，工作流和数据技术的范式。 网格计算正在迅速获得接受，作为一种常规的方式，可以透明地访问生物和生物医学信息学应用程序的计算能力和数据集。 这些技术正在学术界和行业中使用。 但是，使生物医学代码能够在这种环境中运行，需要对此类代码和相关数据存储库的所谓“增强”。将软件程序，数据库和仪器聚集在一起以促进工作流程的过程通常涉及一层软件的开发。我们将这个增强网格的步骤称为代码和数据库的“包装”。 该核心技术研发项目的努力着重于检查几项模范生物医学研究项目的网格计算要求，并通过利用新兴的网格实践和经验来定义和实施具有适当安全性和访问记录的标准接口。 一旦启用网格，就可以更容易地将这些代码组合在一起，以创建生物医学分析管道或工作流程。