DC: Small: Efficient Algorithms for Data-intensive Bio-computing

DC：小型：数据密集型生物计算的高效算法

• 批准号: 0916463
• 负责人: Anantharaman Kalyanaraman
• 金额: $ 43.5万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916463&HistoricalAwards=false
• 关键词: DC; Small; Efficient; Algorithms; Data

The field of bioinformatics and computational biology is experiencing a data revolution unlike any other scientific computing field. Experimental techniques to procure data have increased in throughput, improved in accuracy, and reduced in costs. The preponderance of data has limited the scalability of existing software tools. In a pursuit to understand the complexities and challenges that stem from designing algorithms for data-intensive biocomputing, this project is developing new approaches for two major problems in protein bioinformatics: i) identification of protein families and homology clusters; and ii) peptide identification from large-scale mass spectrometry data. The former requires large-scale graph analysis and the latter requires large-scale database search. The project is investigating a multi-faceted approach which involves designing space-efficient algorithms for massively parallel machines, developing algorithmic heuristics for reducing the time to solution, evaluating the MapReduce paradigm as an alternate computing model, and deploying multicore architectures for fine-grain parallelism. Project outcomes will include new algorithms and open-source software libraries for large-scale protein bioinformatics, including a more generic library for data-intensive biocomputing. The project is addressing a critical need for scalable methods in protein bioinformatics and in doing so will usher in state-of-the-art computing models and concepts from both software and hardware into mainstream biocomputing. Broader impacts include creating interdisciplinary research opportunities for undergraduate and graduate students, and new interdisciplinary curricula at high school, undergraduate and graduate levels. Education materials will be disseminated through a partnership program with Shodor Education Foundation, Inc.Project homepage: http://www.eecs.wsu.edu/~ananth/DataIntensive-Biocomputing/

生物信息学和计算生物学领域正在经历一场不同于任何其他科学计算领域的数据革命。获取数据的实验技术增加了吞吐量，提高了准确性，并降低了成本。数据的优势限制了现有软件工具的可扩展性。为了理解设计数据密集型生物计算算法的复杂性和挑战，该项目正在为蛋白质生物信息学中的两个主要问题开发新方法：i）蛋白质家族和同源簇的识别; ii）从大规模质谱数据中识别肽。前者需要大规模的图分析，后者需要大规模的数据库搜索。该项目正在研究一种多方面的方法，包括为大规模并行机设计空间高效算法，开发算法算法以减少解决方案的时间，评估MapReduce范式作为替代计算模型，以及部署多核架构以实现细粒度并行。项目成果将包括用于大规模蛋白质生物信息学的新算法和开源软件库，包括用于数据密集型生物计算的更通用的库。该项目正在解决蛋白质生物信息学中可扩展方法的关键需求，并将最先进的计算模型和概念从软件和硬件引入主流生物计算。更广泛的影响包括为本科生和研究生创造跨学科研究机会，以及在高中、本科生和研究生层面开设新的跨学科课程。教育材料将通过与Shodor教育基金会的合作计划传播。http://www.eecs.wsu.edu/~ananth/DataIntensive-Biocomputing/