Algorithms for the Analysis of Data from Massively-parallel Genome Sequencing

大规模并行基因组测序数据分析算法

• 批准号: 0844494
• 负责人: Mihai Pop
• 金额: $ 37.99万
• 依托单位: University of Maryland, College Park
• 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: 2009-04-01 至 2013-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0844494&HistoricalAwards=false
• 关键词: Algorithms; Analysis; Data; Massively; parallel

New generation DNA sequencing technologies are revolutionizing modern biological research. Scientists can now generate the rough equivalent of an entire human genome (~3 billion base-pairs of DNA) in just a few days with one single sequencing instrument. Until recently, such amounts of data could only be generated at large genome centers using hundreds of sequencers. The analysis of these data is complicated by their size - a single run of a sequencing instrument yields terabytes of information, often requiring a significant scale-up of the existing computational infrastructure. This project is developing parallel algorithms for analyzing new generation sequencing data with a specific focus on the Map-Reduce paradigm implemented on a highly-distributed computing cluster supported by Google and IBM. The project is primarily focused on developing algorithms for sequence alignment and sequence assembly ? critical tasks in the analysis of genomic data ? and involves the adaptation of string matching and graph algorithms to the Map-Reduce paradigm. This work will potentially lead to parallelism-enabled genomic analysis software that will allow researchers to analyze new generation sequencing data through web-scale computational resources, thereby obviating the need for establishing and maintaining a local high-performance computing infrastructure. The software developed during this project is being made available under an open-source license in order to encourage broad use and to enable future research. The research is integrated with teaching and mentoring of graduate and undergraduate students and the results of the work will be disseminated through journal publications and conference presentations.

新一代DNA测序技术正在彻底改变现代生物学研究。 科学家们现在可以在短短几天内用一台测序仪器生成相当于整个人类基因组的粗略数据（约30亿个碱基对的DNA）。直到最近，这样大量的数据只能在大型基因组中心使用数百个测序仪生成。这些数据的分析因其大小而变得复杂-测序仪器的单次运行产生TB的信息，通常需要显着扩大现有的计算基础设施。 该项目正在开发用于分析新一代测序数据的并行算法，特别关注在Google和IBM支持的高度分布式计算集群上实现的Map-Reduce范式。 该项目主要集中于开发序列比对和序列组装的算法。基因组数据分析中的关键任务？并且涉及到字符串匹配和图形算法对Map-Reduce范例的适应。 这项工作将可能导致并行化的基因组分析软件，使研究人员能够通过网络规模的计算资源分析新一代测序数据，从而避免建立和维护本地高性能计算基础设施的需要。 在该项目期间开发的软件将在开放源码许可证下提供，以鼓励广泛使用，并使未来的研究成为可能。该研究与研究生和本科生的教学和指导相结合，工作成果将通过期刊出版物和会议演示文稿传播。