BIGDATA: Mid-Scale: DA: Collaborative Research: Genomes Galore - Core Techniques, Libraries, and Domain Specific Languages for High-Throughput DNA Sequencing

大数据：中规模：DA：协作研究：基因组丰富 - 高通量 DNA 测序的核心技术、库和领域特定语言

• 批准号: 1247701
• 负责人: Oyekunle Olukotun
• 金额: $ 35万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1247701&HistoricalAwards=false
• 关键词: BIGDATA; Mid; Scale; DA; Collaborative

The recent emergence of a variety of high-throughput DNA sequencing instrumentation, and the concomitant rapid decline in the cost per base, is causing severe data deluge in all areas of life sciences. The heterogeneity of sequencing instrumentation and the vast diversity of applications enabled by them are creating numerous analytics problems for the bioinformatics community to address. In addition, the conventional serial algorithms that have been the mainstay of bioinformatics research are severely challenged by the ever increasing data sets. The goal of the proposed project is to develop core techniques and software libraries to enable scalable, efficient, high performance computing solutions for high-throughput DNA sequencing, also known as next-generation sequencing (NGS). To empower the larger community, the project seeks to 1) identify a set of core functionalities that frequently occur in many types of high-throughput sequencing applications, 2) develop efficient parallel algorithms and high performance implementations for them, 3) pursue mapping to HPC architectures including clusters, multicores, and GPUs, 4) develop software libraries encapsulating these functionalities with the goal of enabling the bioinformatics community to exploit HPC architectures, and 5) design a domain specific language to enable bioinformatics researchers unfamiliar with parallel processing to benefit from this work through automatic generation of parallel codes. The research will be conducted in the context of challenging problems in human genetics and metagenomics, in collaboration with domain specialists.This project is focused on a key capacity building activity to facilitate pervasive use of parallelism by NGS bioinformatics researchers and practitioners. The goal is to empower the broader community to benefit from clever parallel algorithms, highly tuned implementations, and specialized HPC hardware, without requiring expertise in any of these. The software libraries will be released as open source for use, further development, enhancements, and incorporation by the community. The project will provide opportunities for training postdoctoral and graduate students in bigdata analytics and computer science driven interdisciplinary research. Diverse existing mechanisms at the partner institutions will be leveraged to advance goals of minority and women recruitment, undergraduate participation in research, and K-12 outreach.

最近出现了各种高通量DNA测序仪器，以及每个基础成本的迅速下降，在生命科学的所有领域都引起了严重的数据洪水。测序仪器的异质性以及它们启用的大量应用程序正在为生物信息学界创造许多分析问题。此外，由于不断增加的数据集，曾经是生物信息学研究中的主要序列算法受到了严重的挑战。该项目的目标是开发核心技术和软件库，以实现可扩展，高效，高性能计算解决方案的高通量DNA测序，也称为下一代测序（NGS）。为了增强较大的社区的能力，该项目寻求1）确定一组在许多类型的高通量测序应用中经常出现的核心功能，2）为其开发有效的并行算法和高性能实现，3）追求映射到HPC体系结构，包括插图集，Multicores和GPUS，将这些功能与这些功能相关联，4）利用HPC体系结构和5）设计特定的域语言，以使生物信息学研究人员不熟悉并行处理，从而通过自动生成的并行代码从这项工作中受益。这项研究将在与领域专家合作的人类遗传学和宏基因组学中挑战性问题的背景下进行。该项目侧重于关键能力建设活动，以促进NGS生物信息学研究人员和从业者对平行性的普遍使用。目的是使更广泛的社区能够从聪明的并行算法，高度调整的实现和专业的HPC硬件中受益，而无需在任何这些方面都有专业知识。软件库将作为开源，进一步开发，增强和社区合并。该项目将为培训BigData分析和计算机科学驱动的跨学科研究的博士后和研究生提供机会。合作伙伴机构的各种机制将被利用，以促进少数族裔和妇女招募，本科参与研究的目标和K-12外展。