CI-P: A flexible platform for accelerating biological sequence analysis

最近DNA测序成本的下降使生物学家能够进行实验，产生非常大的DNA和蛋白质序列数据集。理解这些数据需要计算分析来识别已知的序列，并根据相似性对新序列进行分组。随着数据集的增长，这些分析成为进步的严重瓶颈。因此，计算机科学家尝试使用混合计算架构来加速序列分析，这种架构结合了多核CPU和加速器，如现场可编程门阵列和图形引擎，其性能相当于数十或数百个CPU核心。为了更有效地加速生物序列分析任务，需要新的基础设施来促进加速分析工具的开发和生物学家的部署。该项目是为加速生物序列分析应用程序创建开发和部署基础设施的计划工作。pi正在为首选硬件平台和一套软件工具制定设计标准，以加快生物序列加速器的创建、验证和部署。关键活动包括确定硬件平台，开发原型软件和固件，以及为加速序列分析工具咨询开发人员和用户社区，以指导计划工作。特别值得一提的是，pi正在一个大型加速器设计会议上组织一个特别专题，征求对拟议基础设施的投入。开发拟议的基础设施将刺激低成本、快速部署和大型支持开发人员和用户社区的生物序列分析加速器的创建。更敏捷的开发将推动生物学家采用加速器，使实验室能够分析大量生物序列数据集并加速发现。

Recent declines in the cost of DNA sequencing have enabled biologists to conduct experiments that produce very large DNA and protein sequence data sets. Understanding this data requires computational analyses to recognize known sequences and group new ones by similarity. As data sets grow, these analyses become a serious bottleneck to progress. Computer scientists have therefore tried to accelerate sequence analysis using hybrid computing architectures that combine multicore CPUs with accelerators, such as field-programmable gate arrays and graphics engines, whose performance equals that of tens or hundreds of CPU cores. To more effectively accelerate biosequence analysis tasks, new infrastructure is needed to facilitate both development of accelerated analytical tools and their deployment to biologists. This project is a planning effort to create development and deployment infrastructure for accelerated biosequence analysis applications. The PIs are developing design criteria for a preferred hardware platform and set of software tools to speed the creation, validation, and deployment of biosequence accelerators. Key activities include qualifying hardware platforms, developing prototype software and firmware, and consulting developer and user communities for accelerated sequence analysis tools to guide the planning effort. In particular, the PIs are organizing a special track at a major accelerator design conference to solicit input on proposed infrastructure. Developing the proposed infrastructure will stimulate creation of biosequence analysis accelerators with low cost, rapid deployment, and a large supporting developer and user community. More agile development will boost adoption of accelerators by biologists, empowering labs to analyze massive biosequence data sets and speeding discovery.