RAPID: Democratizing Genome Sequence Analysis for COVID-19 Using CloudLab

新冠肺炎疫情已在美国和世界各地造成无数人死亡。人类基因信息可能掌握着新冠肺炎药物发现的答案。凭借以低成本对人类基因组进行测序的能力，该项目旨在使云实验室(https://cloudlab.us/，，一家由美国国家科学基金会资助的云计算研究基础设施)上的基因组序列(GS)分析大众化，以加快寻找新冠肺炎治愈方法的进程。因此，任何研究人员都将能够免费使用云实验室来研究受新冠肺炎影响的个体的基因信息差异。该项目将研究高效的计算解决方案，以执行分析个体基因组差异的数据密集型任务。它还将为学生提供培训机会。该项目将使用CloudLab实现基因组序列(GS)分析的民主化。研究人员可以从个体的基因组信息中提取出更深层次的见解。这项工作将有助于更好地理解如何为大规模GS存储、处理和分析设计商品集群、云基础设施和开源软件。具体地说，它将产生新的穷举变量分析(EVA)算法、高效执行变量分析任务的调度策略，以及加速EVA和最大化商品集群资源利用率的优化技术。它将在处理大规模GS工作量时为网络优化提供底层测量数据。通过为研究人员提供公开可用的软件工具和计算基础设施进行大规模变异分析，该项目可以通过揭示个体基因组变异中的深层关系来促进对个体如何应对新冠肺炎感染的理解。它可以为新冠肺炎实现新药发现和治疗策略。该工具的原型将公开用于研究和教育。调查结果将以出版物和软件包的形式传播。将开发新的课程模块；将为高中生举办研讨会。项目网站为https://github.com/MU-Data-Science/EVA.这个储存库将在项目完成后保留5年。这一奖励反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The COVID-19 pandemic has caused numerous deaths in the United States and around the world. Human genetic information may hold the answers for COVID-19 drug discovery. With the ability to sequence the human genome at low cost, this project aims to democratize genome sequence (GS) analysis on CloudLab (https://cloudlab.us/, an NSF-funded cloud computing research infrastructure) for accelerating the process of finding a cure for COVID-19. As a result, any researcher will be able to study differences in the genetic information of individuals affected by COVID-19 using CloudLab at no charge. This project will investigate efficient computing solutions to perform the data-intensive task of analyzing differences in individuals' genomes. It will also provide training opportunities for students.The project will democratize genome sequence (GS) analysis using CloudLab. Deep insights from genomic information of individuals can be extracted by researchers at scale. This work will lead to improved understanding of how commodity clusters, cloud infrastructure, and open-source software could be designed for large-scale GS storage, processing, and analysis. Specifically, it will result in new algorithms for exhaustive variant analysis (EVA), scheduling strategies for efficient execution of variant analysis tasks, and optimization techniques to speedup EVA and maximize resource utilization in a commodity cluster. It will provide low-level measurement data for network optimization when processing large-scale GS workloads.By empowering researchers with publicly available software tools and computing infrastructure for variant analysis at scale, this project could advance the understanding of how individuals respond to COVID-19 infection by uncovering deep relationships in genomic variants of individuals. It can enable new drug discovery and treatment strategies for COVID-19. A prototype of the tool will be made publicly available for research and education. The findings will be disseminated in the form of publications and software packages. New course modules will be developed; a workshop for high school students will be conducted.The project website is at https://github.com/MU-Data-Science/EVA. This repository will be maintained for 5 years after the completion of the project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.