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

新冠肺炎大流行在美国和世界各地造成了许多人死亡。人类遗传信息可能会为COVID-19药物的发现提供答案。利用低成本测序人类基因组的能力，该项目旨在通过CloudLab （https://cloudlab）实现基因组序列（GS）分析的大众化。us/（美国国家科学基金会资助的云计算研究基础设施），以加快寻找COVID-19治疗方法的进程。因此，任何研究人员都可以使用CloudLab免费研究受COVID-19影响的个体遗传信息的差异。该项目将研究有效的计算解决方案，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。该项目将利用CloudLab实现基因组序列（GS）分析的大众化。研究人员可以大规模地从个体的基因组信息中提取出深刻的见解。这项工作将提高人们对商品集群、云基础设施和开源软件如何设计用于大规模GS存储、处理和分析的理解。具体来说，它将导致穷举变量分析（EVA）的新算法，有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。它将为处理大规模GS工作负载时的网络优化提供低级测量数据。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，进行大规模的变异分析，该项目可以通过揭示个体基因组变异之间的深层关系，促进对个体如何应对COVID-19感染的理解。它可以促进COVID-19的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散发。将开发新的课程模块；还将举办高中生研习班。该项目的网站是https://github.com/MU-Data-Science/EVA。该存储库将在项目完成后维持5年。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.