Scalable and Translational Analysis Tools on the Cloud for Deep Integrative Omics Data

摘要 NHLBI Trans-Omics for Precision Medicine(TOPMed)计划旨在提供高质量的 利用高质量的基因组数据优先研究心、肺、血液和睡眠障碍(HLBS)。 今年，该计划将对6万个基因组进行深度测序，以确定DNA序列的特征 规模上的变化。预计将识别出4亿个基因变异。在以后 阶段，预计丰富的基因组分析将应用于同样数量的 样本。在试点阶段，这些额外的分析将包括~3,000个转录本，~2,000个 甲基化图谱和~2,000个代谢组学图谱。 这种规模的数据为发现和分析打开了许多机会，但也提出了 重大挑战。RFA-HL-17-011，标题：NHLBI TOPMed Program：整合式Omics 《TOPMed数据分析方法(U01)》旨在促进 支持创新和可扩展分析的计算和统计方法和工具 基因组资源。我们集团在开发专业的、最先进的 用于处理和分析大型基因组数据集的ART方法和工具。我们有一个 在各种资源中的领导力历史，从鼠标HapMap项目到1000 基因组计划，到编码，并包括NHLBI的TOPMed计划。 在这项应用中，我们建议开发创新和实用的方法，以实现 大规模的信息性基因组分析。这些方法包括计算工具，以 快速扩展深度GWA，用于稳健和强大的综合组学分析的统计方法， 以及组学遗传学结果综合解释的可视化方法。我们会 将这些方法实施到经济高效、易于使用且文档齐全的软件中 有助于了解涉及HLBS疾病的分子机制的包。一个 该提案的关键部分是在商业云上部署这些工具， 为调查人员提供可访问的接口，而无需直接访问本地高吞吐量 计算机和数据存储设施。由此产生的工具将使广泛的科学家能够 运行一流的方法，加速发现HLBS疾病的新治疗方法。

SUMMARY The NHLBI Trans-Omics for Precision Medicine (TOPMed) program aims to provide high- priority studies of heart, lung, blood and sleep disorders (HLBS) with high-quality genomic data. This year, the program will deeply sequence >60,000 genomes to characterize DNA sequence variation at scale. It is expected that >400 million genetic variants will be identified. In later phases, it is expected that rich genomic assays will be applied to an equally large number of samples. In a pilot phase, these additional assays will include ~3,000 transcriptomes, ~2,000 methylation profiles, and ~2,000 metabolomics profiles. Data on this scale opens up many opportunities for discovery and analysis but also poses significant challenges. RFA-HL-17-011, entitled “NHLBI TOPMed Program: Integrative Omics Approaches for Analysis of TOPMed Data (U01)” is intended to stimulate development of computational and statistical methods and tools that enable innovative and scalable analyses genomic resource. Our group has a long history in the development of specialized, state-of-the- art methods and tools for the processing and analysis of large genomic datasets. We have a history of leadership in varied resources, ranging from the Mouse HapMap Project, to 1000 Genomes Project, to ENCODE, and including the NHLBI’s TOPMed program. In this application, we propose to develop innovative and practical methods to enable informative genomic analysis at scale. These methods encompass computational tools to rapidly scale deep GWAS, statistical methods for robust and powerful integrative omics analysis, and visualization methods for integrative interpretation of omics genetics results. We will implement these methods into cost-effective, easy-to-use, and well-documented software packages that facilitate understanding of molecular mechanisms involved in HLBS disorders. A key component of the proposal is the deployment of these tools on commercial clouds, providing accessible interface to investigators without direct access to a local high-throughput compute and data storage facility. The resulting tools will empower a wide range of scientists to run best-in-class methods to accelerate discovery of new treatments for HLBS disorders.