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

用于深度整合组学数据的云上可扩展和转化分析工具

• 批准号: 9312552
• 负责人: Hyun Min Kang
• 金额: $ 54.09万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-15 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9312552
• 关键词: Affinity; Alleles; Base Sequence; Binding; Biological; Biological Assay; Biology; Clinical; Collaborations; Computer software; Computing Methodologies; DNA Sequence; DNA sequencing; Data; Data Analyses; Data Set; Data Storage and Retrieval; Development; Disease; Etiology; Gene Expression; Gene Expression Regulation; Genetic; Genetic Models; Genome; Genomics; Genotype; Heart Diseases; Hematological Disease; Imagery; Individual; Joints; Lead; Leadership; Linear Algebra; Lung diseases; Mediation; Metabolic; Methods; Methylation; Modeling; Molecular; Mus; National Heart, Lung, and Blood Institute; Non-Insulin-Dependent Diabetes Mellitus; Peptide Sequence Determination; Phase; Phenotype; Population; Proteomics; Recording of previous events; Records; Research Personnel; Resource Informatics; Resources; Running; Sampling; Scientist; Sleep Disorders; Software Tools; Source; Statistical Data Interpretation; Statistical Methods; Structure; Technology; Testing; Time; Trans-Omics for Precision Medicine; Variant; base; computer framework; computerized tools; cost effective; epigenomics; genetic variant; genome wide association study; genome-wide; genomic data; graphical user interface; human disease; improved; innovation; metabolomics; novel; operation; phenome; phenotypic data; programs; tool; trait; transcription factor; transcriptome; transcriptomics

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.

总结 NHLBI精准医学跨组学（TOPMed）计划旨在提供高水平的 心脏，肺，血液和睡眠障碍（HLBS）的高质量基因组数据的优先研究。 今年，该计划将对超过60，000个基因组进行深度测序，以表征DNA序列 在规模上的变化。预计将识别出超过4亿个遗传变异。在以后的 阶段，预计丰富的基因组测定将应用于同样大量的 样品在试点阶段，这些额外的检测将包括约3，000个转录组，约2，000个 甲基化图谱和约2，000个代谢组学图谱。 这种规模的数据为发现和分析提供了许多机会，但也带来了 重大挑战。RFA-HL-17-011，标题为“NHLBI TOPMed计划：集成组学 TOPM数据分析方法（U 01）”旨在促进 计算和统计方法和工具，使创新和可扩展的分析 基因组资源我们集团在专业化、国家级的发展方面有着悠久的历史， 用于处理和分析大型基因组数据集的现有技术方法和工具。我们有一个 领导各种资源的历史，从小鼠HapMap项目到1000 基因组计划，编码，并包括NHLBI的TOPMed计划。 在本申请中，我们建议开发创新和实用的方法， 大规模的基因组分析。这些方法包括计算工具， 快速扩展深度GWAS，用于强大的综合组学分析的统计方法， 以及用于组学遗传学结果的综合解释的可视化方法。我们将 将这些方法实现为具有成本效益、易于使用且文档齐全的软件 有助于理解HLBS疾病的分子机制的软件包。一 该提案的关键组成部分是在商业云上部署这些工具， 为调查人员提供无障碍界面，而无需直接访问本地高通量 计算和数据存储设施。由此产生的工具将使广大科学家能够 运行一流的方法，以加速发现HLBS疾病的新疗法。