Support for the use and evaluation of large cloud-based genomic datasets.

支持大型基于云的基因组数据集的使用和评估。

• 批准号: 10827800
• 负责人: J. Michael Cherry
• 金额: $ 21.71万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-07 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10827800
• 关键词: Address; Benchmarking; Biological; Biological Assay; Biomedical Research; CRISPR screen; Cells; Chromatin; Clustered Regularly Interspaced Short Palindromic Repeats; Communities; Complex; Computer Analysis; Computer software; Computing Methodologies; Consensus; DNase I hypersensitive sites sequencing; Data; Data Analyses; Data Coordinating Center; Data Files; Data Set; Development; Disease; Educational workshop; Enhancers; Environment; Evaluation; Foundations; Funding; Future; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genomics; Goals; Hi-C; Human Genome; Informatics; Laboratories; Link; Metadata; Methods; Modality; Modeling; Multiomic Data; Participant; Pathway Analysis; Phenotype; Public Health; Quantitative Trait Loci; RNA; Regulation; Regulator Genes; Regulatory Element; Reporter; Research Personnel; Resources; Role; Running; Science; Services; Software Tools; Structure; Students; Synapses; Technical Expertise; Training; United States National Institutes of Health; Untranslated RNA; Variant; XCL1 gene; analysis pipeline; cell type; cloud based; cloud platform; community setting; comparative; computerized tools; data integration; data management; data sharing; deep learning; design; experience; genome wide association study; genomic data; genomic variation; knowledgebase; large scale data; multiple omics; platform as a service; predictive modeling; privacy preservation; skills; tool; transcriptome sequencing

Project Summary/Abstract The goal of this project is to develop cloud-focused training and benchmarks for the use of cloud resources provided from selected NIH funded large data consortium. There is a need for training via interactive jamborees in the use of existing software for large-scale data analysis via the cloud. We will also evaluate the usefulness of the cloud by comparing the NIH AnVIL service as a platform versus the native Google Cloud Platform for developing and comparing pipelines, interactive analyses such as setup and use of Jupyter notebooks, and for running benchmarks via automated leaderboards. These leaderboards will be compared with what is provided by Synapse as an alternative. We have experience in designing, implementing, and hosting jamborees or workshops for the education of trainees and staff researchers in the tools and methods available to utilize large cloud-based data, and to integrate these data into computational analysis pipelines. The cloud is becoming a critical component for computational biologists and the greater biomedical research community in exploring the human genome, its regulation, association with disease, and structure. However, because of the complexity of using cloud- based resource this has not trickled down to students and resources without advanced computing skills. In our 10 years of experience implementing and managing analysis pipelines on the cloud we have shown the superior advantage for use of cloud-based large-scale analyses. The goal of this project is to provide interactive training for the use of cloud-based analysis software, easy to use data sharing, and evaluate two platforms in their ability to assist in creating effective tools. We have cross-cutting and unparalleled technical expertise in data management, genomics, informatics, network analysis, and privacy-preserving applications as well as our roles leading large data coordination centers, managing, and coordinating data and metadata, as well as creating gold standard knowledgebases.

项目概要/摘要 该项目的目标是开发以云为中心的培训和使用云的基准 由选定的 NIH 资助的大数据联盟提供的资源。需要通过以下方式进行培训 使用现有软件通过云进行大规模数据分析的互动盛会。我们将 还通过将 NIH AnVIL 服务作为平台与 原生 Google Cloud Platform，用于开发和比较管道、交互式分析，例如 设置和使用 Jupyter Notebook，以及通过自动排行榜运行基准测试。这些 排行榜将与 Synapse 提供的替代方案进行比较。我们有经验 设计、实施和主办培训学员和工作人员的聚会或讲习班 研究人员研究可用于利用基于云的大型数据并集成这些数据的工具和方法 数据进入计算分析管道。云正在成为计算的关键组成部分 生物学家和更大的生物医学研究界在探索人类基因组、其 调节、与疾病的关联和结构。然而，由于使用云的复杂性 基于资源，这还没有惠及没有高级计算技能的学生和资源。 在我们 10 年在云上实施和管理分析管道的经验中，我们拥有 显示了使用基于云的大规模分析的优越优势。该项目的目标是 提供基于云的分析软件使用的交互式培训，易于使用的数据共享，以及 评估两个平台协助创建有效工具的能力。我们有跨领域和 在数据管理、基因组学、信息学、网络分析和 隐私保护应用程序以及我们领导大型数据协调中心、管理、 协调数据和元数据，以及创建黄金标准知识库。