WebMeV: A Robust Platform for Intuitive Genomic Data Analysis

WebMeV：用于直观基因组数据分析的强大平台

• 批准号: 10251317
• 负责人: John Quackenbush
• 金额: $ 64.6万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10251317
• 关键词: Bioinformatics; Cancer Research Project; Cloud Computing; Communities; Complex; Computer Literacy; Computing Methodologies; Data; Data Analyses; Data Set; Data Storage and Retrieval; Development; Documentation; Drops; Educational workshop; Encyclopedia of DNA Elements; Ensure; Funding; Gene Expression Profiling; Genes; Genome; Genotype-Tissue Expression Project; Goals; Informatics; Infrastructure; International; Intuition; Java; Libraries; Link; Malignant Neoplasms; Maps; Methods; Modernization; Multiomic Data; Network-based; Pathway interactions; Personal Computers; Physicians; Pilot Projects; Privatization; Proteins; Public Domains; Regulator Genes; Reproducibility; Research; Research Personnel; Research Subjects; Scientist; Software Tools; Statistical Methods; Stress; System; Systems Biology; Technology; Testing; The Cancer Genome Atlas; Training; Visual; Work; analytical method; anticancer research; base; cancer genomics; cloud based; cloud platform; computing resources; data tools; data visualization; differential expression; genomic data; genomic tools; genomics cloud; graphical user interface; high dimensionality; improved; large datasets; meetings; network models; next generation sequence data; novel sequencing technology; online tutorial; open source; programs; statistics; success; tool; transcriptome; transcriptome sequencing; web app; web-enabled

Project Summary/Abstract: Improvements in sequencing technology have allowed genome and transcriptome profiling of large groups of research subjects. Projects such as The Cancer Genome Atlas (TCGA), the Encyclopedia of DNA Elements (ENCODE), the Genotype-Tissue Expression Project (GTEx), and other have placed large, complex, multi- omic data into the public domain. While these large projects and the use of new sequencing technologies has made an unprecedented quantity of data available, technical challenges such as moving and analyzing large multi-omic data sets, and the lack of intuitive and easy to use tools for data analysis, have limited broad exploration of the available data, often separating experimental biologists and domain experts from directly exploring relationships within the data. More than 15 years ago, we began development of MeV, a freely-available, open source software tool for intuitive analysis of genomic data. The simple graphical user interface and the extensive library of state-of-the- art analytical methods made MeV one of the most widely used software tools in bioinformatics, with nearly 260,000 downloads since we began keeping statistics in 2008 and downloads of nearly 30,000 per year for the past few years. Despite the success of MeV and its continued use, we recognized that large-scale, multi-omic data sets can no longer be analyzed easily using a desktop application. To keep pace with the data, we needed to develop a new platform that draws on modern computing technologies, including cloud-based computing and scalable data storage. The solution, funded by the NCI through the ITCR program (5U01CA151118), is a cloud-based, web- enabled version of MeV (WebMeV; http://mev.tm4.org). WebMeV uses Google Cloud Platform (GCP) and its Compute Engine infrastructure to leverage cloud-computing resources for analyzing large public genomic data sets. In April 2016, we released a robust version of WebMeV and have seen use of the system grow dramatically. The system has already been used to perform more than 350,000 analyses; WebMeV currently performing more than 100 analyses per day, 3,735 users who have registered with the system and that group is growing by 400 per month (registration is not required). To ensure wide use, we have done numerous online tutorials, including two “sold out” tutorials for intramural investigators at the NCI where WebMeV has become a critical tool for genomic analysis. In this application, we propose to continue to maintain and improve WebMeV, to expand its capabilities by implementing methods for network inference and representation, to integrate with the Cancer Genomics Cloud Pilots program, and to implement methods that can advance reproducible research.

项目摘要/摘要： 测序技术的进步使得能够对大群体进行基因组和转录组分析 癌症基因组图谱 (TCGA)、DNA 元素百科全书等项目。 （ENCODE）、基因型组织表达项目（GTEx）和其他人已经放置了大型、复杂、多 而这些大型项目和新测序技术的使用已经进入公共领域。 提供了前所未有的数据量，技术挑战，例如移动和分析大量数据 多组学数据集以及缺乏直观且易于使用的数据分析工具限制了广泛的研究 探索可用数据，通常将实验生物学家和领域专家直接与 探索数据中的关系。 15 多年前，我们开始开发 MeV，这是一个免费提供的开源软件工具，用于 直观的基因组数据分析。简单的图形用户界面和广泛的最新库。 艺术分析方法使 MeV 成为生物信息学中使用最广泛的软件工具之一，近乎 自 2008 年开始统计以来，下载量已达 26 万次，每年下载量接近 3 万次 尽管 MeV 取得了成功并持续使用，但我们认识到大规模、多组学的作用。 无法再使用桌面应用程序轻松分析数据集，为了跟上数据的步伐，我们。 需要开发一个利用现代计算技术（包括基于云的技术）的新平台 计算和可扩展的数据存储。 该解决方案由 NCI 通过 ITCR 计划 (5U01CA151118) 资助，是一个基于云的 Web- MeV 的启用版本（WebMeV；http://mev.tm4.org）。WebMeV 使用 Google Cloud Platform (GCP) 及其 Compute Engine 基础设施利用云计算资源分析大型公共基因组数据 2016 年 4 月，我们发布了 WebMeV 的强大版本，并且看到该系统的使用量不断增长。 目前，该系统已用于执行超过 350,000 次 WebMeV 分析； 每天执行超过 100 次分析，已注册系统和该组的用户有 3,735 名 每月增长 400 个（不需要注册）为了确保广泛使用，我们在网上做了很多工作。 教程，其中包括两个为 NCI 校内调查人员提供的“已售罄”教程，其中 WebMeV 已成为 在此应用程序中，我们建议继续维护和改进 WebMeV， 通过实施网络推理和表示方法来扩展其功能，与 癌症基因组学云试点计划，并实施可以推进可重复性的方法 研究。