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: 10454298
• 负责人: John Quackenbush
• 金额: $ 63.29万
• 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/10454298
• 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; 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; gene network; gene regulatory network; 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： 测序技术的改进允许大型基因组和转录组分析 研究对象。 （编码），基因型 - 组织表达项目（GTEX），其他已经放置了大型，复杂，多的 大型项目和使用新测序技术的数据 提出了空前的数据可用数据，诸如移动和分析大型的技术挑战 多摩尼克数据集以及缺乏直观且易于使用的数据分析工具，广泛的范围有限 探索可用数据，通常将实验生物学家和领域专家与直接分开 探索数据中的关系。 15年前，我们开始开发Mev，这是一种自由使用的开源软件TOOLE，for for For For For For For For For For For For For For For For For For For For For For For For For For For 基因组数据的直观分析。 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 自从我们从2008年开始宣传宣传以来，下载260,000个 过去的几个耳朵。 可以使用桌面应用程序轻松地分析数据集。 需要开发一个利用现代计算技术的新平台，包括基于云的计算技术 计算和可扩展数据存储。 该解决方案由NCI ITCR计划（5U01CA151118）资助，是一个云 - 低音的Web- 启用版本的Mev（WebMev; http://mev.tm4.org）。 计算英语基础架构以利用云计算资源来分析大型公共基因组数据 集合。2016年4月，我们发布了强大的Webmev版本 当前，该系统已经进行了350,000多个分析 每天进行100多次分析，有3,735位HABE在系统注册的用户和该组 每月增长400个（不需要注册）。 教程，包括NCI Webmev的两个“售罄”教程，已成为一个 用于基因组分析的关键工具。 通过实施网络推理和压抑方法来扩展其功能，与 癌症基因组云飞行员计划，并实施可以推进可重现的方法 研究。