WebMeV: A Robust Platform for Intuitive Genomic Data Analysis

项目概要/摘要： 测序技术的改进使得大群体的基因组和转录组分析成为可能。 的研究对象。癌症基因组图谱（TCGA）、DNA元素百科全书等项目 （ENCODE），基因型组织表达计划（GTEx），和其他已经放置了大型，复杂，多- 数据进入公共领域。虽然这些大型项目和新测序技术的使用 提供了前所未有的数据量，技术挑战，如移动和分析大型 多组学数据集，以及缺乏直观和易于使用的数据分析工具，限制了广泛的 探索可用的数据，经常将实验生物学家和领域专家从直接 探索数据中的关系。 超过15年前，我们开始开发MeV，这是一个免费的开源软件工具， 基因组数据的直观分析。简单的图形用户界面和广泛的国家图书馆- 艺术分析方法使MeV成为生物信息学中最广泛使用的软件工具之一， 自2008年开始统计以来，下载量为260，000次， 过去几年尽管MeV的成功和它的持续使用，我们认识到，大规模，多组 使用桌面应用程序不再能够容易地分析数据集。为了跟上数据的步伐，我们 需要开发一个新的平台，利用现代计算技术，包括基于云的 计算和可扩展的数据存储。 该解决方案由NCI通过ITCR计划（5 U 01 CA 151118）资助，是一个基于云的网络解决方案， MeV的启用版本（WebMeV; http://mev.tm4.org）。WebMeV使用Google Cloud Platform（GCP）及其 计算引擎基础设施可利用云计算资源分析大型公共基因组数据 集.在2016年4月，我们发布了WebMeV的强大版本，并看到该系统的使用增长 戏剧性地。该系统已被用于执行超过350，000次分析; WebMeV目前 每天进行100多项分析，3 735名用户在系统中注册， 每月增长400人（不需要注册）。为了确保广泛使用，我们在网上做了大量的 教程，包括两个“售罄”的教程，为内部调查人员在NCI，WebMeV已成为一个 基因组分析的重要工具。在这个应用程序中，我们建议继续维护和改进WebMeV， 通过实现网络推理和表示方法来扩展其功能， 癌症基因组学云试点计划，并实施可以提高可重复性的方法， research.

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.