MeV: Software for Next Generation Genomic Data Analysis

MeV：下一代基因组数据分析软件

• 批准号: 8706074
• 负责人: John Quackenbush
• 金额: $ 55.73万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-07 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8706074
• 关键词: Address; Algorithms; Area; Base Sequence; Bioconductor; Bioinformatics; Biological; Biological Assay; Biology; Biomedical Research; Calendar; ChIP-seq; Code; Communities; Computer software; DNA Microarray Chip; Data; Data Analyses; Data Set; Development; Disease; Documentation; Educational workshop; Ensure; Funding; Gene Expression Microarray Analysis; Genomics; Goals; Housing; Human Genome Project; Information Sciences; Institutes; International; Investigation; Java; Laboratories; Learning; Link; Linux; Literature; Lung; Maintenance; Messenger RNA; Meta-Analysis; Methods; Methylation; MicroRNAs; Microarray Analysis; Process; Proteomics; Public Domains; Publications; Published Database; Research; Research Personnel; Research Project Grants; Resources; Sampling; Science; Scientist; Series; Software Tools; Solutions; Source; System; Systems Analysis; Systems Biology; Technology; The Cancer Genome Atlas; United States National Institutes of Health; Work; analytical method; base; biological systems; data mining; experience; foot; functional genomics; genome sequencing; graphical user interface; improved; meetings; metabolomics; new technology; next generation; next generation sequencing; novel; novel strategies; open source; software development; software systems; statistics; tool; transcriptomics; user-friendly

DESCRIPTION (provided by applicant): Project Summary: The most transformative aspect of the Human Genome Project was not the sequence of the genome itself, but the technologies that the project has spawned. Genomics, functional genomics, proteomics, metabolomics, systems biology, and other research areas are fundamentally driven by new technologies. The 1995 publication of methods microarray gene expression analysis launched a revolution in biological inquiry in which reliable technologies changed the scale at which we were able to generate data from biological systems and started the transformation of biomedical research from a purely laboratory science to an information science. One of the greatest challenges in this information-rich era of biomedical science has been our ability to effectively collect, manage, and analyze the data that even small laboratories now regularly produce. As early adopters of DNA microarray technology, our group combined laboratory inquiry with software development to address biological questions. By applying state-of-the-art statistical and data mining approaches, we created a series of open-source, easy-to-use software tools that provide access to these methods in a manner that allows even those with limited bioinformatics experience to effectively explore their data and reach testable hypotheses regarding the underlying biology. Of these, MeV, has become one of the most widely used software tools for the analysis DNA microarray data, with more than 22,500 downloads in the past calendar year and more than 1,233 total citations-statistics we believe underestimate the system's overall use. MeV's development and maintenance during the past 10 years has been supported by a series of research grants and other sources. Here we propose to further develop and maintain the codebase, expanding the utility and functionality of the software to meet the challenges of genomic analysis in the coming years. The greatest challenge in keeping MeV and similar tools relevant is the onslaught of data arising from the new sequencing technologies that are poised to replace array-based analysis in many applications. These tools will open up new avenues of investigation by placing genomic analysis on a footing equal with microarrays, providing an opportunity for new approaches to integrative genomic data analysis. In this application, we will describe our plans to expand the utility of MeV by incorporating an ever-increasing number of public domain tools through integration with Cytoscape and Bioconductor, expand the number of novel algorithms developed through our work and development, extend the software and tools to work with Next Generation sequence- based genomic assays, and provide support to the growing community of users of the software and tools that exist. In doing so, we hope to advance understanding of a wide range of diseases, provide resources for interpreting data from such projects as The Cancer Genome Atlas (TCGA) and the Lung Genomics Research Consortium (LGRC), and enable and accelerate research far beyond that of our own.

项目概述：人类基因组计划最具变革性的方面不是基因组本身的序列，而是该项目所产生的技术。基因组学、功能基因组学、蛋白质组学、代谢组学、系统生物学和其他研究领域从根本上说是由新技术驱动的。1995年出版的《微阵列基因表达分析方法》引发了生物学研究的一场革命，可靠的技术改变了我们从生物系统中产生数据的规模，并开始了生物医学研究从纯粹的实验室科学向信息科学的转变。在这个信息丰富的生物医学科学时代，最大的挑战之一是我们有效收集、管理和分析数据的能力，即使是小实验室现在也经常产生这些数据。 作为DNA微阵列技术的早期采用者，我们的团队将实验室调查与软件开发相结合，以解决生物学问题。通过应用最先进的统计和数据挖掘方法，我们创建了一系列开源，易于使用的软件工具，以允许即使是那些生物信息学经验有限的人也能有效地探索他们的数据并达到关于基础生物学的可测试假设的方式提供这些方法。其中，MeV已经成为分析DNA微阵列数据的最广泛使用的软件工具之一，在过去的一年中有超过22，500次下载和超过1，233次总引用-我们认为统计数据低估了该系统的整体使用。在过去10年中，MeV的开发和维护得到了一系列研究赠款和其他来源的支持。在这里，我们建议进一步开发和维护代码库，扩展软件的实用性和功能，以应对未来几年基因组分析的挑战。保持MeV和类似工具相关性的最大挑战是新测序技术产生的数据冲击，这些技术有望在许多应用中取代基于阵列的分析。这些工具将开辟新的研究途径，将基因组分析置于与微阵列平等的基础上，为整合基因组数据分析的新方法提供机会。在本申请中，我们将描述我们的计划，通过与Cytoscape和Bioconductor集成来合并越来越多的公共领域工具，扩展MeV的效用，扩展通过我们的工作和开发的新颖算法的数量，扩展软件和工具以与下一代基于序列的基因组测定一起工作，并为现有软件和工具的日益增长的用户社区提供支持。在这样做的过程中，我们希望促进对各种疾病的理解，为解释癌症基因组图谱（TCGA）和肺基因组学研究联盟（LGRC）等项目的数据提供资源，并实现和加速远远超出我们自己的研究。