MeV: Software for Next Generation Genomic Data Analysis

MeV:下一代基因组数据分析软件

基本信息

  • 批准号:
    8518262
  • 负责人:
  • 金额:
    $ 54万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

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.
描述(由申请人提供): 项目摘要: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,而是该项目催生的技术。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》引发了生物学探究的一场革命,其中可靠的技术改变了我们从生物系统生成数据的规模,并开始将生物医学研究从纯粹的实验室科学转变为信息科学。在这个信息丰富的生物医学时代,最大的挑战之一是我们有效收集、管理和分析数据的能力,即使是小型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,我们小组将实验室探究与软件开发相结合来解决生物学问题。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,这些工具提供了对这些方法的访问,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得出有关基础生物学的可检验的假设。其中,MeV 已成为用于分析 DNA 微阵列数据的最广泛使用的软件工具之一,在过去的一年中下载量超过 22,500 次,总引用次数超过 1,233 次——我们认为统计数据低估了该系统的整体使用情况。 MeV 在过去 10 年的开发和维护得到了一系列研究经费和其他来源的支持。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。这些工具将基因组分析置于与微阵列同等的基础上,开辟新的研究途径,为综合基因组数据分析的新方法提供机会。在此应用程序中,我们将描述我们的计划,通过与 Cytoscape 和 Bioconductor 集成,纳入不断增加的公共领域工具来扩展 MeV 的实用性,扩大通过我们的工作和开发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分析一起使用,并为现有软件和工具的不断增长的用户社区提供支持。在此过程中,我们希望增进对多种疾病的了解,为解释癌症基因组图谱 (TCGA) 和肺基因组研究联盟 (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。

项目成果

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John Quackenbush其他文献

John Quackenbush的其他文献

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{{ truncateString('John Quackenbush', 18)}}的其他基金

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:
    10676979
  • 财政年份:
    2019
  • 资助金额:
    $ 54万
  • 项目类别:
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
  • 财政年份:
    2019
  • 资助金额:
    $ 54万
  • 项目类别:
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
  • 财政年份:
    2019
  • 资助金额:
    $ 54万
  • 项目类别:
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:
    10001456
  • 财政年份:
    2019
  • 资助金额:
    $ 54万
  • 项目类别:
Unraveling the Complexities of Risk and Mechanism in Cancer
揭示癌症风险和机制的复杂性
  • 批准号:
    9762881
  • 财政年份:
    2018
  • 资助金额:
    $ 54万
  • 项目类别:
Unraveling the Complexities of Risk and Mechanism in Cancer
揭示癌症风险和机制的复杂性
  • 批准号:
    10462799
  • 财政年份:
    2018
  • 资助金额:
    $ 54万
  • 项目类别:
Unraveling the Complexities of Risk and Mechanism in Cancer
揭示癌症风险和机制的复杂性
  • 批准号:
    10665644
  • 财政年份:
    2018
  • 资助金额:
    $ 54万
  • 项目类别:
Unraveling the Complexities of Risk and Mechanism in Cancer
揭示癌症风险和机制的复杂性
  • 批准号:
    10246935
  • 财政年份:
    2018
  • 资助金额:
    $ 54万
  • 项目类别:
Statistical and Quantitative Training in Big Data Health Science
大数据健康科学统计与定量培训
  • 批准号:
    9115368
  • 财政年份:
    2016
  • 资助金额:
    $ 54万
  • 项目类别:
Statistical and Quantitative Training in Big Data Health Science
大数据健康科学统计与定量培训
  • 批准号:
    9248431
  • 财政年份:
    2016
  • 资助金额:
    $ 54万
  • 项目类别:

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