MeV: Software for Next Generation Genomic Data Analysis

描述（由申请人提供）：项目概述：人类基因组计划最具变革性的方面不是基因组序列本身，而是该计划催生的技术。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基本上都是由新技术驱动的。1995年发表的《微阵列基因表达分析方法》引发了一场生物探究的革命，在这场革命中，可靠的技术改变了我们能够从生物系统中生成数据的规模，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，这些工具提供了访问这些方法的方式，即使是那些生物信息学经验有限的人也可以有效地探索他们的数据，并得出关于潜在生物学的可测试假设。其中，MeV已成为最广泛使用的分析DNA微阵列数据的软件工具之一，在过去的日历年中有超过22500次下载和超过1233次总引用-我们认为统计数据低估了该系统的整体使用。在过去的十年中，MeV的发展和维护得到了一系列研究资助和其他来源的支持。在此，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，这些技术准备在许多应用中取代基于阵列的分析。这些工具将通过将基因组分析置于与微阵列平等的基础上，开辟新的研究途径，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，通过整合越来越多的公共领域工具来扩展MeV的效用，扩展通过我们的工作和开发开发的新算法的数量，扩展软件和工具以与下一代基于序列的基因组分析一起工作，并为现有软件和工具不断增长的用户社区提供支持。通过这样做，我们希望促进对广泛疾病的理解，为解释来自癌症基因组图谱（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。

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.