Bioconductor: An Open Computing Resource for Genomics

描述（由申请人提供）：该项目的长期目标是使用先进的统计和生物信息学方法理解和分析高通量基因组数据。实现这一目标的工具是Bioconductor项目，该项目以R统计编程语言为基础，包括一个现有的400多个软件包的储存库。Bioconductor软件包被用于与健康相关的学术、政府、制药和临床追求的许多领域。典型的用途包括病例/对照或更精细的实验设计中的微阵列或RNA-seq差异表示;用微阵列或序列数据测定的拷贝数、单核苷酸多态性或其他变体的表征;用阵列或基于序列的（例如，ChIP-seq）技术;流式细胞术数据的高通量分析;以及其他高通量（例如，成像）测定。该提案的具体目标包括：1、使Bioconductor软件能够被广大的用户和开发人员社区分发和使用; 2、开发跟踪快速出现的和计算要求高的数据类型所需的计算分析基础设施;为基因组规模生物学贡献新的统计方法，包括微阵列和RNA-seq数据的临床应用，基因表达的遗传学分析，和分析方法的比较评估方法。总体方法包括制作和维护用于软件分发和支持的互联网资源，确保Bioconductor软件包符合高标准的软件保证，以及大量的文件和培训工作，使Bioconductor能够被广泛的社区使用和开发;致力于软件实现，用于表示和处理大型数据集和新数据类型，在第三方项目中有效部署和重用软件的集成解决方案，以及促进可重复研究的数据结构和方法;以及作为新Bioconductor软件包的统计方法开发和实施。Bioconductor pro\eci的软件将有助于在国家和国际环境中每天分析和理解基因组规模的数据。Bioconductor \N\作为引入复杂方法的管道，以适当地分析现有的和新的高通量数据类型。Bioconductor提供了一个重要的平台，在这个平台上，博士和博士后个人接受培训，使用统计学上适当的方法来处理高通量数据。

DESCRIPTION (provided by applicant): The long-term objective of this project is to enable comprehension and analysis of high-throughput genomic data using advanced statistical and bioinformatics approaches. The vehicle for attaining this objective is the Bioconductor project, based on the R statistical programming language and encompassing an established repository of more than 400 software packages. Bioconductor software packages are used in many areas of health-related academic, government, pharmaceutical, and clinical pursuits. Typical uses include microarray or RNA-seq differential representation in case / control or more elaborate experimental designs; characterization of copy number, single nucleotide polymorphism, or other variants assayed with microarray or sequence data; assaying regulatory or epigenetic changes with array or sequence-based (e.g., ChlP-seq) technologies; high-throughput analysis of flow cytometric data; and other high-throughput (e.g., imaging) assays. Specific aims of this proposal include: 1, Enabling Bioconductor software distribution and use by a wide community of users and developers; 2, Developing computational analytic infrastructure needed to track rapidly emerging and computationally demanding data types; and 3, Contributing new statistical methods for genome scale biology, including clinical application of microarray and RNA-seq data, analysis of the genetics of gene expression, and approaches to comparative assessment of analytic methodologies. The overall approach involves production and maintenance of internet-based resources for software distribution and support, ensuring conformance of Bioconductor packages to high standards of software assurance, and significant documentation and training efforts to enable Bioconductor use and development by a broad community; focused efforts on software implementations for the representation and processing of large data sets and new data types, integrated solutions for efficient software deployment and re-use in third party projects, and data structures and approaches that foster reproducible research; and statistical methods development and implementation as new Bioconductor packages. Software from the Bioconductor pro\eci will contribute to analysis and comprehension of genome-scale data on a daily basis in national and international settings. Bioconductor \N\\\ serve as a conduit for introduction of sophisticated methods to appropriately analyze existing and new high throughput data types. Bioconductor provides an important platform on which doctoral and post-doctoral individuals are trained to engage high-throughput data using statistically appropriate methodologies.