Bioconductor: An Open Computing Resource for Genomics

Bioconductor：基因组学的开放计算资源

• 批准号: 8337802
• 负责人: Martin T Morgan
• 金额: $ 123.53万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-28 至 2016-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8337802
• 关键词: Address; Affinity; Algorithms; Archives; Area; Base Sequence; Binding; Bioconductor; Bioinformatics; Biological; Biological Assay; Biology; Biotechnology; Books; Clinical; Collaborations; Collection; Communities; Comprehension; Computer software; Data; Data Analyses; Data Set; Development; Documentation; Ensure; Epigenetic Process; Evaluation; Experimental Designs; Flow Cytometry; Fostering; Gene Expression; Gene Expression Profile; Genetic; Genome; Genomics; Genotype; Government; Health; Health Sciences; Histocompatibility Testing; Image; Imagery; Individual; International; International Aspects; Internet; Logistics; Maintenance; Medicine; Memory; Metadata; Methodology; Methods; Metric; Modeling; Morphologic artifacts; Output; Performance; Pharmacologic Substance; Postdoctoral Fellow; Process; Production; Programming Languages; Publishing; RNA; Reading; Reagent; Reproducibility; Research; Research Infrastructure; Research Personnel; Resources; Sampling; Scanning; Scientist; Signal Transduction; Single Nucleotide Polymorphism; Software Design; Software Engineering; Solutions; Standardization; Statistical Methods; Stream; Structure; Techniques; Technology; Testing; Training; Transcript; Translating; Variant; Work; base; cancer genome; case control; clinical application; cluster computing; comparative; computing resources; cost effective; density; experience; flexibility; genome sequencing; high standard; high throughput analysis; high throughput technology; improved; interoperability; meetings; method development; open source; operation; parallel computing; quality assurance; repository; research study; skills; symposium; tool

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.

描述（由申请人提供）：该项目的长期目标是利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，该项目基于 R 统计编程语言，并包含一个包含 400 多个软件包的已建立存储库。 Bioconductor 软件包用于与健康相关的学术、政府、制药和临床研究的许多领域。典型用途包括病例/对照或更复杂的实验设计中的微阵列或 RNA-seq 差异表达； 使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；使用基于阵列或序列（例如 ChlP-seq）技术分析调控或表观遗传变化；流式细胞术数据的高通量分析；和其他高通量（例如成像）测定。该提案的具体目标包括： 1、使 Bioconductor 软件能够被广泛的用户和开发者社区分发和使用； 2、开发跟踪快速新兴且计算要求高的数据类型所需的计算分析基础设施； 3、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以及分析方法的比较评估方法。总体方法包括生产和维护基于互联网的软件分发和支持资源，确保 Bioconductor 软件包符合高标准的软件保证，以及重要的文档和培训工作，以使 Bioconductor 能够被广泛的社区使用和开发；重点关注用于表示和处理大型数据集和新数据类型的软件实现、第三方项目中高效软件部署和重用的集成解决方案，以及促进可重复研究的数据结构和方法；以及统计方法的开发和实施作为新的 Bioconductor 包。 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\\\ 充当引入复杂方法的渠道，以适当分析现有和新的高通量数据类型。 Bioconductor 提供了一个重要的平台，在该平台上培训博士和博士后人员使用统计上适当的方法来处理高通量数据。