Bioconductor: An Open Computing Resource for Genomics

Bioconductor：基因组学的开放计算资源

• 批准号: 8723329
• 负责人: Martin T Morgan
• 金额: $ 20万
• 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/8723329
• 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; 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; transcriptome sequencing

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使用先进的统计和生物信息学方法对高通量基因组数据进行理解和分析。实现此目标的工具是基于R统计编程语言，并涵盖了400多个软件包的既定存储库。生物导体软件包用于与健康相关的学术，政府，药物和临床追求的许多领域。典型用途包括在情况 /对照中的微阵列或RNA-Seq差分表示，或更精细的实验设计； 拷贝数，单核苷酸多态性或用微阵列或序列数据测定的其他变体的表征；用基于阵列或基于序列的技术（例如CHLP-SEQ）技术分析调节或表观遗传变化；流式细胞仪数据的高通量分析；和其他高通量（例如，成像）测定法。该提案的具体目的包括：1，启用生物导体软件的分布以及广泛的用户和开发人员的使用； 2，开发需要跟踪快速出现和计算要求数据类型所需的计算分析基础架构； 3，为基因组量表生物学的新统计方法，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比较评估的方法。总体方法涉及生产和维护基于Internet的资源，以进行软件分发和支持，确保生物通用套件符合高标准的软件保证，以及大量的文档和培训工作，以实现广泛社区的生物通用器使用和开发；专注于用于表示和处理大型数据集和新数据类型的软件实施，集成解决方案，用于在第三方项目中进行有效的软件部署和重复使用，以及促进可重复研究的数据结构和方法；以及统计方法开发和实施作为新的生物导体包。来自生物导体Pro \ ECI的软件将每天在国家和国际环境中每天对基因组规模数据进行分析和理解。生物导体\ n \\\作为引入复杂方法的渠道，以适当分析现有和新的高吞吐量数据类型。 BioConductor提供了一个重要的平台，在该平台上，培训了博士和博士后个体使用统计上适当的方法来参与高通量数据的培训。