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能够使用先进的统计和生物信息学方法来理解和分析高通量基因组数据。实现这一目标的载体是BioConductor项目，该项目以R统计编程语言为基础，包括400多个软件包的已建立的储存库。BioConductor软件包被用于与健康相关的学术、政府、制药和临床研究的许多领域。典型的应用包括在病例/对照或更复杂的实验设计中的微阵列或RNA-SEQ差异表示；利用微阵列或序列数据分析拷贝数、单核苷酸多态或其他变体的特征；利用基于阵列或序列的技术(例如ChLP-SEQ)分析调节或表观遗传变化；高通量分析流式细胞仪数据；以及其他高通量(例如成像)分析。这项建议的具体目标包括：1，使BioConductor软件能够被广大用户和开发人员分发和使用；2，开发跟踪迅速出现的和计算要求很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用于软件分发和支持的基于互联网的资源，确保BioConductor包符合软件保证的高标准，以及为使BioConductor能够被广大社区使用和开发所做的大量文件和培训工作；将重点放在用于表示和处理大数据集和新数据类型的软件实施、用于在第三方项目中有效部署和重复使用的综合解决方案以及促进可重复研究的数据结构和方法上；以及作为新的BioConductor包开发和实施统计方法。BioConductor PRO\ECI的软件将有助于分析和理解国家和国际环境中每天的基因组规模数据。BioConductor用作引入复杂方法的管道，以适当地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，在这个平台上，博士和博士后个人可以接受培训，以便使用统计上合适的方法处理高通量数据。