Cancer Genomics: Integrative and Scalable Solutions in R/Bioconductor

癌症基因组学：R/Bioconductor 中的集成且可扩展的解决方案

• 批准号: 10449603
• 负责人: Martin T Morgan
• 金额: $ 56.94万
• 依托单位: GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10449603
• 关键词: Address; Adoption; Architecture; Archives; Area; Bioconductor; Biological; Biological Assay; Cancer Biology; Cells; Cellular Assay; Communities; Community Participation; Complex; Comprehension; Computer software; DNA Methylation; DNA Sequence; Data; Data Set; Development; Documentation; Ecosystem; Environment; Epigenetic Process; Event; Exposure to; FAIR principles; Funding; Gene Expression; Genes; Genome; Genomic DNA; Genomics; Goals; Growth; Individual; Infrastructure; Institution; Link; Maintenance; Malignant Neoplasms; Memory; Messenger RNA; Methods; Molecular; Molecular Biology; Point Mutation; Precision therapeutics; Programming Languages; Proteins; Protocols documentation; Recurrence; Regulatory Element; Reporting; Reproducibility; Research; Research Personnel; Resources; Role; Specimen; Standardization; Statistical Data Interpretation; Structure; System; Technology; The Cancer Genome Atlas; Training; Training Support; Untranslated RNA; Variant; Visualization; anticancer research; base; cancer cell; cancer genomics; clinically relevant; cloud based; computer infrastructure; cost; data resource; differential expression; experimental study; falls; genetic information; genomic data; improved; interoperability; learning materials; multiple omics; navigation aid; neoplastic cell; novel; novel strategies; open source; operation; outreach; programs; residence; response; single cell analysis; software development; software infrastructure; training opportunity; tumor heterogeneity; tumor microenvironment; tumor-immune system interactions; usability

Abstract Bioconductor is an ecosystem of more than 1,500 open-source software and data packages for the statistical analysis and comprehension of high-throughput genomic data. It is widely used by the cancer genomics research community for statistical analysis and visualization. This software ecosystem is supported by core data classes and methods, reused by both users and developers, that provide convenient representations and efficient operations for many kinds of high-throughput molecular data. Falling sequencing costs and single-cell assays enable increasingly resolved study of the molecular biology of cancer, through combined assaying of DNA sequence, epigenetics, gene expression, protein, and other aspects, even at the single-cell level, for a single specimen. These developments present new challenges in complexity, size, and interpretability of the data. The overarching goal of this project is to create and adapt core Bioconductor software infrastructure to meet these challenges, through the following aims. First, we develop infrastructure for the analysis of single-cell multi-omic experiments. Second, we implement FAIR principles for improved somatic variant prioritization, by defining performant data architecture that harmonizes and integrates the large amount of experimental and annotation data available through Bioconductor. Users of our system will be able to create provenance-rich interoperable reports on structural and functional contexts of somatic variants for use in prioritization. Third, we develop scalable infrastructure for the curation, distribution, maintenance, discoverability, and usability of cancer data resources within and externally to Bioconductor. Finally, we develop a program of user training and new outreach approaches to support adoption of advanced Bioconductor infrastructure by developers of new cancer-related packages and existing packages critical to the cancer research community.

抽象的 Bioconductor 是一个由 1,500 多个开源软件和数据包组成的生态系统，用于统计 高通量基因组数据的分析和理解。它被癌症基因组学广泛使用 统计分析和可视化研究社区。该软件生态系统由核心支持 数据类和方法，由用户和开发人员重用，提供方便的表示和 对多种高通量分子数据进行高效操作。测序成本下降和单细胞 通过联合检测，癌症分子生物学的研究日益深入 DNA 序列、表观遗传学、基因表达、蛋白质和其他方面，甚至在单细胞水平上， 单个标本。这些发展在复杂性、规模和可解释性方面提出了新的挑战。 数据。该项目的总体目标是创建和调整核心 Bioconductor 软件基础设施 通过以下目标应对这些挑战。首先，我们开发用于分析的基础设施 单细胞多组学实验。其次，我们实施公平原则来改进体细胞变异 通过定义协调和集成大量数据的高性能数据架构来确定优先级 可通过 Bioconductor 获得实验和注释数据。我们系统的用户将能够创建 关于体细胞变异的结构和功能背景的来源丰富的可互操作报告，用于 优先顺序。第三，我们开发可扩展的基础设施，用于管理、分发、维护、 Bioconductor 内部和外部的癌症数据资源的可发现性和可用性。最后，我们 制定用户培训计划和新的推广方法，以支持先进技术的采用 新的癌症相关包和现有包的开发人员开发的生物导体基础设施对于癌症至关重要 癌症研究团体。