Cancer Genomics: Integrative and Salable Solutions in R/Bioconductor

摘要 BioConductor是一个由1500多个开源软件和数据包组成的生态系统，用于 高通量基因组数据的分析和理解。它被广泛应用于癌症基因组学 用于统计分析和可视化的研究社区。该软件生态系统由核心支持 由用户和开发人员重用的数据类和方法，它们提供了方便的表示形式和 高效操作多种类型的高通量分子数据。测序成本下降和单细胞 通过联合分析癌症的分子生物学，分析使癌症的分子生物学研究变得更加清晰。 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，对于一个 单一样本。这些发展在复杂性、大小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，以 通过以下目标迎接这些挑战。首先，我们开发用于分析的基础设施 单细胞多组体实验。第二，我们对改良的体细胞变异实行公平的原则 通过定义性能数据体系结构来协调和集成大量 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 种源丰富的可互操作报告的结构和功能背景的体细胞变异用于 确定优先顺序。第三，我们开发可扩展的基础设施，用于管理、分发、维护、 生物导体内部和外部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，以支持采用先进的 生物导体基础设施，由癌症相关新包和现有包的开发商提供，这些包对 癌症研究社区。

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.