Cancer Genomics: Integrative and Scalable Solutions in R/Bioconductor

摘要 Bioconductor是一个由1,500多个开源软件和数据包组成的生态系统， 分析和理解高通量基因组数据。它被广泛用于癌症基因组学 研究社区的统计分析和可视化。这个软件生态系统由核心 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的高效操作。测序成本下降和单细胞 通过组合测定， DNA序列，表观遗传学，基因表达，蛋白质和其他方面，即使在单细胞水平上， 单个样本。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， 通过以下目标迎接这些挑战。首先，我们开发分析的基础设施， 单细胞多组学实验。其次，我们对改进的体细胞变异体实施FAIR原则 优先级，通过定义协调和集成大量 实验和注释数据可通过Bioconductor获得。我们系统的用户将能够创建 关于体细胞变异体的结构和功能背景的来源丰富的可互操作报告， 优先化。第三，我们开发可扩展的基础设施，用于策展，分发，维护， Bioconductor内部和外部癌症数据资源的可扩展性和可用性。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 生物导体基础设施的开发人员的新的癌症相关的软件包和现有的软件包的关键， 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.