Bioconductor: An Open-Source, Open-Development Computing Resource for Genomics

项目摘要 Bioconductor是一个致力于分析和解释高通量基因组数据的项目，包括： 测序、微阵列、流式细胞术、蛋白质组学和成像数据。Bioconductor是基于R sta- 程序设计语言它包括软件、注释和开发和贡献的数据包 由该赠款资助的个人以及国家和国际科学界资助。生物导体是 备受推崇，在全球生物信息学界广泛使用，被引用率高，并有正式的合作 与人类细胞图谱和NHGRI的基因组分析、可视化和信息学实验室空间对齐 （AnVIL）。在此更新申请中提出的工作反映了该项目对开源/开放的承诺。 为基因组数据科学开发、创建和分发便携式工具， 支持用户和开发人员，使计算方法适应云规模数据的新技术 科学和有效培训基因组生物学和个性化基因组医学的劳动力。特别- CI的目标是（1）维护和增强www.example.com上的系统，以组织和分发 分析软件、参考数据和精心策划的实验数据，（2）核心基础设施软件的强化 软件包，以提高基于系统的分析的可靠性和吞吐量，（3）进行研究和开发， 最佳实践的选择，以利用可扩展的计算策略， 分析，以及（4）加强社区参与和教育实践，这些都是项目的固有内容 自其成立以来，通过追求这些目标，项目调查人员和贡献者增加了可用性，相关性， 一个系统和社区的强大性，是独一无二的，并处于独特的地位，以加速许多方面的进展， 基因组数据科学领域，最终有助于生物知识和改善人类健康。

PROJECT SUMMARY Bioconductor is a project dedicated to the analysis and interpretation of high throughput genomic data, includ- ing sequencing, microarray, flow cytometry, proteomics, and imaging data. Bioconductor is based on the R sta- tistical programming language. It consists of software, annotation, and data packages developed and contributed by individuals funded by this grant, and by the national and international scientific community. Bioconductor is highly respected, widely used in the global bioinformatics community, highly cited, and has formal collaborative alignments with the Human Cell Atlas and NHGRI's Genomic Analysis, Visualization, and Informatics Lab Space (AnVIL). Work proposed in this renewal application reflects the commitment of the project to open-source/open- development creation and distribution of portable tools for genomic data science, high-quality documentation and support for users and developers, adaptation of computational methods to new technologies for cloud-scale data science, and effective training of the workforce for genome biology and personalized genomic medicine. The spe- cific aims are (1) maintenance and enhancement of the system at bioconductor.org for organizing and distributing analytic software, reference data, and curated experimental data, (2) hardening of core infrastructural software packages to increase reliability and throughput of analyses based on the system, (3) conduct research and devel- opment of best practices for taking advantage of scalable computing strategies for integrative cloud-scale genomic analysis, and (4) enhance community engagement and education practices that have been intrinsic to the project since its inception. By pursuing these aims, project investigators and contributors add to the usability, relevance, and robustness of a system and community that is unique and is uniquely situated to accelerate progress in many areas of genomic data science, ultimately contributing to biological knowledge and improvement of human health.