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

项目总结 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 fl测序、微阵列、DNA流式细胞术、蛋白质组学和成像数据。生物导体是基于R-Sta- 有组织的编程语言。它由开发和贡献的软件、注释和数据包组成 由这笔赠款资助的个人以及国家和国际科学界fic社区。生物导体是 备受推崇，在全球生物信息学社区广泛使用，引用率高，并与 与人类细胞图谱和NHGRI基因组分析、可视化和信息学实验室空间的比对 (砧板)。在此续订申请中提出的工作fl反映了该项目对开源/开放的承诺。 开发、创建和分发便携工具，用于基因组数据科学、高质量文件和 为用户和开发人员提供支持，使计算方法适应云级数据的新技术 科学，以及对基因组生物学和个性化基因组医学劳动力的有效培训。斯派西-- CIfic的目标是：(1)维护和加强BioConductor.org的系统，以便组织和分发 分析软件、参考数据和精选的实验数据，(2)核心基础设施软件的加固 提高基于系统的分析的可靠性和吞吐量的包，(3)进行研究和开发- 利用集成云规模基因组的可扩展计算策略的最佳实践 分析，以及(4)加强社区参与和项目固有的教育实践 从一开始就是这样。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、 独一无二的系统和社区的健壮性，以及独特的位置，以加速许多 在基因组数据科学领域，最终为生物学知识和改善人类健康作出贡献。

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.