The Department of Biochemistry & Molecular Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA.
Seven Bridges, Charlestown, MA 02129, USA.
Database (Oxford). 2021 Mar 30;2021. doi: 10.1093/database/baab008.
Developments in high-throughput sequencing (HTS) result in an exponential increase in the amount of data generated by sequencing experiments, an increase in the complexity of bioinformatics analysis reporting and an increase in the types of data generated. These increases in volume, diversity and complexity of the data generated and their analysis expose the necessity of a structured and standardized reporting template. BioCompute Objects (BCOs) provide the requisite support for communication of HTS data analysis that includes support for workflow, as well as data, curation, accessibility and reproducibility of communication. BCOs standardize how researchers report provenance and the established verification and validation protocols used in workflows while also being robust enough to convey content integration or curation in knowledge bases. BCOs that encapsulate tools, platforms, datasets and workflows are FAIR (findable, accessible, interoperable and reusable) compliant. Providing operational workflow and data information facilitates interoperability between platforms and incorporation of future dataset within an HTS analysis for use within industrial, academic and regulatory settings. Cloud-based platforms, including High-performance Integrated Virtual Environment (HIVE), Cancer Genomics Cloud (CGC) and Galaxy, support BCO generation for users. Given the 100K+ userbase between these platforms, BioCompute can be leveraged for workflow documentation. In this paper, we report the availability of platform-dependent and platform-independent BCO tools: HIVE BCO App, CGC BCO App, Galaxy BCO API Extension and BCO Portal. Community engagement was utilized to evaluate tool efficacy. We demonstrate that these tools further advance BCO creation from text editing approaches used in earlier releases of the standard. Moreover, we demonstrate that integrating BCO generation within existing analysis platforms greatly streamlines BCO creation while capturing granular workflow details. We also demonstrate that the BCO tools described in the paper provide an approach to solve the long-standing challenge of standardizing workflow descriptions that are both human and machine readable while accommodating manual and automated curation with evidence tagging. Database URL: https://www.biocomputeobject.org/resources.
高通量测序（HTS）的发展导致测序实验产生的数据量呈指数级增长，生物信息学分析报告的复杂性增加，产生的数据类型也增加。这些数据量、多样性和复杂性的增加，以及它们的分析，暴露了对结构化和标准化报告模板的必要性。BioCompute Objects (BCO) 为 HTS 数据分析的通信提供了必要的支持，包括对工作流程的支持，以及数据、策展、可访问性和通信的可重复性。BCO 标准化了研究人员报告来源的方式，以及工作流程中使用的既定验证和验证协议，同时也足够强大，可以在知识库中传达内容集成或策展。封装工具、平台、数据集和工作流程的 BCO 符合 FAIR（可发现、可访问、可互操作和可重复使用）标准。提供操作工作流程和数据信息有助于平台之间的互操作性，并在 HTS 分析中纳入未来的数据集，以便在工业、学术和监管环境中使用。包括 High-performance Integrated Virtual Environment (HIVE)、Cancer Genomics Cloud (CGC) 和 Galaxy 在内的基于云的平台为用户支持 BCO 的生成。鉴于这些平台之间有 10 万+ 的用户群，BioCompute 可以用于工作流程文档。在本文中，我们报告了依赖于平台和独立于平台的 BCO 工具的可用性：HIVE BCO App、CGC BCO App、Galaxy BCO API 扩展和 BCO 门户。我们利用社区参与来评估工具的功效。我们证明，这些工具进一步推进了 BCO 的创建，超越了标准早期版本中使用的文本编辑方法。此外，我们证明了在现有分析平台中集成 BCO 的创建可以极大地简化 BCO 的创建，同时捕获更细粒度的工作流程细节。我们还证明了本文中描述的 BCO 工具提供了一种方法来解决标准化工作流程描述的长期挑战，这些描述既对人类可读，也对机器可读，同时适应了带有证据标记的手动和自动策展。数据库 URL：https://www.biocomputeobject.org/resources。
