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

中文翻译：

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为支持 BioCompute 对象而开发的生物信息学工具


高通量测序（HTS）的发展导致测序实验生成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以及生成的数据类型增加。生成的数据及其分析的数量、多样性和复杂性的增加揭示了结构化和标准化报告模板的必要性。 BioCompute 对象 (BCO) 为 HTS 数据分析的通信提供必要的支持，包括对工作流程以及通信的数据、管理、可访问性和可重复性的支持。 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管理。封装工具、平台、数据集和工作流程的 BCO 符合 FAIR（可查找、可访问、可互操作和可重用）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，并将未来数据集纳入 HTS 分析中，以便在工业、学术和监管环境中使用。基于云的平台，包括高性能集成虚拟环境（HIVE）、癌症基因组云（CGC）和Galaxy，支持用户生成BCO。鉴于这些平台之间有超过 10 万个用户群，BioCompute 可用于工作流程文档编制。在本文中，我们报告了平台相关和平台无关的 BCO 工具的可用性：HIVE BCO 应用程序、CGC BCO 应用程序、Galaxy BCO API 扩展和 BCO 门户。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标准早期版本中使用的文本编辑方法的 BCO 创建。此外，我们还证明，将 BCO 生成集成到现有分析平台中可以极大地简化 BCO 创建，同时捕获精细的工作流程细节。我们还证明，本文中描述的 BCO 工具提供了一种解决长期存在的挑战的方法，即标准化人类和机器可读的工作流程描述，同时适应带有证据标记的手动和自动管理。数据库网址：https://www.biocomputeobject。组织/资源