As the scale of biological data generation has increased, the bottleneck of research has shifted from data generation to analysis. Researchers commonly need to build computational workflows that include multiple analytic tools and require incremental development as experimental insights demand tool and parameter modifications. These workflows can produce hundreds to thousands of intermediate files and results that must be integrated for biological insight. Data-centric workflow systems that internally manage computational resources, software, and conditional execution of analysis steps are reshaping the landscape of biological data analysis and empowering researchers to conduct reproducible analyses at scale. Adoption of these tools can facilitate and expedite robust data analysis, but knowledge of these techniques is still lacking. Here, we provide a series of strategies for leveraging workflow systems with structured project, data, and resource management to streamline large-scale biological analysis. We present these practices in the context of high-throughput sequencing data analysis, but the principles are broadly applicable to biologists working beyond this field.

中文翻译：

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使用工作流系统简化数据密集型生物学

随着生物数据生成规模的增加，研究的瓶颈已经从数据生成转向分析。研究人员通常需要构建包含多种分析工具的计算工作流，并且需要增量开发作为实验洞察需求工具和参数修改。这些工作流程可以产生成百上千的中间文件和结果，这些文件和结果必须集成起来才能获得生物学洞察力。在内部管理计算资源、软件和分析步骤的条件执行的以数据为中心的工作流系统正在重塑生物数据分析的格局，并使研究人员能够大规模进行可重复的分析。采用这些工具可以促进和加快稳健的数据分析，但仍然缺乏对这些技术的了解。这里，我们提供了一系列策略来利用具有结构化项目、数据和资源管理的工作流系统来简化大规模生物分析。我们在高通量测序数据分析的背景下介绍这些实践，但这些原则广泛适用于该领域以外的生物学家。