Streamlining data-intensive biology with workflow systems.

ABSTRACT: 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 kno

SUBMITTER: Reiter T 

PROVIDER: S-EPMC8631065 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature