title: Computing Workflows for Biologists: A Roadmap.
abstract: Extremely large datasets have become routine in biology. However, performing a computational analysis of a large dataset can be overwhelming, especially for novices. Here, we present a step-by-step guide to computing workflows with the biologist end-user in mind. Starting from a foundation of sound data management practices, we make specific recommendations on how to approach and perform computational analyses of large datasets, with a view to enabling sound, reproducible biological research.
authors.affiliation: Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, United States of America.|BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, United States of America.
authors.forename: Ashley
authors.initials: A
authors.lastname: Shade
authors.affiliation: BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, United States of America.|Data Carpentry, datacarpentry.org.
authors.forename: Tracy K
authors.initials: TK
authors.lastname: Teal
mesh_terms: D000818:Animals; D001695:Biology / Q000639:trends; D019295:Computational Biology / Q000379:methods* / Q000639:trends; D003205:Computing Methodologies; D003628:Database Management Systems / Q000639:trends; D066264:Datasets as Topic / Q000639:trends; D017408:Guidelines as Topic; D006801:Humans; D015203:Reproducibility of Results; D009626:Terminology as Topic; D057188:Workflow; D000078329:Workforce