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Streamlining Data-Intensive Biology With Workflow Systems

View ORCID ProfileTaylor Reiter, View ORCID ProfilePhillip T. Brooks, View ORCID ProfileLuiz Irber, View ORCID ProfileShannon E.K. Joslin, View ORCID ProfileCharles M. Reid, View ORCID ProfileCamille Scott, View ORCID ProfileC. Titus Brown, View ORCID ProfileN. Tessa Pierce
doi: https://doi.org/10.1101/2020.06.30.178673
Taylor Reiter
1Department of Population Health and Reproduction, University of California, Davis · Funded by Moore Foundation GBMF4551
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Phillip T. Brooks
2Department of Population Health and Reproduction, University of California, Davis
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Luiz Irber
1Department of Population Health and Reproduction, University of California, Davis · Funded by Moore Foundation GBMF4551
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Shannon E.K. Joslin
3Department of Animal Science, University of California, Davis · Funded by State and Federal Water Contractors A19-1844
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Charles M. Reid
1Department of Population Health and Reproduction, University of California, Davis · Funded by Moore Foundation GBMF4551
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Camille Scott
1Department of Population Health and Reproduction, University of California, Davis · Funded by Moore Foundation GBMF4551
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C. Titus Brown
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N. Tessa Pierce
4Department of Population Health and Reproduction, University of California, Davis · Funded by NSF 1711984
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  • For correspondence: ntpierce@gmail.com
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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. The maturation of 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 practices and strategies for leveraging workflow systems with structured project, data, and resource management to streamline large-scale biological analysis.

Author Summary We present a guide for workflow-enabled biological sequence data analysis, developed through our own teaching, training and analysis projects. We recognize that this is based on our own use cases and experiences, but we hope that our guide will contribute to a larger discussion within the open source and open science communities and lead to more comprehensive resources. Our main goal is to accelerate the research of scientists conducting sequence analyses by introducing them to organized workflow practices that not only benefit their own research but also facilitate open and reproducible science.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • taylorreiter · ReiterTaylor

  • brooksph · brooksph

  • luizirber · luizirber

  • shannonekj · IntrprtngGnmcs

  • charlesreid1

  • camillescott · camille_codon

  • ctb · ctitusbrown

  • bluegenes · saltyscientist

  • https://github.com/dib-lab/2020-workflows-paper

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 01, 2020.
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Streamlining Data-Intensive Biology With Workflow Systems
Taylor Reiter, Phillip T. Brooks, Luiz Irber, Shannon E.K. Joslin, Charles M. Reid, Camille Scott, C. Titus Brown, N. Tessa Pierce
bioRxiv 2020.06.30.178673; doi: https://doi.org/10.1101/2020.06.30.178673
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Streamlining Data-Intensive Biology With Workflow Systems
Taylor Reiter, Phillip T. Brooks, Luiz Irber, Shannon E.K. Joslin, Charles M. Reid, Camille Scott, C. Titus Brown, N. Tessa Pierce
bioRxiv 2020.06.30.178673; doi: https://doi.org/10.1101/2020.06.30.178673

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