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Tutorial: Assessing metagenomics software with the CAMI benchmarking toolkit

View ORCID ProfileFernando Meyer, Till-Robin Lesker, View ORCID ProfileDavid Koslicki, Adrian Fritz, Alexey Gurevich, Aaron E. Darling, Alexander Sczyrba, Andreas Bremges, View ORCID ProfileAlice C. McHardy
doi: https://doi.org/10.1101/2020.08.11.245712
Fernando Meyer
1Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
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Till-Robin Lesker
1Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
2German Center for Infection Research (DZIF), Braunschweig, Germany
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David Koslicki
3Computer Science and Engineering, Biology, and The Huck Institutes of the Life Sciences, Penn State University, State College, PA, USA
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Adrian Fritz
1Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
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Alexey Gurevich
4Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia
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Aaron E. Darling
5The ithree institute, University of Technology Sydney, Sydney, Australia
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Alexander Sczyrba
6Faculty of Technology and Center for Biotechnology, Bielefeld University, Bielefeld, Germany
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Andreas Bremges
1Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
2German Center for Infection Research (DZIF), Braunschweig, Germany
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Alice C. McHardy
1Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
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  • ORCID record for Alice C. McHardy
  • For correspondence: alice.mchardy@helmholtz-hzi.de
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Abstract

Computational methods are key in microbiome research, and obtaining a quantitative and unbiased performance estimate is important for method developers and applied researchers. For meaningful comparisons between methods, to identify best practices, common use cases, and to reduce overhead in benchmarking, it is necessary to have standardized data sets, procedures, and metrics for evaluation. In this tutorial, we describe emerging standards in computational metaomics benchmarking derived and agreed upon by a larger community of researchers. Specifically, we outline recent efforts by the Critical Assessment of Metagenome Interpretation (CAMI) initiative, which supplies method developers and applied researchers with exhaustive quantitative data about software performance in realistic scenarios and organizes community-driven benchmarking challenges. We explain the most relevant evaluation metrics to assess metagenome assembly, binning, and profiling results, and provide step-by-step instructions on how to generate them. The instructions use simulated mouse gut metagenome data released in preparation for the second round of CAMI challenges and showcase the use of a repository of tool results for CAMI data sets. This tutorial will serve as a reference to the community and facilitate informative and reproducible benchmarking in microbiome research.

Competing Interest Statement

The authors have declared no competing interest.

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 August 12, 2020.
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Tutorial: Assessing metagenomics software with the CAMI benchmarking toolkit
Fernando Meyer, Till-Robin Lesker, David Koslicki, Adrian Fritz, Alexey Gurevich, Aaron E. Darling, Alexander Sczyrba, Andreas Bremges, Alice C. McHardy
bioRxiv 2020.08.11.245712; doi: https://doi.org/10.1101/2020.08.11.245712
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Tutorial: Assessing metagenomics software with the CAMI benchmarking toolkit
Fernando Meyer, Till-Robin Lesker, David Koslicki, Adrian Fritz, Alexey Gurevich, Aaron E. Darling, Alexander Sczyrba, Andreas Bremges, Alice C. McHardy
bioRxiv 2020.08.11.245712; doi: https://doi.org/10.1101/2020.08.11.245712

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