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Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit

Nature Protocols volume 16, pages 1785–1801 (2021)Cite this article

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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 and common use cases, and to reduce overhead in benchmarking, it is necessary to have standardized datasets, procedures and metrics for evaluation. In this tutorial, we describe emerging standards in computational meta-omics 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 for assessing 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 datasets. This tutorial will serve as a reference for the community and facilitate informative and reproducible benchmarking in microbiome research.

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Fig. 1: CAMI benchmarking workflow.
Fig. 2: MetaQUAST assembly benchmarking metrics.
Fig. 3: Assessing metagenome cross-sample assembly quality with MetaQUAST for the CAMI II mouse gut dataset.
Fig. 4: Assessing genome binners on the gold-standard assembly of the CAMI II mouse gut dataset.
Fig. 5: Assessing taxonomic binning results on the CAMI II mouse gut dataset.
Fig. 6: Number of high-quality taxon bins predicted from the CAMI II mouse gut dataset for the phylum to species ranks.
Fig. 7: Assessing taxonomic profiling results on the CAMI II mouse gut dataset.

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Data availability

The results of all benchmarked methods and gold standards are available at https://zenodo.org/communities/cami. Links to individual results and DOIs are available in Supplementary Tables 1, 4, 8, and 11. The gold-standard assembly is provided with the CAMI II mouse gut dataset (Table 2). Assembly results and code used to generate Fig. 3 are available at https://github.com/CAMI-challenge/BenchmarkingToolkitTutorial. Genome and taxonomic binning, and taxonomic profiling results used in Figs. 4–7 are available, respectively, in the AMBER and OPAL GitHub repositories at https://github.com/CAMI-challenge/AMBER and https://github.com/CAMI-challenge/OPAL. The code in this paper has been peer-reviewed.

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Acknowledgements

The authors thank P. B. Pope for helpful comments. A.E.D.’s contribution was facilitated in part by the Australian Research Council’s Discovery Projects funding scheme (project DP180101506). A.G.’s contribution was facilitated by St. Petersburg State University, Russia (grant ID PURE 51555639).

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Authors and Affiliations

  1. Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany

    Fernando Meyer, Till-Robin Lesker, Adrian Fritz, Andreas Bremges & Alice C. McHardy

  2. German Center for Infection Research (DZIF), Braunschweig, Germany

    Till-Robin Lesker & Andreas Bremges

  3. Computer Science and Engineering, Biology, and The Huck Institutes of the Life Sciences, Penn State University, State College, PA, USA

    David Koslicki

  4. Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia

    Alexey Gurevich

  5. The ithree institute, University of Technology Sydney, Sydney, Australia

    Aaron E. Darling

  6. Faculty of Technology and Center for Biotechnology, Bielefeld University, Bielefeld, Germany

    Alexander Sczyrba

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Contributions

F.M. and T.-R.L. performed the experiments; F.M., A.F., T.-R.L., and A.S. prepared the data; A.C.M., A.B., and A.S. conceived the experiments; A.C.M., F.M., and A.B. wrote the manuscript with comments by others; F.M., T.-R.L., D.K., A.F., A.G., A.E.D., A.S., A.B., and A.C.M. interpreted the results, and read and approved the final manuscript.

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Correspondence to Alice C. McHardy.

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Supplementary information

Supplementary Information

Supplementary Tables 1–13, Supplementary Figs. 1 and 2 and Supplementary Note.

Supplementary Data 1

Supplementary Results: MetaQUAST metrics

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Meyer, F., Lesker, TR., Koslicki, D. et al. Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit. Nat Protoc 16, 1785–1801 (2021). https://doi.org/10.1038/s41596-020-00480-3

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