Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit

被引:0
|
作者
Fernando Meyer
Till-Robin Lesker
David Koslicki
Adrian Fritz
Alexey Gurevich
Aaron E. Darling
Alexander Sczyrba
Andreas Bremges
Alice C. McHardy
机构
[1] Helmholtz Centre for Infection Research,Computational Biology of Infection Research
[2] German Center for Infection Research (DZIF),Computer Science and Engineering, Biology, and The Huck Institutes of the Life Sciences
[3] Penn State University,Center for Algorithmic Biotechnology
[4] St. Petersburg State University,The ithree institute
[5] University of Technology Sydney,Faculty of Technology and Center for Biotechnology
[6] Bielefeld University,undefined
来源
Nature Protocols | 2021年 / 16卷
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摘要
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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页码:1785 / 1801
页数:16
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