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教程:使用 CAMI 基准测试工具包评估宏基因组学软件。

Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit.

机构信息

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

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

出版信息

Nat Protoc. 2021 Apr;16(4):1785-1801. doi: 10.1038/s41596-020-00480-3. Epub 2021 Mar 1.

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.

摘要

计算方法是微生物组研究的关键,对于方法开发人员和应用研究人员来说,获得定量和无偏的性能估计是很重要的。为了在方法之间进行有意义的比较,确定最佳实践和常见用例,并减少基准测试的开销,有必要为评估制定标准化的数据集、程序和指标。在本教程中,我们描述了由更大的研究人员社区得出并达成一致的计算宏基因组学基准测试的新兴标准。具体来说,我们概述了宏基因组分析评估(CAMI)倡议的最新努力,该倡议为方法开发人员和应用研究人员提供了关于软件在现实场景中性能的详尽定量数据,并组织了社区驱动的基准测试挑战。我们解释了评估宏基因组组装、分类和分析结果的最相关评估指标,并提供了生成这些指标的逐步说明。这些说明使用了为第二轮 CAMI 挑战准备的模拟鼠肠微生物组数据,并展示了如何使用 CAMI 数据集的工具结果存储库。本教程将作为社区的参考,并促进微生物组研究中信息丰富且可重复的基准测试。

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