标杆管理
基因组
计算机科学
数据科学
软件
计算生物学
软件工程
生物信息学
遗传学
生物
基因
业务
营销
程序设计语言
作者
Fernando Meyer,Till-Robin Lesker,David Koslicki,Adrian Fritz,Alexey Gurevich,Aaron E. Darling,Alexander Sczyrba,Andreas Bremges,Alice C. McHardy
出处
期刊:Nature Protocols
[Springer Nature]
日期:2021-03-01
卷期号:16 (4): 1785-1801
被引量:42
标识
DOI:10.1038/s41596-020-00480-3
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI