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. 2021 Jun 11;12:3562. doi: 10.1038/s41467-021-23821-6

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a Study design and approach. First, synthetic microbial communities were designed mimicking three distinct ecological scenarios. Second, metagenomic sequencing of these communities and determination of their microbial loads were simulated. Third, sequencing data were transformed according to a selection of methods being benchmarked. Finally, these methods were evaluated based on their performance in relevant microbiome analyses. b Examples of the three types of ecological scenarios simulated: succession (left panel), blooming (mid panel), and dysbiosis (right panel). Samples are ordered in function of microbial density. The top 10 most abundant taxa as well as the bloomer and opportunist taxon are colored for each population.