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. 2024 Nov 4;12:226. doi: 10.1186/s40168-024-01949-z

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© The Author(s) 2024

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — The MAGqual pipeline. MAGs in FASTA format are run through CheckM for quality assessment, SeqKit to calculate basic statistics and Bakta for MAG annotation, and the output from these is classified according to the MIMAG standards using a bespoke Python script. This outputs a CSV file containing recommended metadata, the MAGs organised according to their classification and plots and a report are generated to visualise the classification and assembly statistics