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. 2019 Feb 11;568(7753):499–504. doi: 10.1038/s41586-019-0965-1

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© The Author(s) 2019

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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Extended Data Fig. 3 — a, MAGs resulting from the MetaWRAP pipeline (left, n = 9,552) and from a modified co-assembly approach (right, n = 4,404) compared to the original MAGs generated with SPAdes and MetaBAT for 1,000 random datasets. A good match was defined as ≥95% ANI over ≥60% of alignment fraction, whereas an excellent match indicates ≥98% ANI over ≥80% alignment. b, Proportion of MAGs generated with each pipeline (MetaWRAP and co-assembly) coloured by their level of match to the original set.