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A comparison of single-coverage and multi-coverage metagenomic binning reveals extensive hidden contamination

Nature Methods volume 20, pages 1170–1173 (2023)Cite this article

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Abstract

Metagenomic binning has revolutionized the study of uncultured microorganisms. Here we compare single- and multi-coverage binning on the same set of samples, and demonstrate that multi-coverage binning produces better results than single-coverage binning and identifies contaminant contigs and chimeric bins that other approaches miss. While resource expensive, multi-coverage binning is a superior approach and should always be performed over single-coverage binning.

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Fig. 1: A comparison of single- and multi-coverage metagenomic binning.
Fig. 2: The worst-performing single-coverage bin according to the mean pairwise correlation coefficient.

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Data availability

Raw rumen FASTQ datasets are available under BioProject accession PRJEB21624. Raw human FASTQ datasets are available under BioProject accession PRJNA278393. Bins from Rampelli et al., assembled by Pasolli et al., are available from http://segatalab.cibio.unitn.it/data/Pasolli_et_al.html. Metagenome assemblies of the Rampelli et al. data, assembled by Pasolli et al., are available from https://www.dropbox.com/s/5qqtbyuufmgycp6/RampelliS_2015.tar.bz2. Finally, our analysis of the rumen and human datasets and bins can be downloaded from https://doi.org/10.6084/m9.figshare.19733509.

Code availability

Code for producing single- and multi-coverage assemblies and bins is available at https://github.com/WatsonLab/single_and_multiple_binning.

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Acknowledgements

The Roslin Institute forms part of, and is supported by, the Royal (Dick) School of Veterinary Studies, University of Edinburgh. This project was supported by the Biotechnology and Biological Sciences Research Council (BBSRC; BB/S006680/1, BB/R015023/1 and BB/V018450/1), including institute strategic program grant BBS/E/D/30002276.

Author information

Authors and Affiliations

  1. The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK

    Jennifer Mattock

  2. Centre for Digital Innovation, DSM Biotechnology Center, Delft, The Netherlands

    Mick Watson

  3. Scotland’s Rural College, Peter Wilson Building, King’s Buildings, Edinburgh, UK

    Mick Watson

Authors
  1. Jennifer Mattock
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  2. Mick Watson
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Contributions

J.M. and M.W. carried out all analyses and wrote the paper.

Corresponding author

Correspondence to Mick Watson.

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Competing interests

Mick Watson is an employee of DSM, and the remaining authors declare no competing interests

Peer review

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Nature Methods thanks Anders Andersson, C. Titus Brown and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling editor: Lin Tang, in collaboration with the Nature Methods team. Peer reviewer reports are available.

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Supplementary information

Supplementary Information

Replication of results in human microbiome data, rationale for using Pearson correlation coefficient, challenges in implementation of our approach and Supplementary Fig. 1.

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Mattock, J., Watson, M. A comparison of single-coverage and multi-coverage metagenomic binning reveals extensive hidden contamination. Nat Methods 20, 1170–1173 (2023). https://doi.org/10.1038/s41592-023-01934-8

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