AMBER: Assessment of Metagenome BinnERs

  1. Fernando Meyer
  2. Peter Hofmann
  3. Peter Belmann
  4. Ruben Garrido-Oter
  5. Adrian Fritz
  6. Alexander Sczyrba
  7. Alice C McHardy

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Abstract

Reconstructing the genomes of microbial community members is key to the interpretation of shotgun metagenome samples. Genome binning programs deconvolute reads or assembled contigs of such samples into individual bins. However, assessing their quality is difficult due to the lack of evaluation software and standardized metrics. Here, we present Assessment of Metagenome BinnERs (AMBER), an evaluation package for the comparative assessment of genome reconstructions from metagenome benchmark datasets. It calculates the performance metrics and comparative visualizations used in the first benchmarking challenge of the initiative for the Critical Assessment of Metagenome Interpretation (CAMI). As an application, we show the outputs of AMBER for 11 binning programs on two CAMI benchmark datasets. AMBER is implemented in Python and available under the Apache 2.0 license on GitHub.

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  1. GigaScience

    Now published in GigaScience doi: 10.1093/gigascience/giy069

    Fernando Meyer 1Department of Computational Biology of Infection Research, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany2Braunschweig Integrated Centre of Systems Biology (BRICS), Braunschweig, GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this sitePeter Hofmann 1Department of Computational Biology of Infection Research, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany2Braunschweig Integrated Centre of Systems Biology (BRICS), Braunschweig, GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this sitePeter Belmann 1Department of Computational Biology of Infection Research, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany2Braunschweig Integrated Centre of Systems Biology (BRICS), Braunschweig, Germany3Faculty of Technology, Bielefeld University, Bielefeld, Germany4Center for Biotechnology, Bielefeld University, Bielefeld, GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteRuben Garrido-Oter 5Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany6Cluster of Excellence on Plant Sciences (CEPLAS)Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteAdrian Fritz 1Department of Computational Biology of Infection Research, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany2Braunschweig Integrated Centre of Systems Biology (BRICS), Braunschweig, GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteAlexander Sczyrba 3Faculty of Technology, Bielefeld University, Bielefeld, Germany4Center for Biotechnology, Bielefeld University, Bielefeld, GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteAlice C. McHardy 1Department of Computational Biology of Infection Research, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany2Braunschweig Integrated Centre of Systems Biology (BRICS), Braunschweig, GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteFor correspondence: Alice.McHardy@helmholtz-hzi.de

    A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giy069 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

    These peer reviews were as follows:

    Reviewer 1: http://dx.doi.org/10.5524/REVIEW.101202 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.101203

  2. Version published to 10.1093/gigascience/giy069
  3. Version published to 10.1101/239582v1 on bioRxiv