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AMBER: Assessment of Metagenome BinnERs

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 softw...

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Autores principales: Meyer, Fernando, Hofmann, Peter, Belmann, Peter, Garrido-Oter, Ruben, Fritz, Adrian, Sczyrba, Alexander, McHardy, Alice C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022608/
https://www.ncbi.nlm.nih.gov/pubmed/29893851
http://dx.doi.org/10.1093/gigascience/giy069
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author Meyer, Fernando
Hofmann, Peter
Belmann, Peter
Garrido-Oter, Ruben
Fritz, Adrian
Sczyrba, Alexander
McHardy, Alice C
author_facet Meyer, Fernando
Hofmann, Peter
Belmann, Peter
Garrido-Oter, Ruben
Fritz, Adrian
Sczyrba, Alexander
McHardy, Alice C
author_sort Meyer, Fernando
collection PubMed
description 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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spelling pubmed-60226082018-07-10 AMBER: Assessment of Metagenome BinnERs Meyer, Fernando Hofmann, Peter Belmann, Peter Garrido-Oter, Ruben Fritz, Adrian Sczyrba, Alexander McHardy, Alice C Gigascience Technical Note 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. Oxford University Press 2018-06-08 /pmc/articles/PMC6022608/ /pubmed/29893851 http://dx.doi.org/10.1093/gigascience/giy069 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Note
Meyer, Fernando
Hofmann, Peter
Belmann, Peter
Garrido-Oter, Ruben
Fritz, Adrian
Sczyrba, Alexander
McHardy, Alice C
AMBER: Assessment of Metagenome BinnERs
title AMBER: Assessment of Metagenome BinnERs
title_full AMBER: Assessment of Metagenome BinnERs
title_fullStr AMBER: Assessment of Metagenome BinnERs
title_full_unstemmed AMBER: Assessment of Metagenome BinnERs
title_short AMBER: Assessment of Metagenome BinnERs
title_sort amber: assessment of metagenome binners
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022608/
https://www.ncbi.nlm.nih.gov/pubmed/29893851
http://dx.doi.org/10.1093/gigascience/giy069
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