Critical Assessment of Metagenome Interpretation: A benchmark of metagenomics software

Alexander Sczyrba; Peter Hofmann; Peter Belmann; David Koslicki; Stefan Janssen; Johannes Dröge; Ivan Gregor; Stephan Majda; Jessika Fiedler; Eik Dahms; Andreas Bremges; Adrian Fritz; Ruben Garrido-Oter; Tue Sparholt Jørgensen; Nicole Shapiro; Philip D. Blood; Alexey Gurevich; Yang Bai; Dmitrij Turaev; Matthew Z. Demaere; Rayan Chikhi; Niranjan Nagarajan; Christopher Quince; Fernando Meyer; Monika Balvočiutė; Lars Hestbjerg Hansen; Søren J. Sørensen; Burton K.H. Chia; Bertrand Denis; Jeff L. Froula; Zhong Wang; Robert Egan; Dongwan Don Kang; Jeffrey J. Cook; Charles Deltel; Michael Beckstette; Claire Lemaitre; Pierre Peterlongo; Guillaume Rizk; Dominique Lavenier; Yu Wei Wu; Steven W. Singer; Chirag Jain; Marc Strous; Heiner Klingenberg; Peter Meinicke; Michael D. Barton; Thomas Lingner; Hsin Hung Lin; Yu Chieh Liao; Genivaldo Gueiros Z. Silva; Daniel A. Cuevas; Robert A. Edwards; Surya Saha; Vitor C. Piro; Bernhard Y. Renard; Mihai Pop; Hans Peter Klenk; Markus Göker; Nikos C. Kyrpides; Tanja Woyke; Julia A. Vorholt; Paul Schulze-Lefert; Edward M. Rubin; Aaron E. Darling; Thomas Rattei; Alice C. McHardy

doi:10.1038/nmeth.4458

Critical Assessment of Metagenome Interpretation: A benchmark of metagenomics software

Alexander Sczyrba, Peter Hofmann, Peter Belmann, David Koslicki, Stefan Janssen, Johannes Dröge, Ivan Gregor, Stephan Majda, Jessika Fiedler, Eik Dahms, Andreas Bremges, Adrian Fritz, Ruben Garrido-Oter, Tue Sparholt Jørgensen, Nicole Shapiro, Philip D. Blood, Alexey Gurevich, Yang Bai, Dmitrij Turaev, Matthew Z. DemaereRayan Chikhi, Niranjan Nagarajan, Christopher Quince, Fernando Meyer, Monika Balvočiutė, Lars Hestbjerg Hansen, Søren J. Sørensen, Burton K.H. Chia, Bertrand Denis, Jeff L. Froula, Zhong Wang, Robert Egan, Dongwan Don Kang, Jeffrey J. Cook, Charles Deltel, Michael Beckstette, Claire Lemaitre, Pierre Peterlongo, Guillaume Rizk, Dominique Lavenier, Yu Wei Wu, Steven W. Singer, Chirag Jain, Marc Strous, Heiner Klingenberg, Peter Meinicke, Michael D. Barton, Thomas Lingner, Hsin Hung Lin, Yu Chieh Liao, Genivaldo Gueiros Z. Silva, Daniel A. Cuevas, Robert A. Edwards, Surya Saha, Vitor C. Piro, Bernhard Y. Renard, Mihai Pop, Hans Peter Klenk, Markus Göker, Nikos C. Kyrpides, Tanja Woyke, Julia A. Vorholt, Paul Schulze-Lefert, Edward M. Rubin, Aaron E. Darling, Thomas Rattei, Alice C. McHardy

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Abstract

Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.

Original language	English
Pages (from-to)	1063-1071
Number of pages	9
Journal	NATURE METHODS
Volume	14
Issue number	11
DOIs	https://doi.org/10.1038/nmeth.4458
Publication status	Published - Nov 2017
Externally published	Yes

Bibliographical note

This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Keywords

Metagenomics
Classification and taxonomy
Computational biology and bioinformatics
Critical Assessment
Metagenome Interpretation
metagenomics software
Critical Assessment of Metagenome Interpretation
CAMI

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Sczyrba, A., Hofmann, P., Belmann, P., Koslicki, D., Janssen, S., Dröge, J., Gregor, I., Majda, S., Fiedler, J., Dahms, E., Bremges, A., Fritz, A., Garrido-Oter, R., Jørgensen, T. S., Shapiro, N., Blood, P. D., Gurevich, A., Bai, Y., Turaev, D., ... McHardy, A. C. (2017). Critical Assessment of Metagenome Interpretation: A benchmark of metagenomics software. NATURE METHODS, 14(11), 1063-1071. https://doi.org/10.1038/nmeth.4458

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title = "Critical Assessment of Metagenome Interpretation: A benchmark of metagenomics software",

abstract = "Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.",

keywords = "Metagenomics, Classification and taxonomy, Computational biology and bioinformatics, Critical Assessment, Metagenome Interpretation, metagenomics software, Critical Assessment of Metagenome Interpretation, CAMI",

author = "Alexander Sczyrba and Peter Hofmann and Peter Belmann and David Koslicki and Stefan Janssen and Johannes Dr{\"o}ge and Ivan Gregor and Stephan Majda and Jessika Fiedler and Eik Dahms and Andreas Bremges and Adrian Fritz and Ruben Garrido-Oter and J{\o}rgensen, {Tue Sparholt} and Nicole Shapiro and Blood, {Philip D.} and Alexey Gurevich and Yang Bai and Dmitrij Turaev and Demaere, {Matthew Z.} and Rayan Chikhi and Niranjan Nagarajan and Christopher Quince and Fernando Meyer and Monika Balvo{\v c}iutė and Hansen, {Lars Hestbjerg} and S{\o}rensen, {S{\o}ren J.} and Chia, {Burton K.H.} and Bertrand Denis and Froula, {Jeff L.} and Zhong Wang and Robert Egan and {Don Kang}, Dongwan and Cook, {Jeffrey J.} and Charles Deltel and Michael Beckstette and Claire Lemaitre and Pierre Peterlongo and Guillaume Rizk and Dominique Lavenier and Wu, {Yu Wei} and Singer, {Steven W.} and Chirag Jain and Marc Strous and Heiner Klingenberg and Peter Meinicke and Barton, {Michael D.} and Thomas Lingner and Lin, {Hsin Hung} and Liao, {Yu Chieh} and Silva, {Genivaldo Gueiros Z.} and Cuevas, {Daniel A.} and Edwards, {Robert A.} and Surya Saha and Piro, {Vitor C.} and Renard, {Bernhard Y.} and Mihai Pop and Klenk, {Hans Peter} and Markus G{\"o}ker and Kyrpides, {Nikos C.} and Tanja Woyke and Vorholt, {Julia A.} and Paul Schulze-Lefert and Rubin, {Edward M.} and Darling, {Aaron E.} and Thomas Rattei and McHardy, {Alice C.}",

note = "This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. The images or other third party material in this article are included in the article{\textquoteright}s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/",

year = "2017",

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doi = "10.1038/nmeth.4458",

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Sczyrba, A, Hofmann, P, Belmann, P, Koslicki, D, Janssen, S, Dröge, J, Gregor, I, Majda, S, Fiedler, J, Dahms, E, Bremges, A, Fritz, A, Garrido-Oter, R, Jørgensen, TS, Shapiro, N, Blood, PD, Gurevich, A, Bai, Y, Turaev, D, Demaere, MZ, Chikhi, R, Nagarajan, N, Quince, C, Meyer, F, Balvočiutė, M, Hansen, LH, Sørensen, SJ, Chia, BKH, Denis, B, Froula, JL, Wang, Z, Egan, R, Don Kang, D, Cook, JJ, Deltel, C, Beckstette, M, Lemaitre, C, Peterlongo, P, Rizk, G, Lavenier, D, Wu, YW, Singer, SW, Jain, C, Strous, M, Klingenberg, H, Meinicke, P, Barton, MD, Lingner, T, Lin, HH, Liao, YC, Silva, GGZ, Cuevas, DA, Edwards, RA, Saha, S, Piro, VC, Renard, BY, Pop, M, Klenk, HP, Göker, M, Kyrpides, NC, Woyke, T, Vorholt, JA, Schulze-Lefert, P, Rubin, EM, Darling, AE, Rattei, T & McHardy, AC 2017, 'Critical Assessment of Metagenome Interpretation: A benchmark of metagenomics software', NATURE METHODS, vol. 14, no. 11, pp. 1063-1071. https://doi.org/10.1038/nmeth.4458

TY - JOUR

T1 - Critical Assessment of Metagenome Interpretation

T2 - A benchmark of metagenomics software

AU - Sczyrba, Alexander

AU - Hofmann, Peter

AU - Belmann, Peter

AU - Koslicki, David

AU - Janssen, Stefan

AU - Dröge, Johannes

AU - Gregor, Ivan

AU - Majda, Stephan

AU - Fiedler, Jessika

AU - Dahms, Eik

AU - Bremges, Andreas

AU - Fritz, Adrian

AU - Garrido-Oter, Ruben

AU - Jørgensen, Tue Sparholt

AU - Shapiro, Nicole

AU - Blood, Philip D.

AU - Gurevich, Alexey

AU - Bai, Yang

AU - Turaev, Dmitrij

AU - Demaere, Matthew Z.

AU - Chikhi, Rayan

AU - Nagarajan, Niranjan

AU - Quince, Christopher

AU - Meyer, Fernando

AU - Balvočiutė, Monika

AU - Hansen, Lars Hestbjerg

AU - Sørensen, Søren J.

AU - Chia, Burton K.H.

AU - Denis, Bertrand

AU - Froula, Jeff L.

AU - Wang, Zhong

AU - Egan, Robert

AU - Don Kang, Dongwan

AU - Cook, Jeffrey J.

AU - Deltel, Charles

AU - Beckstette, Michael

AU - Lemaitre, Claire

AU - Peterlongo, Pierre

AU - Rizk, Guillaume

AU - Lavenier, Dominique

AU - Wu, Yu Wei

AU - Singer, Steven W.

AU - Jain, Chirag

AU - Strous, Marc

AU - Klingenberg, Heiner

AU - Meinicke, Peter

AU - Barton, Michael D.

AU - Lingner, Thomas

AU - Lin, Hsin Hung

AU - Liao, Yu Chieh

AU - Silva, Genivaldo Gueiros Z.

AU - Cuevas, Daniel A.

AU - Edwards, Robert A.

AU - Saha, Surya

AU - Piro, Vitor C.

AU - Renard, Bernhard Y.

AU - Pop, Mihai

AU - Klenk, Hans Peter

AU - Göker, Markus

AU - Kyrpides, Nikos C.

AU - Woyke, Tanja

AU - Vorholt, Julia A.

AU - Schulze-Lefert, Paul

AU - Rubin, Edward M.

AU - Darling, Aaron E.

AU - Rattei, Thomas

AU - McHardy, Alice C.

N1 - This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

PY - 2017/11

Y1 - 2017/11

N2 - Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.

AB - Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.

KW - Metagenomics

KW - Classification and taxonomy

KW - Computational biology and bioinformatics

KW - Critical Assessment

KW - Metagenome Interpretation

KW - metagenomics software

KW - Critical Assessment of Metagenome Interpretation

KW - CAMI

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UR - http://purl.org/au-research/grants/ARC/LP150100912

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DO - 10.1038/nmeth.4458

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EP - 1071

JO - NATURE METHODS

JF - NATURE METHODS

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ER -

Critical Assessment of Metagenome Interpretation: A benchmark of metagenomics software

Abstract

Bibliographical note

Keywords

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