Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software
Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S, Dröge J, Gregor I, Majda S, Fiedler J, Dahms E, Bremges A, et al. (2017)
Nature Methods 14(11): 1063-1071.
Zeitschriftenaufsatz
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Autor*in
Sczyrba, AlexanderUniBi 
;
Hofmann, Peter;
Belmann, PeterUniBi;
Koslicki, David;
Janssen, StefanUniBi ;
Dröge, Johannes;
Gregor, Ivan;
Majda, Stephan;
Fiedler, Jessika;
Dahms, Eik;
Bremges, AndreasUniBi ;
Fritz, Adrian
Alle
;
Hofmann, Peter;
Belmann, PeterUniBi;
Koslicki, David;
Janssen, StefanUniBi ;
Dröge, Johannes;
Gregor, Ivan;
Majda, Stephan;
Fiedler, Jessika;
Dahms, Eik;
Bremges, AndreasUniBi ;
Fritz, Adrian
Alle
Einrichtung
Abstract / Bemerkung
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.
Erscheinungsjahr
2017
Zeitschriftentitel
Nature Methods
Band
14
Ausgabe
11
Seite(n)
1063-1071
ISSN
1548-7091, 1548-7105
Page URI
https://pub.uni-bielefeld.de/record/2914367
Zitieren
Sczyrba A, Hofmann P, Belmann P, et al. Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software. Nature Methods. 2017;14(11):1063-1071.
Sczyrba, A., Hofmann, P., Belmann, P., Koslicki, D., Janssen, S., Dröge, J., Gregor, I., et al. (2017). Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software. Nature Methods, 14(11), 1063-1071. https://doi.org/10.1038/nmeth.4458
Sczyrba, Alexander, Hofmann, Peter, Belmann, Peter, Koslicki, David, Janssen, Stefan, Dröge, Johannes, Gregor, Ivan, et al. 2017. “Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software”. Nature Methods 14 (11): 1063-1071.
Sczyrba, A., Hofmann, P., Belmann, P., Koslicki, D., Janssen, S., Dröge, J., Gregor, I., Majda, S., Fiedler, J., Dahms, E., et al. (2017). Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software. Nature Methods 14, 1063-1071.
Sczyrba, A., et al., 2017. Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software. Nature Methods, 14(11), p 1063-1071.
A. Sczyrba, et al., “Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software”, Nature Methods, vol. 14, 2017, pp. 1063-1071.
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., DeMaere, M.Z., Chikhi, R., Nagarajan, N., Quince, C., Meyer, F., Balvočiūtė, M., Hansen, L.H., Sørensen, S.J., Chia, B.K.H., Denis, B., Froula, J.L., Wang, Z., Egan, R., Don Kang, D., Cook, J.J., Deltel, C., Beckstette, M., Lemaitre, C., Peterlongo, P., Rizk, G., Lavenier, D., Wu, Y.-W., Singer, S.W., Jain, C., Strous, M., Klingenberg, H., Meinicke, P., Barton, M.D., Lingner, T., Lin, H.-H., Liao, Y.-C., Silva, G.G.Z., Cuevas, D.A., Edwards, R.A., Saha, S., Piro, V.C., Renard, B.Y., Pop, M., Klenk, H.-P., Göker, M., Kyrpides, N.C., Woyke, T., Vorholt, J.A., Schulze-Lefert, P., Rubin, E.M., Darling, A.E., Rattei, T., McHardy, A.C.: Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software. Nature Methods. 14, 1063-1071 (2017).
Sczyrba, Alexander, Hofmann, Peter, Belmann, Peter, Koslicki, David, Janssen, Stefan, Dröge, Johannes, Gregor, Ivan, Majda, Stephan, Fiedler, Jessika, Dahms, Eik, Bremges, Andreas, Fritz, Adrian, Garrido-Oter, Ruben, Jørgensen, Tue Sparholt, Shapiro, Nicole, Blood, Philip D, Gurevich, Alexey, Bai, Yang, Turaev, Dmitrij, DeMaere, Matthew Z, Chikhi, Rayan, Nagarajan, Niranjan, Quince, Christopher, Meyer, Fernando, Balvočiūtė, Monika, Hansen, Lars Hestbjerg, Sørensen, Søren J, Chia, Burton K H, Denis, Bertrand, Froula, Jeff L, Wang, Zhong, Egan, Robert, Don Kang, Dongwan, Cook, Jeffrey J, Deltel, Charles, Beckstette, Michael, Lemaitre, Claire, Peterlongo, Pierre, Rizk, Guillaume, Lavenier, Dominique, Wu, Yu-Wei, Singer, Steven W, Jain, Chirag, Strous, Marc, Klingenberg, Heiner, Meinicke, Peter, Barton, Michael D, Lingner, Thomas, Lin, Hsin-Hung, Liao, Yu-Chieh, Silva, Genivaldo Gueiros Z, Cuevas, Daniel A, Edwards, Robert A, Saha, Surya, Piro, Vitor C, Renard, Bernhard Y, Pop, Mihai, Klenk, Hans-Peter, Göker, Markus, Kyrpides, Nikos C, Woyke, Tanja, Vorholt, Julia A, Schulze-Lefert, Paul, Rubin, Edward M, Darling, Aaron E, Rattei, Thomas, and McHardy, Alice C. “Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software”. Nature Methods 14.11 (2017): 1063-1071.
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Daten bereitgestellt von European Bioinformatics Institute (EBI)
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