Critical Assessment of Metagenome Interpretation: the second round of challenges

Fernando Meyer^#^{1

2}, Adrian Fritz^#^{1

2

3}, Zhi-Luo Deng^{1

2

4}, David Koslicki⁵, Till Robin Lesker^{3

6}, Alexey Gurevich⁷, Gary Robertson^{1

2}, Mohammed Alser⁸, Dmitry Antipov⁹, Francesco Beghini¹⁰, Denis Bertrand¹¹, Jaqueline J Brito¹², C Titus Brown¹³, Jan Buchmann¹⁴, Aydin Buluç^{15

16}, Bo Chen^{15

16}, Rayan Chikhi¹⁷, Philip T L C Clausen¹⁸, Alexandru Cristian^{19

20}, Piotr Wojciech Dabrowski^{21

22}, Aaron E Darling²³, Rob Egan^{24

25}, Eleazar Eskin²⁶, Evangelos Georganas²⁷, Eugene Goltsman^{24

25}, Melissa A Gray^{19

28}, Lars Hestbjerg Hansen²⁹, Steven Hofmeyr^{15

16}, Pingqin Huang³⁰, Luiz Irber¹³, Huijue Jia^{31

32}, Tue Sparholt Jørgensen^{33

34}, Silas D Kieser^{35

36}, Terje Klemetsen³⁷, Axel Kola³⁸, Mikhail Kolmogorov³⁹, Anton Korobeynikov^{9

40}, Jason Kwan⁴¹, Nathan LaPierre²⁶, Claire Lemaitre⁴², Chenhao Li¹¹, Antoine Limasset⁴³, Fabio Malcher-Miranda⁴⁴, Serghei Mangul¹², Vanessa R Marcelino^{45

46}, Camille Marchet⁴³, Pierre Marijon⁴⁷, Dmitry Meleshko⁹, Daniel R Mende⁴⁸, Alessio Milanese^{49

50}, Niranjan Nagarajan^{51

52}, Jakob Nissen⁵³, Sergey Nurk⁵⁴, Leonid Oliker^{15

16}, Lucas Paoli⁴⁹, Pierre Peterlongo⁴², Vitor C Piro⁴⁴, Jacob S Porter⁵⁵, Simon Rasmussen⁵⁶, Evan R Rees⁴¹, Knut Reinert⁵⁷, Bernhard Renard^{44

58}, Espen Mikal Robertsen³⁷, Gail L Rosen^{19

28

59}, Hans-Joachim Ruscheweyh⁴⁹, Varuni Sarwal²⁶, Nicola Segata¹⁰, Enrico Seiler⁵⁷, Lizhen Shi⁶⁰, Fengzhu Sun⁶¹, Shinichi Sunagawa⁴⁹, Søren Johannes Sørensen⁶², Ashleigh Thomas^{24

63}, Chengxuan Tong¹¹, Mirko Trajkovski^{35

64}, Julien Tremblay⁶⁵, Gherman Uritskiy⁶⁶, Riccardo Vicedomini¹⁷, Zhengyang Wang³⁰, Ziye Wang⁶⁷, Zhong Wang^{68

69

70}, Andrew Warren⁵⁵, Nils Peder Willassen³⁷, Katherine Yelick^{15

16}, Ronghui You³⁰, Georg Zeller⁵⁰, Zhengqiao Zhao¹⁹, Shanfeng Zhu^{71

72}, Jie Zhu^{31

32}, Ruben Garrido-Oter⁷³, Petra Gastmeier³⁸, Stephane Hacquard⁷³, Susanne Häußler⁶, Ariane Khaledi⁶, Friederike Maechler³⁸, Fantin Mesny⁷³, Simona Radutoiu⁷⁴, Paul Schulze-Lefert⁷³, Nathiana Smit⁶, Till Strowig⁶, Andreas Bremges^{1

3}, Alexander Sczyrba⁷⁵, Alice Carolyn McHardy^{76

77

78

79}

Affiliations

¹ Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany.
² Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany.
³ German Center for Infection Research (DZIF), Hannover-Braunschweig Site, Braunschweig, Germany.
⁴ Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, Hannover, Germany.
⁵ Pennsylvania State University, State College, PA, USA.
⁶ Helmholtz Centre for Infection Research, Braunschweig, Germany.
⁷ Saint Petersburg State University, Saint Petersburg, Russia.
⁸ Department of Information Technology and Electrical Engineering, ETH Zürich, Zurich, Switzerland.
⁹ Center for Algorithmic Biotechnology, Saint Petersburg State University, Saint Petersburg, Russia.
¹⁰ Department CIBIO, University of Trento, Trento, Italy.
¹¹ Genome Institute of Singapore, Singapore, Singapore.
¹² University of Southern California, Los Angeles, CA, USA.
¹³ University of California, Davis, Davis, CA, USA.
¹⁴ Institute for Biological Data Science, Heinrich-Heine-University, Düsseldorf, Germany.
¹⁵ Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
¹⁶ University of California, Berkeley, Berkeley, CA, USA.
¹⁷ Institut Pasteur, Paris, France.
¹⁸ National Food Institute, Division of Global Surveillance, Technical University of Denmark, Lyngby, Denmark.
¹⁹ Drexel University, Philadelphia, PA, USA.
²⁰ Google Inc., Philadelphia, PA, USA.
²¹ Robert Koch-Institut, Berlin, Germany.
²² Hochschule für Technik und Wirtschaft Berlin, Berlin, Germany.
²³ University of Technology Sydney, Sydney, Australia.
²⁴ DOE Joint Genome Institute, Berkeley, CA, USA.
²⁵ Lawrence Berkeley National Laboratories, Berkeley, CA, USA.
²⁶ University of California, Los Angeles, Los Angeles, CA, USA.
²⁷ Intel Corporation, Santa Clara, CA, USA.
²⁸ Ecological and Evolutionary Signal-Processing and Informatics Laboratory, Philadelphia, PA, USA.
²⁹ University of Copenhagen, Department of Plant and Environmental Science, Frederiksberg, Denmark.
³⁰ School of Computer Science, Fudan University, Shanghai, China.
³¹ BGI-Shenzhen, Shenzhen, China.
³² Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, BGI-Shenzhen, Shenzhen, China.
³³ Technical University of Denmark, Novo Nordisk Foundation Center for Biosustainability, Lyngby, Denmark.
³⁴ Aarhus University, Department of Environmental Science, Roskilde, Denmark.
³⁵ Department of Cell Physiology and Metabolism, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
³⁶ Swiss Institute of Bioinformatics, Geneva, Switzerland.
³⁷ The Arctic University of Norway, Tromsø, Norway.
³⁸ Charité-Universitätsmedizin Berlin, Berlin, Germany.
³⁹ Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA.
⁴⁰ Department of Statistical Modelling, Saint Petersburg State University, Saint Petersburg, Russia.
⁴¹ University of Wisconsin-Madison, Madison, WI, USA.
⁴² Univ. Rennes, Inria, CNRS, IRISA, Rennes, France.
⁴³ Université Lille, CNRS, CRIStAL, Lille, France.
⁴⁴ Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany.
⁴⁵ Sydney Medical School, The University of Sydney, Sydney, Australia.
⁴⁶ Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, Australia.
⁴⁷ Department of Computer Science, Inria, University of Lille, CNRS, Lille, France.
⁴⁸ Amsterdam University Medical Center, Amsterdam, the Netherlands.
⁴⁹ Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich, Switzerland.
⁵⁰ Structural and Computational Biology Unit, EMBL, Heidelberg, Germany.
⁵¹ Genome Institute of Singapore, A*STAR, Singapore, Singapore.
⁵² National University of Singapore, Singapore, Singapore.
⁵³ DTU Health Tech, Kongens, Lyngby, Denmark.
⁵⁴ Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
⁵⁵ University of Virginia, Charlottesville, VA, USA.
⁵⁶ Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
⁵⁷ Institute for Bioinformatics, FU Berlin, Berlin, Germany.
⁵⁸ Bioinformatics Unit (MF1), Robert Koch Institute, Berlin, Germany.
⁵⁹ Center for Biological Discovery from Big Data, Philadelphia, PA, USA.
⁶⁰ Florida Polytechnic University, Lakeland, FL, USA.
⁶¹ Quantitative and Computational Biology Department, University of Southern California, Los Angeles, CA, USA.
⁶² University of Copenhagen, Copenhagen, Denmark.
⁶³ University of British Columbia, Vancouver, British Columbia, Canada.
⁶⁴ Diabetes Center, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
⁶⁵ Energy, Mining and Environment, National Research Council Canada, Montreal, Quebec, Canada.
⁶⁶ Phase Genomics, Seattle, WA, USA.
⁶⁷ School of Mathematical Sciences, Fudan University, Shanghai, China.
⁶⁸ Department of Energy Joint Genome Institute, Berkeley, CA, USA.
⁶⁹ Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
⁷⁰ School of Natural Sciences, University of California at Merced, Merced, CA, USA.
⁷¹ Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
⁷² Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
⁷³ Max Planck Institute for Plant Breeding Research, Köln, Germany.
⁷⁴ Aarhus University, Aarhus, Denmark.
⁷⁵ Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany.
⁷⁶ Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany. amc14@helmholtz-hzi.de.
⁷⁷ Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany. amc14@helmholtz-hzi.de.
⁷⁸ German Center for Infection Research (DZIF), Hannover-Braunschweig Site, Braunschweig, Germany. amc14@helmholtz-hzi.de.
⁷⁹ Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, Hannover, Germany. amc14@helmholtz-hzi.de.

^# Contributed equally.

PMID: 35396482
PMCID: PMC9007738
DOI: 10.1038/s41592-022-01431-4

Critical Assessment of Metagenome Interpretation: the second round of challenges

Fernando Meyer et al. Nat Methods. 2022 Apr.

. 2022 Apr;19(4):429-440.

doi: 10.1038/s41592-022-01431-4. Epub 2022 Apr 8.

Authors

Affiliations

¹ Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany.
² Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany.
³ German Center for Infection Research (DZIF), Hannover-Braunschweig Site, Braunschweig, Germany.
⁴ Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, Hannover, Germany.
⁵ Pennsylvania State University, State College, PA, USA.
⁶ Helmholtz Centre for Infection Research, Braunschweig, Germany.
⁷ Saint Petersburg State University, Saint Petersburg, Russia.
⁸ Department of Information Technology and Electrical Engineering, ETH Zürich, Zurich, Switzerland.
⁹ Center for Algorithmic Biotechnology, Saint Petersburg State University, Saint Petersburg, Russia.
¹⁰ Department CIBIO, University of Trento, Trento, Italy.
¹¹ Genome Institute of Singapore, Singapore, Singapore.
¹² University of Southern California, Los Angeles, CA, USA.
¹³ University of California, Davis, Davis, CA, USA.
¹⁴ Institute for Biological Data Science, Heinrich-Heine-University, Düsseldorf, Germany.
¹⁵ Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
¹⁶ University of California, Berkeley, Berkeley, CA, USA.
¹⁷ Institut Pasteur, Paris, France.
¹⁸ National Food Institute, Division of Global Surveillance, Technical University of Denmark, Lyngby, Denmark.
¹⁹ Drexel University, Philadelphia, PA, USA.
²⁰ Google Inc., Philadelphia, PA, USA.
²¹ Robert Koch-Institut, Berlin, Germany.
²² Hochschule für Technik und Wirtschaft Berlin, Berlin, Germany.
²³ University of Technology Sydney, Sydney, Australia.
²⁴ DOE Joint Genome Institute, Berkeley, CA, USA.
²⁵ Lawrence Berkeley National Laboratories, Berkeley, CA, USA.
²⁶ University of California, Los Angeles, Los Angeles, CA, USA.
²⁷ Intel Corporation, Santa Clara, CA, USA.
²⁸ Ecological and Evolutionary Signal-Processing and Informatics Laboratory, Philadelphia, PA, USA.
²⁹ University of Copenhagen, Department of Plant and Environmental Science, Frederiksberg, Denmark.
³⁰ School of Computer Science, Fudan University, Shanghai, China.
³¹ BGI-Shenzhen, Shenzhen, China.
³² Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, BGI-Shenzhen, Shenzhen, China.
³³ Technical University of Denmark, Novo Nordisk Foundation Center for Biosustainability, Lyngby, Denmark.
³⁴ Aarhus University, Department of Environmental Science, Roskilde, Denmark.
³⁵ Department of Cell Physiology and Metabolism, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
³⁶ Swiss Institute of Bioinformatics, Geneva, Switzerland.
³⁷ The Arctic University of Norway, Tromsø, Norway.
³⁸ Charité-Universitätsmedizin Berlin, Berlin, Germany.
³⁹ Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA.
⁴⁰ Department of Statistical Modelling, Saint Petersburg State University, Saint Petersburg, Russia.
⁴¹ University of Wisconsin-Madison, Madison, WI, USA.
⁴² Univ. Rennes, Inria, CNRS, IRISA, Rennes, France.
⁴³ Université Lille, CNRS, CRIStAL, Lille, France.
⁴⁴ Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany.
⁴⁵ Sydney Medical School, The University of Sydney, Sydney, Australia.
⁴⁶ Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, Australia.
⁴⁷ Department of Computer Science, Inria, University of Lille, CNRS, Lille, France.
⁴⁸ Amsterdam University Medical Center, Amsterdam, the Netherlands.
⁴⁹ Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich, Switzerland.
⁵⁰ Structural and Computational Biology Unit, EMBL, Heidelberg, Germany.
⁵¹ Genome Institute of Singapore, A*STAR, Singapore, Singapore.
⁵² National University of Singapore, Singapore, Singapore.
⁵³ DTU Health Tech, Kongens, Lyngby, Denmark.
⁵⁴ Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
⁵⁵ University of Virginia, Charlottesville, VA, USA.
⁵⁶ Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
⁵⁷ Institute for Bioinformatics, FU Berlin, Berlin, Germany.
⁵⁸ Bioinformatics Unit (MF1), Robert Koch Institute, Berlin, Germany.
⁵⁹ Center for Biological Discovery from Big Data, Philadelphia, PA, USA.
⁶⁰ Florida Polytechnic University, Lakeland, FL, USA.
⁶¹ Quantitative and Computational Biology Department, University of Southern California, Los Angeles, CA, USA.
⁶² University of Copenhagen, Copenhagen, Denmark.
⁶³ University of British Columbia, Vancouver, British Columbia, Canada.
⁶⁴ Diabetes Center, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
⁶⁵ Energy, Mining and Environment, National Research Council Canada, Montreal, Quebec, Canada.
⁶⁶ Phase Genomics, Seattle, WA, USA.
⁶⁷ School of Mathematical Sciences, Fudan University, Shanghai, China.
⁶⁸ Department of Energy Joint Genome Institute, Berkeley, CA, USA.
⁶⁹ Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
⁷⁰ School of Natural Sciences, University of California at Merced, Merced, CA, USA.
⁷¹ Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
⁷² Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
⁷³ Max Planck Institute for Plant Breeding Research, Köln, Germany.
⁷⁴ Aarhus University, Aarhus, Denmark.
⁷⁵ Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany.
⁷⁶ Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany. amc14@helmholtz-hzi.de.
⁷⁷ Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany. amc14@helmholtz-hzi.de.
⁷⁸ German Center for Infection Research (DZIF), Hannover-Braunschweig Site, Braunschweig, Germany. amc14@helmholtz-hzi.de.
⁷⁹ Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, Hannover, Germany. amc14@helmholtz-hzi.de.

^# Contributed equally.

PMID: 35396482
PMCID: PMC9007738
DOI: 10.1038/s41592-022-01431-4

Abstract

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

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Conflict of interest statement

A.E.D. cofounded Longas Technologies Pty Ltd, a company aimed at development of synthetic long-read sequencing technologies, and is employed by Illumina Australia Pty Ltd. A.C. is employed by Google LLC. L.I. is employed by 10X Genomics. E.R.R. conducted an internship at Empress Therapeutics. E. Georganas is employed by Intel Corporation. G.U. is employed by Amazon.com, Inc. The remaining authors declare no competing interests.

Figures

**Fig. 1. Metagenome assembler performances on the marine and strain-madness datasets.**
a, Radar plots of genome fraction. b, Mismatches per 100 kilobases (kb). c, Misassemblies. d, NGA50. e, Strain recall. f, Strain precision. For methods with multiple evaluated versions, the best ranked version on the marine data is shown (Supplementary Fig. 1 and Supplementary Table 3). Absolute values for metrics are log scaled. Lines indicate different subsets of genomes analyzed, and the value of the GSAs indicates the upper bound for a metric. The metrics are shown for both unique and common strain genomes. g, Genome recovery fraction versus genome sequencing depth (coverage) on the marine dataset. Blue indicates unique genomes (<95% ANI), green common genomes (ANI ≥ 95%) and orange high-copy circular elements. Gray lines indicate the coverage at which the first genome is recovered with ≥90% genome fraction. Source data

**Fig. 2. Performance of genome binners on short-read assemblies (GSA and MA, MEGAHIT) of the marine, strain-madness, and plant-associated data.**
a, Boxplots of average completeness, purity, ARI, percentage of binned bp and fraction of genomes recovered with moderate or higher quality (>50% completeness, <10% contamination) across methods from each dataset (Methods). Arrows indicate the average. b–g, Boxplots of completeness per genome and purity per bin, and bar charts of ARI, binned bp and moderate or higher quality genomes recovered, by method, for each dataset: marine GSA (b), marine MA (c), strain-madness GSA (d), strain-madness MA (e), plant-associated GSA (f) and plant-associated MA (g). The submission with the highest F1-score per method on a dataset is shown (Supplementary Tables 9–15). Boxes in boxplots indicate the interquartile range of n results, the center line the median and arrows the average. Whiskers extend to 1.5 × interquartile range or to the maximum and minimum if there is no outlier. Outliers are results represented as points outside 1.5 × interquartile range above the upper quartile and below the lower quartile. Source data

**Fig. 3. Taxonomic binning performance across ranks per dataset.**
a, Marine. b, Strain-madness. c, Plant-associated. Metrics were computed over unfiltered (solid lines) and 1%-filtered (that is, without the 1% smallest bins in bp, dashed lines) predicted bins of short reads (SR), long reads (LR) and contigs of the GSA. Shaded bands show the standard error across bins. Source data

**Fig. 4. Taxonomic profiling results for the marine and strain-madness datasets at genus level.**
a,b, Marine datasets. c,d, Strain-madness datasets. Results are shown for the overall best ranked submission per software version (Supplementary Tables 33 and 35, and Supplementary Fig. 12). a,c, Purity versus completeness. b,d, Upper bound of L1 norm (2) minus actual L1 norm versus upper bound of weighted UniFrac error (16) minus actual weighted UniFrac error. Symbols indicate the mean over ten marine and 100 strain-madness samples, respectively, and error bars the standard deviation. Metrics were determined using OPAL with default settings. Source data

**Fig. 5. Computational requirements of software from all categories.**
a, Runtime. b, Maximum memory usage. Results are reported for the marine and strain-madness read data or GSAs (Supplementary Table 40). The x axes are log scaled and the numbers given are the software version numbers. Source data

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References

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Critical Assessment of Metagenome Interpretation: the second round of challenges

Affiliations

Critical Assessment of Metagenome Interpretation: the second round of challenges

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

LinkOut - more resources

Full Text Sources