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Challenges in benchmarking metagenomic profilers

Nature Methods volume 18, pages 618–626 (2021)Cite this article

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Abstract

Accurate microbial identification and abundance estimation are crucial for metagenomics analysis. Various methods for classification of metagenomic data and estimation of taxonomic profiles, broadly referred to as metagenomic profilers, have been developed. Nevertheless, benchmarking of metagenomic profilers remains challenging because some tools are designed to report relative sequence abundance while others report relative taxonomic abundance. Here we show how misleading conclusions can be drawn by neglecting this distinction between relative abundance types when benchmarking metagenomic profilers. Moreover, we show compelling evidence that interchanging sequence abundance and taxonomic abundance will influence both per-sample summary statistics and cross-sample comparisons. We suggest that the microbiome research community pay attention to potentially misleading biological conclusions arising from this issue when benchmarking metagenomic profilers, by carefully considering the type of abundance data that were analyzed and interpreted and clearly stating the strategy used for metagenomic profiling.

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Fig. 1: Comparison of profiling results.
Fig. 2: Correlation between sequence and taxonomic abundance in synthetic profiles based on different kingdoms.
Fig. 3: Quantitative and qualitative benchmarking results of four representative metagenomic profilers using 25 simulated communities.
Fig. 4: Alpha diversity based on sequence and taxonomic abundance.
Fig. 5: Ordination analyses of simulated profiles based on rJSD.

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Data availability

All simulated datasets can be downloaded from https://figshare.com/projects/Challenges_in_Benchmarking_Metagenomic_Profilers/79916.Source data are provided with this paper.

Code availability

R scripts used in this paper are available at https://github.com/shihuang047/re-benchmarking

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Acknowledgements

Research reported in this publication was supported by grant nos. R01AI141529, R01HD093761, R01AG067744, UH3OD023268, U19AI095219 and U01HL089856 from the National Institutes of Health. This work was also supported by IBM Research through the AI Horizons Network, UC San Diego AI for Healthy Living program in partnership with the UC San Diego Center for Microbiome Innovation.

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Author notes

  1. These authors contributed equally: Zheng Sun, Shi Huang.

Authors and Affiliations

  1. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Zheng Sun & Yang-Yu Liu

  2. Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA

    Shi Huang, Qiyun Zhu, Yoshiki Vázquez-Baeza & Rob Knight

  3. Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, USA

    Shi Huang, Qiyun Zhu, Yoshiki Vázquez-Baeza & Rob Knight

  4. Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, China

    Meng Zhang

  5. IBM T. J. Watson Research Center, Yorktown Heights, NY, USA

    Niina Haiminen & Laxmi Parida

  6. IBM Research Europe, The Hartree Centre, Warrington, UK

    Anna Paola Carrieri

  7. AI and Cognitive Software, IBM Research-Almaden, San Jose, CA, USA

    Ho-Cheol Kim

  8. Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, USA

    Rob Knight

  9. Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA

    Rob Knight

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Contributions

Y.-Y.L. and R.K. conceived and designed the analysis. Z.S. and S.H. led the analysis. M.Z., Q.Z., N.H., A.P.C., Y.V.-B, L.P. and H.-C.K. contributed evaluation strategies. All authors analyzed the results. Z.S., S.H., Y.-Y.L. and R.K. wrote the paper. All authors edited the paper.

Corresponding authors

Correspondence to Rob Knight or Yang-Yu Liu.

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Competing interests

This work received support from IBM Research through the AI Horizons Network. Coauthors N.H., A.P.C., L.P. and H.-C.K. are employees of IBM. The authors declare no other competing interests.

Additional information

Peer review information Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Lin Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

Supplementary Information

Supplementary Note, Figs. 1–6 and references.

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Supplementary Data

An Excel file that includes the source data used in each of the Supplementary Figures.

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Source Data Fig. 1

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Sun, Z., Huang, S., Zhang, M. et al. Challenges in benchmarking metagenomic profilers. Nat Methods 18, 618–626 (2021). https://doi.org/10.1038/s41592-021-01141-3

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  • DOI: https://doi.org/10.1038/s41592-021-01141-3

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