Greengenes2 unifies microbial data in a single reference tree

Daniel McDonald¹, Yueyu Jiang², Metin Balaban³, Kalen Cantrell⁴, Qiyun Zhu^{5

6}, Antonio Gonzalez¹, James T Morton⁷, Giorgia Nicolaou⁸, Donovan H Parks⁹, Søren M Karst¹⁰, Mads Albertsen¹¹, Philip Hugenholtz⁹, Todd DeSantis¹², Se Jin Song¹³, Andrew Bartko¹³, Aki S Havulinna^{14

15}, Pekka Jousilahti¹⁴, Susan Cheng^{16

17}, Michael Inouye^{18

19}, Teemu Niiranen^{14

20}, Mohit Jain²¹, Veikko Salomaa¹⁴, Leo Lahti²², Siavash Mirarab², Rob Knight^{23

24

25

26}

Affiliations

¹ Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA.
² Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
³ Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
⁴ Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
⁵ School of Life Sciences, Arizona State University, Tempe, AZ, USA.
⁶ Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA.
⁷ Biostatistics & Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
⁸ Halicioglu Data Science Institute, University of California San Diego, La Jolla, CA, USA.
⁹ Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Queensland, Australia.
¹⁰ Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA.
¹¹ Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark.
¹² Department of Informatics, Second Genome, Brisbane, CA, USA.
¹³ Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
¹⁴ Finnish Institute for Health and Welfare, Helsinki, Finland.
¹⁵ Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland.
¹⁶ Division of Cardiology, Brigham and Women's Hospital, Boston, MA, USA.
¹⁷ Cedars-Sinai Medical Center, Los Angeles, CA, USA.
¹⁸ Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
¹⁹ Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
²⁰ Division of Medicine, Turku University Hospital and University of Turku, Turku, Finland.
²¹ Sapient Bioanalytics, LLC, San Diego, CA, USA.
²² Department of Computing, University of Turku, Turku, Finland.
²³ Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA. robknight@eng.ucsd.edu.
²⁴ Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA. robknight@eng.ucsd.edu.
²⁵ Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA. robknight@eng.ucsd.edu.
²⁶ Department of Bioengineering, University of California San Diego, La Jolla, CA, USA. robknight@eng.ucsd.edu.

PMID: 37500913
PMCID: PMC10818020
DOI: 10.1038/s41587-023-01845-1

Greengenes2 unifies microbial data in a single reference tree

Daniel McDonald et al. Nat Biotechnol. 2024 May.

. 2024 May;42(5):715-718.

doi: 10.1038/s41587-023-01845-1. Epub 2023 Jul 27.

Authors

Affiliations

¹ Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA.
² Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
³ Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
⁴ Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
⁵ School of Life Sciences, Arizona State University, Tempe, AZ, USA.
⁶ Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA.
⁷ Biostatistics & Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
⁸ Halicioglu Data Science Institute, University of California San Diego, La Jolla, CA, USA.
⁹ Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Queensland, Australia.
¹⁰ Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA.
¹¹ Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark.
¹² Department of Informatics, Second Genome, Brisbane, CA, USA.
¹³ Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
¹⁴ Finnish Institute for Health and Welfare, Helsinki, Finland.
¹⁵ Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland.
¹⁶ Division of Cardiology, Brigham and Women's Hospital, Boston, MA, USA.
¹⁷ Cedars-Sinai Medical Center, Los Angeles, CA, USA.
¹⁸ Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
¹⁹ Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
²⁰ Division of Medicine, Turku University Hospital and University of Turku, Turku, Finland.
²¹ Sapient Bioanalytics, LLC, San Diego, CA, USA.
²² Department of Computing, University of Turku, Turku, Finland.
²³ Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA. robknight@eng.ucsd.edu.
²⁴ Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA. robknight@eng.ucsd.edu.
²⁵ Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA. robknight@eng.ucsd.edu.
²⁶ Department of Bioengineering, University of California San Diego, La Jolla, CA, USA. robknight@eng.ucsd.edu.

PMID: 37500913
PMCID: PMC10818020
DOI: 10.1038/s41587-023-01845-1

Erratum in

Author Correction: Greengenes2 unifies microbial data in a single reference tree.
McDonald D, Jiang Y, Balaban M, Cantrell K, Zhu Q, Gonzalez A, Morton JT, Nicolaou G, Parks DH, Karst SM, Albertsen M, Hugenholtz P, DeSantis T, Song SJ, Bartko A, Havulinna AS, Jousilahti P, Cheng S, Inouye M, Niiranen T, Jain M, Salomaa V, Lahti L, Mirarab S, Knight R. McDonald D, et al. Nat Biotechnol. 2024 May;42(5):813. doi: 10.1038/s41587-023-02026-w. Nat Biotechnol. 2024. PMID: 37853258 Free PMC article. No abstract available.

Abstract

Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR amplification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree.

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Conflict of interest statement

D.M. is a consultant for BiomeSense, Inc., has equity and receives income. The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. R.K. is a scientific advisory board member, and consultant for BiomeSense, Inc., has equity and receives income. He is a scientific advisory board member and has equity in GenCirq. He is a consultant and scientific advisory board member for DayTwo, and receives income. He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc., and has equity. He is a cofounder of Micronoma, and has equity and is a scientific advisory board member. The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. The remaining authors declare no competing interests.

Figures

**Fig. 1. Greengenes2 overview and harmonization of 16S rRNA ASVs with shotgun metagenomic data.**
a, The Greengenes2 phylogeny rendered using Empress, with ASV multifurcations collapsed; tip color indicates representation in the American Gut Project (AGP), the EMP, both or neither, with the top 20 represented phyla depicted in the outer bar. b, The same collapsed phylogeny colored by the presence or absence of the best BLAST hit from SILVA 138. The bar depicts the same coloring as the tips. c, EMP samples and the amount of novel branch length (normalized by the total backbone branch length) added to the tree through ASV fragment placement. Note that sample counts are not even across EMPO3 categories. d, Bray–Curtis applied to paired 16S V4 rRNA ASVs and whole-genome shotgun samples from THDMI subset of The Microsetta Initiative; PC, principal coordinate. e, Same data as d but computing Bray–Curtis on collapsed genus data. f, Same data as d and e but using weighted UniFrac at the ASV and genome identifier levels. Source data

**Fig. 2. Taxonomic and effect size consistency between 16S rRNA ASVs and shotgun metagenomic data.**
a–c, Per-sample taxonomy comparisons between 16S and whole-genome shotgun profiles from THDMI. The solid bar depicts the 50th percentile, and the dashed lines are 25th and 75th percentiles. a, Assessment of 16S taxonomy with SILVA 138 using the default q2-feature-classifier naive Bayes model (note, SILVA does not annotate at the species level); GG2, Greengenes2. b, Assessment of 16S taxonomy with Greengenes 13_8 (GG13_8) using the default q2-feature-classifier naive Bayes model. c, Assessment of 16S taxonomy performed by reading the lineages directly from the phylogeny or through naive Bayes trained on the V4 regions of the Greengenes2 backbone. d,e, Effect size calculations performed with Evident on paired 16S and whole-genome shotgun samples from THDMI. Calculations were performed at maximal resolution using ASVs for 16S and genome identifiers for shotgun samples. The data represented here are human gut microbiome samples. The stars denote variables that are drawn out specifically in the plot (for example, population) and were arbitrarily selected as comparison points to help highlight differences between d and e. Bray–Curtis distances (d) and weighted normalized UniFrac (e) are shown. Source data

See this image and copyright information in PMC

References

1. Zhu Q, et al. Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains Bacteria and Archaea. Nat. Commun. 2019;10:5477. doi: 10.1038/s41467-019-13443-4. - DOI - PMC - PubMed
1. Parks DH, et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res. 2022;50:D785–D794. doi: 10.1093/nar/gkab776. - DOI - PMC - PubMed
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1. McDonald D, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of Bacteria and Archaea. ISME J. 2012;6:610–618. doi: 10.1038/ismej.2011.139. - DOI - PMC - PubMed
1. Balaban, M. et al. Generation of accurate, expandable phylogenomic trees with uDANCE. Nat. Biotechnol.10.1038/s41587-023-01868-8 (2023). - PMC - PubMed

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Greengenes2 unifies microbial data in a single reference tree

Affiliations

Greengenes2 unifies microbial data in a single reference tree

Authors

Affiliations

Erratum in

Abstract

Conflict of interest statement

Figures

References

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources