Greengenes2 unifies microbial data in a single reference tree

Daniel McDonald; Yueyu Jiang; Metin Balaban; Kalen Cantrell; Qiyun Zhu; Antonio Gonzalez; James T Morton; Giorgia Nicolaou; Donovan H Parks; Søren M Karst; Mads Albertsen; Philip Hugenholtz; Todd DeSantis; Se Jin Song; Andrew Bartko; Aki S Havulinna; Pekka Jousilahti; Susan Cheng; Michael Inouye; Teemu Niiranen; Mohit Jain; Veikko Salomaa; Leo Lahti; Siavash Mirarab; Rob Knight

doi:10.1038/s41587-023-01845-1

Greengenes2 unifies microbial data in a single reference tree

Nat Biotechnol. 2024 May;42(5):715-718. doi: 10.1038/s41587-023-01845-1. Epub 2023 Jul 27.

Authors

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.

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.

MeSH terms

Bacteria / classification
Bacteria / genetics
Databases, Genetic
Metagenome / genetics
Metagenomics*
Phylogeny*
RNA, Ribosomal, 16S* / genetics

Abstract

MeSH terms

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