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Greengenes2 unifies microbial data in a single reference tree


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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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Nature Biotechnology
nature biotechnology
Brief Communication
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. Parks  9, Søren M. Karst10, Mads Albertsen  11,
Philip Hugenholtz  9, Todd DeSantis12, Se Jin Song13, Andrew Bartko  13,
Aki S. Havulinna  14,15, Pekka Jousilahti14, Susan Cheng16,17, Michael Inouye18,19,
Teemu Niiranen14,20, Mohit Jain21, Veikko Salomaa  14, Leo Lahti22,
Siavash Mirarab  2 & Rob Knight  1,4,13,23
Studies using 16S rRNA and shotgun metagenomics typically yield
dierent results, usually attributed to PCR amplication biases. We
introduce Greengenes2, a reference tree that unies 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 eect size when analyzed
with the same tree.
Shotgun metagenomics and 16S rRNA gene amplicon (16S) studies are
widely used in microbiome research, but investigators using these dif-
ferent methods typically find their results hard to reconcile. This lack
of standardization across methods limits the utility of the microbiome
for reproducible biomarker discovery.
A key problem is that whole-genome resources and rRNA resources
depend on different taxonomies and phylogenies. For example, Web
of Life (WoL)
and the Genome Taxonomy Database (GTDB)
whole-genome trees that cover only a small fraction of known bacteria
and archaea, while SILVA3 and Greengenes4 are more comprehensive
but are most often not linked to genome records.
We reasoned that an iterative approach could yield a single mas-
sive reference tree that unifies these different data layers (for example,
genome and 16S rRNA records), which we call Greengenes2. We began
with a whole-genome catalog of 15,953 bacterial and archaeal genomes
that were evenly sampled from NCBI, and we reconstructed an accu-
rate phylogenomic tree by summarizing evolutionary trajectories of
380 global marker genes using the new workflow uDance5. This work,
namely WoL version 2 (WoL2), represents a substantial upgrade from
the previously released WoL1 (10,575 genomes)1,6. We then added 18,356
full-length 16S rRNA sequences from the Living Tree Project (LTP)
January 2022 release
, 1,725,274 near-complete 16S rRNA genes from
Received: 16 December 2022
Accepted: 25 May 2023
Published online: xx xx xxxx
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1Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA. 2Department of Electrical and Computer Engineering,
University of California San Diego, La Jolla, CA, USA. 3Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA,
USA. 4Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA. 5School of Life Sciences, Arizona State
University, Tempe, AZ, USA. 6Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA. 7Biostatistics &
Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD,
USA. 8Halicioglu Data Science Institute, University of California San Diego, La Jolla, CA, USA. 9Australian Centre for Ecogenomics, School of Chemistry
and Molecular Biosciences, The University of Queensland, St Lucia, Queensland, Australia. 10Department of Obstetrics and Gynecology, Columbia
University, New York, NY, USA. 11Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark. 12Department of Informatics, Second
Genome, Brisbane, CA, USA. 13Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA,
USA. 14Finnish Institute for Health and Welfare, Helsinki, Finland. 15Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland. 16Division
of Cardiology, Brigham and Women’s Hospital, Boston, MA, USA. 17Cedars-Sinai Medical Center, Los Angeles, CA, USA. 18Cambridge Baker Systems
Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia. 19Cambridge Baker Systems Genomics Initiative, Department of
Public Health and Primary Care, University of Cambridge, Cambridge, UK. 20Division of Medicine, Turku University Hospital and University of Turku, Turku,
Finland. 21Sapient Bioanalytics, LLC, San Diego, CA, USA. 22Department of Computing, University of Turku, Turku, Finland. 23Department of Bioengineering,
University of California San Diego, La Jolla, CA, USA. e-mail:
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Brief Communication
(Fig. 1a). Our use of uDance ensured that the genome-based relation-
ships are kept fixed, and relationships between full-length 16S rRNA
sequences are inferred. For short fragments, we kept genome and
full-length relationships fixed and inserted fragments independently
from each other. Following deduplication and quality control on frag-
ment placement, this yielded a tree covering 21,074,442 sequences
from 31 different EMP Ontology 3 (EMPO3) environments, of which
46.5% of species-level leaves were covered by a complete genome. Taxo-
nomic labels were decorated onto the phylogeny using tax2tree v1.1
(ref. 4). The input taxonomy for decoration used GTDB r207, combined
Karst et al.8 and the Earth Microbiome Project 500 (EMP500)9 and all
full-length 16S rRNA sequences from GTDB r207 to the genome-based
backbone with uDance v1.1.0, producing a genome-supported phylog-
eny with 16S rRNA explicitly represented. Finally, we inserted 23,113,447
short V4 16S rRNA Deblur v1.1.0 (ref. 10) amplicon sequence variants
(ASVs) from Qiita (retrieved 14 December 2021)
and mitochondria and
chloroplast 16S rRNA from SILVA v138 using deep-learning-enabled
phylogenetic placement (DEPP) v0.3 (ref. 12). This final step represents
ASVs from over 300,000 public and private samples in Qiita, includ-
ing the entirety of the EMP
and American Gut Project/Microsetta
Not in SILVA 138
Branch length
PC1 (30.73%)
PC2 (5.81%)
d e f
Plant corpus
Sediment (saline)
Animal corpus
Animal surface
Animal secretion
Animal (non-saline)
Solid (non-saline)
Soil (non-saline)
Water (saline)
Plant rhizosphere
Surface (saline)
Sterile water blank
Sediment (non-saline)
Animal distal gut
Plant surface
Aerosol (non-saline)
Water (non-saline)
Animal proximal gut
Surface (non-saline)
PC1 (48.94%)
PC2 (11.34%)
PC1 (46.07%)
PC2 (9.76%)
a b
Fig. 1 | Greengenes2 overview and harmonization of 16S rRNA ASVs with
shotgun metagenomic data. a, The Greengenes2 phylogeny rendered using
Empress23, 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 BLAST24 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.
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Brief Communication
with the LTP January 2022 release. Taxonomy was harmonized prioritiz-
ing GTDB, including preserving the polyphyletic labels of GTDB (see
also Methods). The taxonomy will be updated every 6 months using
the latest versions of GTDB and LTP.
Greengenes2 is much larger than past resources in its phylogenetic
coverage, as compared to SILVA (Fig. 1b), Greengenes (Supplementary
Fig. 1a) and GTDB (Supplementary Fig. 1b). Moreover, because our
amplicon library is linked to environments labeled with EMPO cat-
egories, we can easily identify the environments that contain samples
that can fill out the tree. Because metagenome assembled genome
(MAG) assembly efforts can only cover abundant taxa, for each EMPO
category, we plotted the amount of new branch length added to the
tree by taxa whose minimum abundance is 1% in each sample (Fig. 1c).
The results show, on average, which environment types will best yield
new MAGs and which environments harbor individual samples that
will have a large impact when sequenced.
Past efforts to reconcile 16S and shotgun datasets have led to
non-overlapping distributions, and only techniques such as Pro-
crustes analysis can show relationships between the results15. In
two large human stool cohorts
where both 16S and shotgun data
were generated on the same samples, we find that Bray–Curtis17
(non-phylogenetic) ordination fails to reconcile at the feature level
(Fig. 1d) and is poor at the genus level (Fig. 1e and Supplementary
Fig. 1c). However, UniFrac
, a phylogenetic method, used with our
Greengenes2 tree provides better concordance (Fig. 1f and Supplemen-
tary Fig. 1d). To examine applicability of Greengenes2 to non-human
environments, we next computed both Bray–Curtis and weighted
UniFrac at the feature level on the 16S and shotgun data from the EMP
As with the human data, we observe better concordance with the use
of the Greengenes2 phylogeny (Supplementary Fig. 2) despite limited
representation of whole genomes from non-human sources, as these
environments are not as well characterized in general.
White wine
White wine
Red wine
Red wine
Pearson correlation
Eect size WGS
Eect size 16S
Eect size 16S
Eect size WGS
Class Order
SILVA 138 naive Bayes versus GG2 WGS Bray–Curtis eect sizes (Cohen’s d and f)
Weighted UniFrac eect sizes (Cohen’s d and f)
Pearson r2 = 0.57; P = 1.35 × 10–16
Pearson r2 = 0.86; P = 2.67 × 10–37
GG 13_8 naive Bayes versus GG2 WGS
GG2 16S versus GG2 WGS
Family Genus
Class Order Family Genus Species
Class Order Family Genus Species
Pearson correlation
Pearson correlation
Naive Bayes
Phylogenetic taxonomy
Fig. 2 | Taxonomic and effect size consistency between 16S rRNA ASVs and
shotgun metagenomic data. ac, 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.
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We also find that the per-sample shotgun and 16S taxonomy rela-
tive abundance profiles are concordant even to the species level. We
first computed taxonomy profiles for shotgun data using the Woltka
. Using a naive Bayes classifier from q2-feature-classifier
v2022.2 (ref. 20) to compare GTDB r207 taxonomy results at each
level down to the genus level against SILVA v138 (Fig. 2a) or down to
the species level against Greengenes v13_8 (Fig. 2b), no species-level
reconciliation was possible. By contrast, Greengenes2 provided excel-
lent concordance at the genus level (Pearson r = 0.85) and good con-
cordance at the species level (Pearson r = 0.65; Fig. 2c). Interestingly,
the tree is now sufficiently complete such that exact matching of 16S
ASVs followed by reading the taxonomy off the tree performs even
better than the naive Bayes classifier (naive Bayes, Pearson r = 0.54 at
the species level and r = 0.84 at the genus level).
Finally, a critical reason to assign taxonomy is downstream use of
biomarkers and indicator taxa. Microbiome science has been described
as having a reproducibility crisis21, but much of this problem stems from
incompatible methods
. We initially used the The Human Diet Microbi-
ome Initiative (THDMI) dataset, which is a multipopulation expansion
of The Microsetta Initiative14 that contains samples with paired 16S and
shotgun preparations, to test whether a harmonized resource would
provide concordant rankings for the variables that affect the human
microbiome similarly. Using Greengenes2, the concordance was good
with Bray–Curtis (Fig. 2d; Pearson r
 = 0.57), better using UniFrac with
different phylogenies (SILVA 138 and Greengenes2; Supplementary
Fig. 1e; Pearson r2 = 0.77) and excellent with UniFrac on the same phy-
logeny (Fig. 2e; Pearson r2 = 0.86). We confirmed these results with an
additional cohort
(Supplementary Fig. 1f,g). Intriguingly, the ranked
effect sizes across different cohorts were concordant.
Taken together, these results show that use of a consistent, inte-
grated taxonomic resource dramatically improves the reproducibility
of microbiome studies using different data types and allows varia-
bles of large versus small effect to be reliably recovered in different
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tributions and competing interests; and statements of data and code
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Human research protocols
THDMI participant informed consent was obtained under University
of California, San Diego, institutional review board protocol 141853.
FINRISK participant informed consent was obtained under the Coordi-
nating Ethical Committee of the Helsinki and Uusimaa Hospital District
protocol reference number 558/E3/2001.
Phylogeny construction
WoL2 (ref. 1; a tree inferred using genome-wide data) was used as the
starting backbone. Full-length 16S sequences from the LTP
, full-length
mitochondria and chloroplast from SILVA 138 (ref. 3), full-length 16S
from GTDB r207 (ref. 2), full-length 16S from Karst et al.
and full-length
16S from the EMP500 (ref. 9; samples selected and sequenced spe-
cifically for Greengenes2) were collected and deduplicated. Sequences
were then aligned using UPP25, and gappy sequences with less than
1,000 base pairs were removed. The resulting set of 321,210 unique
sequences was used with uDance v1.1.0 to update the WoL2 backbone.
Briefly, uDance updates an existing tree with new sequences and (unlike
placement methods) also infers the relationship of existing sequences.
uDance has two modes, one that allows updates to the backbone and
one that keeps the backbone fixed, where the former mode is intended
for use with whole genomes. In our analyses, we kept the backbone tree
(inferred using genomic data) fixed. To extend the genomic tree with
16S data, we identified 13,249 (of 15,953 total) genomes in the WoL2
backbone tree with at least one 16S copy and used them to train a DEPP
model with the weighted average method detailed later to handle multi-
ple copies. We then used DEPP to insert all 16S copies of all genomes into
the backbone and measured the distance between the genome position
and the 16S position. We removed copies that were placed much further
than others, as identified using a two-means approach with centroids
equal to at least 13 branches. We repeated this process in a second round.
For every remaining genome, we selected as its representative the copy
with the minimum placement error and computed the consensus with
ties. At the end, we were left with 12,344 unique 16S sequences across
all WoL2 genomes. For tree inference, uDance used IQ-TREE2 (ref. 26) in
fast tree search with model GTR+ Γ after removing duplicate sequences.
Next, we collected 16S V4 ASVs from Qiita
using redbiom
performed 14 December 2021) from contexts ‘Deblur_2021.09-Illumina-
16S-V4-90nt-dd6875’, ‘Deblur_2021.09-Illumina-16S-V4-100nt-50b3a2’,
‘Deblur_2021.09-Illumina-16S-V4-125nt-92f954’, ‘Deblur_2021.09-Illumina-
16S-V4-150nt-ac8c0b’, ‘Deblur_2021.09-Illumina-16S-V4-200nt-
0b8b48’ and ‘Deblur_2021.09-Illumina-16S-V4-250nt-8b2bff’ and
aligned them to the existing 16S alignment of sequences in WoL2 using
UPP, setting the maximum alignment subset size to 200 (to help with
scalability). The collected 16S V4 ASVs are aligned to the V4 region of
the existing ‘backbone’ alignments. A DEPP model was then trained
on the full-length 16S sequences from the backbone. DEPP constructs
a neural network model that embeds sequences in high-dimensional
spaces such that embedded points resemble the phylogeny in their
distances. Such a model then allows insertion of new sequences into a
tree using the distance-based phylogenetic insertion method APPLES-2
(ref. 28). The ASVs from redbiom were then inserted into the backbone
using the trained DEPP model. To enable analyses of large datasets, we
used a clustering approach with DEPP. We trained an ensemble of DEPP
models corresponding to different parts of the tree and used a classifier
to detect the correct subtree. During training, for species with multiple
16S, all the copies are mapped to the same leaf in the backbone tree.
To train the DEPP models with multiple sequences mapped to a leaf,
each site in each sequence is encoded as a probability vector of four
nucleotides across all the copies.
Integrating the GTDB and LTP taxonomies
GTDB and LTP are not directly compatible due to differences in their
curation. As a result, it is not always possible to map a species from
one resource to the other because parts of a species lineage are not
present, are described using different names or have an ambiguous asso-
ciation due to polyphyletic taxa in GTDB (for example, Firmicutes_A,
Firmicutes_B and so on;
We integrated taxonomic data from LTP into GTDB as LTP includes spe-
cies that are not yet represented in GTDB. Additionally, GTDB is actively
curated, while LTP generally uses the NCBI taxonomy. To account for
these differences, we first mapped any species that had a perfect species
name association and revised its ancestral lineage to match GTDB. Next,
we generated lineage rewrite rules using the GTDB record metadata.
Specifically, we limited the metadata to records that are GTDB repre
sentatives and NCBI-type material and defined a lineage renaming from
the recorded NCBI taxonomy to the GTDB taxonomy. These rewrite
rules were applied from most- to least-specific taxa, and through this
mechanism, we could revise much of the higher ranks of LTP. We then
identified incertae sedis records in LTP that we could not map, removed
their lineage strings and did not attempt to provide taxonomy for them,
instead opting to rely on downstream taxonomy decoration to resolve
their lineages. Next, any record that was ambiguous to map was split
into a secondary taxonomy for use in backfilling in the downstream
taxonomy decoration. Finally, we instrumented numerous consistency
checks in the taxonomy through the process to capture inconsistent
parents in the taxonomic hierarchy and consistent numbers of ranks in a
lineage and to ensure that the resulting taxonomy was a strict hierarchy.
Taxonomy decoration
The original tax2tree algorithm was not well suited for a large volume
of species-level records in the backbone, as the algorithm requires an
internal node to place a name. If two species are siblings, the tree would
lack a node to contain the species label for both taxa. To account for
this, we updated the algorithm to insert ‘placeholder’ nodes with zero
branch length as the parents of backbone records, which could accept
these species labels. We further updated tax2tree to operate directly on
.jplace data29, preserving edge numbering of the original edges before
adding ‘placeholder’ nodes. To support LTP records that could not be
integrated into GTDB, we instrumented a secondary taxonomy mode
for tax2tree. Specifically, following the standard decoration, backfilling
and name promotion procedures, we determine on a per-record basis
for the secondary taxonomy what portion of the lineage is missing
and place the missing labels on the placeholder node. We then issue a
second round of name promotion using the existing tax2tree methods.
The actual taxonomy decoration occurs on the backbone tree,
which contains only full-length 16S records and does not contain
ASVs. This is done as ASV placements are independent, do not modify
the backbone and would substantially increase the computational
resources required. After the backbone is decorated, fragment place-
ments from DEPP are resolved using a multifurcation strategy using
the balanced-parentheses library30.
Phylogenetic collapse for visualization
We are unaware of phylogenetic visualization software that can display
a tree with over 20,000,000 tips. To produce the visualizations in Fig.
1, we reduced the dimension of the tree by collapsing fragment multi-
furcations to single nodes, dropping the tree to 522,849 tips.
MAG target environments
A feature table for the 27,015 16S rRNA V4 90-nucleotide EMP samples
was obtained from redbiom. The ASVs were filtered to the overlap of
ASVs present in Greengenes2. Any feature with <1% relative abundance
within a sample was removed. The feature table was then rarefied to
1,000 sequences per sample. The amount of novel branch length was
then computed per sample by summing the branch length of each ASV’s
placement edge. The per-sample branch length was then normalized
by the total tree branch length (excluding length contributed by ASVs).
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Per-sample taxonomy correlations
All comparisons used THDMI
16S and Woltka processed shotgun
data. These data were accessed from Qiita study 10317 and filtered
the set of features that overlap with Greengenes2 using the QIIME 2
(ref. 31) q2-greengenes2 plugin. The 16S taxonomy was assessed using
either a traditional naive Bayes classifier with q2-feature-classifier
and default references from QIIME 2 2022.2 or by reading the lineage
directly from the phylogeny. To help improve correlations between
SILVA and Greengenes2 and between Greengenes and Greengenes2,
we stripped polyphyletic labelings from those data; we did not strip
polyphyletic labels from the phylogenetic taxonomy comparison or
the Greengenes2 16S versus Greengenes2 whole-genome shotgun
(WGS) naive Bayes comparison. Shotgun taxonomy was determined
by the specific observed genome records. Once the 16S taxonomy was
assigned, those tables and the WGS Woltka WoL2 table were collapsed
at the species, genus, family, order and class levels. We then computed
a minimum relative abundance per sample in the dataset from THDMI.
In each sample, we removed any feature, either 16S or WGS, below the
per-sample minimum (that is, max(min(16S), min(WGS))), forming a
common minimal basis for taxonomy comparison. Following filtering,
Pearson correlation was computed per sample using SciPy
. These
correlations were aggregated per 16S taxonomy assignment method
and by each taxonomic rank. The 25th, 50th and 75th percentiles were
then plotted with Matplotlib33.
Principal coordinates
THDMI Deblur 16S and Woltka processed shotgun sequencing data,
against WoL2, were obtained from Qiita study 10317. Both feature tables
were filtered against Greengenes2 2022.10, removing any feature not
present in the tree. For the genus collapsed plot (Fig. 1e), both the 16S
and WGS data features were collapsed using the same taxonomy. For
all three figures, the 16S data were subsampled, with replacement,
to 10,000 sequences per sample. The WGS data were subsampled,
with replacement, to 1,000,000 sequences per sample. Bray–Curtis,
weighted UniFrac and principal coordinates analysis were computed
using q2-diversity 2022.2. The resulting coordinates were visualized
with q2-emperor34.
The EMP ‘EMP500’ 16S and Woltka processed shotgun sequencing
data, against WoL2, were obtained from Qiita study 13114. Both feature
tables were filtered against Greengenes2 2022.10. The 16S data were
subsampled, with replacement, to 1,000 sequences per sample. The
WGS data were subsampled, with replacement, to 50,000 sequences
per sample. The sequencing depth for WGS data was selected based
on Supplementary Fig. 6 of Shaffer et al.
, which noted low levels of
read recruitment to publicly available whole genomes. Bray–Curtis,
weighted UniFrac and principal coordinates analysis were computed
using q2-diversity 2022.2. The resulting coordinates were visualized
with q2-emperor.
Effect size calculations
Similar to principal coordinates, data from THDMI were rarefied to
9,000 and 2,000,000 sequences per sample for 16S and WGS, respec-
tively. Bray–Curtis and weighted normalized UniFrac were computed
on both sets of data. The variables for THDMI were subset to those with
at least two category values having more than 50 samples. For UniFrac
with SILVA (Supplementar y Fig. 1e), we performed fragment insertion
using q2-fragment-insertion
into the standard QIIME 2 SILVA refer-
ence, followed by rarefaction to 9,000 sequences per sample, and then
computed weighted normalized UniFrac.
For FINRISK, the data were rarefied to 1,000 and 500,000
sequences per sample for 16S and WGS, respectively. A different
depth was used to account for the overall lower amount of sequenc-
ing data for FINRISK. As with THDMI, the variables selected were
reduced to those with at least two category values having more than
50 samples.
Support for computing paired effect sizes is part of the QIIME2
Greengenes2 plugin q2-greengenes2, which performs effect size cal-
culations using Evident36.
Reporting summary
Further information on research design is available in the Nature Port-
folio Reporting Summary linked to this article.
Data availability
The official location of the Greengenes2 releases is http://ftp.microbio.
me/greengenes_release/. The data are released under a BSD-3 clause
license. Data from THDMI are part of Qiita study 10317 and European
Bioinformatics Institute accession number PRJEB11419. The FINRISK
data and including the data presented in Supplementary Fig. 1c–g are
protected; details on data access are available in the European Genome–
Phenome Archive under accession number EGAD00001007035. The
data presented in Supplementary Fig. 1a,b are not compatible with
Excel. The EMP data are part of Qiita study 13114 and European Bio-
informatics Institute accession number ERP125879. Source data are
provided with this paper.
Code availability
A QIIME 2 plugin is available to facilitate use with the resource that
can be obtained from ref. 37 (version 2023.3;
zenodo.7758134). Taxonomy construction, decoration and release
processing is part of ref. 38 (version 2023.3;
zenodo.7758138). uDance is available at GitHub39 (version v1.1.0; Phylogeny insertion using
DEPP is available at ref. 40 (version 0.3;
zenodo.7768798). The trained model can be accessed via Zenodo at Code used for the figures
in this manuscript is available in ref. 41. Finally, an interactive website
to explore the Greengenes2 data is available at https://greengenes2.
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alignments using phylogeny-aware proiles. Genome Biol. 16,
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characterization system. mSystems 4, e00215-19 (2019).
28. Balaban, M., Jiang, Y., Roush, D., Zhu, Q. & Mirarab, S. Fast and
accurate distance-based phylogenetic placement using divide
and conquer. Mol. Ecol. Resour. 22, 1213–1227 (2022).
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A format for phylogenetic placements. PLoS ONE 7, e31009
30. McDonald, D. Improved-octo-waddle. GitHub
biocore/improved-octo-waddle/ (2023).
31. Bolyen, E. et al. Reproducible, interactive, scalable and extensible
microbiome data science using QIIME 2. Nat. Biotechnol. 37,
852–857 (2019).
32. Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientiic
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34. Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. EMPeror:
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Gigascience 2, 16 (2013).
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mSystems 3, e00021-18 (2018).
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Nature Biotechnology
Brief Communication
36. Rahman, G. et al. Determination of eect sizes for power analysis
of microbiome studies using large mircrobiome datasets. Genes (2023).
37. McDonald, D. q2-greengenes2. GitHub
biocore/q2-greengenes2/ (2023).
38. McDonald, D. greengenes2. GitHub
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39. Balaban, M. uDance. GitHub
uDance (2023).
40. Jiang, Y. DEPP. GitHub (2023).
41. McDonald, D. Greengenes2 analyses. GitHub
knightlab-analyses/greengenes2 (2023).
This work was supported, in part, by NSF XSEDE BIO210103 (Q.Z.), NSF
RAPID 20385.09 (R.K.), NIH 1R35GM14272 (S.M.), NIH U19AG063744
(R.K.), NIH U24DK131617 (R.K.), NIH DP1-AT010885 (R.K.) and Emerald
Foundation 3022 (R.K.). J.T.M. was funded by the intramural research
program of the Eunice Kennedy Shriver National Institute of Child
Health and Human Development. The dataset from THDMI was
generated through support from Danone Nutricia Research and the
Center for Microbiome Innovation. This work used Expanse at the San
Diego Supercomputing Center through allocation ASC150046 from
the Advanced Cyberinfrastructure Coordination Ecosystem: Services
& Support (ACCESS) program, which is supported by NSF grants
2138259, 2138286, 2138307, 2137603 and 2138296.
Author contributions
D.M. and R.K. conceived, initiated and coordinated the project
and performed analyses. D.M. and A.G. wrote infrastructure and
analysis code. Y.J., M.B., Q.Z. and S.M. coordinated phylogenetic
placements and reconstruction. K.C. wrote visualization code.
G.N. and J.T.M. performed analyses. S.M.K. and M.A. generated
16S rRNA operons. D.H.P., P.H. and T.D. provided guidance on the
genome taxonomy. S.J.S., A.B., A.S.H., P.J., S.C., M.I., T.N., M.J., V.S.
and L.L. provided data used for analysis. All authors reviewed and
edited the manuscript.
Competing interests
R.K. is a scientiic advisory board member, and consultant
for BiomeSense, Inc., has equity and receives income. The
terms of this arrangement have been reviewed and approved
by the University of California, San Diego in accordance with its
conlict of interest policies. The remaining authors declare no
competing interests.
Additional information
Supplementary information The online version
contains supplementary material available at
Correspondence and requests for materials should be addressed to
Rob Knight.
Peer review information Nature Biotechnology thanks Robin Rohwer,
C. Titus Brown and the other, anonymous, reviewer(s) for their
contribution to the peer review of this work.
Reprints and permissions information is available at
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