Evolution of microbiomes and the evolution of the study and politics of microbiomes (or, how can something be both ridiculously overhyped and horrifically under-appreciated)

Evolution of microbiomes and the evolution of the study
and politics of microbiomes
(or, how can something be both ridiculously overhyped
and horrifically under-appreciated).
Microbiome Virtual International Forum
December 7, 2021 (PST)
Jonathan A. Eisen
University of California, Davis
@phylogenomics
http://phylogenomics.me

Google Trends Hits to Microbiome
The Rise of the Microbiome (2016)

The Rise of the Microbiome (2016)
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Pubmed Hits to Microbiome vs. Year

Why Now I: Appreciation of Microbial Diversity

Why Now II: Post Genome Blues
The Microbiome
Transcriptome
Variome
Epigenome
Overselling the Human Genome?

Why Now III: Technological Advances

Why Now IV: Microbiome Functions
Turnbaugh et al Nature. 2006 444(7122):1027-31.

Why Now IV: Microbiome Functions
Turnbaugh et al Nature. 2006 444(7122):1027-31.
#1: Microbiome impacts key trait
#2: Microbiome is transferable / modifiable

Why Now V: Importance of Other Microbiomes

Phylogenomics and Evolvability
•Mutation
•Duplication
•Deletion
•Rearrangement
•Recombination
Intrinsic
Novelty Origin
Evolvability: variation in these
processes w/in & between taxa
Phylogenomics: integrating
genomics & evolution, helps
interpret / predict evolvability

•Mutation
•Duplication
•Deletion
•Rearrangement
•Recombination
Intrinsic
Extrinsic
Novelty Origin
Evolvability &
Phylogenomics of
Extrinsic Novelties
•Recombination
•Gene transfer

•Mutation
•Duplication
•Deletion
•Rearrangement
•Recombination
Intrinsic
•Symbiosis
•Symbioses
•Microbiomes
Extrinsic
Novelty Origin
Evolvability &
Phylogenomics of
Extrinsic Novelties
•Recombination
•Gene transfer

Eisen Lab “Topics”
Phylogenomic
Methods
& Tools
Microbial
Phylogenomics
&
Evolvability
Phylogenomic
Resources
&
Reference Data
Communication
&
Participation
In Microbiology
& Science
Research
Projects

Phylogenomic
Methods
& Tools
Phylogenomic
Resources
&
Reference Data
Communication
&
Participation
In Microbiology
& Science
Research
Projects
Microbial
Phylogenomics
&
Evolvability
A Brief Tour of Projects

Phylogenomic
Methods
& Tools
Extrinsic:
Symbiosis
Symbioses
Communities
Phylogenomic
Resources
&
Reference Data
Communication
&
Participation
In Microbiology
& Science
Research
Projects
Area 2: Extrinsic Novelty
E2
Extrinsic

Host Microbe Stress (HMS) Triangle
Host
Microbe Stress
E2
Extrinsic

Host
Microbiome Stress
Host Microbe Stress (HMS) Triangle
E2
Extrinsic

Symbiosis Under Stress
When organisms are placed under selective
pressure or stress where novelty would be
beneficial, can we predict which pathway
they will use?
What leads to interactions / symbioses
being a potential solution?
Can we manipulate interactions and/or force
new ones upon systems?
Extrinsic
Novelty

HMS Type 1: Nutrient Acquisition
Host
Microbiome Nutrients
E2
Extrinsic

HMS Type 1: Chemosymbioses
Marine Invertebrates
Endosymbionts Carbon
Colleen
Cavanaugh
E2
Extrinsic
Eisen JA, et al.. 1992. Phylogenetic relationships of chemoautotrophic bacterial symbionts of Solemya velum Say (Mollusca: Bivalvia) determined by 16S rRNA gene sequence analysis. Journal of Bacteriology 174: 3416-3421. PMID: 1577710. PMCID:
PMC206016.
Newton ILG, et al 2007. The Calyptogena magnifica chemoautotrophic symbiont genome. Science 315: 998-1000
Dmytrenko O, et al. 2014. The genome of the intracellular bacterium of the coastal bivalve, Solemya velum: a blueprint for thriving in and out of symbiosis. BMC Genomics 15: 924.
Roeselers G, et al.. 2010. Complete genome sequence of Candidatus Ruthia magnifica.

HMS Type 1: Xylem Feeders
Glassy Winged Sharpshooter
Gut
Endosymbionts
Trying to
Live on
Xylem Fluid
Nancy Moran
Dongying Wu
E2
Extrinsic
Wu D, Daugherty SC, Van Aken SE, Pai GH, Watkins KL, Khouri H, et al. (2006) Metabolic Complementarity and Genomics of the Dual Bacterial Symbiosis of Sharpshooters. PLoS Biol 4(6): e188. https://doi.org/10.1371/journal.pbio.0040188

HMS Type 1: Nitrogen Acquisition
Oloton
Corn
Mucilage
Microbiome
Low
N
Van Deynze A, Zamora P, Delaux PM, Heitmann C, Jayaraman D, Rajasekar S, Graham D, Maeda J, Gibson D, Schwartz KD, Berry AM, Bhatnagar S, Jospin G, Darling A, Jeannotte R, Lopez J, Weimer BC, Eisen JA, Shapiro
HY, Ané JM, Bennett AB. 2018. Nitrogen fixation in a landrace of maize is supported by a mucilage-associated diazotrophic microbiota. PLoS Biology 16(8):e2006352. doi: 10.1371/journal.pbio.2006352. PMID: 30086128.
PMCID: PMC6080747.
E2
Extrinsic

HMS Type 1: Nutrients and Odor
Host
Microbiome Nutrients
Yamaguchi MS, Ganz HH, Cho AW, Zaw TH, Jospin G, McCartney MM, et al. (2019) Bacteria isolated from Bengal cat (Felis catus × Prionailurus bengalensis) anal sac secretions produce
volatile compounds potentially associated with animal signaling. PLoS ONE 14(9): e0216846. https://doi.org/10.1371/journal.pone.0216846

HMS Type 2: Pathogens
Host
Microbiome Pathogen
E2
Extrinsic

HMS Type 2: Flu & Ducks
Ducks
Gut
Microbiome
Flu
Walter

Boyce
Holly
Ganz
Sarah
Hird
Ladan
Daroud
Alana

Firl
Hird SM, Ganz H, Eisen JA, Boyce WM. 2018. The cloacal microbiome of five wild duck species varies by species and influenza A virus infection status. mSphere 3:e00382-18. https:// doi.org/10.1128/mSphere.00382-18
Ganz, H.H., Doroud, L., Firl, A.J., Hird, S.M., Eisen, J.A. and Boyce, W.M., 2017. Community-level differences in the microbiome of healthy wild mallards and those infected by influenza A viruses. mSystems, 2(1) .e00188-16.
E2
Extrinsic

HMS Type 2: Koalas & Chlamydia
Koala
Gut
Microbiome
Chlamydia
&
Antibiotics
Katherine
Dahlhausen
E2
Extrinsic
Dahlhausen KE, Doroud L, Firl AJ, Polkinghorne A, Eisen JA. 2018. Characterization of shifts of koala (Phascolarctos cinereus) intestinal microbial communities associated with antibiotic treatment. PeerJ 6:e4452 https://doi.org/
10.7717/peerj.4452
Dahlhausen KE, Jospin G, Coil DA, Eisen JA, Wilkins LGE. 2020. Isolation and sequence-based characterization of a koala symbiont: Lonepinella koalarum. PeerJ 8:e10177 https://doi.org/10.7717/peerj.10177

Frogs
Skin
Microbiome
Chytrid
Sonia Ghose
Marina De León
HMS Type 2: Frogs and Chytrids
E2
Extrinsic

Host
Microbiome Changing
Environment
HMS Type 3: Environmental Change
E2
Extrinsic

HMS Type 3: Rice Microbiome
Rice
Root
Microbiome Domestication
E2
Extrinsic
Sundar Lab
Srijak
Bhatnagar
Edwards J, Johnson C, Santos-Medellin C, Lurie E, Podishetty NK, Bhatnagar S, Eisen JA, Sundaresan V. 2015. Structure, variation, and assembly of the root-associated microbiomes of
rice. Proceedings of the National Academy of Sciences USA 12(8): E911-20.

Seagrass
Microbiome Returning to
The Sea
HMS Type 3: Seagrass Land to Sea
Jenna

Lang
Jessica

Green
Jay

Stachowicz
David
Coil
E2
Extrinsic
https://seagrassmicrobiome.org

Seagrass
Microbiome Returning to
The Sea
HMS Type 3: Seagrass Land to Sea
Jenna

Lang
Jessica

Green
Jay

Stachowicz
David
Coil
E2
Extrinsic
https://seagrassmicrobiome.org
Jay

Stachowicz
Maggie

Sogin
Gina

Chaput

HMS Type 3: Panamanian Isthmus
1000s of Species
Microbiome
Rise of
Panamanian
Isthmus
Laetitia
Wilkins
Bill
Wcislo
Matt
Leray
E2
Extrinsic
https://istmobiome.rbind.io
https://istmobiome.net
· This work is funded by a grant from the Gordon and Betty Moore Foundation doi:10.37807/GBMF5603
Jarrod
Scott
David
Coil

Phylogenomic
Methods
& Tools
Phylogenomic
Resources
&
Reference Data
Communication
&
Participation
In Microbiology
& Science
Research
Projects
Microbial
Phylogenomics
&
Evolvability
Phylogenomic Methods and Tools
A Brief Tour of Methods

Tools: rRNA Phylogeny Driven Methods
rRNA
Phylogeny Driven
Methods
Microbial
Phylogenomics
&
Evolvability
Phylogenomic
Resources
&
Reference Data
Communication
&
Participation
In Microbiology
& Science
Model
Systems

Eisen et al.
1992
Eisen et al. 1992. J. Bact.174: 3416
Colleen Cavanaugh
Chemosynthetic Symbioses

Phylogeny As a Tool in rRNA Analysis
Similarity
≠
Relatedness

STAP
An Automated Phylogenetic Tree-Based Small Subunit
rRNA Taxonomy and Alignment Pipeline (STAP)
Dongying Wu1
*, Amber Hartman1,6
, Naomi Ward4,5
, Jonathan A. Eisen1,2,3
1 UC Davis Genome Center, University of California Davis, Davis, California, United States of America, 2 Section of Evolution and Ecology, College of Biological Sciences,
University of California Davis, Davis, California, United States of America, 3 Department of Medical Microbiology and Immunology, School of Medicine, University of
California Davis, Davis, California, United States of America, 4 Department of Molecular Biology, University of Wyoming, Laramie, Wyoming, United States of America,
5 Center of Marine Biotechnology, Baltimore, Maryland, United States of America, 6 The Johns Hopkins University, Department of Biology, Baltimore, Maryland, United
States of America
Abstract
Comparative analysis of small-subunit ribosomal RNA (ss-rRNA) gene sequences forms the basis for much of what we know
about the phylogenetic diversity of both cultured and uncultured microorganisms. As sequencing costs continue to decline
and throughput increases, sequences of ss-rRNA genes are being obtained at an ever-increasing rate. This increasing flow of
data has opened many new windows into microbial diversity and evolution, and at the same time has created significant
methodological challenges. Those processes which commonly require time-consuming human intervention, such as the
preparation of multiple sequence alignments, simply cannot keep up with the flood of incoming data. Fully automated
methods of analysis are needed. Notably, existing automated methods avoid one or more steps that, though
computationally costly or difficult, we consider to be important. In particular, we regard both the building of multiple
sequence alignments and the performance of high quality phylogenetic analysis to be necessary. We describe here our fully-
automated ss-rRNA taxonomy and alignment pipeline (STAP). It generates both high-quality multiple sequence alignments
and phylogenetic trees, and thus can be used for multiple purposes including phylogenetically-based taxonomic
assignments and analysis of species diversity in environmental samples. The pipeline combines publicly-available packages
(PHYML, BLASTN and CLUSTALW) with our automatic alignment, masking, and tree-parsing programs. Most importantly,
this automated process yields results comparable to those achievable by manual analysis, yet offers speed and capacity that
are unattainable by manual efforts.
Citation: Wu D, Hartman A, Ward N, Eisen JA (2008) An Automated Phylogenetic Tree-Based Small Subunit rRNA Taxonomy and Alignment Pipeline (STAP). PLoS
ONE 3(7): e2566. doi:10.1371/journal.pone.0002566
multiple alignment and phylogeny was deemed unfeasible.
However, this we believe can compromise the value of the results.
For example, the delineation of OTUs has also been automated
via tools that do not make use of alignments or phylogenetic trees
(e.g., Greengenes). This is usually done by carrying out pairwise
comparisons of sequences and then clustering of sequences that
have better than some cutoff threshold of similarity with each
other). This approach can be powerful (and reasonably efficient)
but it too has limitations. In particular, since multiple sequence
alignments are not used, one cannot carry out standard
phylogenetic analyses. In addition, without multiple sequence
alignments one might end up comparing and contrasting different
regions of a sequence depending on what it is paired with.
The limitations of avoiding multiple sequence alignments and
phylogenetic analysis are readily apparent in tools to classify
sequences. For example, the Ribosomal Database Project’s
Classifier program [29] focuses on composition characteristics of
each sequence (e.g., oligonucleotide frequency) and assigns
taxonomy based upon clustering genes by their composition.
Though this is fast and completely automatable, it can be misled in
cases where distantly related sequences have converged on similar
composition, something known to be a major problem in ss-rRNA
sequences [30]. Other taxonomy assignment systems focus
classification tools it does have some limitations. For example,
the generation of new alignments for each sequence is both
computational costly, and does not take advantage of available
curated alignments that make use of ss-RNA secondary structure
to guide the primary sequence alignment. Perhaps most
importantly however is that the tool is not fully automated. In
addition, it does not generate multiple sequence alignments for all
sequences in a dataset which would be necessary for doing many
analyses.
Automated methods for analyzing rRNA sequences are also
available at the web sites for multiple rRNA centric databases,
such as Greengenes and the Ribosomal Database Project (RDPII).
Though these and other web sites offer diverse powerful tools, they
do have some limitations. For example, not all provide multiple
sequence alignments as output and few use phylogenetic
approaches for taxonomy assignments or other analyses. More
importantly, all provide only web-based interfaces and their
integrated software, (e.g., alignment and taxonomy assignment),
cannot be locally installed by the user. Therefore, the user cannot
take advantage of the speed and computing power of parallel
processing such as is available on linux clusters, or locally alter and
potentially tailor these programs to their individual computing
needs (Table 1).
Table 1. Comparison of STAP’s computational abilities relative to existing commonly-used ss-RNA analysis tools.
STAP ARB Greengenes RDP
Installed where? Locally Locally Web only Web only
User interface Command line GUI Web portal Web portal
Parallel processing YES NO NO NO
Manual curation for taxonomy assignment NO YES NO NO
Manual curation for alignment NO YES NO* NO
Open source YES** NO NO NO
Processing speed Fast Slow Medium Medium
It is important to note, that STAP is the only software that runs on the command line and can take advantage of parallel processing on linux clusters and, further, is
more amenable to downstream code manipulation.
*
Note: Greengenes alignment output is compatible with upload into ARB and downstream manual alignment.
**
The STAP program itself is open source, the programs it depends on are freely available but not open source.
doi:10.1371/journal.pone.0002566.t001
ss-rRNA Taxonomy Pipeline
STAP database, and the query sequence is aligned to them using
the CLUSTALW profile alignment algorithm [40] as described
above for domain assignment. By adapting the profile alignment
algorithm, the al
while gaps are in
sequence accord
Figure 1. A flow chart of the STAP pipeline.
doi:10.1371/journal.pone.0002566.g001
STAP database, and the query sequence is aligned to them using
the CLUSTALW profile alignment algorithm [40] as described
above for domain assignment. By adapting the profile alignment
algorithm, the alignments from the STAP database remain intact,
while gaps are inserted and nucleotides are trimmed for the query
sequence according to the profile defined by the previous
alignments from the databases. Thus the accuracy and quality of
the alignment generated at this step depends heavily on the quality
of the Bacterial/Archaeal ss-rRNA alignments from the
Greengenes project or the Eukaryotic ss-rRNA alignments from
the RDPII project.
Phylogenetic analysis using multiple sequence alignments rests on
the assumption that the residues (nucleotides or amino acids) at the
same position in every sequence in the alignment are homologous.
Thus, columns in the alignment for which ‘‘positional homology’’
cannot be robustly determined must be excluded from subsequent
analyses. This process of evaluating homology and eliminating
questionable columns, known as masking, typically requires time-
consuming, skillful, human intervention. We designed an automat-
ed masking method for ss-rRNA alignments, thus eliminating this
bottleneck in high-throughput processing.
First, an alignment score is calculated for each aligned column
by a method similar to that used in the CLUSTALX package [42].
Specifically, an R-dimensional sequence space representing all the
possible nucleotide character states is defined. Then for each
aligned column, the nucleotide populating that column in each of
the aligned sequences is assigned a score in each of the R
dimensions (Sr) according to the IUB matrix [42]. The consensus
‘‘nucleotide’’ for each column (X) also has R dimensions, with the
Figure 2. Domain assignment. In Step 1, STAP assigns a domain to
each query sequence based on its position in a maximum likelihood
tree of representative ss-rRNA sequences. Because the tree illustrated
Figure 1. A flow chart of the STAP pipeline.
doi:10.1371/journal.pone.0002566.g001
ss-rRNA Taxonomy Pipeline

WATERS
Hartman et al. BMC Bioinformatics 2010, 11:317
http://www.biomedcentral.com/1471-2105/11/317
Open Access
SOFTWARE
Software
Introducing W.A.T.E.R.S.: a Workflow for the
Alignment, Taxonomy, and Ecology of Ribosomal
Sequences
Amber L Hartman†1,3, Sean Riddle†2, Timothy McPhillips2, Bertram Ludäscher2 and Jonathan A Eisen*1
Abstract
Background: For more than two decades microbiologists have used a highly conserved microbial gene as a
phylogenetic marker for bacteria and archaea. The small-subunit ribosomal RNA gene, also known as 16 S rRNA, is
encoded by ribosomal DNA, 16 S rDNA, and has provided a powerful comparative tool to microbial ecologists. Over
time, the microbial ecology field has matured from small-scale studies in a select number of environments to massive
collections of sequence data that are paired with dozens of corresponding collection variables. As the complexity of
data and tool sets have grown, the need for flexible automation and maintenance of the core processes of 16 S rDNA
sequence analysis has increased correspondingly.
Results: We present WATERS, an integrated approach for 16 S rDNA analysis that bundles a suite of publicly available 16
S rDNA analysis software tools into a single software package. The "toolkit" includes sequence alignment, chimera
removal, OTU determination, taxonomy assignment, phylogentic tree construction as well as a host of ecological
analysis and visualization tools. WATERS employs a flexible, collection-oriented 'workflow' approach using the open-
source Kepler system as a platform.
Conclusions: By packaging available software tools into a single automated workflow, WATERS simplifies 16 S rDNA
analyses, especially for those without specialized bioinformatics, programming expertise. In addition, WATERS, like
some of the newer comprehensive rRNA analysis tools, allows researchers to minimize the time dedicated to carrying
out tedious informatics steps and to focus their attention instead on the biological interpretation of the results. One
advantage of WATERS over other comprehensive tools is that the use of the Kepler workflow system facilitates result
interpretation and reproducibility via a data provenance sub-system. Furthermore, new "actors" can be added to the
workflow as desired and we see WATERS as an initial seed for a sizeable and growing repository of interoperable, easy-
to-combine tools for asking increasingly complex microbial ecology questions.
Background
Microbial communities and how they are surveyed
Microbial communities abound in nature and are crucial
for the success and diversity of ecosystems. There is no
end in sight to the number of biological questions that
can be asked about microbial diversity on earth. From
animal and human guts to open ocean surfaces and deep
sea hydrothermal vents, to anaerobic mud swamps or
boiling thermal pools, to the tops of the rainforest canopy
and the frozen Antarctic tundra, the composition of
microbial communities is a source of natural history,
intellectual curiosity, and reservoir of environmental
health [1]. Microbial communities are also mediators of
insight into global warming processes [2,3], agricultural
success [4], pathogenicity [5,6], and even human obesity
[7,8].
In the mid-1980 s, researchers began to sequence ribo-
somal RNAs from environmental samples in order to
characterize the types of microbes present in those sam-
ples, (e.g., [9,10]). This general approach was revolution-
ized by the invention of the polymerase chain reaction
(PCR), which made it relatively easy to clone and then
* Correspondence: jaeisen@ucdavis.edu
1 Department of Medical Microbiology and Immunology and the Department
of Evolution and Ecology, Genome Center, University of California Davis, One
Shields Avenue, Davis, CA, 95616, USA
† Contributed equally
Full list of author information is available at the end of the article
11:317
105/11/317
Page 2 of 14
bosomal RNA) in partic-
osomal RNA (ss-rRNA).
e amount of previously
[1,11-13]. Researchers
t rRNA gene not only
it can be PCR amplified,
e and highly conserved
ersally distributed among
ful for inferring phyloge-
e then, "cultivation-inde-
ught a revolution to the
ng scientists to study a
Align
Check
chimeras
Cluster Build
Tree
Assign
Taxonomy
Tree w/
Taxonomy
Diversity
statistics &
graphs
Unifrac
files
Cytoscape
network
OTU table
Page 3 of 14
Motivations
As outlined above, successfully processing microbial
sequence collections is far from trivial. Each step is com-
plex and usually requires significant bioinformatics
expertise and time investment prior to the biological
interpretation. In order to both increase efficiency and
ensure that all best-practice tools are easily usable, we
sought to create an "all-inclusive" method for performing
all of these bioinformatics steps together in one package.
To this end, we have built an automated, user-friendly,
workflow-based system called WATERS: a Workflow for
the Alignment, Taxonomy, and Ecology of Ribosomal
Sequences (Fig. 1). In addition to being automated and
simple to use, because WATERS is executed in the Kepler
scientific workflow system (Fig. 2) it also has the advan-
tage that it keeps track of the data lineage and provenance
of data products [23,24].
Automation
The primary motivation in building WATERS was to
minimize the technical, bioinformatics challenges that
arise when performing DNA sequence clustering, phylo-
genetic tree, and statistical analyses by automating the 16
S rDNA analysis workflow. We also hoped to exploit
additional features that workflow-based approaches
entail, such as optimized execution and data lineage
tracking and browsing [23,25-27]. In the earlier days of 16
S rDNA analysis, simply knowing which microbes were
present and whether they were biologically novel was a
noteworthy achievement. It was reasonable and expected,
therefore, to invest a large amount of time and effort to
get to that list of microbes. But now that current efforts
are significantly more advanced and often require com-
parison of dozens of factors and variables with datasets of
thousands of sequences, it is not practically feasible to
process these large collections "by hand", and hugely inef-
ficient if instead automated methods can be successfully
employed.
Broadening the user base
A second motivation and perspective is that by minimiz-
ing the technical difficulty of 16 S rDNA analysis through
the use of WATERS, we aim to make the analysis of these
datasets more widely available and allow individuals with
Figure 2 Screenshot of WATERS in Kepler software. Key features: the library of actors un-collapsed and displayed on the left-hand side, the input
and output paths where the user declares the location of their input files and desired location for the results files. Each green box is an individual Kepler
actor that performs a single action on the data stream. The connectors (black arrows) direct and hook up the actors in a defined sequence. Double-
clicking on any actor or connector allows it to be manipulated and re-arranged.
Page 9
default is 97% and 99%), and they are also generated for
every metadata variable comparison that the user
includes.
Data pruning
To assist in troubleshooting and quality con
WATERS returns to the user three fasta files of seque
Figure 3 Biologically similar results automatically produced by WATERS on published colonic microbiota samples. (A) Rarefaction curves
ilar to curves shown in Eckburg et al. Fig. 2; 70-72, indicate patient numbers, i.e., 3 different individuals. (B) Weighted Unifrac analysis based on ph
genetic tree and OTU data produced by WATERS very similar to Eckburg et al. Fig. 3B. (C) Neighbor-joining phylogenetic tree (Quicktree) represent
the sequences analyzed by WATERS, which is clearly similar to Fig. S1 in Eckburg et al.
B
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alignment used to build the profile, resulting in a multiple
sequence alignment of full-length reference sequences and
metagenomic reads. The final step of the alignment process is a
quality control filter that 1) ensures that only homologous SSU-
rRNA sequences from the appropriate phylogenetic domain are
included in the final alignment, and 2) masks highly gapped
alignment columns (see Text S1).
We use this high quality alignment of metagenomic reads and
references sequences to construct a fully-resolved, phylogenetic
tree and hence determine the evolutionary relationships between
the reads. Reference sequences are included in this stage of the
analysis to guide the phylogenetic assignment of the relatively
short metagenomic reads. While the software can be easily
extended to incorporate a number of different phylogenetic tools
capable of analyzing metagenomic data (e.g., RAxML [27],
pplacer [28], etc.), PhylOTU currently employs FastTree as a
default method due to its relatively high speed-to-performance
PD versus PID clustering, 2) to explore overlap between PhylOTU
clusters and recognized taxonomic designations, and 3) to quantify
the accuracy of PhylOTU clusters from shotgun reads relative to
those obtained from full-length sequences.
PhylOTU Clusters Recapitulate PID Clusters
We sought to identify how PD-based clustering compares to
commonly employed PID-based clustering methods by applying
the two methods to the same set of sequences. Both PID-based
clustering and PhylOTU may be used to identify OTUs from
overlapping sequences. Therefore we applied both methods to a
dataset of 508 full-length bacterial SSU-rRNA sequences (refer-
ence sequences; see above) obtained from the Ribosomal Database
Project (RDP) [25]. Recent work has demonstrated that PID is
more accurately calculated from pairwise alignments than multiple
sequence alignments [32–33], so we used ESPRIT, which
Figure 1. PhylOTU Workflow. Computational processes are represented as squares and databases are represented as cylinders in this generalize
workflow of PhylOTU. See Results section for details.
doi:10.1371/journal.pcbi.1001061.g001
Finding Metagenomic OTUs
Sharpton TJ, Riesenfeld SJ, Kembel SW, Ladau J, O'Dwyer
JP, Green JL, Eisen JA, Pollard KS. (2011) PhylOTU: A High-
Throughput Procedure Quantifies Microbial Community
Diversity and Resolves Novel Taxa from Metagenomic Data.
PLoS Comput Biol 7(1): e1001061. doi:10.1371/
journal.pcbi.1001061
OTUs via Phylogeny (PhylOTU)
Tom
Sharpton
Katie
Pollard
Jessica
Green
Finding Metagenomic OTUs

rRNA Copy # vs. Phylogeny
Steven
Kembel
Jessica
Green
Martin 
Wu
Kembel SW, Wu M, Eisen JA, Green JL (2012)
Incorporating 16S Gene Copy Number
Information Improves Estimates of Microbial
Diversity and Abundance. PLoS Comput Biol
8(10): e1002743. doi:10.1371/
journal.pcbi.1002743

Other
Marker
Genes
Microbial
Phylogenomics
&
Evolvability
Phylogenomic
Resources
&
Reference Data
Communication
&
Participation
In Microbiology
& Science
Model
Systems
Tools: Other Marker Genes

Metagenomics
DNA
RecA RecA
RecA
RpoB RpoB
RpoB
Rpl4 Rpl4
Rpl4 rRNA rRNA
rRNA
Hsp70 Hsp70
Hsp70
EFTu EFTu
EFTu
http://genomebiology.com/2008/9/10/R151 Genome Biology 2008, Volume 9, Issue 10, Article R151 Wu and Eisen R151.7
Genome Biology 2008, 9:R151
sequences are not conserved at the nucleotide level [29]. As a
result, the nr database does not actually contain many more
protein marker sequences that can be used as references than
those available from complete genome sequences.
Comparison of phylogeny-based and similarity-based phylotyping
Although our phylogeny-based phylotyping is fully auto-
mated, it still requires many more steps than, and is slower
than, similarity based phylotyping methods such as a
MEGAN [30]. Is it worth the trouble? Similarity based phylo-
typing works by searching a query sequence against a refer-
ence database such as NCBI nr and deriving taxonomic
information from the best matches or 'hits'. When species
that are closely related to the query sequence exist in the ref-
erence database, similarity-based phylotyping can work well.
However, if the reference database is a biased sample or if it
contains no closely related species to the query, then the top
hits returned could be misleading [31]. Furthermore, similar-
ity-based methods require an arbitrary similarity cut-off
value to define the top hits. Because individual bacterial
genomes and proteins can evolve at very different rates, a uni-
versal cut-off that works under all conditions does not exist.
As a result, the final results can be very subjective.
In contrast, our tree-based bracketing algorithm places the
query sequence within the context of a phylogenetic tree and
only assigns it to a taxonomic level if that level has adequate
sampling (see Materials and methods [below] for details of
the algorithm). With the well sampled species Prochlorococ-
cus marinus, for example, our method can distinguish closely
related organisms and make taxonomic identifications at the
species level. Our reanalysis of the Sargasso Sea data placed
672 sequences (3.6% of the total) within a P. marinus clade.
On the other hand, for sparsely sampled clades such as
Aquifex, assignments will be made only at the phylum level.
Thus, our phylogeny-based analysis is less susceptible to data
sampling bias than a similarity based approach, and it makes
Major phylotypes identified in Sargasso Sea metagenomic data
Figure 3
Major phylotypes identified in Sargasso Sea metagenomic data. The metagenomic data previously obtained from the Sargasso Sea was reanalyzed using
AMPHORA and the 31 protein phylogenetic markers. The microbial diversity profiles obtained from individual markers are remarkably consistent. The
breakdown of the phylotyping assignments by markers and major taxonomic groups is listed in Additional data file 5.
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rplE
rplF
rplK
rplL
rplM
rplN
rplP
rplS
rplT
rpmA
rpoB
rpsB
rpsC
rpsE
rpsI
rpsJ
rpsK
rpsM
rpsS
smpB
tsf
Relative
abundance
Many other genes
better than rRNA

Sargasso Phylotypes
Weighted
%
of
Clones
0.000
0.125
0.250
0.375
0.500
Major Phylogenetic Group
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EFG EFTu HSP70 RecA RpoB rRNA
Venter et al., Science 304: 66. 2004
Marker Phylotyping - Sargasso Metagenome

AMPHORA
http://genomebiology.com/2008/9/10/R151 Genome Biology 2008, Volume 9, Issue 10, Article R151 Wu and Eisen R151.7
Major phylotypes identified in Sargasso Sea metagenomic data
Figure 3
Major phylotypes identified in Sargasso Sea metagenomic data. The metagenomic data previously obtained from the Sargasso Sea was reanalyzed using
AMPHORA and the 31 protein phylogenetic markers. The microbial diversity profiles obtained from individual markers are remarkably consistent. The
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a
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s
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f
i
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d
b
a
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t
e
r
i
a
dnaG
frr
infC
nusA
pgk
pyrG
rplA
rplB
rplC
rplD
rplE
rplF
rplK
rplL
rplM
rplN
rplP
rplS
rplT
rpmA
rpoB
rpsB
rpsC
rpsE
rpsI
rpsJ
rpsK
rpsM
rpsS
smpB
tsf
Relative
abundance AMPHORA Phylotyping w/ Protein Markers
Martin 
Wu

Phylosift - Bayesian Phylotyping
Input Sequences
rRNA workflow
protein workflow
profile HMMs used to align
candidates to reference alignment
Taxonomic
Summaries
parallel option
hmmalign
multiple alignment
LAST
fast candidate search
pplacer
phylogenetic placement
LAST
LAST
search input against references
hmmalign
multiple alignment
hmmalign
multiple alignment
Infernal
multiple alignment
LAST
<600 bp
>600 bp
Sample Analysis &
Comparison
Krona plots,
Number of reads placed
for each marker gene
Edge PCA,
Tree visualization,
Bayes factor tests
each
input
sequence
scanned
against
both
workflows
Aaron
Darling
Erik
Matsen
Holly
Bik
Guillaume
Jospin
Darling AE, Jospin G, Lowe E,
Matsen FA IV, Bik HM, Eisen JA.
(2014) PhyloSift: phylogenetic
analysis of genomes and
metagenomes. PeerJ 2:e243
http://dx.doi.org/10.7717/
peerj.243
Erik
Lowe

PD from Metagenomes
typically used as a qualitative measure because duplicate s
quences are usually removed from the tree. However, the
test may be used in a semiquantitative manner if all clone
even those with identical or near-identical sequences, are i
cluded in the tree (13).
Here we describe a quantitative version of UniFrac that w
call “weighted UniFrac.” We show that weighted UniFrac b
haves similarly to the FST test in situations where both a
FIG. 1. Calculation of the unweighted and the weighted UniFr
measures. Squares and circles represent sequences from two differe
environments. (a) In unweighted UniFrac, the distance between t
circle and square communities is calculated as the fraction of t
branch length that has descendants from either the square or the circ
environment (black) but not both (gray). (b) In weighted UniFra
branch lengths are weighted by the relative abundance of sequences
the square and circle communities; square sequences are weight
twice as much as circle sequences because there are twice as many tot
circle sequences in the data set. The width of branches is proportion
to the degree to which each branch is weighted in the calculations, an
gray branches have no weight. Branches 1 and 2 have heavy weigh
since the descendants are biased toward the square and circles, respe
tively. Branch 3 contributes no value since it has an equal contributio
from circle and square sequences after normalization.
Kembel SW, Eisen JA, Pollard KS, Green JL (2011) The Phylogenetic Diversity of
Metagenomes. PLoS ONE 6(8): e23214. doi:10.1371/journal.pone.0023214
Jessica
Green
Steven
Kembel
Katie
Pollard

Tools: Phylogenomic Functional Prediction
Phylogenomic
Functional
Prediction
Microbial
Phylogenomics
&
Evolvability
Phylogenomic
Resources
&
Reference Data
Communication
&
Participation
In Microbiology
& Science
Model
Systems

Phylogenomic
Functional
Prediction
To understand how functions evolve,
We need to be able to predict
functions well from sequence data.
Tools: Phylogenomic Functional Prediction

PSA
Similarity
≠
Relatedness

PHYLOGENENETIC PREDICTION OF GENE FUNCTION
IDENTIFY HOMOLOGS
OVERLAY KNOWN
FUNCTIONS ONTO TREE
INFER LIKELY FUNCTION
OF GENE(S) OF INTEREST
1 2 3 4 5 6
3 5
3
1A 2A 3A 1B 2B 3B
2A 1B
1A
3A
1B
2B
3B
ALIGN SEQUENCES
CALCULATE GENE TREE
1
2
4
6
CHOOSE GENE(S) OF INTEREST
2A
2A
5
3
Species 3
Species 1 Species 2
1
1 2
2
2 3
1
1A 3A
1A 2A 3A
1A 2A 3A
4 6
4 5 6
4 5 6
2B 3B
1B 2B 3B
1B 2B 3B
ACTUAL EVOLUTION
(ASSUMED TO BE UNKNOWN)
Duplication?
EXAMPLE A EXAMPLE B
Duplication?
Duplication?
Duplication
5
METHOD
Ambiguous
Based on
Eisen, 1998
Genome Res 8:
163-167.
Phylogenomics

Phylotyping
Eisen et al.
1992
Eisen et al. 1992. J. Bact.174: 3416

Shotmap
Simulate)
metagenomic)
library)
Translate)
metagenomic)
reads)
Search)
metagenomic)
pep6des)
Classify)
metagenomic)
pep6des)
Es6mate)
protein)family)
abundance)
Taxonomic)
proﬁles)from)real)
metagenomes)
Protein)family)
database)
IMG/ER)
reference)
genomes)
Construct))
mock))
community)
1"
Annotate)
genes)in)
genomes)
2"
Expected)
abundance)of)
gene)families)
3"
4"
5"
Protein)family)
database)
Evaluate)
es6ma6on)
accuracy)
6" 7"
8"
9"
Tom Sharpton
Katie Pollard
https://github.com/sharpton/shotmap
Shotmap

Tools: Phylogenetic Profiling
Phylogenetic
Profiling
Microbial
Phylogenomics
&
Evolvability
Phylogenomic
Resources
&
Reference Data
Communication
&
Participation
In Microbiology
& Science
Model
Systems

Sporulation Gene Profile
Wu et al. 2005 PLoS Genetics 1: e65.

B. subtilis new sporulation genes
Bjorn Traag
Richard Losick
Antonia Pugliese
J Bacteriol. 2013 Jan;195(2):253-60. doi: 10.1128/JB.01778-12

Tools: Whole Genome Phylogeny
Whole
Genome
Phylogeny
Microbial
Phylogenomics
&
Evolvability
Phylogenomic
Resources
&
Reference Data
Communication
&
Participation
In Microbiology
& Science
Model
Systems

Whole
Genome
Phylogeny
To understand how functions evolve,
We need to know how organisms are
related to each other
Tools: Whole Genome Phylogeny

Automated WGT: Amphora
W
Martin 
Wu

Automated WGT: Phylosift
Input Sequences
rRNA workflow
protein workflow
profile HMMs used to align
candidates to reference alignment
Taxonomic
Summaries
parallel option
hmmalign
multiple alignment
LAST
pplacer
phylogenetic placement
LAST
LAST
search input against references
hmmalign
multiple alignment
hmmalign
multiple alignment
Infernal
multiple alignment
LAST
<600 bp
>600 bp
Sample Analysis &
Comparison
Krona plots,
Number of reads placed
for each marker gene
Edge PCA,
Tree visualization,
Bayes factor tests
each
input
sequence
scanned
against
both
workflows
Aaron
Darling
Erik
Matsen
Holly
Bik
Guillaume
Jospin
Darling AE, Jospin G, Lowe E,
Matsen FA IV, Bik HM, Eisen JA.
(2014) PhyloSift: phylogenetic
analysis of genomes and
metagenomes. PeerJ 2:e243
http://dx.doi.org/10.7717/
peerj.243
Erik
Lowe

Normalizing Across Genes Tree OTU
Wu, D., Doroud, L, Eisen, JA 2013. arXiv. TreeOTU:
Operational Taxonomic Unit Classification Based on
Phylogenetic
Dongying Wu

Tools: Linking Phylogeny and Function
Linking
Phylogeny
&
Function
Microbial
Phylogenomics
&
Evolvability
Phylogenomic
Resources
&
Reference Data
Communication
&
Participation
In Microbiology
& Science
Model
Systems

Resources and Reference Data
Phylogenomic
Methods
& Tools
Microbial
Phylogenomics
&
Evolvability
Phylogenomic
Resources
&
Reference Data
Communication
&
Participation
In Microbiology
& Science
Model
Systems
A Brief Tour of Resources

Phylogeny can
guide generation
of reference data

Phylogeny Guided Genome Sequencing

SFAMs (Sifting Families)
Representative
Genomes
Extract
Protein
Annotation
All v. All
BLAST
Homology
Clustering
(MCL)
SFams
Align &
Build
HMMs
HMMs
Screen for
Homologs
New
Genomes
Extract
Protein
Annotation
Figure 1
Sharpton et al. 2012.BMC bioinformatics, 13(1), 264.
A
B
C

PhyEco Markers
Phylogenetic group Genome Number Gene Number Maker Candidates
Archaea 62 145415 106
Actinobacteria 63 267783 136
Alphaproteobacteria 94 347287 121
Betaproteobacteria 56 266362 311
Gammaproteobacteria 126 483632 118
Deltaproteobacteria 25 102115 206
Epislonproteobacteria 18 33416 455
Bacteriodes 25 71531 286
Chlamydae 13 13823 560
Chloroflexi 10 33577 323
Cyanobacteria 36 124080 590
Firmicutes 106 312309 87
Spirochaetes 18 38832 176
Thermi 5 14160 974
Thermotogae 9 17037 684
Wu D, Jospin G, Eisen JA (2013) Systematic Identification of Gene Families
for Use as “Markers” for Phylogenetic and Phylogeny-Driven Ecological
Studies of Bacteria and Archaea and Their Major Subgroups. PLoS ONE
8(10): e77033. doi:10.1371/journal.pone.0077033

Phylogenomic
Methods
& Tools
Microbial
Phylogenomics
&
Evolvability
Phylogenomic
Resources
&
Reference Data
Communication
&
Participation
In Microbiology
& Science
Model
Systems

Phylogenomic
Methods
& Tools
Key
Lessons
Phylogenomic
Resources
&
Reference Data
Communication
&
Participation
In Microbiology
& Science
Model
Systems

Lesson 1
Microbiome-host interactions
are way way way way way way
way way way way way more
complicated than single host-
microbe interactions

Phylogenomic
Methods
& Tools
Microbial
Phylogenomics
&
Evolvability
Phylogenomic
Resources
&
Reference Data
Communication
&
Participation
In Microbiology
& Science
Model
Systems

The Rise of the Microbiome Downsides

Microbiomania vs. Germophobia
Germophobia Microbiomania

Germophobia Microbiomania
All Microbes Are Bad
Use Antimicrobials
in Everything
Avoid all Microbes
All Microbes Are Good
Use Probiotics
in Everything
Embraces all Microbes
Lick Subway Poles
Fecal Transplants
Will Save World
Avoid Animals
Too
Swab Stories

Underselling Overselling
All Microbes Are Bad
Use Antimicrobials
in Everything
Avoid all Microbes
All Microbes Are Good
Use Probiotics
in Everything
Embraces all Microbes
Lick Subway Poles
Fecal Transplants
Will Save World
Avoid Animals
Too
Swab Stories

Overselling 1: Correlations
Correlation ≠ causation
Lesson: Some microbiome correlations with health states are
due to microbiomes playing a causal role in health state. But
most are not due to causal connections.

Autism - Microbiome - Diet
•

Overselling 2: Contamination
Lesson: Some “observations” of microbes being present in a
system are mistakes

Overselling 3: Presence vs. Importance
Lesson: Even when microbes are actually present somewhere,
this does not mean they are important

Overselling 4: Non pathogen ≠ probiotic
https://phylogenomics.blogspot.com/2013/12/cvs-marketing-probiotics-for-everyone.html?spref=tw
Lesson: Some probiotics really work, but you can’t just throw a
non pathogenic microbe at something and call it a probiotic

Probiotics That Kill …
https://phylogenomics.blogspot.com/2012/07/quick-post-story-about-ucdavis.html

Overselling 5: Personalized ≠ Health
Lesson: Most claims of personalized microbiome health and
diet plans are bogus

Overselling 6: Some Microbes Are Bad
Lesson: Hygiene hypothesis is important but imbibing all the
microbes in the world is not a good plan

Other Overselling Issues
• Big number systems lead to spurious
associations
• Massive complexity
• Just because fecal transplants work for C.diff
does not mean they should work for
everything

Underselling 1: Kill Everything
Lesson: We have gone completely bonkers with overuse of
sterilization and antimicrobials

Underselling 2: Swab Stories
Lesson: Germaphobia leads to crazy behaviors and great
underselling of the possible benefits of microbes

Other Underselling Issues
• Related to a pathogen does not mean
pathogenic
• Microbes with subtle effects have been
ignored in most systems (i.e., if they are not
pathogens or obligate mutualists)
• Microbiomes ignored in many experimental
studies of plants and animals
• Microbes ignored in most conservation
studies

Solution 1: Complain a lot
See http://microbiomania.net

http://microBE.net
http://gut-check.net
Solution 2: Education & Outreach

Kitty Microbiome
Georgia Barguil
Jack Gilbert
Project MERCCURI
Phone
and
Shoes
tinyurl/kittybiome
Holly Ganz
David Coil
Solution 3: Citizen Science

Solution 4: Engage Students Too

Balance?
Goal:
Evolve microbiome related
communications to be
balanced, even though most
microbiomes are not

Evolution of microbiomes and the evolution of the study and politics of microbiomes (or, how can something be both ridiculously overhyped and horrifically under-appreciated)

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