Phylogenomics and the Diversity and Diversification of Microbes

Phylogenomics and the Diversity
and Diversification of Microbes
October 14, 2022
Talk at UC Merced
Quantitative and Systems Biology Colloqiuim
Jonathan A. Eisen
University of California, Davis
@phylogenomics
http://phylogenomics.me

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
A Brief Tour

Phylogenomic
Projects
Microbial
Phylogenomics
&
Evolvability

Phylogenomic
Symbiosis
Symbioses
Communities
Phylogenomic
Projects
Extrinsic Novelty 2
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: 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

Marine
Invertebrates
HMS Type 1: Chemosymbioses
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 magni
fi
ca 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 magni
fi
ca.

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
Connie Rojas

Chemical communication
Many animals communicate via odors and chemical
substances (pheromones; volatile organic compounds) to:
deter predators mark territories advertise fertility
and emit identifying information
Wood 1990; Jordan et al. 2007; Kucklich et al. 2019
Modification of Slide by Connie Rojas

Anal glands produce semi-viscous and odorous
secretions in mammals

Anal glands produce semi-viscous and odorous
secretions in mammals
fermentative bacteria within
anal glands can produce
odorous metabolites involved
in host chemical signaling
Studied in badgers, hyenas,
and meerkats, but severely
understudied in other
animals
Rosell et al. 1998, Theis et al. 2013, Roberts et al. 2014, Leclaire et al. 2014

Limited understanding in domestic cats (Felis
catus)
Yamaguchi et al. (2019) examined the microbiome and
volatile organic compounds found in the anal gland
secretions of a Bengal cat

Limited understanding in domestic cats (Felis
catus)
Yamaguchi et al. (2019) examined the microbiome and
volatile organic compounds found in the anal gland
secretions of a Bengal cat
Bacteria isolated from the anal gland produced the same
volatiles found in anal gland secretions

Expanding this research to include more
cat individuals
Metagenomics
(microbiome)
Culturing
Swabbed the anal glands of 23 domestic cats
Metabolomics
(volatiles)
Stanley Marks, Hira Lesea,
and Cristina Davis

Microbiome
composition
dominated by
Bacteroides,
Corynebacterium,
and Lactobacillus
Samples Modification of Slide by Connie Rojas

Recovered 89 MAGs from the felid anal sac
Genus # of MAGs
Corynebacterium 5
Porphyromonas 5
Bacteroides 3
Peptoniphilus 3
Bifidobacterium 2
Blautia 2
Campylobacter 2
Collinsella 2
Faecalimonas 2
Finegoldia 2
Fusobacterium 2
Mediterraneibacter 2
Pauljensenia 2
Peptostreptococcus 2
Urinicoccus 2

0
25
50
75
100
Number
of
Isolates
Bacterial Genus
Anaerococcus
Bacteroides
Clostridium
Corynebacterium
Enterococcus
Escherichia
Lacticaseibacillus
Lactobacillus
Other
Pediococcus
Pepstostreptococcus
Proteus
Shigella
Streptococcus
105 bacteria isolated from the cat anal gland
represented 22 genera and 35 species
Culture Collection

Seven bacterial species were recovered as MAGs and
as cultured isolates
MAG
Corynebacterium pyruviciproducens
Corynebacterium frankenforstense
Bacteroides fragilis
Escherichia coli
Lactobacillus johnsonii
Pediococcus acidilactici
Proteus mirabilis
Streptococcus canis
Corynebacterium spp.
possess type I fatty acid
synthases
Lactobacillus plantarum can
make volatile phenols, esters,
and ketones from
fermentation of gram sprouts
Proteus mirabilis produce
odorants in carcasses that
attract blowflies
Streptococcus sp. found in
the anal gland secretions
of red foxes and dogs, and
in the human axillae

In the process of characterizing the volatile
compounds found in anal sac secretions

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
fi
ve wild duck species varies by species and in
fl
uenza 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

Sonia Ghose’s Research
Characterizing the impact of restoration
on the Rana sierrae skin microbiome
across restoration histories and sites
Sonia L. Ghose
Collaborators:
• Vance Vredenburg
(SFSU)
• Roland Knapp (SNARL)
• Jessie Bushell (SF Zoo)
Funding:
• NIH Animal Models of
Infectious Diseases T32
• Alfred P. Sloan Foundation
• Center for Population
Biology
Modification of Slide by Sonia Ghose

Rana sierrae
Study system: Rana sierrae
• The Sierra Nevada yellow-legged frog
• Endangered species
• Highly susceptible to Bd
• Few populations remain
• Persisting with Bd
• Restoration efforts underway
• Role of skin microbiome?
IUCNredlist.org
Sonia Ghose’s Research

One genus dominates samples but unstudied
Sonia Ghose Modification of Slide by Sonia Ghose

Variovorax
Pseudorhodoferax
Curvibacter, Rhodoferax
Xylophilus, Curvibacter, Acidovorax, Delftia, Comam
Hydrogenophaga
Symbiobacter
Roseateles, Paucibacter, Pelomonas, Mitsuaria
Frog01
Rubrivivax, Azohydromonas, Aquincola, Vitreoscilla, JOSH
Rhizobacter, Ideonella
Methylibium, Aquabacterium
AAP99
Janthinobacterium
Duganella
Massilia
Massilia
AVCC01
Herbaspirillum
Undibacterium
Profftella
Polynucleobacter, Burkholderia
BOG-994
Caldimonas
Thiomonas
Fusobacterium
Brachymonas, Comamonas
= MAG
representative
= Clade contains
known violacein
producer
Black text = Present in 16S
data
Grey text = Not present in
16S data
Frog 01 Mag
Family
Burkholderiaceae
Close relatives of
Frog 01 known to
make anti-fungal
pigment violacein

Discovery and Taxonomy of Pigmented Bacteria
Marina E. De León
Marina De León
Modification of Slide by Marina De León

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.

HMS Type 3: Panamanian Isthmus
1000s of Species
Microbiome
Rise of
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

Seagrass
Zostera marina
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

PLENTY OF FUNGI IN THE SEA:
INSIGHTS FROM PROFILING THE
SEAGRASS MYCOBIOME
CASSIE ETTINGER, PHD
@CASETTRON
NSF OCE POSTDOC
STAJICH LAB, UC RIVERSIDE
Modification of Slide by Cassie Ettinger

FOCUS ON ONE SEAGRASS
SPECIES: ZOSTERA MARINA
◆ Focus on one seagrass
species: Zostera marina (ZM)
or eelgrass
◆ Most abundant seagrass
species in the Northern
hemisphere
https://www.iucnredlist.org/
Fig 1, Fonseca & Uhrin Mar Fisheries Rev (2009)
Leaves
Rhizome
Roots

MYCOBIOME: THE FUNGI ON/IN A HOST

NOT MUCH IS KNOWN ABOUT
MARINE FUNGI
◆ Very few cultured isolates
exist
◆ Thought to include
members of the “early
diverging lineages” or
“dark matter” fungi
◆ Likely involved in
symbioses with many
marine organisms
◆ Harder to study than
bacteria / archaea
Microbial isolates associated with Zostera marina
Ettinger & Eisen, PLoS One (2020)

FUNGI WERE IMPORTANT FOR PLANT
TRANSITION TO LAND BUT MARINE
ENVIRONMENT HAS DIFFERENT SELECTION
PRESSURES
POSSIBLE LOSS OF MYCORRHIZAL FUNGI?
POSSIBLE GAIN OF NOVEL ASSOCIATIONS
WITH MARINE FUNGI??

GOALS FOR PROFILING THE
SEAGRASS MYCOBIOME
1) To characterize the taxonomic diversity of fungi associated
with the seagrass, ZM, from Bodega Bay, CA
2) To isolate and identify a diverse culture collection of fungi
associated with ZM from Bodega Bay, CA
3) To survey the taxonomic diversity of fungi associated with the
seagrass, ZM, globally

THE MYCOBIOME OF ZM FROM BODEGA BAY, CA: FUNGAL
TAXA VARY ACROSS DIFFERENT PARTS OF THE PLANT
Ettinger & Eisen, Frontiers in Microbiology (2019)
Leaves
Roots
Rhizome
Glomerellales (Colletotrichum) =
Possible dark septate endophytes

DARK SEPTATE ENDOPHYTES (DSE)
◆ Morphological, not phylogenetic group
◆ Largely uncharacterized
◆ Can transfer nitrogen and receive carbon
from land plants
◆ Can increase overall land plant nutrient
content and growth
◆ But negative, neutral and positive effects
have all been observed in land plants
◆ DSE have been previously observed in
the Mediterranean seagrass, Posidonia
oceanica
Fig 3E, 3F, Vohník et al Mycorrhiza (2015)

Mr Bayes + RAxML phylogeny
of ITS2 & partial 28S rRNA gene
Ettinger & Eisen, Frontiers in Microbiology (2019)
MOST ABUNDANT ASV (SV8) CLUSTERS WITHIN NOVEL
CLADE SW-I IN LOBULOMYCETALES (CHYTRIDIOMYCOTA)
ASV
=
amplicon
sequence
variant

COULD THESE CHYTRIDS BE SEAGRASS
PARASITES OR MUTUALISTIC SYMBIONTS?
◆ Unclassified chytrids previously seen
associated with Thalassia testudinum
◆ Found only on/in living leaf tissue
◆ Were unable to culture it without the
host plant
◆ Hypothesized it was potential
mutualistic symbiont or weak parasite
◆ Thought might be ubiquitous, but
could easily be misidentified as the
seagrass pathogen Labyrinthula
Newell & Fell Botanica Marina (1980)

SEAGRASS MYCOBIOME
1) Culture-independent profiling reveals putative associations
with dark septate endophytes and marine chytrids
2) To isolate and identify a diverse culture collection of fungi
associated with ZM from Bodega Bay, CA

ISOLATIONS PERFORMED BY
UNDERGRADUATES
◆ Cultured fungi associated with:
◆ ZM leaves, ZM roots, ZM rhizomes, sediment,
seawater from Bodega Bay, CA
◆ Epiphytes and endophytes
◆ Sanger identifications using ITS/28S
Neil Brahmbahatt
Katie Somers
Kate Jones & Tess McDaniel

108 FUNGI, 40 BACTERIA & 2
OOMYCETES
◆ Fungi
◆ Mainly Ascomycota in the
Eurotiomycetes,
Dothideomycetes &
Sordariomycetes
◆ No chytrids
◆ Bacteria
◆ Majority were ubiquitous marine
lineages (e.g. Vibrio,
Pseudoalteromonas)
◆ Actinomycetes isolates
◆ Phyllobacterium sp.
◆ Oomycota
◆ Halophytophthora sp.
Ettinger & Eisen, PLoS One (2020)

SEAGRASS MYCOBIOME
1) Culture-independent profiling reveals putative associations
with dark septate endophytes and marine chytrids
2) Culture-based surveys capture ZM generalist and specialist
fungi and enable comparative genomics

Leaves (n = 12), roots (n = 12),
sediment (n = 12) taken from a shallow
ZM bed at each site
Sediment Roots
Leaves
GLOBAL SAMPLING EFFORT
16 SITES ACROSS NORTHERN HEMISPHERE
Site map courtesy of J. Stachowicz Ettinger et al, AEM (2021)

ACKNOWLEDGEMENTS:
The Eisen & Stajich labs
Three fun-gals & one fun-guy: Kate Jones,
Tess McDaniel, Katie Somers & Neil Brahmbahatt
Collaborators: Jay Stachowicz, Sofie Voerman,
Susan Williams, Jessica Abbott, Jeanine Olsen, ZEN,
Marina LaForgia, Victoria Watson-Zink, Dante Torio & more
Want to know more?
email: cassande@ucr.edu
website: casett.github.io
twitter: @casettron
https://xkcd.com/

Microbiome Returning to
The Sea
HMS Type 3: Seagrass Land to Sea
Jenna
Lang
Jessica
Green
Jay
Stachowicz
David
Zostera marina
Part 2
Develop Zm into a
model system

Catherine Collier. IAN Image Library. https://ian.umces.edu/
imagelibrary/
Jay
Stachowicz
Jonathan
Eisen
Laura
Vann
Jeanine
Olsen
Thorsten B.H.
Reusch
Resequencing of Zostera marina Across the
Northern Hemisphere
Modification of Slide by Gina Chaput

imagelibrary/
• 10.4% reads not mapped to
Z. marina
• 421 MAGs constructed
• 121 MAGs of high quality
Assembling Non Zostera reads in the data

imagelibrary/
• 10.4% reads not mapped to
Z. marina
• 421 MAGs constructed
• 121 MAGs of high quality
Df Sumof Squares R2 F Pr(>F)
Latitude 1 51144 0.03172 11.2517 1e-04 ***
Water Body 1 48178 0.02988 10.5994 1e-04 ***
Latitude:Water Body 1 44813 0.02779 9.8591 1e-04 ***
Residual 323 1468163 0.91060
Total 326 1612299 1.0000
Assembling Non Zostera reads in the data

Phylogenetic Diversity of High Quality MAGs
Chaput et al. Unpublished
(31)
Df Sumof Squares R2 F Pr(>F)
Latitude 1 41270 0.06962 26.666 1e-04 ***
Water Body 1 29210 0.04927 18.874 1e-04 ***
Latitude:Water Body 1 22437 0.03785 14.498 1e-04 ***
Residual 323 499890 0.84326
Total 326 592807 1.0000
Modification of Slide by
Gina Chaput

MAG Composition Clusters Based on Body of
Water & Latitude
RDA1 (7%)
RDA2
(4.8%)
Chaput et al. Unpublished Modification of Slide by Gina Chaput

Plant-Microbe
Interactions:
Host Response
Observations
in the Environment
Method Development
From Field to the Lab: Designing Microbiome
Experiments

Observations
in the Environment
From Field to the Lab: Designing Microbiome
Experiments
Application of Microbes (Example: Seagrass Restoration)
Plant-Microbe
Interactions:
Host Response
Method Development

Assembly Rules of Plant-Microbe Interactions: Z. marina
seedlings
Host Filtering Effect
Priority Effect of Seed MicrobiomePriming Effect of
Sediment Microbiome
Seven Stages of Seedling
Development
Xu et al. 2016 (DOI: 10.7717/peerj.2697)
EcoFAB 2.0

Phylogenomic
Methods
& Tools
Phylogenomic
&
Evolvability
Phylogenomic Methods and Tools
A Brief Tour of Methods

Major Work Last Three Years
• COVID
• COVID
• COVID
• COVID
• COVID
• COVID
• COVID

Lab Manager Gone …
David Coil Modification of Slide by David Coil

Community Testing
Modification of Slide by David Coil

Air Filters in Schools

UCDMC Sampling

Monitoring wastewater to inform
COVID-19 public health response
Heather N. Bischel
Assistant Professor, Department of Civil & Environmental Engineering
hbischel@ucdavis.edu

Major Work Last Three Years
• COVID
• COVID
• COVID
• COVID
• COVID
• COVID
• COVID
HELP NEEDED

Covid-19
outbreak?
• Phylogenetic analysis of
clinical sequence data
clusters related Covid-19
infections
Main finding:
• Infection clusters are not
seen for students living off
campus
• Infection clusters are seen
for students in on-campus
residential housing [RH &
TG]
Modification of Slide by Mo Kaze

Covid-19 outbreak:
local school
• Phylogenetic analysis of
clinical sequence data
identified local school
outbreak
Main finding:
• High sequence similarity
indicated strong support
for Covid-19 transmission
between students,
teachers, and parents

Wastewater Epidemiological
Surveillance

WWES Expansion
**
** polio
tbd
Future
goals:
Infectious viruses, bacteria, fungi
Antibiotic resistance
Phage identification

Tools: rRNA Phylogeny Driven Methods
rRNA
&
Evolvability
Phylogenomic

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
A
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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
&
Evolvability
Phylogenomic

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
&
Evolvability
Phylogenomic

We need to be able to predict
functions well from sequence data.
Tools: Phylogenomic Functional Prediction

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

PSA
Similarity
≠
Relatedness

Tools: Phylogenetic Profiling
Phylogenetic
&
Evolvability
Phylogenomic

We need to know how organisms are
related to each other
Tools: Whole Genome Phylogeny

HMS Type 1: Xylem Feeders
Glassy Winged Sharpshooter
Gut
Endosymbionts
Trying to
Live on
Xylem Fluid
Nancy Moran
Dongying Wu
E2
Extrinsic

WGT: Higher Evolutionary Rates in Endosymbionts
Wu et al. 2006 PLoS Biology 4: e188. Collaboration with Nancy Moran’ s Lab
Higher
Evolutionary
Rates in
Endosymbionts

MutS MutL
+ +
+ +
+ +
+ +
_ _
_ _
Variation in Evolution Rates Correlated with Repair Gene Presence
Highest Rates
In Those Missing
Mismatch Repair
Genes

MutS MutL
+ +
+ +
+ +
+ +
_ _
_ _
Variation in Evolution Rates Correlated with Repair Gene Presence
Important Use of
Whole Genome Trees

Whole Genome Trees: Many Possible Methods
Lang JM, Darling AE, Eisen JA (2013) Phylogeny of
Bacterial and Archaeal Genomes Using Conserved
Genes: Supertrees and Supermatrices. PLoS ONE
8(4): e62510. doi:10.1371/journal.pone.0062510
Jenna Lang

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
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

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

Tools: Linking Phylogeny and Function
Linking
&
Evolvability
Phylogenomic

&
Evolvability
Phylogenomic
Resources
&
Reference Data
Communication

• Tanja Woyke
• Jonathan Eisen
• Duane Moser
• Tullis Onstott

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
&
Evolvability
Phylogenomic
&
Participation
In Microbiology
& Science
Model

#2: Microbiome is transferable / modifiable

Why Now V: Importance of Other Microbiomes

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

Phylogenomics and the Diversity and Diversification of Microbes

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