Phylogenomics and the Diversity and Diversification of Microbes
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The document discusses a talk given by Jonathan A. Eisen on phylogenomics and microbial diversity at UC Merced. It outlines methods and topics related to microbial phylogenomics, evolvability, and interactions between hosts and microbes under stress, while highlighting several specific research studies related to symbiosis and microbiomes in various organisms. Additionally, the document emphasizes the importance of understanding microbial interactions in ecological contexts and their implications for evolution and disease.
![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](https://image.slidesharecdn.com/eisen-221019184219-7a327c12/85/Phylogenomics-and-the-Diversity-and-Diversification-of-Microbes-80-320.jpg)
![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](https://image.slidesharecdn.com/eisen-221019184219-7a327c12/85/Phylogenomics-and-the-Diversity-and-Diversification-of-Microbes-88-320.jpg)
![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
Hartman et al. BMC Bioinformatics 2010, 11:317
http://www.biomedcentral.com/1471-2105/11/317
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.
Hartman et al. BMC Bioinformatics 2010, 11:317
http://www.biomedcentral.com/1471-2105/11/317
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](https://image.slidesharecdn.com/eisen-221019184219-7a327c12/85/Phylogenomics-and-the-Diversity-and-Diversification-of-Microbes-90-320.jpg)
Phylogenomics and the Diversity and Diversification of Microbes
- 1. 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
- 3. 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
- 4. •Mutation •Duplication •Deletion •Rearrangement •Recombination Intrinsic Extrinsic Novelty Origin Evolvability & Phylogenomics of Extrinsic Novelties Phylogenomics and Evolvability •Recombination •Gene transfer
- 5. •Mutation •Duplication •Deletion •Rearrangement •Recombination Intrinsic •Symbiosis •Symbioses •Microbiomes Extrinsic Novelty Origin Evolvability & Phylogenomics of Extrinsic Novelties Phylogenomics and Evolvability •Recombination •Gene transfer
- 6. Eisen Lab “Topics” Phylogenomic Methods & Tools Microbial Phylogenomics & Evolvability Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Research Projects A Brief Tour
- 8. Phylogenomic Symbiosis Symbioses Communities Phylogenomic Projects Extrinsic Novelty 2 E2 Extrinsic
- 9. Host Microbe Stress (HMS) Triangle Host Microbe Stress E2 Extrinsic
- 10. Host Microbiome Stress Host Microbe Stress (HMS) Triangle E2 Extrinsic
- 11. 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
- 12. HMS Type 1: Nutrient Acquisition Host Microbiome Nutrients E2 Extrinsic
- 13. 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
- 14. 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
- 15. 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.
- 16. 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
- 17. 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
- 18. Anal glands produce semi-viscous and odorous secretions in mammals Modification of Slide by Connie Rojas
- 19. 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 Modification of Slide by Connie Rojas
- 20. 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 Modification of Slide by Connie Rojas
- 21. 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 Modification of Slide by Connie Rojas
- 22. 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 Modification of Slide by Connie Rojas
- 23. Microbiome composition dominated by Bacteroides, Corynebacterium, and Lactobacillus Samples Modification of Slide by Connie Rojas
- 24. 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 Modification of Slide by Connie Rojas
- 25. 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 Modification of Slide by Connie Rojas
- 26. 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 Modification of Slide by Connie Rojas
- 27. In the process of characterizing the volatile compounds found in anal sac secretions Modification of Slide by Connie Rojas
- 28. HMS Type 2: Pathogens Host Microbiome Pathogen E2 Extrinsic
- 29. 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
- 30. 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
- 31. Frogs Skin Microbiome Chytrid Sonia Ghose Marina De León HMS Type 2: Frogs and Chytrids E2 Extrinsic
- 32. 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
- 33. 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 Modification of Slide by Sonia Ghose
- 34. One genus dominates samples but unstudied Sonia Ghose Modification of Slide by Sonia Ghose
- 35. 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 Modification of Slide by Sonia Ghose
- 36. Discovery and Taxonomy of Pigmented Bacteria Marina E. De León Marina De León Modification of Slide by Marina De León
- 37. Host Microbiome Changing Environment HMS Type 3: Environmental Change E2 Extrinsic
- 38. 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.
- 39. 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
- 40. 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
- 41. 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
- 42. 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 Modification of Slide by Cassie Ettinger
- 43. MYCOBIOME: THE FUNGI ON/IN A HOST Modification of Slide by Cassie Ettinger
- 44. 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) Modification of Slide by Cassie Ettinger
- 45. 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?? Modification of Slide by Cassie Ettinger
- 46. 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 Modification of Slide by Cassie Ettinger
- 47. 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 Modification of Slide by Cassie Ettinger
- 48. 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 Modification of Slide by Cassie Ettinger
- 49. 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) Modification of Slide by Cassie Ettinger
- 50. 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 Modification of Slide by Cassie Ettinger
- 51. 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) Modification of Slide by Cassie Ettinger
- 52. GOALS FOR PROFILING THE 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 3) To survey the taxonomic diversity of fungi associated with the seagrass, ZM, globally Modification of Slide by Cassie Ettinger
- 53. 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 Modification of Slide by Cassie Ettinger
- 54. 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) Modification of Slide by Cassie Ettinger
- 55. GOALS FOR PROFILING THE 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 3) To survey the taxonomic diversity of fungi associated with the seagrass, ZM, globally Modification of Slide by Cassie Ettinger
- 56. 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) Modification of Slide by Cassie Ettinger
- 57. 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: [email protected] website: casett.github.io twitter: @casettron https://xkcd.com/ Modification of Slide by Cassie Ettinger
- 58. 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
- 59. 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
- 60. 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
- 61. Catherine Collier. IAN Image Library. https://ian.umces.edu/ imagelibrary/ • 10.4% reads not mapped to Z. marina • 421 MAGs constructed • 121 MAGs of high quality Assembling Non Zostera reads in the data Modification of Slide by Gina Chaput
- 62. Catherine Collier. IAN Image Library. https://ian.umces.edu/ imagelibrary/ • 10.4% reads not mapped to Z. marina • 421 MAGs constructed • 121 MAGs of high quality Assembling Non Zostera reads in the data Modification of Slide by Gina Chaput
- 63. Catherine Collier. IAN Image Library. https://ian.umces.edu/ 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 Modification of Slide by Gina Chaput
- 64. 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
- 65. Modification of Slide by Gina Chaput
- 66. MAG Composition Clusters Based on Body of Water & Latitude RDA1 (7%) RDA2 (4.8%) Chaput et al. Unpublished Modification of Slide by Gina Chaput
- 67. Plant-Microbe Interactions: Host Response Observations in the Environment Method Development From Field to the Lab: Designing Microbiome Experiments Modification of Slide by Gina Chaput
- 68. 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 Modification of Slide by Gina Chaput
- 69. 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 Modification of Slide by Gina Chaput
- 70. Swabs to Genomes
- 71. Phylogenomic Methods & Tools Phylogenomic & Evolvability Phylogenomic Methods and Tools A Brief Tour of Methods
- 72. Major Work Last Three Years • COVID • COVID • COVID • COVID • COVID • COVID • COVID
- 73. Lab Manager Gone … David Coil Modification of Slide by David Coil
- 74. Community Testing Modification of Slide by David Coil
- 75. Air Filters in Schools Modification of Slide by David Coil
- 76. UCDMC Sampling Modification of Slide by David Coil
- 77. Monitoring wastewater to inform COVID-19 public health response Heather N. Bischel Assistant Professor, Department of Civil & Environmental Engineering [email protected]
- 78. Major Work Last Three Years • COVID • COVID • COVID • COVID • COVID • COVID • COVID HELP NEEDED
- 79. Major Work Last Three Years
- 80. 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
- 81. 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 Modification of Slide by Mo Kaze
- 82. Modification of Slide by Mo Kaze
- 83. Wastewater Epidemiological Surveillance Modification of Slide by Mo Kaze
- 84. WWES Expansion ** ** polio tbd Future goals: Infectious viruses, bacteria, fungi Antibiotic resistance Phage identification Modification of Slide by Mo Kaze
- 85. Tools: rRNA Phylogeny Driven Methods rRNA & Evolvability Phylogenomic
- 87. ≠ Relatedness
- 88. 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
- 89. 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: [email protected] 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 Hartman et al. BMC Bioinformatics 2010, 11:317 http://www.biomedcentral.com/1471-2105/11/317 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. Hartman et al. BMC Bioinformatics 2010, 11:317 http://www.biomedcentral.com/1471-2105/11/317 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 !"#$ !"#% !"#& "#" "#& '&(!(')*+),-(./*0/-01,()234/0,)5(67#7 !"#% !"#& "#" "#& "#% "#$ "#6 "#9 '%(!(')*+),-(./*0/-01,()234/0,)5(%	%8 :"; :"< :"= :"> :" :"@ :" :&; :&< :&= :&> :&? :&@ :&A :%; :%< :%= :%> :%? :%@ :%A '=;(!('&(.B('% " :9" &9"" %%9" $""" " 9" &"" &9" %"" %9" :% :& :" C !"#$%&'()%$%* !"#$%&'()"+%* )%+$",&'$%'!"#$%&(" "#$(-'!"#$%&(" .%&&/#'0(#&'!(" %,*(+'-,&'$%'!"#$%&(" 1(&0(#/$%* #+'*$&()(" #+'*$&()("+%* 2324 5"00",&'$%'!"#$%&(" #6"-'!"#$%&(" "+,7",&'$%'!"#$%&(" 1/*'!"#$%&(" 1(&0(#/$%* !"#(++( 1(&0(#/$%* 0'++(#/$%*
- 90. 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
- 91. 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
- 94. 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
- 95. 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
- 96. Tools: Phylogenomic Functional Prediction Phylogenomic & Evolvability Phylogenomic
- 97. We need to be able to predict functions well from sequence data. Tools: Phylogenomic Functional Prediction
- 98. 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
- 99. Phylotyping Eisen et al. 1992 Eisen et al. 1992. J. Bact.174: 3416
- 105. We need to know how organisms are related to each other Tools: Whole Genome Phylogeny
- 106. HMS Type 1: Xylem Feeders Glassy Winged Sharpshooter Gut Endosymbionts Trying to Live on Xylem Fluid Nancy Moran Dongying Wu E2 Extrinsic
- 107. 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
- 108. Wu et al. 2006 PLoS Biology 4: e188. Collaboration with Nancy Moran’ s Lab MutS MutL + + + + + + + + _ _ _ _ Variation in Evolution Rates Correlated with Repair Gene Presence Highest Rates In Those Missing Mismatch Repair Genes
- 109. Wu et al. 2006 PLoS Biology 4: e188. Collaboration with Nancy Moran’ s Lab MutS MutL + + + + + + + + _ _ _ _ Variation in Evolution Rates Correlated with Repair Gene Presence Important Use of Whole Genome Trees
- 110. 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
- 112. 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 fast candidate search LAST fast candidate search search input against references hmmalign multiple alignment hmmalign multiple alignment Infernal multiple alignment LAST fast candidate search <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
- 113. 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
- 114. Tools: Linking Phylogeny and Function Linking & Evolvability Phylogenomic
- 122. • Tanja Woyke • Jonathan Eisen • Duane Moser • Tullis Onstott
- 123. MAGs
- 124. 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
- 125. 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
- 126. Eisen Lab “Topics” Phylogenomic & Evolvability Phylogenomic & Participation In Microbiology & Science Model
- 136. #2: Microbiome is transferable / modifiable
- 137. Why Now V: Importance of Other Microbiomes
- 138. The Rise of the Microbiome Downsides
- 139. Microbiomania vs. Germophobia Germophobia Microbiomania
- 140. Microbiomania vs. Germophobia 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
- 141. Microbiomania vs. Germophobia 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
- 142. Overselling 1: Correlations Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation 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.
- 143. Autism - Microbiome - Diet •
- 144. Overselling 2: Contamination Lesson: Some “observations” of microbes being present in a system are mistakes
- 145. Placenta Microbiome?
- 146. Overselling 3: Presence vs. Importance Lesson: Even when microbes are actually present somewhere, this does not mean they are important
- 147. 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
- 148. Probiotics That Kill … https://phylogenomics.blogspot.com/2012/07/quick-post-story-about-ucdavis.html
- 149. Overselling 5: Personalized ≠ Health Lesson: Most claims of personalized microbiome health and diet plans are bogus
- 150. Overselling 6: Some Microbes Are Bad Lesson: Hygiene hypothesis is important but imbibing all the microbes in the world is not a good plan
- 151. 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
- 152. Underselling 1: Kill Everything Lesson: We have gone completely bonkers with overuse of sterilization and antimicrobials
- 153. Underselling 2: Swab Stories Lesson: Germaphobia leads to crazy behaviors and great underselling of the possible benefits of microbes
- 154. 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
- 155. Solution 1: Complain
- 156. Solution 1: Complain a lot See http://microbiomania.net
- 157. Many others complaining too
- 158. http://microBE.net http://gut-check.net Solution 2: Education & Outreach
- 160. Kitty Microbiome Georgia Barguil Jack Gilbert Project MERCCURI Phone and Shoes tinyurl/kittybiome Holly Ganz David Coil Solution 3: Citizen Science
- 161. Solution 4: Engage Students Too
- 162. Microbiomania vs. Germophobia 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
- 163. Microbiomania vs. Germophobia 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
- 164. Balance? Goal: Evolve microbiome related communications to be balanced, even though most microbiomes are not
- 165. Eisen Lab • Rules