iroki: automatic customization for phylogenetic trees25 groups. however, phylogenetic analyses of...

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Iroki: automatic customization for phylogenetic trees 1

2

Ryan M. Moore1*, Amelia O. Harrison2, Sean M. McAllister2, Rachel L. Marine3, Clara 3

Chan2, and K. Eric Wommack1 4

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1Center for Bioinformatics & Computational Biology, University of Delaware, Newark, DE, 6

USA 7

2School of Marine Science and Policy, University of Delaware, Newark, DE, USA 8

3Department of Biological Sciences, University of Delaware, Newark, DE, USA 9

10

Corresponding author’s information 11

*To whom correspondence should be addressed 12

Address: Delaware Biotechnology Institute, 15 Innovation Way, Newark, Delaware 13

19711 14

(Tel): (302) 831-4362 15

(Fax): (302) 831-3447 16

(E-mail): [email protected] 17

18

Abstract 19

Background 20

Phylogenetic trees are an important analytical tool for examining species and community 21

diversity, and the evolutionary history of species. In the case of microorganisms, 22

.CC-BY 4.0 International licensenot certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which wasthis version posted February 13, 2017. . https://doi.org/10.1101/106138doi: bioRxiv preprint

https://doi.org/10.1101/106138

http://creativecommons.org/licenses/by/4.0/

2

decreasing sequencing costs have enabled researchers to generate ever-larger sequence 23

datasets, which in turn have begun to fill gaps in the evolutionary history of microbial 24

groups. However, phylogenetic analyses of large sequence datasets present challenges to 25

extracting meaningful trends from complex trees. Scientific inferences made by visual 26

inspection of phylogenetic trees can be simplified and enhanced by customizing various 27

parts of the tree, including label color, branch color, and other features. Yet, manual 28

customization is time-consuming and error prone, and programs designed to assist in 29

batch tree customization often require programming experience. To address these 30

limitations, we developed Iroki, a program for fast, automatic customization of 31

phylogenetic trees. Iroki allows the user to incorporate information on a broad range of 32

metadata for each experimental unit represented in the tree. 33

34

Results 35

Iroki was applied to four existing microbial sequence datasets to demonstrate its utility in 36

data exploration and presentation. Iroki was used to highlight connections between viral 37

phylogeny and host taxonomy, to explore the abundance of microbial groups across 38

samples of cattle hide, to examine short-term temporal dynamics of virioplankton 39

communities, and to search for trends in the biogeography of Zetaproteobacteria. 40

41

Conclusions 42

Iroki is an easy-to-use web app and command line application for fast, automatic 43

customization of phylogenetic trees based on user-provided categorical or continuous 44


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metadata. Iroki allows for rapid hypothesis testing through visualizing custom 45

phylogenetic trees, streamlining the process of phylogenetic data exploration and 46

presentation. 47

48

Availability 49

Iroki can be accessed through a web app or via installation through RubyGems, from 50

source, or through the Iroki Docker image. All source code and documentation is 51

available under the GPLv3 license at https://github.com/mooreryan/iroki. The Iroki web-52

app is accessible at www.iroki.net or through the Virome portal 53

(http://virome.dbi.udel.edu), and its source code is released under GPLv3 license at 54

https://github.com/mooreryan/iroki_web. The Docker image can be found here: 55

https://hub.docker.com/r/mooreryan/iroki. 56

57

Keywords 58

Phylogeny, visualization, sequence analysis, bioinformatics, metagenomics 59

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Iroki: automatic customization for phylogenetic trees 61

62

Background 63

Studies in microbial ecology often use phylogenetic trees as a means for assessing the 64

diversity and evolutionary history of microorganisms. As the cost of sequencing has 65

declined, researchers have been able to gather ever-larger sequence datasets. While large 66

sequence datasets have begun to fill in the gaps in the evolutionary history of microbial 67

groups [1–5]; they have also posed new analytical challenges as extracting meaningful 68

trends within such highly dimensional datasets can be cumbersome. In particular, 69

scientific inferences made by visual inspection of phylogenetic trees can be simplified and 70

enhanced by customizing various parts of the tree including label and branch color, 71

branch width, and other features. Though many tree visualization packages allow for 72

manual modifications [6–9], the process can be time consuming and error prone 73

especially when the tree contains many nodes. While a handful of existing programs 74

address the issue of tree visualization, most are not capable of batch customization and 75

those that do often require programming experience [10–13]. 76

77

Iroki, a program for fast, automatic customization of phylogenetic trees, was developed to 78

address these limitations and enable users to incorporate information on a broad range of 79

metadata for each experimental unit represented in the tree. Iroki is available for use 80

through a web interface at www.iroki.net, through the Virome portal 81

(http://virome.dbi.udel.edu), and through a UNIX command line tool. Results are saved in 82


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the widely used Nexus format with color metadata tailored for use with FigTree [8] (a 83

freely available and efficient tree viewer). 84

85

Implementation 86

Iroki enhances visualization of phylogenetic trees by coloring node labels and branches 87

according to categorical metadata criteria or numerical data such as abundance 88

information. Iroki can also rename nodes in a batch process according to user 89

specifications so that node names are more descriptive. A tree file in Newick format 90

containing a phylogenetic tree is always required. Additional required input files depend 91

on the operation(s) desired. Coloring functions require a color map or a biom [14] file. 92

Node renaming functions require a name map. The color map, name map, and biom files 93

are created by the user and, along with the Newick file, form the inputs for Iroki. 94

95

Explicit tree coloring 96

Iroki’s principle functionality involves coloring node labels and/or branches based on 97

information provided by the user in the color map. The color map text file contains either 98

two or three tab-delimited columns depending on how branches and labels are to be 99

colored. Two columns, pattern and color, are used when labels and branches are to have 100

the same color. Three columns, pattern, label color, and branch color, are used when 101

branches and labels are to have different colors. Patterns are searched against node labels 102

either as regular expressions or exact string matches. 103

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Entries in the color column can be any of the 657 named colors in the R programming 105

language [15] (e.g., skyblue, tomato, goldenrod2, lightgray, black) or any valid 106

hexadecimal color code (e.g., #FF78F6). In addition, Iroki provides a 19 color palette 107

with complementary colors based on Kelly’s color scheme for maximum contrast [16]. 108

Nodes in the tree that are not in the color map will remain black. 109

110

Depending on user-specified options, a pattern match to node label(s) will trigger coloring 111

of the label and/or the branch directly connected to that label. Inner branches will be 112

colored to match their descendent branches if all descendants are the same color, 113

allowing quick identification of common ancestors and clades that share common 114

metadata. 115

116

Tree coloring based on numerical data 117

Iroki provides the ability to generate color gradients based on numerical data, such as 118

absolute or relative abundance, from a tab-delimited biom format file. Single-color 119

gradients use color saturation to illustrate numerical differences, with nodes at a higher 120

level being more saturated than those at a lower level. For example, highly abundant 121

nodes will be represented by more highly saturated colors. Two-color gradients show 122

numerical differences through both color mixing and luminosity. Additionally, the biom 123

file may specify numerical information for one group (e.g., abundance in a particular 124

sample) or for two groups (e.g., abundance in the treatment group vs. abundance in the 125


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control group). For biom files with one group, single- or two-color gradients may be used. 126

However, biom files specifying two-group metadata may only use the two-color gradient. 127

128

Renaming nodes 129

Some packages for generating phylogenetic trees restrict the use of special characters and 130

spaces or require node names to be shorter than a specified length or (RAxML [17], 131

PHYLIP [18], etc.). Name restrictions present challenges to scientific interpretation of 132

phylogenetic trees. Iroki’s renaming function uses a two-column, tab-delimited name map 133

to associate current node names, exactly matching those in the tree file, with new names. 134

The new name column has no restrictions on name length or character type. Iroki ensures 135

name uniqueness by appending integers to the ends of names, if necessary. 136

137

Combining the color map, name map, and biom files 138

Iroki can be used to make complex combinations of customizations by combining the 139

color map, name map, and biom files. For example, a biom file can be used to apply a 140

color gradient based on numerical data to the labels of a tree, a color map can be used to 141

separately color the branches based on user-specified conditions, and a name map can be 142

used to rename nodes in a single command or web request. Iroki follows a specific order 143

of precedence when applying multiple customizations. First, the color gradient inferred 144

from the biom file is applied. Next, the color map is applied to specified labels or 145

branches, overriding the gradient applied in the previous step if necessary. Finally, the 146

name map is used to map current names to the new names (Fig. 1). 147


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148

Output 149

Iroki outputs the modified tree in the Nexus format. When building the phylogenetic tree, 150

FigTree uses the Nexus format file and interprets the color metadata output of Iroki. 151

152

Results & Discussion 153

154

Global diversity of bacteriophage 155

Viruses are the most abundant biological entity on Earth, providing an enormous reservoir 156

of genetic diversity, driving evolution of their hosts, influencing composition of microbial 157

communities, and affecting global biogeochemical cycles [19,20]. The current viral 158

taxonomic system is based on a suite of physical characteristics of the virion rather than 159

on genome sequences. The phage proteomic tree, created to provide a genome-based 160

taxonomic system for bacteriophage classification [21], was recently updated to include 161

hundreds of new phage genomes from the Phage SEED reference database [22], as well as 162

long assembled contigs from viral shotgun metagenomes (viromes) collected from the 163

Chesapeake Bay (SERC) [23] and the Mediterranean Sea [24]. 164

165

Taxonomy and host information metadata was collected for the viral genome sequences, a 166

color map was created to assign colors based on viral family and host phyla, and Iroki was 167

used to add color metadata to branches and labels of the phage proteomic tree. Since a 168


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large number of colors were required on the tree, Iroki’s Kelly color palette was used to 169

provide clear color contrasts. The tree was rendered with FigTree (Fig. 2). 170

171

Adding color to the phage proteomic tree with Iroki shows trends in the data that would 172

be difficult to discern without color. Uncultured phage contigs from the SERC and 173

Mediterranean viromes make up a large portion of all phage sequences shown on the tree, 174

and are widely distributed among known phage. In general, viruses in the same family 175

claded together, e.g., branch coloring highlights large groups of closely related 176

Siphoviridae and Myoviridae. This label-coloring scheme also shows that viruses infecting 177

hosts within same phylum are, in general, phylogenetically similar. For example, viruses 178

within one of the multiple large groups of Siphoviridae across the tree infect almost 179

exclusively host species within the same phylum, e.g., Siphoviridae infecting 180

Actinobacteria clade away from Siphoviridae infecting Firmicutes or Proteobacteria. 181

182

Bacterial community diversity and prevalence of E. coli in beef cattle 183

Shiga toxin-producing Escherichia coli (STEC) are dangerous human pathogens that 184

colonize the lower gastrointestinal tracts of cattle and other ruminants. STEC-185

contaminated beef and STEC shed in the feces of these animals are major sources of 186

foodborne illness. To identify possible interactions between STEC populations and the 187

commensal cattle microbiome, a recent study examined the diversity of the bacterial 188

community associated with beef cattle hide [25]. Fecal and hide samples were collected 189

over twelve weeks and SSU rRNA amplicon libraries were constructed and analyzed by 190


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Illumina sequencing [26]. The study indicated that the community structure of hide 191

bacterial communities was altered when the hides were positive for STEC contamination. 192

193

Iroki was used to visualize changes in the relative abundance of each cattle hide bacterial 194

OTU according to the presence or absence of STEC. A Mann-Whitney U test comparing 195

OTU abundance between STEC positive and STEC negative samples was performed, and 196

those bacterial OTUs showing a significant change in relative abundance (p < 0.5) were 197

placed on a phylogenetic tree according to the 16S rRNA sequence. Branches of the tree 198

were colored based on whether there was a significant change in relative abundance with 199

STEC contamination (red: p <= 0.05, blue: p > 0.05). Node labels were colored along a 200

blue-green color gradient representing the abundance ratio of OTUs between samples 201

with STEC (blue) and without (green). Additionally, label luminosity was determined 202

based on overall abundance (lighter: less abundant, darker: more abundant) (Fig. 3). Iroki 203

makes it clear that most OTUs on the tree showed a significant difference in abundance 204

(branch coloring) between STEC positive and STEC negative samples (node coloring). 205

Furthermore, we can see that most OTUs are at low abundance with only a few highly 206

abundant OTUs (label luminosity). The color gradient added by Iroki allows us to see that 207

the abundant OTUs were evolutionarily distant from one another and thus spread out 208

across many phylogenetic groups. 209

210

Iroki can be used to quickly test hypotheses without investing a large amount of time 211

annotating trees manually. A UPGMA tree was created based on unweighted UniFrac 212


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distance [27] between 356 bacterial community profiles based on SSU rRNA amplicon 213

sequences from cattle hide and feces samples (Fig. 4). Iroki was used to evaluate 214

similarities in sample bacterial communities according to the sampling location. Iroki 215

colored branches based on whether the sample originated from feces (blue) or from hide 216

(red). The coloring added by Iroki shows a clear partitioning of bacterial communities on 217

the tree based on their sampling location (hide or feces). However, four fecal samples 218

claded with hide samples, and two hide samples claded with fecal samples, highlighting 219

the ability of Iroki to easily identify good candidates for more in-depth examination. 220

Additionally, Iroki was used to illustrate a correlation between one of the most abundant 221

bacterial families, Ruminococcaceae, and sampling location. Iroki colored node labels 222

with a color gradient based on Ruminococcaceae family abundance, utilizing both a 223

single color gradient (Fig. 4A) and a two color gradient (Fig. 4B). Custom trees were 224

visualized using FigTree. Iroki’s automatic color gradient and ability to label branches 225

and nodes based on different criteria clearly show that Ruminococcaceae is more 226

abundant in fecal samples than in hide samples. 227

228

Short-term dynamics of virioplankton 229

The gene encoding Ribonuclotide reductase (RNR) is common within viral genomes and 230

thus can be used as a marker gene for studying viral diversity. Moreover, RNR 231

polymorphism is predictive of some of the biological and ecological features of viral 232

populations [28]. A mesocosm experiment examined the short-term dynamics of phage 233

populations using RNR amplicon sequences, specifically, sequences of class II RNRs of 234


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bacteriophages infecting cyanobacterial hosts. A phylogenetic tree was created from the 235

Cyano II RNR amplicon sequences and Iroki was used to color nodes and branches based 236

on the time point (0 h, 6 h, 12 h) at which each amplicon sequence was observed. The 237

customized tree was then visualized using FigTree (Fig. 5). Iroki’s coloring showed that no 238

phylogenetic clade was dominated by OTUs observed in any particular time point; rather, 239

time points were spread relatively evenly across clades. This analysis demonstrates Iroki’s 240

utility for exploring sequence datasets, allowing the researcher to quickly and easily test 241

hypotheses. 242

243

Phylogeny of Zetaproteobacteria within a biogeographic context 244

Biogeographical studies assess the distribution of an organism’s biodiversity across space 245

and time. The extent to which microorganisms exhibit biogeography is an open question 246

in microbial ecology. The isolated nature of the microbial communities associated with 247

deep-ocean hydrothermal vents provides an ideal system for studying the biogeography of 248

microbes. In particular, iron-oxidizing bacteria have been shown to thrive in vent fluids, 249

sediments, and iron-rich microbial mats associated with the vents. Globally, iron-250

oxidizing bacteria make significant contributions to the iron and carbon cycles. A recent 251

study analyzed multiple SSU rRNA clone libraries to investigate the biogeography of 252

Zetaproteobacteria, a phyla containing many iron-oxidizing bacterial species, between 253

three sampling regions of the Pacific Ocean (central Pacific—Loihi seamount, western 254

Pacific—Southern Mariana Trough, and southern Pacific (Vailulu’u Seamount/Tonga 255

Arc/East Lau Spreading Center/Kermadec Arc) [29]. Sequences were aligned and a 256


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phylogeny was inferred as described in [29]. Iroki was used to examine the relationship 257

between sampling location and phylotype by adding branch and label color based on 258

geographic location and renaming original node labels with OTU and location metadata. 259

The custom tree was visualized using FigTree (Fig. 6). In some cases, OTUs contained 260

sequences from only one sampling location (e.g., OTUs 12, 15, and 16), whereas other 261

OTUs are distributed among more than one sampling location (e.g., OTUs 1, 2, and 4). 262

Often, sequences sampled from the same geographic location are in the same phylotype 263

despite being members of different OTUs (e.g., OTUs 10 and 19). 264

265

Availability and requirements 266

A web-based version of Iroki can be accessed online at www.iroki.net or through the 267

Virome portal (http://virome.dbi.udel.edu/). For users who wish to run Iroki locally, a 268

command line version of the program is installable via RubyGems, from GitHub 269

(https://github.com/mooreryan/iroki). A Docker image is available for users who desire the 270

flexibility of the command line tool, but do not want to install Iroki or manage its 271

dependencies (https://hub.docker.com/r/mooreryan/iroki). Docker is a convenient method 272

for packaging an application with all of its dependencies that is guaranteed to run the 273

same regardless of the user’s environment [30,31]. The README provided with the source 274

code provides detailed instructions for setting up and running Iroki. Further 275

documentation and tutorials can be found at the Iroki Wiki 276

(https://github.com/mooreryan/iroki/wiki). 277

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License 279

Iroki and its associated programs are released under the GNU General Public License 280

version 3 [32]. 281

282

Conclusions 283

Iroki is a command line program and web app for fast, automatic customization of large 284

phylogenetic trees based on user specified configuration files describing categorical or 285

continuous metadata information. The output files include Nexus tree files with color 286

metadata tailored specifically for use with FigTree. Various example datasets from 287

microbial ecology studies were analyzed to demonstrate Iroki’s utility. In each case, Iroki 288

simplified the processes of data exploration, data presentation, and hypothesis testing. 289

Iroki provides a simple and convenient way to rapidly customize phylogenetic trees, 290

especially in cases where the tree in question is too large to annotate manually or in 291

studies with many trees to annotate. 292

293

List of Abbreviations 294

OTU: operational taxonomic 295

RNR: Ribonucleotide reductase 296

STEC: Shiga-toxengenic Escherichia coli 297

298

Ethics approval and consent to participate 299

Not applicable 300


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301

Consent for publication 302

Not applicable 303

304

Availability of data and materials 305

Data and code used to generate figures are available on GitHub at 306

https://github.com/mooreryan/iroki_manuscript_data 307

308

Funding 309

This project was supported by the USDA National Institute of Food and Agriculture award 310

number 2012-68003-30155 and the National Science Foundation Advances in 311

Bioinformatics program (award number DBI_1356374). 312

313

Competing Interests 314

The authors declare that they have no competing interests. 315

316

Authors’ contributions 317

RMM and SMM conceived the project. RMM wrote the manuscript and implemented 318

Iroki. AOH and RLM processed and analyzed Cyano II amplicons. All authors read, 319

edited, and approved the final manuscript. 320

321

Acknowledgements 322


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We would like to acknowledge Daniel J. Nasko and Jessica M. Chopyk for their work on 323

the phage proteomic tree, and Barbra D. Ferrell for editing the manuscript. 324

325


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400


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401

Fig. 1: Precedence of Iroki’s customization pipeline 402

Flowchart illustrating the precedence of steps when performing multiple customizations 403

with Iroki. Input/output files are purple, command line options are in blue, and processes 404

are orange. The choice of exact or regular expression matching guides each subsequent 405

step of the process. Iroki gives higher precedence to processes towards the bottom of the 406

diagram. For example, given that a user selects the options for coloring both labels and 407

branches, and provides both a biom file and color map with the color map specifying 408

colors for the labels only, then the branches will be colored according to the color 409

gradient inferred from the biom file, whereas the labels will be colored according to the 410

rules specified in the color map. 411

412

Apply color gradient

Apply specified colors

Rename nodes

Matching option

Exact or regexp mathching?

Color branches, labels, or both?

Coloring options

Biom file

Color map

Name map

Newick file

Nexus file

Lower

Higher

Precedence


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413

Fig. 2: Comparing phage and their host phyla 414

All phage genomes from Phage SEED with assembled virome contigs from the Chesapeake 415

Bay and Mediterranean Sea. Iroki highlights phylogenetic trends after coloring branches 416

according to viral family or sampling location in the case of virome contigs (marked with 417

an asterisk in the legend), and coloring node labels according to host phylum of the 418

phage. 419

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SERC*SiphoviridaeMyoviridaeMediterranean*PodoviridaeMicroviridaeInoviridaeLeviviridaeCystoviridaeOther/Unclassified

ProteobacteriaFimicutesActinobacteriaBacteroidetesCyanobacteriaCrenarchaeotaEuryarchaeotaTenericutesOther/Unclassified

Branches(Viral family)

Labels(Host phylum)


https://doi.org/10.1101/106138


23

421

Fig. 3: Changes in OTU abundance in two sample groups 422

Approximate-maximum likelihood tree of OTUs that showed significant differences in 423

relative abundance between STEC positive and STEC negative cattle hide samples. 424

Branches show significance based on coloring by the p-value of a Mann-Whitney U test 425

examining changes in abundance between samples positive for STEC (p < 0.05 – red) and 426

samples negative for STEC, (p >= 0.05 – blue). Label color on a blue-green color gradient 427


https://doi.org/10.1101/106138


24

highlights OTU occurrence based on the abundance ratio between STEC positive samples 428

(blue) and STEC negative samples (green). For example, labels that are darker green had a 429

higher abundance in STEC negative samples. Node luminosity represents overall 430

abundance with lighter nodes being less abundant than darker nodes. 431


https://doi.org/10.1101/106138


25

432

Fig. 4: Comparing cattle fecal and hide samples and the abundance of Ruminococcaceae 433

Phylogeny based on UPGMA tree of pairwise unweighted UniFrac distance between 356 434

bacterial community profiles based on SSU rRNA amplicon sequences from cattle hide 435

and feces. Branches are colored by feces (blue) and hide (red). Rapid testing of the 436

hypothesis that the abundance of one of the most abundant families, Ruminococcaceae, 437

and sample origin are correlated is enabled through node label coloring by (A) a green 438

single-color gradient (color saturation increases with increasing abundance of 439

Ruminococcaceae OTUs) and (B) a light green (low abundance of Ruminococcaceae 440

OTUs) to dark blue (high abundance of Ruminococcaceae OTUs) color gradient. 441

442


https://doi.org/10.1101/106138


26

443

Fig. 5: Temporal dynamics of virioplankton populations according to Cyano II RNR 444

amplicon phylogeny 445

An approximately-maximum-likelihood phylogenetic tree of 200 randomly selected class 446

II Cyano RNR representative sequences from 98% percent clusters. Iroki was used to color 447

branches by time point: zero hours – yellow, six hours – orange, and twelve hours – 448

purple. 449


https://doi.org/10.1101/106138


27

450

451

452

453

454

455

456

457

458

459

460

461

462

463

Fig. 6: Zetaproteobacteria show biogeographic partitioning 464

Phylogenetic tree showing placement of full-length Zetaproteobacteria SSU rRNA 465

sequences with outgroups. Iroki was used to color labels and branches by geographic 466

location of the sampling site (Loihi – green, Mariana – gold, South Pacific – purple, and 467

Other – gray), as well as to rename the nodes with OTU and sampling site metadata. 468

469

Loihi Mariana South Pacific Other

Location

0.04

Spha

erot

ilus n

atan

s

OTU

9 S. Pacific S49 OTU

2 Lo

ihi S

16

OTU12 Loihi S60

OTU

22 L

oihi

S77

OTU3 Other S26O

TU4 Loihi S29

OTU

9 Mariana S45

Hyphomonas jannasch

iana

OTU2

Loih

i S22

OTU

11 Loihi S58

Aquifex pyrophilus

Lept

othr

ix di

scop

hora

OTU

9 Mariana S46

OTU1 Loihi S8

Nitrospina gracilis

OTU26 S. Pacific S82

OTU

4 Loihi S31

OTU1 S. Pacific S11

OTU

2 Lo

ihi S

20

OTU

9 Other S47

OTU2 L

oihi S

21

OTU20 Mariana S74

OTU6 Loihi S40

OTU2 Loihi S

19

OTU

11 L

oihi

S56

Rhodobacter

gluco

nicum

OTU3 S. Pacific S27

OTU

2 Lo

ihi S

17

OTU1 Mariana S9

OTU

1 S.

Pac

ific

S13

OTU1 Loihi S7

Gallion

ella f

errug

inea

OTU18 Other S72

OTU1 Loihi S4

Thermotoga maritima

OTU15 Mariana S65

OTU

14 Loihi S63

OTU

5 S. Pacific S37

OTU6 Loihi S38

OTU2 S. Pac

ific S24

OTU7 Loihi S41

OTU1 Loihi S2O

TU4 S. Pacific S33

OTU8 S. Pacific S43OTU8 S. Pacific S44

OTU2 Other

S23

Sulfurovum lithotrophicum

OTU21 S. Pacific S75O

TU23

Oth

er S

79

OTU

10 Loihi S53

OTU1 Loihi S3


OTU9 Other S48

OTU

4 S. Pacific S36

Sulfurimonas autotrophica

Nitratiruptor tergarcus

OTU1 Loihi S6

OTU

16 S

. Pac

ific

S69

OTU

28 S

. Pac

ific

S84

Thio

thrix

disc

iform

is

OTU1 S. Pacific S10

OTU15 Mariana S67

OTU6 Loihi S39

OTU

4 S. Pacific S35

Thio

mic

rosp

ira p

sych

roph

ila

OTU1 Loihi S5

OTU

2 Lo

ihi S

14

OTU8 Mariana S42

OTU

22 L

oihi

S78

OTU

11 Other S59

OTU

11 Loihi S55

OTU

10 L

oihi

S51

OTU

16 S

. Pac

ific

S68

OTU18 Other S71OTU13 S. Pacific S62

OTU24 Other S80

OTU

10 L

oihi

S52

OTU3 S. Pacific S28

OTU1 Loihi S1

Mar

inob

acte

r aqu

aeol

ei OTU15 Mariana S66

OTU

25 S. Pacific S81

Desulfurella acetivorans

OTU

4 Loihi S30

OTU3 Loihi S25

Roseobact

er den

itrifican

s

OTU

27 S. Pacific S83

OTU

2 Lo

ihi S

15

OTU

2 Lo

ihi S

18


OTU

14 O

ther

S64

OTU

4 S. Pacific S34

OTU

11 Loihi S57

OTU

9 S. Pacific S50

OTU

19 L

oihi

S73

Hippea maritima

OTU

4 Loihi S32

OTU

1 S. Pacific S12

OTU

10 L

oihi

S54

OTU12 Loihi S61


https://doi.org/10.1101/106138


iroki: automatic customization for phylogenetic trees25 groups. however, phylogenetic analyses of...

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