bayesian taxonomic assignment for the next-generation metagenomics

“Bayesian Taxonomic Assignment for the Next-Generation

Metagenomics”Jonathan A. Eisen

August 7, 2013

DHS Meeting

Wednesday, August 7, 13

Shotgun Metagenomics



DNA



DNA Sequence



DNA Sequence

?????



DNA Sequence

Who is there?

What are they doing?



DNA Sequence



• Which communities are most similar / different?

• What accounts for the differences?

• Natural vs. unnatural• Community level

signatures (of events, stability, biogeography, etc)


Our Approach - Phylogeny

Phylogeny of sequences can reveal details about history, taxonomy, function, and ecology


DNA extraction

PCRSequence

rRNA genes

Sequence alignment = Data matrix

Phylogenetic tree

PCR

rRNA1

rRNA2

Makes lots of copies of the rRNA genes in sample

rRNA1 5’...ACACACATAGGTGGAGCTA

GCGATCGATCGA... 3’

E. coli

Humans

A

T

T

A

G

A

A

C

A

T

C

A

C

A

A

C

A

G

G

A

G

T

T

CrRNA1

E. coli Humans

rRNA2rRNA2

5’..TACAGTATAGGTGGAGCTAGCGACGATCGA... 3’

rRNA phylotyping

rRNA3 5’...ACGGCAAAATAGGTGGATT

CTAGCGATATAGA... 3’

rRNA4 5’...ACGGCCCGATAGGTGGATT

CTAGCGCCATAGA... 3’

rRNA3 C A C T G T

rRNA4 C A C A G T

Yeast T A C A G T

Yeast

rRNA3 rRNA4

Phylotyping


Uses of Phylogeny in Metagenomics

• Taxonomic assessment• Phylogenetic OTUs• Phylogenetic taxonomy assignment• Phylogenetic binning

• Sample comparisons and hypothesis testing• Alpha diversity (i.e., PD)• Beta diversity• Trait evolution• Dispersal• Functional predictions• Rates of evolution• Convergence


Venter et al., Science 304: 66. 2004

rRNA Phylotyping - Sargasso Metagenome


http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15001713&query_hl=2



RecA Phylotyping - Sargasso Metagenome




0

0.125

0.250

0.375

0.500

Alphapro

teobacteria

Betap

roteobacteria

Gamm

aproteobacteria

Epsilo

nproteobacteria

Deltapro

teobacteria

Cyanobacteria

Firmicutes

Actinobacteria

Chlorobi

CFB

Chloroflexi

Spirochaetes

Fusobacteria

Deinococcus-Th

ermus

Euryarchaeota

Crenarchaeota

Sargasso Phylotypes

Wei

ghte

d %

of C

lone

s

Major Phylogenetic Group

EFG EFTu HSP70 RecA RpoB rRNA

Phylotyping - Sargasso Metagenome





GOS 1

GOS 2

GOS 3

GOS 4

GOS 5

Phylogenetic ID of Novel Lineages

Wu et al PLoS One 2011


Wu et al. 2006 PLoS Biology 4: e188.

Baumannia makes vitamins and cofactors

Sulcia makes amino acids

Phylogenetic Binning


http://biology.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pbio.0040188

http://biology.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pbio.0040188

Phylogenetic Functional Prediction





Sequencing Revolution

• More Samples

• Deeper sequencing• The rare biosphere• Relative abundance estimates

• More samples (with barcoding)• Times series• Spatially diverse sampling• Fine scale sampling


http://phylosift.wordpress.com

PhyloSift

Supported by DHS GrantWednesday, August 7, 13

Acknowledgements

Jonathan Eisen

Students and other staff: - Eric Lowe, John Zhang, David Coil

Open source community: - BLAST, LAST, HMMER, Infernal, pplacer, Krona, metAMOS, Bioperl, Bio::Phylo, JSON, etc. etc.

PhyloSift is open source software:- Website: http://phylosift.wordpress.org- Code: http://github.com/gjospin/phylosift

Erick MatsenFHCRC

Todd TreangenBNBI, NBACC

Holly Bik

TiffanieNelson

MarkBrown

Aaron Darling

Guillaume Jospin


http://phylosift.wordpress.org/


Analysis & Summary

PhyloSift


Analysis & Summary

Analysis & Summary

•Metagenomic reads•Contigs•Genes

PhyloSift


Analysis & Summary

Searching inputs against reference family DB

PhyloSift


Analysis & Summary

Align to reference HMMs for each family

PhyloSift


Analysis & Summary

Place reads into reference phylogeny using pplacer

PhyloSift


Analysis & Summary

Summarize results & additional analyses

PhyloSift


Output 1: Taxonomy


Taxonomic summary plots in Krona (Ondov et al 2011)

Taxonomic Summaries (via Krona)


Tree Reconciliation in PhyloSift


Tree Reconciliation in PhyloSift

Environmental Sequences

Named Taxa


Output 2: Phylogenetic Tree of Reads


PhyloSift Tree Browsing

Darling et al Submitted

Placement tree from 2 week old infant gut data


Output 3: Edge PCA Edge PCA for exploratory data analysis (Matsen and Evans 2013) Given E edges and S samples:

− For each edge, calculate difference in placement mass on either side of edge− Results in E x S matrix− Calculate E x E covariance matrix− Calculate eigenvectors, eigenvalues of covariance matrix

Eigenvector: each value indicates how “important” an edge is in explaining differences among the S samples

Example calculating a matrix entry for an edge:This edge gets 5-2=3

mass=5 mass=2


Edge PCA: Identify lineages that explain most variation among samples

Matsen and Evans 2013, Darling et al Submitted.

Edge PCA


QIIME and Edge PCA on 110 fecal metagenomes from

Yatsunenko et al 2012 Nature.

Sequenced with 454, to about 150Mbp/metagenome

Darling et al Submitted.

Edge PCA vs. UNIFRAC PCA


Output 4: Forensics


Analysis & Summary

Analysis & Summary


PhyloSift


Analysis & Summary

Analysis & Summary


PhyloSift

Challenge - Short Non Overlapping Reads


Analysis & Summary


PhyloSift


Markers

• PMPROK – Dongying Wu’s Bac/Arch markers

• Eukaryotic Orthologs – Parfrey 2011 paper• 16S/18S rRNA • Mitochondria - protein-coding genes• Viral Markers – Markov clustering on

genomes• Codon Subtrees – finer scale taxonomy• Extended Markers – plastids, gene families


PMPROK Genes


Analysis & Summary

PhyloSift

Challenges:•Limited ref. genomes•Limited markers, families



Improving I: More Markers

Phylogenetic group Genome Number

Gene Number

Maker Candidates

Archaea 62 145415 106Actinobacteria 63 267783 136Alphaproteobacteria 94 347287 121Betaproteobacteria 56 266362 311Gammaproteobacteria 126 483632 118Deltaproteobacteria 25 102115 206Epislonproteobacteria 18 33416 455Bacteriodes 25 71531 286Chlamydae 13 13823 560Chloroflexi 10 33577 323Cyanobacteria 36 124080 590Firmicutes 106 312309 87Spirochaetes 18 38832 176Thermi 5 14160 974Thermotogae 9 17037 684

Wu et al. PLOS One 2013. In press.


RepresentativeGenomes

ExtractProtein

Annotation

All v. AllBLAST

HomologyClustering

(MCL)

SFams

Align & Build

HMMs

HMMs

Screen forHomologs

NewGenomes

ExtractProtein

Annotation

Figure 1Sharpton et al. 2013

AB

C

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Improving II: More Families


Improving III: Filling in the Tree

Figure from Barton, Eisen et al. “Evolution”, CSHL Press based on Baldauf et al Tree


Genomic Encyclopedia of Bacteria & Archaea

Wu et al. 2009 Nature 462, 1056-1060



http://www.nature.com/nature/journal/v462/n7276/full/nature08656.html


Genomic Encyclopedia of Bacteria & Archaea

Wu et al. 2009 Nature 462, 1056-1060



Family Diversity vs. PD

Wu et al. 2009 Nature 462, 1056-1060




The Dark Matter of Biology

From Wu et al. 2009 Nature 462, 1056-1060Wednesday, August 7, 13



50

Number of SAGs from Candidate Phyla

OD

1

OP

11

OP

3

SA

R4

06

Site A: Hydrothermal vent 4 1 - -Site B: Gold Mine 6 13 2 -Site C: Tropical gyres (Mesopelagic) - - - 2Site D: Tropical gyres (Photic zone) 1 - - -

Sample collections at 4 additional sites are underway.

Phil Hugenholtz

GEBA Uncultured


JGI Dark Matter Project

environmental samples (n=9)

isolation of singlecells (n=9,600)

whole genomeamplification (n=3,300)

SSU rRNA gene based identification

(n=2,000)

genome sequencing, assembly and QC (n=201)

draft genomes(n=201)

SAK

HSM ETLTG

HOT

GOM

GBS

EPR

TAETL T

PR

EBS

AK E

SM G TATTG

OM

OT

seawater brackish/freshwater hydrothermal sediment bioreactor

GN04WS3 (Latescibacteria)GN01

!"#$%&'$LD1

WS1PoribacteriaBRC1

LentisphaeraeVerrucomicrobia

OP3 (Omnitrophica)ChlamydiaePlanctomycetes

NKB19 (Hydrogenedentes)WYOArmatimonadetesWS4

ActinobacteriaGemmatimonadetesNC10SC4WS2

Cyanobacteria()*&2

Deltaproteobacteria

EM19 (Calescamantes)+,-*./'&'012345678#89/,-568/:

GAL35Aquificae

EM3Thermotogae

Dictyoglomi

SPAMGAL15

CD12 (Aerophobetes)OP8 (Aminicenantes)AC1SBR1093

ThermodesulfobacteriaDeferribacteres

Synergistetes

OP9 (Atribacteria)()*&2

CaldisericaAD3

Chloroflexi

AcidobacteriaElusimicrobiaNitrospirae49S1 2B

CaldithrixGOUTA4

*;<%0123=/68>8?8,6@98/:Chlorobi

486?8,A-5BTenericutes4AB@9/,-568/Chrysiogenetes

Proteobacteria

4896@9/,-565BTG3SpirochaetesWWE1 (Cloacamonetes)

C=1ZB3

=D)&'EF58>@,@,,AB&CG56?ABOP1 (Acetothermia)Bacteriodetes

TM7GN02 (Gracilibacteria)

SR1BH1

OD1 (Parcubacteria)

(*1OP11 (Microgenomates)

Euryarchaeota

Micrarchaea

DSEG (Aenigmarchaea)Nanohaloarchaea

Nanoarchaea

Cren MCGThaumarchaeota

Cren C2Aigarchaeota

Cren pISA7

Cren ThermoproteiKorarchaeota

pMC2A384 (Diapherotrites)

BACTERIA ARCHAEA

archaeal toxins (Nanoarchaea)

lytic murein transglycosylase

stringent response (Diapherotrites, Nanoarchaea)

ppGpp

limitingamino acids

SpotT RelA

(GTP or GDP)+ PPi

GTP or GDP+ATP

limitingphosphate,fatty acids,carbon, iron

DksA

Expression of components for stress response

sigma factor (Diapherotrites, Nanoarchaea)

!4

"#$#"%

!2!3 !1

-35 -10

&'()

&*()

+',#-./0123452

oxidoretucase

+ +e- donor e- acceptor

H

'Ribo

ADP

+

'62

O

Reduction

OxidationH

'Ribo

ADP

'6

O

2H

',)##$#6##$#72#####################',)6+ + -

HGT from Eukaryotes (Nanoarchaea)

Eukaryota

O68*62

OH

'6

*8*63

OO

68*62

'6

*8*63

O

tetra-peptide

O68*62

OH

'6

*8*63

OO

68*62

'6

*8*63

O

tetra-peptide

murein (peptido-glycan)

archaeal type purine synthesis (Microgenomates)

PurFPurD9:3'PurL/QPurMPurKPurE9:3*PurB

PurP

?

Archaea

adenine guanine

O

6##'2

+'

'62

'

'

H

H

'

'

'

H

HH' '

H

PRPP ;,<*,+

IMP

,<*,+

A*

GUA *G U

GU

A

*

GU

A UA * U

A * U

Growing AA chain

=+',>?/0@#recognizes

UGA1+',

UGA recoded for Gly (Gracilibacteria)

ribosome

Woyke et al. Nature 2013.




A Genomic Encyclopedia of Microbes (GEM)



Analysis & Summary


PhyloSift


Analysis & Summary


PhyloSift

Challenge:How to align?


Zorro - Automated Masking

ce to

Tru

e Tr

ee

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

200 400 800 1600 3200

Dist

ance

to T

rue

Tree

Sequence Length

200

no maskingzorrogblocks

Wu M, Chatterji S, Eisen JA (2012) Accounting For Alignment Uncertainty in Phylogenomics. PLoS ONE 7(1): e30288. doi:10.1371/journal.pone.0030288


http://www.plosone.org/article/info%253Adoi%252F10.1371%252Fjournal.pone.0030288




Analysis & Summary


PhyloSift


Analysis & Summary


PhyloSift

Challenges:•Trees from short reads•Probabilistic methods


Improving IV: Better Reference Tree

Lang et al. 2013


Analysis & Summary

Summarize results & additional analyses

PhyloSift


Phylosift DB Update

Amino Acid Tree

Run PhyloSift (search + align)

Execute'dbupdate'mode'

A'taxa'set'is'selected'with'a'maxPD'cutoff'of'0.02'and'a'new'

tree'is'inferred'

EBI'Genomes'

Infer Updated Tree

Add'new'sequences'to'marker'packages'

JGI'Genomes'

Private'Genomes'

NCBI'Genomes'

Nucleotide Tree

Prune Tree

Update reference sequences with

new data

New'sequences'added'at'0.25'PD'for'amino'acid'tree;'higher'PD'threshold'enables'more'aggressive'searches'of'reference'database,'since'LAST'searching'is'faster'

with'fewer'sequences.'

Reconcile'NCBI'taxonomy'IDs'with'phylogeneOc'topologies,'for'both'amino'acid'tree'and'codon'subtrees'

Tree Reconciliation

Package Markers

Users’'local'marker'databases'are'automaOcally'scanned'each'Ome'PhyloSiR'is'run'and'any'new'

updates'are'automaOcally'downloaded'if'available'

Automated Download to

PhyloSift Users

Prune Tree A'taxa'set'is'selected'with'a'maxPD'cutoff'of'0.01'and'a'new'tree'is'inferred'


Improving VI: Other Methods

• PhylOTU• Kembel all markers• Kembel copy # correction


Kembel Correction

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


alignment used to build the profile, resulting in a multiplesequence alignment of full-length reference sequences andmetagenomic reads. The final step of the alignment process is aquality control filter that 1) ensures that only homologous SSU-rRNA sequences from the appropriate phylogenetic domain areincluded in the final alignment, and 2) masks highly gappedalignment columns (see Text S1).We use this high quality alignment of metagenomic reads and

references sequences to construct a fully-resolved, phylogenetictree and hence determine the evolutionary relationships betweenthe reads. Reference sequences are included in this stage of theanalysis to guide the phylogenetic assignment of the relativelyshort metagenomic reads. While the software can be easilyextended to incorporate a number of different phylogenetic toolscapable of analyzing metagenomic data (e.g., RAxML [27],pplacer [28], etc.), PhylOTU currently employs FastTree as adefault method due to its relatively high speed-to-performanceratio and its ability to construct accurate trees in the presence ofhighly-gapped data [29]. After construction of the phylogeny,lineages representing reference sequences are pruned from thetree. The resulting phylogeny of metagenomic reads is then used tocompute a PD distance matrix in which the distance between apair of reads is defined as the total tree path distance (i.e., branchlength) separating the two reads [30]. This tree-based distancematrix is subsequently used to hierarchically cluster metagenomicreads via MOTHUR into OTUs in a fashion similar to traditionalPID-based analysis [31]. As with PID clustering, the hierarchicalalgorithm can be tuned to produce finer or courser clusters,corresponding to different taxonomic levels, by adjusting theclustering threshold and linkage method.To evaluate the performance of PhylOTU, we employed

statistical comparisons of distance matrices and clustering resultsfor a variety of data sets. These investigations aimed 1) to compare

PD versus PID clustering, 2) to explore overlap between PhylOTUclusters and recognized taxonomic designations, and 3) to quantifythe accuracy of PhylOTU clusters from shotgun reads relative tothose obtained from full-length sequences.

PhylOTU Clusters Recapitulate PID ClustersWe sought to identify how PD-based clustering compares to

commonly employed PID-based clustering methods by applyingthe two methods to the same set of sequences. Both PID-basedclustering and PhylOTU may be used to identify OTUs fromoverlapping sequences. Therefore we applied both methods to adataset of 508 full-length bacterial SSU-rRNA sequences (refer-ence sequences; see above) obtained from the Ribosomal DatabaseProject (RDP) [25]. Recent work has demonstrated that PID ismore accurately calculated from pairwise alignments than multiplesequence alignments [32–33], so we used ESPRIT, whichimplements pairwise alignments, to obtain a PID distance matrixfor the reference sequences [32]. We used PhylOTU to compute aPD distance matrix for the same data. Then, we used MOTHUR tohierarchically cluster sequences into OTUs based on both PIDand PD. For each of the two distance matrices, we employed arange of clustering thresholds and three different definitions oflinkage in the hierarchical clustering algorithm: nearest-neighbor,average, and furthest-neighbor.To statistically evaluate the similarity of cluster composition

between of each pair of clustering results, we used two summarystatistics that together capture the frequency with which sequencesare co-clustered in both analyses: true conjunction rate (i.e., theproportion of pairs of sequences derived from the same cluster inthe first analysis that also are clustered together in the secondanalysis) and true disjunction rate (i.e., the proportion of pairs ofsequences derived from different clusters in the first analysis thatalso are not clustered together in the second analysis) (see Methods

Figure 1. PhylOTU Workflow. Computational processes are represented as squares and databases are represented as cylinders in this generalizeworkflow of PhylOTU. See Results section for details.doi:10.1371/journal.pcbi.1001061.g001

Finding Metagenomic OTUs

PLoS Computational Biology | www.ploscompbiol.org 3 January 2011 | Volume 7 | Issue 1 | e1001061

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

PhylOTU


http://www.ploscompbiol.org/article/info%253Adoi%252F10.1371%252Fjournal.pcbi.1001061

http://www.ploscompbiol.org/article/info%253Adoi%252F10.1371%252Fjournal.pcbi.1001061

Kembel Combiner

cally defined by a sequence similarity threshold) in the sampleas equally related. Newer ! diversity measures that incorporatephylogenetic information are more powerful because they ac-count for the degree of divergence between sequences (13, 18,29, 30). Phylogenetic ! diversity measures can also be eitherquantitative or qualitative depending on whether abundance istaken into account. The original, unweighted UniFrac measure(13) is a qualitative measure. Unweighted UniFrac measuresthe distance between two communities by calculating the frac-tion of the branch length in a phylogenetic tree that leads todescendants in either, but not both, of the two communities(Fig. 1A). The fixation index (FST), which measures thedistance between two communities by comparing the geneticdiversity within each community to the total genetic diversity ofthe communities combined (18), is a quantitative measure thataccounts for different levels of divergence between sequences.The phylogenetic test (P test), which measures the significanceof the association between environment and phylogeny (18), istypically used as a qualitative measure because duplicate se-quences are usually removed from the tree. However, the Ptest may be used in a semiquantitative manner if all clones,even those with identical or near-identical sequences, are in-cluded in the tree (13).

Here we describe a quantitative version of UniFrac that wecall “weighted UniFrac.” We show that weighted UniFrac be-haves similarly to the FST test in situations where both are

applicable. However, weighted UniFrac has a major advantageover FST because it can be used to combine data in whichdifferent parts of the 16S rRNA were sequenced (e.g., whennonoverlapping sequences can be combined into a single treeusing full-length sequences as guides). We use two differentdata sets to illustrate how analyses with quantitative and qual-itative ! diversity measures can lead to dramatically differentconclusions about the main factors that structure microbialdiversity. Specifically, qualitative measures that disregard rel-ative abundance can better detect effects of different foundingpopulations, such as the source of bacteria that first colonizethe gut of newborn mice and the effects of factors that arerestrictive for microbial growth such as temperature. In con-trast, quantitative measures that account for the relative abun-dance of microbial lineages can reveal the effects of moretransient factors such as nutrient availability.

MATERIALS AND METHODS

Weighted UniFrac. Weighted UniFrac is a new variant of the original un-weighted UniFrac measure that weights the branches of a phylogenetic treebased on the abundance of information (Fig. 1B). Weighted UniFrac is thus aquantitative measure of ! diversity that can detect changes in how many se-quences from each lineage are present, as well as detect changes in which taxaare present. This ability is important because the relative abundance of differentkinds of bacteria can be critical for describing community changes. In contrast,the original, unweighted UniFrac (Fig. 1A) is a qualitative ! diversity measurebecause duplicate sequences contribute no additional branch length to the tree(by definition, the branch length that separates a pair of duplicate sequences iszero, because no substitutions separate them).

The first step in applying weighted UniFrac is to calculate the raw weightedUniFrac value (u), according to the first equation:

u ! !i

n

bi " "Ai

AT#

Bi

BT"

Here, n is the total number of branches in the tree, bi is the length of branch i,Ai and Bi are the numbers of sequences that descend from branch i in commu-nities A and B, respectively, and AT and BT are the total numbers of sequencesin communities A and B, respectively. In order to control for unequal samplingeffort, Ai and Bi are divided by AT and BT.

If the phylogenetic tree is not ultrametric (i.e., if different sequences in thesample have evolved at different rates), clustering with weighted UniFrac willplace more emphasis on communities that contain quickly evolving taxa. Sincethese taxa are assigned more branch length, a comparison of the communitiesthat contain them will tend to produce higher values of u. In some situations, itmay be desirable to normalize u so that it has a value of 0 for identical commu-nities and 1 for nonoverlapping communities. This is accomplished by dividing uby a scaling factor (D), which is the average distance of each sequence from theroot, as shown in the equation as follows:

D ! !j

n

dj " #Aj

AT$

Bj

BT$

Here, dj is the distance of sequence j from the root, Aj and Bj are the numbersof times the sequences were observed in communities A and B, respectively, andAT and BT are the total numbers of sequences from communities A and B,respectively.

Clustering with normalized u values treats each sample equally instead of

TABLE 1. Measurements of diversity

Measure Measurement of " diversity Measurement of ! diversity

Only presence/absence of taxa considered Qualitative (species richness) QualitativeAdditionally accounts for the no. of times that

each taxon was observedQuantitative (species richness and evenness) Quantitative

FIG. 1. Calculation of the unweighted and the weighted UniFracmeasures. Squares and circles represent sequences from two differentenvironments. (a) In unweighted UniFrac, the distance between thecircle and square communities is calculated as the fraction of thebranch length that has descendants from either the square or the circleenvironment (black) but not both (gray). (b) In weighted UniFrac,branch lengths are weighted by the relative abundance of sequences inthe square and circle communities; square sequences are weightedtwice as much as circle sequences because there are twice as many totalcircle sequences in the data set. The width of branches is proportionalto the degree to which each branch is weighted in the calculations, andgray branches have no weight. Branches 1 and 2 have heavy weightssince the descendants are biased toward the square and circles, respec-tively. Branch 3 contributes no value since it has an equal contributionfrom circle and square sequences after normalization.

VOL. 73, 2007 PHYLOGENETICALLY COMPARING MICROBIAL COMMUNITIES 1577

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


NMF in MetagenomesCharacterizing the niche-space distributions of componentsS

ite

s

N orth American E ast C oast_G S 005_E mbayment

N orth American E ast C oast_G S 002_C oasta l






N orth American E ast C oast_G S 011_E stuary


E astern Tropica l Pacific_G S 021_C oasta l

N orth American E ast C oast_G S 006_E stuary


Polynesia Archipelagos_G S 051_C ora l R eef Atoll

G alapagos Islands_G S 036_C oasta l


Indian O cean_G S 117a_C oasta l sample

G alapagos Islands_G S 031_C oasta l upwelling


G alapagos Islands_G S 030_W arm S eep


S argasso S ea_G S 001c_O pen O cean

E astern Tropica l Pacific_G S 022_O pen O cean


Indian O cean_G S 149_H arbor

Indian O cean_G S 123_O pen O cean

C aribbean S ea_G S 016_C oasta l S ea

Indian O cean_G S 148_Fringing R eef


Indian O cean_G S 112a_O pen O cean

C aribbean S ea_G S 017_O pen O cean




C aribbean S ea_G S 018_O pen O cean

Indian O cean_G S 108a_Lagoon R eef


E astern Tropica l Pacific_G S 023_O pen O cean


C aribbean S ea_G S 019_C oasta l

C aribbean S ea_G S 015_C oasta l


G alapagos Islands_G S 026_O pen O cean

Polynesia Archipelagos_G S 049_C oasta l


Polynesia Archipelagos_G S 048a_C ora l R eef

Component 1

Component 2

Component 3

Component 4

Component 5

0 .1 0 .2 0 .3 0 .4 0 .5 0 .6

0 .2 0 .4 0 .6 0 .8 1 .0

Salin

ity

Sam

ple

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>4000m2000!4000m900!2000m100!200m20!100m0!20m

>4000m2000!4000m900!2000m100!200m20!100m0!20m

(a) (b) (c)

Figure 3: a) Niche-space distributions for our five components (HT ); b) the site-similarity matrix (HT H); c) environmental variables for the sites. The matrices arealigned so that the same row corresponds to the same site in each matrix. Sites areordered by applying spectral reordering to the similarity matrix (see Materials andMethods). Rows are aligned across the three matrices.

Figure 3a shows the estimated niche-space distribution for each of the five com-ponents. Components 2 (Photosystem) and 4 (Unidentified) are broadly distributed;Components 1 (Signalling) and 5 (Unidentified) are largely restricted to a handful ofsites; and component 3 shows an intermediate pattern. There is a great deal of overlapbetween niche-space distributions for di�erent components.

Figure 3b shows the pattern of filtered similarity between sites. We see clear pat-terns of grouping, that do not emerge when we calculate functional distances withoutfiltering, or using PCA rather than NMF filtering (Figure 3 in Text S1). As withthe Pfams, we see clusters roughly associated with our components, but there is moreoverlapping than with the Pfam clusters (Figure 2b).

Figure 3c shows the distribution of environmental variables measured at each site.Inspection of Figure 3 reveals qualitative correspondence between environmental factorsand clusters of similar sites in the similarity matrix. For example, the “North AmericanEast Coast” samples are divided into two groups, one in the top left and the other in thebottom right of the similarity matrix. Inspection of the environmental features suggeststhat the split in these samples could be mostly due to the di�erences in insolation andwater depth.

We can also examine patterns of similarity between the components themselves,using niche-site distributions or functional profiles (see Figure 5 in Text S1). All 5

8

Functional biogeography of ocean microbes revealed through non-negative matrixfactorization Jiang et al. In press PLoS One. Comes out 9/18.

w/ Weitz, Dushoff, Langille, Neches, Levin, etc


Other Uses of PhyloSift

• Integration with other tools (e.g., QIIME)• LGT detection• Contamination screening• Synthetic Biology Orders


w

68

Amino Acid Tree

Run PhyloSift (search + align)

Execute'dbupdate'mode'

A'taxa'set'is'selected'with'a'maxPD'cutoff'of'0.02'and'a'new'

tree'is'inferred'

EBI'Genomes'

Infer Updated Tree

Add'new'sequences'to'marker'packages'

JGI'Genomes'

Private'Genomes'

NCBI'Genomes'

Nucleotide Tree

Prune Tree

Update reference sequences with

new data

New'sequences'added'at'0.25'PD'for'amino'acid'tree;'higher'PD'threshold'enables'more'aggressive'searches'of'reference'database,'since'LAST'searching'is'faster'

with'fewer'sequences.'

Reconcile'NCBI'taxonomy'IDs'with'phylogeneOc'topologies,'for'both'amino'acid'tree'and'codon'subtrees'

Tree Reconciliation

Package Markers

Users’'local'marker'databases'are'automaOcally'scanned'each'Ome'PhyloSiR'is'run'and'any'new'

updates'are'automaOcally'downloaded'if'available'

Automated Download to

PhyloSift Users

Prune Tree A'taxa'set'is'selected'with'a'maxPD'cutoff'of'0.01'and'a'new'tree'is'inferred'


Improving VII: More Samples


The Built Environment

ORIGINAL ARTICLE

Architectural design influences the diversity andstructure of the built environment microbiome

Steven W Kembel1, Evan Jones1, Jeff Kline1,2, Dale Northcutt1,2, Jason Stenson1,2,Ann M Womack1, Brendan JM Bohannan1, G Z Brown1,2 and Jessica L Green1,3

1Biology and the Built Environment Center, Institute of Ecology and Evolution, Department ofBiology, University of Oregon, Eugene, OR, USA; 2Energy Studies in Buildings Laboratory,Department of Architecture, University of Oregon, Eugene, OR, USA and 3Santa Fe Institute,Santa Fe, NM, USA

Buildings are complex ecosystems that house trillions of microorganisms interacting with eachother, with humans and with their environment. Understanding the ecological and evolutionaryprocesses that determine the diversity and composition of the built environment microbiome—thecommunity of microorganisms that live indoors—is important for understanding the relationshipbetween building design, biodiversity and human health. In this study, we used high-throughputsequencing of the bacterial 16S rRNA gene to quantify relationships between building attributes andairborne bacterial communities at a health-care facility. We quantified airborne bacterial communitystructure and environmental conditions in patient rooms exposed to mechanical or windowventilation and in outdoor air. The phylogenetic diversity of airborne bacterial communities waslower indoors than outdoors, and mechanically ventilated rooms contained less diverse microbialcommunities than did window-ventilated rooms. Bacterial communities in indoor environmentscontained many taxa that are absent or rare outdoors, including taxa closely related to potentialhuman pathogens. Building attributes, specifically the source of ventilation air, airflow rates, relativehumidity and temperature, were correlated with the diversity and composition of indoor bacterialcommunities. The relative abundance of bacteria closely related to human pathogens was higherindoors than outdoors, and higher in rooms with lower airflow rates and lower relative humidity.The observed relationship between building design and airborne bacterial diversity suggests thatwe can manage indoor environments, altering through building design and operation the communityof microbial species that potentially colonize the human microbiome during our time indoors.The ISME Journal advance online publication, 26 January 2012; doi:10.1038/ismej.2011.211Subject Category: microbial population and community ecologyKeywords: aeromicrobiology; bacteria; built environment microbiome; community ecology; dispersal;environmental filtering

Introduction

Humans spend up to 90% of their lives indoors(Klepeis et al., 2001). Consequently, the way wedesign and operate the indoor environment has aprofound impact on our health (Guenther andVittori, 2008). One step toward better understandingof how building design impacts human healthis to study buildings as ecosystems. Built envi-ronments are complex ecosystems that containnumerous organisms including trillions of micro-organisms (Rintala et al., 2008; Tringe et al., 2008;Amend et al., 2010). The collection of microbiallife that exists indoors—the built environment

microbiome—includes human pathogens and com-mensals interacting with each other and with theirenvironment (Eames et al., 2009). There have beenfew attempts to comprehensively survey the builtenvironment microbiome (Rintala et al., 2008;Tringe et al., 2008; Amend et al., 2010), with moststudies focused on measures of total bioaerosolconcentrations or the abundance of culturable orpathogenic strains (Berglund et al., 1992; Toivolaet al., 2002; Mentese et al., 2009), rather than a morecomprehensive measure of microbial diversity inindoor spaces. For this reason, the factors thatdetermine the diversity and composition of the builtenvironment microbiome are poorly understood.However, the situation is changing. The develop-ment of culture-independent, high-throughputmolecular sequencing approaches has transformedthe study of microbial diversity in a variety ofenvironments, as demonstrated by the recent explo-sion of research on the microbial ecology of aquaticand terrestrial ecosystems (Nemergut et al., 2011)

Received 23 October 2011; revised 13 December 2011; accepted13 December 2011

Correspondence: SW Kembel, Biology and the Built EnvironmentCenter, Institute of Ecology and Evolution, Department of Biology,University of Oregon, Eugene, OR 97405, USA.E-mail: [email protected]

The ISME Journal (2012), 1–11& 2012 International Society for Microbial Ecology All rights reserved 1751-7362/12

www.nature.com/ismej

Microbial Biogeography of Public Restroom SurfacesGilberto E. Flores1, Scott T. Bates1, Dan Knights2, Christian L. Lauber1, Jesse Stombaugh3, Rob Knight3,4,

Noah Fierer1,5*

1 Cooperative Institute for Research in Environmental Science, University of Colorado, Boulder, Colorado, United States of America, 2 Department of Computer Science,

University of Colorado, Boulder, Colorado, United States of America, 3 Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado, United

States of America, 4 Howard Hughes Medical Institute, University of Colorado, Boulder, Colorado, United States of America, 5 Department of Ecology and Evolutionary

Biology, University of Colorado, Boulder, Colorado, United States of America

Abstract

We spend the majority of our lives indoors where we are constantly exposed to bacteria residing on surfaces. However, thediversity of these surface-associated communities is largely unknown. We explored the biogeographical patterns exhibitedby bacteria across ten surfaces within each of twelve public restrooms. Using high-throughput barcoded pyrosequencing ofthe 16 S rRNA gene, we identified 19 bacterial phyla across all surfaces. Most sequences belonged to four phyla:Actinobacteria, Bacteriodetes, Firmicutes and Proteobacteria. The communities clustered into three general categories: thosefound on surfaces associated with toilets, those on the restroom floor, and those found on surfaces routinely touched withhands. On toilet surfaces, gut-associated taxa were more prevalent, suggesting fecal contamination of these surfaces. Floorsurfaces were the most diverse of all communities and contained several taxa commonly found in soils. Skin-associatedbacteria, especially the Propionibacteriaceae, dominated surfaces routinely touched with our hands. Certain taxa were morecommon in female than in male restrooms as vagina-associated Lactobacillaceae were widely distributed in femalerestrooms, likely from urine contamination. Use of the SourceTracker algorithm confirmed many of our taxonomicobservations as human skin was the primary source of bacteria on restroom surfaces. Overall, these results demonstrate thatrestroom surfaces host relatively diverse microbial communities dominated by human-associated bacteria with clearlinkages between communities on or in different body sites and those communities found on restroom surfaces. Moregenerally, this work is relevant to the public health field as we show that human-associated microbes are commonly foundon restroom surfaces suggesting that bacterial pathogens could readily be transmitted between individuals by the touchingof surfaces. Furthermore, we demonstrate that we can use high-throughput analyses of bacterial communities to determinesources of bacteria on indoor surfaces, an approach which could be used to track pathogen transmission and test theefficacy of hygiene practices.

Citation: Flores GE, Bates ST, Knights D, Lauber CL, Stombaugh J, et al. (2011) Microbial Biogeography of Public Restroom Surfaces. PLoS ONE 6(11): e28132.doi:10.1371/journal.pone.0028132

Editor: Mark R. Liles, Auburn University, United States of America

Received September 12, 2011; Accepted November 1, 2011; Published November 23, 2011

Copyright: ! 2011 Flores et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported with funding from the Alfred P. Sloan Foundation and their Indoor Environment program, and in part by the NationalInstitutes of Health and the Howard Hughes Medical Institute. The funders had no role in study design, data collection and analysis, decision to publish, orpreparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected]

Introduction

More than ever, individuals across the globe spend a largeportion of their lives indoors, yet relatively little is known about themicrobial diversity of indoor environments. Of the studies thathave examined microorganisms associated with indoor environ-ments, most have relied upon cultivation-based techniques todetect organisms residing on a variety of household surfaces [1–5].Not surprisingly, these studies have identified surfaces in kitchensand restrooms as being hot spots of bacterial contamination.Because several pathogenic bacteria are known to survive onsurfaces for extended periods of time [6–8], these studies are ofobvious importance in preventing the spread of human disease.However, it is now widely recognized that the majority ofmicroorganisms cannot be readily cultivated [9] and thus, theoverall diversity of microorganisms associated with indoorenvironments remains largely unknown. Recent use of cultiva-tion-independent techniques based on cloning and sequencing ofthe 16 S rRNA gene have helped to better describe these

communities and revealed a greater diversity of bacteria onindoor surfaces than captured using cultivation-based techniques[10–13]. Most of the organisms identified in these studies arerelated to human commensals suggesting that the organisms arenot actively growing on the surfaces but rather were depositeddirectly (i.e. touching) or indirectly (e.g. shedding of skin cells) byhumans. Despite these efforts, we still have an incompleteunderstanding of bacterial communities associated with indoorenvironments because limitations of traditional 16 S rRNA genecloning and sequencing techniques have made replicate samplingand in-depth characterizations of the communities prohibitive.With the advent of high-throughput sequencing techniques, wecan now investigate indoor microbial communities at anunprecedented depth and begin to understand the relationshipbetween humans, microbes and the built environment.

In order to begin to comprehensively describe the microbialdiversity of indoor environments, we characterized the bacterialcommunities found on ten surfaces in twelve public restrooms(six male and six female) in Colorado, USA using barcoded

PLoS ONE | www.plosone.org 1 November 2011 | Volume 6 | Issue 11 | e28132

the stall in), they were likely dispersed manually after women usedthe toilet. Coupling these observations with those of thedistribution of gut-associated bacteria indicate that routine use oftoilets results in the dispersal of urine- and fecal-associated bacteriathroughout the restroom. While these results are not unexpected,they do highlight the importance of hand-hygiene when usingpublic restrooms since these surfaces could also be potentialvehicles for the transmission of human pathogens. Unfortunately,previous studies have documented that college students (who arelikely the most frequent users of the studied restrooms) are notalways the most diligent of hand-washers [42,43].

Results of SourceTracker analysis support the taxonomicpatterns highlighted above, indicating that human skin was theprimary source of bacteria on all public restroom surfacesexamined, while the human gut was an important source on oraround the toilet, and urine was an important source in women’srestrooms (Figure 4, Table S4). Contrary to expectations (seeabove), soil was not identified by the SourceTracker algorithm asbeing a major source of bacteria on any of the surfaces, includingfloors (Figure 4). Although the floor samples contained family-leveltaxa that are common in soil, the SourceTracker algorithmprobably underestimates the relative importance of sources, like

Figure 3. Cartoon illustrations of the relative abundance of discriminating taxa on public restroom surfaces. Light blue indicates lowabundance while dark blue indicates high abundance of taxa. (A) Although skin-associated taxa (Propionibacteriaceae, Corynebacteriaceae,Staphylococcaceae and Streptococcaceae) were abundant on all surfaces, they were relatively more abundant on surfaces routinely touched withhands. (B) Gut-associated taxa (Clostridiales, Clostridiales group XI, Ruminococcaceae, Lachnospiraceae, Prevotellaceae and Bacteroidaceae) were mostabundant on toilet surfaces. (C) Although soil-associated taxa (Rhodobacteraceae, Rhizobiales, Microbacteriaceae and Nocardioidaceae) were in lowabundance on all restroom surfaces, they were relatively more abundant on the floor of the restrooms we surveyed. Figure not drawn to scale.doi:10.1371/journal.pone.0028132.g003

Figure 4. Results of SourceTracker analysis showing the average contributions of different sources to the surface-associatedbacterial communities in twelve public restrooms. The ‘‘unknown’’ source is not shown but would bring the total of each sample up to 100%.doi:10.1371/journal.pone.0028132.g004

Bacteria of Public Restrooms


high diversity of floor communities is likely due to the frequency ofcontact with the bottom of shoes, which would track in a diversityof microorganisms from a variety of sources including soil, which isknown to be a highly-diverse microbial habitat [27,39]. Indeed,bacteria commonly associated with soil (e.g. Rhodobacteraceae,Rhizobiales, Microbacteriaceae and Nocardioidaceae) were, on average,more abundant on floor surfaces (Figure 3C, Table S2).Interestingly, some of the toilet flush handles harbored bacterialcommunities similar to those found on the floor (Figure 2,Figure 3C), suggesting that some users of these toilets may operatethe handle with a foot (a practice well known to germaphobes andthose who have had the misfortune of using restrooms that are lessthan sanitary).

While the overall community level comparisons between thecommunities found on the surfaces in male and female restroomswere not statistically significant (Table S3), there were gender-

related differences in the relative abundances of specific taxa onsome surfaces (Figure 1B, Table S2). Most notably, Lactobacillaceaewere clearly more abundant on certain surfaces within femalerestrooms than male restrooms (Figure 1B). Some species of thisfamily are the most common, and often most abundant, bacteriafound in the vagina of healthy reproductive age women [40,41]and are relatively less abundant in male urine [28,29]. Ouranalysis of female urine samples collected as part of a previousstudy [26] (Figure 1A), found that Lactobacillaceae were dominant inurine, therefore implying that surfaces in the restrooms whereLactobacillaceae were observed were contaminated with urine. Otherstudies have demonstrated a similar phenomenon, with vagina-associated bacteria having also been observed in airplanerestrooms [11] and a child day care facility [10]. As we foundthat Lactobacillaceae were most abundant on toilet surfaces andthose touched by hands after using the toilet (with the exception of

Figure 2. Relationship between bacterial communities associated with ten public restroom surfaces. Communities were clustered usingPCoA of the unweighted UniFrac distance matrix. Each point represents a single sample. Note that the floor (triangles) and toilet (asterisks) surfacesform clusters distinct from surfaces touched with hands.doi:10.1371/journal.pone.0028132.g002

Table 1. Results of pairwise comparisons for unweighted UniFrac distances of bacterial communities associated with varioussurfaces of public restrooms on the University of Colorado campus using the ANOSIM test in Primer v6.

Door in Door out Stall in Stall outFaucethandle

Soapdispenser

Toilet flushhandle Toilet seat Toilet floor

Door in

Door out 20.139

Stall in 0.149 20.053

Stall out 20.074 20.083 20.037

Faucet handle 20.062 20.011 20.092 20.040

Soap dispenser 20.020 0.014 20.060 20.001 0.070

Toilet flush handle 0.376* 0.405* 0.221 0.350* 0.172* 0.470*

Toilet seat 0.742* 0.672* 0.457* 0.586* 0.401* 0.653* 0.187*

Toilet floor 0.995* 0.988* 0.993* 0.961* 0.758* 0.998* 0.577* 0.950*

Sink floor 1.000* 0.995* 1.000* 0.974* 0.770* 1.000* 0.655* 0.982* 20.033

The R-statistic is shown for each comparison with asterisks denoting comparisons that were statistically significant at P#0.01.doi:10.1371/journal.pone.0028132.t001

Bacteria of Public Restrooms


10 FEBRUARY 2012 VOL 335 SCIENCE www.sciencemag.org 650

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In just that short time, the microbes had begun to take on a “signature” of outside air (more types from plants and soil), and 2 hours after the windows were shut again, the proportion of microbes from the human body increased back to pre-vious levels.

The s tudy, which appeared online 26 Janu-ary in The ISME Journal, found that mechanically ventilated rooms had lower microbial diversity than ones with open win-dows. The availability of fresh air translated into lower proportions of microbes associ-ated with the human body, and consequently, fewer potential pathogens. Although this result suggests that having natural airfl ow may be healthier, Green says answering that question requires clinical data; she’s hoping to convince a hospital to participate in a study to see if the incidence of hospital-acquired infections is associated with a room’s micro-bial community.

For his part, Peccia, who is also a Sloan grantee, is merging microbiology and the

physics of aerosols to look more closely at how the movement of air affects microbes. Peccia says his group is building on work by air-quality engineers and scientists, but “we want to add biology to the equation.”

Bacteria in air behave like other particles; their size dictates how they disperse or settle. Humans in a room not only shed microbes from their skin and mouths, but they also drum up microbial material from the fl oor as

they move around. But to quantify those con-tributions, Peccia’s team has had to develop new methods to collect airborne bacteria and extract their DNA, as the microbes are much less abundant in air than on surfaces.

In one recent study, they used air fi lters to sample airborne particles and microbes in a classroom during 4 days during which students were present and 4 days during which the room was vacant. They measured the abundance and type of fungal and bac-terial genomes present and estimated the microbes’ concentrations in the entire room. By accounting for bacteria entering and leav-

ing the room through ventilation, they calculated that people shed or resuspended about 35 million bacterial cells per person per hour. That number is much higher than the several-hundred-thousand maximum previously estimated to be present in indoor air, Peccia reported last fall at the American Association for Aerosol Research Conference in Orlando, Florida.

His group’s data also suggest that rooms have “memories” of past human inhabitants. By kick-ing into the air settled microbes from the fl oor, occupants expose themselves not just to the microbes of a person coughing next to them, but also possibly to those from a person who coughed in the room a few hours or even days ago.

Peccia hopes to come up with ways to describe the distribution of bacteria indoors that can be used in conjunction with exist-ing knowledge about particulate matter and chemicals in designing healthier buildings. “My hope is that we can bring this enough to the forefront that people who do aerosol sci-ence will fi nd it as important to know biology as to know physics and chemistry,” he says.

Still, even though he’s a willing partici-

pant in indoor microbial ecology research, Peccia thinks that the field has yet to gel. And the Sloan Foundation’s Olsiewski shares some of his con-cern. “Everybody’s gen-erating vast amounts of

data,” she says, but looking across data sets can be diffi cult because groups choose dif-ferent analytical tools. With Sloan support, though, a data archive and integrated analyt-ical tools are in the works.

To foster collaborations between micro-biologists, architects, and building scientists, the foundation also sponsored a symposium on the microbiome of the built environment at the 2011 Indoor Air conference in Austin, Texas, and launched a Web site, MicroBE.net, that’s a clearinghouse of information on the fi eld. Although Olsiewski won’t say how long the foundation will fund its indoor microbial ecology program, she says Sloan is committed to supporting all of the current projects for the next few years. The program’s ultimate goal, she says, is to create a new fi eld of scientifi c inquiry that eventually will be funded by tradi-tional government funding agencies focused on basic biology and environmental policy.

Matthew Kane, a microbial ecologist and program director at the U.S. National Sci-ence Foundation (NSF), says that although there was interest in these questions prior to the Sloan program, the Sloan Foundation has taken a directed approach to funding the research, and “I have no doubt that their investment is going to reap great returns.” So far, though, NSF has funded only one study on indoor microbes: a study of Pseudomonas bacteria in human households.

As studies like Green’s building ecology analysis progress, they should shed light on how indoor environments differ from those traditionally studied by microbial ecologists. “It’s important to have a quantitative under-standing of how building design impacts microbial communities indoors, and how these communities impact human health,” Green says. But it remains to be seen whether we’ll someday design and maintain our build-ings with microbes in mind.

–COURTNEY HUMPHRIES

Courtney Humphries is a freelance writer in Boston and author of Superdove.

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Outside infl uence. Students prepare to sample air outside a class-room in China as part of an indoor ecology study.

Bathroom biogeography. By swabbing different surfaces in public restrooms, researchers determined that microbes vary in where they come from depend-ing on the surface (chart).

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Citizen Science


Crowdfunding/Crowdsourcing


Acknowledgements

Jonathan Eisen

Students and other staff: - Eric Lowe, John Zhang, David Coil

Open source community: - BLAST, LAST, HMMER, Infernal, pplacer, Krona, metAMOS, Bioperl, Bio::Phylo, JSON, etc. etc.

PhyloSift is open source software:- Website: http://phylosift.wordpress.org- Code: http://github.com/gjospin/phylosift

Erick MatsenFHCRC

Todd TreangenBNBI, NBACC

Holly Bik

TiffanieNelson

MarkBrown

Aaron Darling

Guillaume Jospin




bayesian taxonomic assignment for the next-generation metagenomics

Technology

c t g t rrna4 c

g t t c rrna1

analysis summary phylosift

rrna3 c

phylosift tree

g t yeast t

pplacer phylosift

family phylosift