"phylogeny-driven studies in genomics and metagenomics" talk by jonathan eisen at...

Phylogeny-Driven Approaches to Genomics and Metagenomics

June 23, 2012Canadian Society for Microbiology

Jonathan A. EisenUniversity of California, Davis

@phylogenomics

Acknowledgements

• $$$• DOE• NSF• GBMF• Sloan• DARPA• DSMZ• DHS

• People, places• DOE JGI: Eddy Rubin, Phil Hugenholtz, Nikos Kyrpides• UC Davis: Aaron Darling, Dongying Wu, Holly Bik, Russell

Neches, Jenna Morgan-Lang• Other: Jessica Green, Katie Pollard, Martin Wu, Tom Slezak,

Jack Gilbert, Steven Kembel, J. Craig Venter, Naomi Ward, Hans-Peter Klenk

Phylogeny: What is it?

Phylogeny: What is it?

• Phylogeny is a description of the evolutionary history of relationships among organisms (or their parts).

• This is frequently portrayed in a diagram called a phylogenetic tree.

• Phylogenies can be more complex than a bifurcating tree (e.g., lateral gene transfer, recombination, hybridization)

Whatever the History: Trying to Incorporate it is Critical

from Lake et al. doi: 10.1098/rstb.2009.0035

Phylogeny

• Applies to • Species• Genes• Genomes

Phylogeny: What is it good for?

Phylogeny: What is it good for?

Uses of Phylogenyin Genomics and Metagenomics

Uses of Phylogenyin Genomics and Metagenomics

Example 1:

Phylotyping

DNA extraction

PCRSequence

rRNA genes

Sequence alignment = Data matrixPhylogenetic 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’

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

rRNA Phylotyping

rRNA Phylotyping

• Collect DNA from environment

• PCR amplify rRNA genes using broad (so-called universal) primers

• Sequence• Align to others• Infer evolutionary tree• Unknowns “identified”

by placement on tree

Era IV: Genomes in Environment

shotgunsequence

Metagenomics

Venter et al., Science 304: 66. 2004

rRNA Phylotyping in Sargasso

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




RecA Phylotyping in Sargasso Data






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

Venter et al., Science 304: 66-74. 2004



Side benefit: binning

Metagenomics

Binning challenge

Binning challenge

Best binning method: reference genomes

Binning challenge

No reference genome? What do you do?

Binning challenge


Composition, Assembly, others

Binning challenge


Phylogeny

Wu et al. 2006 PLoS Biology 4: e188.

Baumannia makes vitamins and cofactors

Sulcia makes amino acids

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

CFB Phyla

Side benefit II: PG Ecology

rRNA survey

• Sequence rRNAs

• Cluster

rRNA survey

• Sequence rRNAs

• Cluster• Identify

“OTUs”

OTU1OTU2OTU3OTU4OTU5OTU6OTU7OTU8OTU9OTU10

OTUs on Tree

OTU1OTU5

OTU4

OTU6

OTU8OTU9

OTU7OTU3OTU2

OTU10

OTUs on Tree

OTU1OTU5

OTU4

OTU6

OTU8OTU9

OTU7OTU3OTU2

OTU10

• Clades• Rates of

change• LGT• Convergence• Character

history

Unifrac

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

Figure 1.Estimates of Phylogenetic Diversity (PD) and PD Gain (G) for the grey community. Theboxes represent taxa from the black, white, and grey communities. (A) PD is the sum of thebranches leading to the grey taxa. (B) G is the sum of the branches leading only to the greytaxa. (C) PD rarefaction curves showing the increase in branch length with sampling effortfor the intestinal and stool bacteria from three healthy individuals. Aligned16S rRNAsequences from the three individuals were available with the Supplementary Materials in(Eckburg, et al., 2005). The Arb parsimony insertion tool was used to add the sequences to atree containing over 9,000 sequences (Hugenholtz, 2002) that is available for download atthe rRNA Database Project II website (Maidak, et al., 2001). The curves represent theaverage values for 50 replicate trials.

Lozupone and Knight Page 24

FEMS Microbiol Rev. Author manuscript; available in PMC 2009 July 1.N

IH-P

A A

uthor Manuscript

NIH

-PA

Author M

anuscriptN

IH-P

A A

uthor Manuscript

Caveat: Not Everything in Groups

GOS 1

GOS 2

GOS 3

GOS 4

GOS 5

RecA, RpoB in GOS

Wu et al PLoS One 2011

Uses of Phylogeny in Genomics and Metagenomics

Example 2:

Functional Diversity and Functional Predictions

Predicting Function

• Key step in genome projects• More accurate predictions help guide

experimental and computational analyses

• Many diverse approaches• All improved both by “phylogenomic”

type analyses that integrate evolutionary reconstructions and understanding of how new functions evolve

Predicting Function

• Identification of motifs– Short regions of sequence similarity that are indicative of

general activity– e.g., ATP binding

• Homology/similarity based methods– Gene sequence is searched against a databases of other

sequences– If significant similar genes are found, their functional

information is used• Problem

– Genes frequently have similarity to hundreds of motifs and multiple genes, not all with the same function

From Eisen et al. 1997 Nature Medicine 3: 1076-1078.







Blast Search of H. pylori “MutS”

• Blast search pulls up Syn. sp MutS#2 with much higher p value than other MutS homologs

• Based on this TIGR predicted this species had mismatch repair

• Assumes functional constancy

Based on Eisen et al. 1997 Nature Medicine 3: 1076-1078.



MutL??

Based on Eisen et al. 1997 Nature Medicine 3: 1076-1078.



Overlaying Functions onto Tree

Aquae Trepa

Rat

FlyXenla

MouseHumanYeast

NeucrArath

BorbuSynsp

Neigo

ThemaStrpy

Bacsu

Ecoli

TheaqDeiraChltr

SpombeYeast

YeastSpombe

MouseHuman

Arath

YeastHumanMouse

Arath

StrpyBacsu

HumanCeleg

YeastMetthBorbu

AquaeSynsp

Deira Helpy

mSaco

YeastCeleg

Human

MSH4

MSH5MutS2

MutS1

MSH1

MSH3

MSH6

MSH2

Based on Eisen, 1998 Nucl Acids Res 26: 4291-4300.





PHYLOGENENETIC PREDICTION OF GENE FUNCTION

IDENTIFY HOMOLOGS

OVERLAY KNOWNFUNCTIONS ONTO TREE

INFER LIKELY FUNCTIONOF GENE(S) OF INTEREST

1 2 3 4 5 6

3 5

3

1A 2A 3A 1B 2B 3B

2A 1B

1A

3A

1B2B

3B

ALIGN SEQUENCES

CALCULATE GENE TREE

12

4

6

CHOOSE GENE(S) OF INTEREST

2A

2A

5

3

Species 3Species 1 Species 2

1

1 2

2

2 31

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.





Based on Eisen, 1998 Genome Res 8: 163-167.

PHYLOGENENETIC PREDICTION OF GENE FUNCTION

IDENTIFY HOMOLOGS

OVERLAY KNOWNFUNCTIONS ONTO TREE

INFER LIKELY FUNCTIONOF GENE(S) OF INTEREST

1 2 3 4 5 6

3 5

3

1A 2A 3A 1B 2B 3B

2A 1B

1A

3A

1B2B

3B

ALIGN SEQUENCES

CALCULATE GENE TREE

12

4

6

CHOOSE GENE(S) OF INTEREST

2A

2A

5

3

Species 3Species 1 Species 2

1

1 2

2

2 31

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





Diversity of Proteorhodopsins

Venter et al., 2004

Carboxydothermus sporulates

Wu et al. 2005 PLoS Genetics 1: e65.

http://www.ncbi.nlm.nih.gov/entrez/utils/lofref.fcgi?PrId=4656&uid=16311624&db=pubmed&url=http://genetics.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pgen.0010065


Wu et al. 2005 PLoS Genetics 1: e65.



Uses of Phylogeny in Genomics and Metagenomics

Example 3:

Selecting Organisms for Study

Acidobacteria

Bacteroides

Fibrobacteres

Gemmimonas

Verrucomicrobia

Planctomycetes

Chloroflexi

Proteobacteria

Chlorobi

FirmicutesFusobacteria Actinobacteria

Cyanobacteria

Chlamydia

Spriochaetes

Deinococcus-Thermus

Aquificae

Thermotogae

TM6OS-K

Termite GroupOP8

Marine GroupAWS3

OP9

NKB19

OP3

OP10

TM7

OP1OP11

Nitrospira

SynergistesDeferribacteres

Thermudesulfobacteria

Chrysiogenetes

Thermomicrobia

Dictyoglomus

Coprothmermobacter

• At least 40 phyla of bacteria

As of 2002

Based on Hugenholtz, 2002

Acidobacteria

Bacteroides

Fibrobacteres

Gemmimonas

Verrucomicrobia

Planctomycetes

Chloroflexi

Proteobacteria

Chlorobi


Cyanobacteria

Chlamydia

Spriochaetes

Deinococcus-Thermus

Aquificae

Thermotogae

TM6OS-K

Termite GroupOP8

Marine GroupAWS3

OP9

NKB19

OP3

OP10

TM7

OP1OP11

Nitrospira



Chrysiogenetes

Thermomicrobia

Dictyoglomus

Coprothmermobacter


• Most genomes from three phyla

As of 2002


Acidobacteria

Bacteroides

Fibrobacteres

Gemmimonas

Verrucomicrobia

Planctomycetes

Chloroflexi

Proteobacteria

Chlorobi


Cyanobacteria

Chlamydia

Spriochaetes

Deinococcus-Thermus

Aquificae

Thermotogae

TM6OS-K

Termite GroupOP8

Marine GroupAWS3

OP9

NKB19

OP3

OP10

TM7

OP1OP11

Nitrospira



Chrysiogenetes

Thermomicrobia

Dictyoglomus

Coprothmermobacter



• Some studies in other phyla

As of 2002


Acidobacteria

Bacteroides

Fibrobacteres

Gemmimonas

Verrucomicrobia

Planctomycetes

Chloroflexi

Proteobacteria

Chlorobi


Cyanobacteria

Chlamydia

Spriochaetes

Deinococcus-Thermus

Aquificae

Thermotogae

TM6OS-K

Termite GroupOP8

Marine GroupAWS3

OP9

NKB19

OP3

OP10

TM7

OP1OP11

Nitrospira



Chrysiogenetes

Thermomicrobia

Dictyoglomus

Coprothmermobacter



• Some other phyla are only sparsely sampled

• Same trend in Eukaryotes

As of 2002


Acidobacteria

Bacteroides

Fibrobacteres

Gemmimonas

Verrucomicrobia

Planctomycetes

Chloroflexi

Proteobacteria

Chlorobi


Cyanobacteria

Chlamydia

Spriochaetes

Deinococcus-Thermus

Aquificae

Thermotogae

TM6OS-K

Termite GroupOP8

Marine GroupAWS3

OP9

NKB19

OP3

OP10

TM7

OP1OP11

Nitrospira



Chrysiogenetes

Thermomicrobia

Dictyoglomus

Coprothmermobacter



• Some other phyla are only sparsely sampled

• Same trend in Viruses

As of 2002


GEBA

http://www.jgi.doe.gov/programs/GEBA/pilot.html



GEBA: Components• Project overview (Phil Hugenholtz, Nikos Kyrpides, Jonathan

Eisen, Eddy Rubin, Jim Bristow)• Project management (David Bruce, Eileen Dalin, Lynne

Goodwin)• Culture collection and DNA prep (DSMZ, Hans-Peter Klenk)• Sequencing and closure (Eileen Dalin, Susan Lucas, Alla

Lapidus, Mat Nolan, Alex Copeland, Cliff Han, Feng Chen, Jan-Fang Cheng)

• Annotation and data release (Nikos Kyrpides, Victor Markowitz, et al)

• Analysis (Dongying Wu, Kostas Mavrommatis, Martin Wu, Victor Kunin, Neil Rawlings, Ian Paulsen, Patrick Chain, Patrik D’Haeseleer, Sean Hooper, Iain Anderson, Amrita Pati, Natalia N. Ivanova, Athanasios Lykidis, Adam Zemla)

• Adopt a microbe education project (Cheryl Kerfeld)• Outreach (David Gilbert)• $$$ (DOE, Eddy Rubin, Jim Bristow)

GEBA Now

• 300+ genomes• Rich sampling of major groups of

cultured organisms

GEBA Lesson 1:The rRNA Tree of Life is a Useful Tool

From Wu et al. 2009 Nature 462, 1056-1060

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


GEBA Lesson 2:The rRNA Tree of Life is not perfect ...

Badger et al. 2005 Int J System Evol Microbiol 55: 1021-1026.

16s WGT, 23S



GEBA Lesson 3: Phylogeny improves genome annotation

• Took 56 GEBA genomes and compared results vs. 56 randomly sampled new genomes

• Better definition of protein family sequence “patterns”• Greatly improves “comparative” and “evolutionary”

based predictions• Conversion of hypothetical into conserved hypotheticals• Linking distantly related members of protein families• Improved non-homology prediction

GEBA Lesson 4 :Metadata Important

GEBA Lesson 5:Improves discovering new genetic diversity

Phylogenetic Distribution Novelty: Bacterial Actin Related Protein

Haliangium ochraceum DSM 14365 Patrik D’haeseleer, Adam Zemla, Victor Kunin

A. cliftonii gi14269497U. pertusa gi50355609

C. boidinii gi57157304S. cerevisiae gi14318479L. starkeyi gi166080363 S. japonicus gi213407080

H. sapiens gi4501889M. cerebralis gi46326807

C. cinerea gi169844021N. crassa gi85101929I. scapularis gi215507378 H. sapiens gi5031569

S. japonicus gi213404844S. cerevisiae gi6320175D. melanogaster gi24642545G. gallus gi45382569C. neoformans gi58266690S. cerevisiae gi6322525D. melanogaster gi17737543H. sapiens gi5031573 H. ochraceum gi227395998

P. patens gi168051992 A. thaliana gi18394608

S. cerevisiae gi1008244

D. melanogaster gi17737347

D. hansenii gi218511921S. cerevisiae gi6323114

S. japonicus gi213408393 S. cerevisiae gi1301932

D. discoideum gi66802418

O. sativa gi182657420 A. thaliana gi1841 1737

D. melanogater gi19920358M. musculus gi226246593

99

67

100100

65

100

100

75

100

100

51

9973

10097

94100

74

100

87

100

0.5

ACTIN

ARP1

ARP2

ARP3

BARP

ARP4

ARP5

ARP6

ARP7

ARP10

See also Guljamow et al. 2007 Current Biology. Wu et al. 2009 Nature 462, 1056-1060





Protein Family Rarefaction

• Take data set of multiple complete genomes

• Identify all protein families using MCL• Plot # of genomes vs. # of protein families

Wu et al. 2009 Nature 462, 1056-1060



Synapomorphies exist

Wu et al. 2009 Nature 462, 1056-1060



GEBA Lesson 6:Improves Analysis of Uncultured

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


EFGEFTuHSP70RecARpoBrRNA

Metagenomic Phylotyping

GEBA Project improves metagenomic analysis




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


EFGEFTuHSP70RecARpoBrRNA

But not a lot


Metagenomic Phylotyping



• AND THEN ALL OF THEM WERE DECEIVED

• For each of these areas - need to do a MUCH better job ...

Improving Phylotyping

Major Issues in Phylotpying

Beyond Moore’s Law Metagenomics

Short reads

Beyond Moore’s Law Metagenomics

Short reads

WE NEED NEW METHODS

Major Issues in Phylotpying

Method 1: Each is an island

• Each new sequences is an island

• Take reference data• Build alignment, models, trees• Add new sequence to reference alignment

and build tree

STAP

STAP database, and the query sequence is aligned to them usingthe CLUSTALW profile alignment algorithm [40] as describedabove 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 querysequence according to the profile defined by the previousalignments from the databases. Thus the accuracy and quality ofthe alignment generated at this step depends heavily on the qualityof the Bacterial/Archaeal ss-rRNA alignments from theGreengenes project or the Eukaryotic ss-rRNA alignments fromthe RDPII project.

Phylogenetic analysis using multiple sequence alignments rests onthe assumption that the residues (nucleotides or amino acids) at thesame 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 subsequentanalyses. This process of evaluating homology and eliminatingquestionable columns, known as masking, typically requires time-consuming, skillful, human intervention. We designed an automat-ed masking method for ss-rRNA alignments, thus eliminating thisbottleneck in high-throughput processing.

First, an alignment score is calculated for each aligned columnby a method similar to that used in the CLUSTALX package [42].Specifically, an R-dimensional sequence space representing all thepossible nucleotide character states is defined. Then for eachaligned column, the nucleotide populating that column in each ofthe aligned sequences is assigned a score in each of the Rdimensions (Sr) according to the IUB matrix [42]. The consensus‘‘nucleotide’’ for each column (X) also has R dimensions, with thescore for each dimension (Xr) calculated as the average of thescores for that column in that dimension (average of Sr). Thus thescore of the consensus nucleotide is a mathematical expressiondescribing the average ‘‘nucleotide’’ in that column for thatalignment.

Figure 2. Domain assignment. In Step 1, STAP assigns a domain toeach query sequence based on its position in a maximum likelihoodtree of representative ss-rRNA sequences. Because the tree illustratedhere is not rooted, domain assignment would not be accurate andreliable (sequence similarity based methods cannot make an accurateassignment in this case either). However the figure illustrates animportant role of the tree-based domain assignment step, namelyautomatic identification of deep-branching environmental ss-rRNAs.doi:10.1371/journal.pone.0002566.g002

Figure 1. A flow chart of the STAP pipeline.doi:10.1371/journal.pone.0002566.g001

ss-rRNA Taxonomy Pipeline

PLoS ONE | www.plosone.org 5 July 2008 | Volume 3 | Issue 7 | e2566

Wu et al. 2008 PLoS One









Each sequence analyzed separately

AMPHORA

Guide tree

Wu and Eisen Genome Biology 2008 9:R151 doi:10.1186/gb-2008-9-10-r151

Phylotyping w/ Proteins


Whole Genome Tree


Method 2: Most in the Family

Phylogenetic Challenge

A single tree with everything?

xxxxxxxxxxxxxxxxxxxxxxx

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xxxxxxxxxxxxxx

xxxxxxxxxxxxxx

xxxxxxxxxxxxxxxxxxxxxxx

xxxxxxxxxxxxxxxxxxx

xxxxxxxxxxxxxx

xxxxxxxxxxxxxx

A single tree with everything(as long as there is a lot of overlap)



rRNA in Sargasso Metagenome





STAP All









Combine all into one alignment


RecA in Sargasso





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


EFG EFTu HSP70 RecA RpoB rRNA


Protein vs. rRNA Sargasso Data



Kembel Correction

Method 3: All in the family

• Combine new sequences into one tree

• Take reference data• Build alignment, models, trees• Add all sequences to reference alignment

and build tree

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

PhylOTU - Sharpton et al. PLoS Comp. Bio 2011

PhylOTU

Phylosift/ pplacer

Method 4: All in the genome

• Combine new sequences from different gene families into one tree

• Take reference data• Build alignment, models• Concatenate• Add all sequences to reference alignment

and build tree

Challenge

• Each gene poorly sampled in metagenomes

• Can we combine all into a single tree?

Kembel et al. The phylogenetic diversity of metagenomes. PLoS One 2011

Kembel Combiner

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

Improving Phylotyping II

• We need to analyze more gene families

Families/PD not uniform

� �

��6��

3��1��

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

Improving Functional Predictions

Improving Functional Predictions

• We need to analyze even more gene families

Sifting FamiliesRepresentative

Genomes

ExtractProtein

Annotation

All v. AllBLAST

HomologyClustering

(MCL)

SFams

Align & Build

HMMs

HMMs

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NewGenomes

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Figure 1

Sharpton et al. submitted

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Sharpton et al. submitted

Phylogenetic Contrasts

NMF in MetagenomesCharacterizing the niche-space distributions of components

Sit

es

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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ph

yll

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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

Non-negative matrix factorizationJiang et al. submitted

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

Better Genome Phylogenies

IV: Better Reference Tree

Morgan et al. submitted

Most Important Issue

We have still only scratched the surface of microbial diversity

rRNA Tree of Life

Figure from Barton, Eisen et al. “Evolution”, CSHL Press. 2007.

Based on tree from Pace 1997 Science 276:734-740

Archaea

Eukaryotes

Bacteria

PD: Genomes













PD: Genomes + GEBA











PD: Isolates




0.01

Legend:

Dataset genes

MA ammonialyase

MA mutase S subunit

MA mutase E subunit

PHA synthatase

cellulase

CRISPRs

CAS

Color ranges:

New Genomes

Haloferax mediterraneiHaloferax mucosumHaloferax volcaniiHaloferax sulfurifontisHaloferax denitrificans

Halogeometricum borinquenseHaloquadratum walsbyiHalorubrum lacusprofundi

Halalkalicoccus jeotgali

Natrialba magadiiHaloterrigena turkmenica

Halopiger xanaduensis

Haloarcula vallismortisHaloarcula marismortuiHaloarcula sinaiiensisHaloarcula californiae

Halomicrobium mukohataeiHalorhabdus utahensisNatronomonas pharaonis

Halobacterium sp. NRC�1Halobacterium salinarum R1

0.320.93

0.551

0.83

0.900.34

0.32

0.411

0.52

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##

Figure 8. Independent expansion of the TATA-binding protein family in two haloarchaeal genera. Phylogeny of TATA-binding protein (TBP) homologs identified by RAST with Bootstrap values shown. Colored branches represent duplication events (with the dark blue branch representing four duplications). Ancestral TBP (found in all genomes) is shown on the purple branch. Successive duplications are shown in darkening shades of green (Halobacterium) or blue (Haloferax). Lynch et al. in preparation

PD: All




Uncultured Lineages:

• Get into culture• Enrichment cultures• If abundant in low diversity ecosystems• Flow sorting• Microbeads• Microfluidic sorting• Single cell amplification

120

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

Phylogenomics Future 3

Need Experiments from Across the Tree of Life too

Acidobacteria

Bacteroides

Fibrobacteres

Gemmimonas

Verrucomicrobia

Planctomycetes

Chloroflexi

Proteobacteria

Chlorobi


Cyanobacteria

Chlamydia

Spriochaetes

Deinococcus-Thermus

Aquificae

Thermotogae

TM6OS-K

Termite GroupOP8

Marine GroupAWS3

OP9

NKB19

OP3

OP10

TM7

OP1OP11

Nitrospira



Chrysiogenetes

Thermomicrobia

Dictyoglomus

Coprothmermobacter


As of 2002


Acidobacteria

Bacteroides

Fibrobacteres

Gemmimonas

Verrucomicrobia

Planctomycetes

Chloroflexi

Proteobacteria

Chlorobi


Cyanobacteria

Chlamydia

Spriochaetes

Deinococcus-Thermus

Aquificae

Thermotogae

TM6OS-K

Termite GroupOP8

Marine GroupAWS3

OP9

NKB19

OP3

OP10

TM7

OP1OP11

Nitrospira



Chrysiogenetes

Thermomicrobia

Dictyoglomus

Coprothmermobacter


• Experimental studies are mostly from three phyla

As of 2002


Acidobacteria

Bacteroides

Fibrobacteres

Gemmimonas

Verrucomicrobia

Planctomycetes

Chloroflexi

Proteobacteria

Chlorobi


Cyanobacteria

Chlamydia

Spriochaetes

Deinococcus-Thermus

Aquificae

Thermotogae

TM6OS-K

Termite GroupOP8

Marine GroupAWS3

OP9

NKB19

OP3

OP10

TM7

OP1OP11

Nitrospira



Chrysiogenetes

Thermomicrobia

Dictyoglomus

Coprothmermobacter




As of 2002


Acidobacteria

Bacteroides

Fibrobacteres

Gemmimonas

Verrucomicrobia

Planctomycetes

Chloroflexi

Proteobacteria

Chlorobi


Cyanobacteria

Chlamydia

Spriochaetes

Deinococcus-Thermus

Aquificae

Thermotogae

TM6OS-K

Termite GroupOP8

Marine GroupAWS3

OP9

NKB19

OP3

OP10

TM7

OP1OP11

Nitrospira



Chrysiogenetes

Thermomicrobia

Dictyoglomus

Coprothmermobacter




• Same trend in Eukaryotes

As of 2002


0.1

Acidobacteria

Bacteroides

Fibrobacteres

Gemmimonas

Verrucomicrobia

Planctomycetes

Chloroflexi

Proteobacteria

Chlorobi


Cyanobacteria

Chlamydia

Spriochaetes

Deinococcus-Thermus

Aquificae

Thermotogae

TM6OS-K

Termite GroupOP8

Marine GroupAWS3

OP9

NKB19

OP3

OP10

TM7

OP1OP11

Nitrospira



Chrysiogenetes

Thermomicrobia

Dictyoglomus

Coprothmermobacter

Tree based on Hugenholtz (2002) with some modifications.

Need experimental studies from across the tree too

0.1

Acidobacteria

Bacteroides

Fibrobacteres

Gemmimonas

Verrucomicrobia

Planctomycetes

Chloroflexi

Proteobacteria

Chlorobi


Cyanobacteria

Chlamydia

Spriochaetes

Deinococcus-Thermus

Aquificae

Thermotogae

TM6OS-K

Termite GroupOP8

Marine GroupAWS3

OP9

NKB19

OP3

OP10

TM7

OP1OP11

Nitrospira



Chrysiogenetes

Thermomicrobia

Dictyoglomus

Coprothmermobacter

Tree based on Hugenholtz (2002) with some modifications.

Adopt a Microbe

A Happy Tree of Life

Acknowledgements

• $$$• DOE• NSF• GBMF• Sloan• DARPA• DSMZ• DHS

• People, places• DOE JGI: Eddy Rubin, Phil Hugenholtz, Nikos Kyrpides• UC Davis: Aaron Darling, Dongying Wu, Holly Bik, Russell

Neches, Jenna Morgan-Lang• Other: Jessica Green, Katie Pollard, Martin Wu, Tom Slezak,

Jack Gilbert, Steven Kembel, J. Craig Venter, Naomi Ward, Hans-Peter Klenk

iSEEM Project

"phylogeny-driven studies in genomics and metagenomics" talk by jonathan eisen at...

Education

c t g t

g t rrna3rrna3 c

g t ctagcgccataga

g t ctagcgatataga

duplicate seweighted

yeast t

unweighted unifrac

coli humansrrna4 c