Jonathan Eisen @phylogenomics talk for #LAMG12

Phylogenomic Approaches to the
Study of Microbial Diversity
September 16, 2012
Lake Arrowhead Microbial Genomes
#LAMG12

Jonathan A. Eisen
University of California, Davis
@phylogenomics

Sunday, September 16, 12

A Bit of History

• For the real story about the Lake
Arrowhead Microbial Genomes meetings
see http://tinyurl.com/LAMG12
• But the key to LAMG meetings are ...


Quotes


Quotes

• Space-time continuum of genes and
genomes


Quotes

genomes
• Microbes not only have a lot of sex, they
have a lot of weird sex


Quotes

genomes
• Gene sequences are the wormhole that
allows one to tunnel into the past


Quotes

genomes
• This is how you do metagenomics on 50
dollars, and that’s Canadian dollars


Quotes

genomes
• The human guts are a real milieu of stuff


Quotes

genomes
• The human guts are a real milieu of stuff
• Antibiotics do not kill things, they corrupt
them

Quotes

• There comes a point in life when you have
to bring chemists into the picture


Quotes

• The rectal swabs are here in tan color


Quotes

• If I have time I will tell you about a dream


Quotes

• Another thing you need to know" pause
"Actually you don't NEED to know any of
this


Quotes

this
• I have been influenced by Fisher Price
throughout my life


Quotes

this
• I have been influenced by Fisher Price
throughout my life
• This is going to be ironic coming from
someone who studies circumcision

Quotes

• And we will bring out the unused cheese
from yesterday


Quotes

from yesterday
• A paper came out next year


Quotes

from yesterday
• It takes 1000 nanobiologists to make one
microbiologist


Quotes

from yesterday
• It takes 1000 nanobiologists to make one
microbiologist
• In an engineering sense, the vagina is a
simple plug flow reactor


Phylogenomic Approaches to
Studying Microbial Diversity

Example 1:

Phylotyping
and
Phylogenetic Diversity


rRNA Phylotyping
DNA
extraction PCR

Makes lots of Sequence
PCR copies of the rRNA genes
rRNA genes
in sample

rRNA1
5’...ACACACATAGGTGGAGCTA
GCGATCGATCGA... 3’
Sequence alignment = Data matrix
rRNA2
rRNA1 A C A C A C 5’..TACAGTATAGGTGGAGCTAG
CGACGATCGA... 3’
rRNA2 T A C A G T
rRNA3
rRNA3 C A C T G T 5’...ACGGCAAAATAGGTGGATT
rRNA4 C A C A G T CTAGCGATATAGA... 3’

E. coli A G A C A G rRNA4
5’...ACGGCCCGATAGGTGGATT
Humans T A T A G T CTAGCGCCATAGA... 3’
Yeast T A C A G T


rRNA Phylotyping


rRNA Phylotyping

E. coli Humans

Yeast


rRNA Phylotyping

E. coli Humans

Yeast

OTU2 OTU1

OTU4
OTU3

E. coli Humans

Yeast


rRNA Phylotyping
B
A

Cluster C


rRNA Phylotyping
B
A

Cluster C

B
A

OTUs C


rRNA Phylotyping
B
A

Cluster C

B
A

OTUs C

OTU1

OTU2

OTU3

OTU4


rRNA Phylotyping
B
A

Cluster C

B
A

OTUs C

OTU2 OTU1

OTU1 OTU4
OTU3
OTU2

OTU3 E. coli Humans
OTU4 Yeast


rRNA Phylotyping

Just
E. coli Humans
Phylogeny
Yeast


rRNA Phylotyping
B
A

Cluster C

Just
B E. coli Humans
Phylogeny
A

Yeast
OTUs C

OTU2 OTU1

OTU1 OTU4
OTU3
OTU2

OTU3 E. coli Humans
OTU4 Yeast


rRNA Phylotyping
• OTUs
• Taxonomic lists
• Relative abundance of taxa
• Ecological metrics (alpha and beta diversity)
• Phylogenetic metrics
• Binning
• Identification of novel groups
• Clades
• Rates of change
• LGT
• Convergence
• PD
• Phylogenetic ecology (e.g., Unifrac)

What’s New in Phylotyping


What’s New in Phylotyping I

• More PCR products

• Deeper sequencing
• The rare biosphere
• Relative abundance estimates

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


intense research (5–9), as such studies of β-diversity (variation in mental variation or dispersal limitation
community composition) yield insights into the maintenance of vary by spatial scale? Because most bac
Beta-Diversity
biodiversity. These studies are still relatively rare for micro-
organisms, however, and thus our understanding of the mecha-
and hardy, we predicted that dispersa
primarily across continents, resulting
nisms underlying microbial diversity—most of the tree of life— microbial “provinces” (15). At the sam
remains limited. environmental factors would contrib
β-Diversity, and therefore distance-decay patterns, could be decay at all scales, resulting in the steepe
driven solely by differences in environmental conditions across scale as reported in plant and animal c
space, a hypothesis summed up by microbiologists as, “every-
thing is everywhere—the environmental selects” (10). Under this Results and Discussion
model, a distance-decay curve is observed because environmen- We characterized AOB community co
tal variables tend to be spatially autocorrelated, and organisms Sanger sequencing of 16S rRNA gene
with differing niche preferences are selected from the available primer sets. Here we focus on the resu
pool of taxa as the environment changes with distance. sequences from the order Nitrosomo
Dispersal limitation can also give rise to β-diversity, as it per- primers specific for AOB within the β-
mits historical contingencies to influence present-day biogeo- The second primer set (18) generate
graphic patterns. For example, neutral niche models, in which an
organism’s marshes 1.sampled marshes sampled for details). for details). its environmental
Fig. 1. The 13
abundance (see Table S1 (see Table S1 Marshes com- com-
Fig. The 13 is not influenced by Marshes
pared with one another within regions are circled. (Inset) The arrangement
preferences, predict apoints within marshes. Six pointsThe arrangement a 100-m relatively
pared with one another within regions are circled. (Inset) were sampled along On
of sampling distance-decay curve (8, 11).
Author contributions: J.B.H.M. and M.C.H.-D. designed
of sampling points within marshes. Six points births ∼1 kmalongTwo marshescontribute to
transect, and a seventh point was sampled
were sampled away. a 100-m in the
short time seventh pointstochastic km away.were sampled morethe
scales, was sampled(outlined stars) Two marshes in intensively,
and deaths
Northeast United States M.C.H.-D. performed research; J.B.H.M., S.D.A., and M
transect, and a ∼1 a grid pattern.
a Northeast United Statesdistributionweretaxa (ecological drift). On longer
heterogeneous (outlined stars) of sampled more intensively,
along four 100-m transects in
and M.C.H.-D. wrote the paper.
time four 100-m transects in a rangegenetic processes allow results taxon Distance-decay curves for the declare no conflict of interest.
along scales, stochastic pattern.
a broader
grid
of Proteobacteria, but yielded similar
for Fig. 2. di- The authors
Nitrosomadales communities. The
versification across the Tables S2 and S3).
(Fig. S1 and landscape (evolutionary drift). If dispersal denotes thearticle is alinear regression across all spatial
dashed, blue line This least-squares PNAS Direct Submission.
Across all samples, we identified 4,931 quality Nitrosomadales scales. The solid lines denote separate regressions within each of the three
isa limiting, then current environmental or (operational taxo- 2.spatial scales: within marshes, regional the Nitrosomadales communities. The acces
broader range of Proteobacteria, but yielded similar results conditions will
sequences, which grouped into 176 OTUs biotic Fig. Distance-decay Freely available marshes within regions circledPNAS open
curves for (across online through the in

notAcrossand samples, theidentified 4,931 qualitycurve, and thusdashed,Thebluelinelines significantlyregions). The slopes of all withinsolid theof thespatialthis pape
(Fig. S1
fully all Tables S2 units)retained a arbitrary 99% Nitrosomadales cutoff. Fig. 1),solidcontinental Dataseparate zero. linear regression across all three
nomic and S3). an
explain we distance-decay sequence similarity but light andline)denotes(acrossleast-squares The slopeslinesthe each solid in
This cutoff
using blue the
geographicare denote deposition: The sequences red lines
less than regressions of
(except
reported
high amount of sequence diversity, scales. are significantly different from the slope of the all scale (blue dashed) line.
distance will begrouped the chance of including diversity similarity even after marshes, regional (across marshes within regions circled in
correlated with community because se-
sequences, which minimized into 176 OTUs (operational taxo- of spatial scales: within Bank database (accession nos. HQ271472–HQ276885
quencing or PCR99% sequence similarity cutoff. appear 1), and continental (across regions). The slopes of all lines (except the solid
errors. Most (95%) of the sequences Fig.
controlling for closelyarbitraryeither(2).the marine Nitrosospira-like clade, blue line) are significantly less than zero. The slopesdistancesolid red lines E-m
nomic units) using another factors to
related
1
light somonadales community similarity. Geographic of the con- addressed.
To whom correspondence should be
Drivers of bacterial β-diversity depend on spatial scale
This cutoff retained a to be abundant inof sequence diversity,ref. 19) orare significantly different from the slope of the all scale (blue dashed) line.
known high amount estuarine sediments (e.g., but
For macroorganisms, the relative because contribution of environ- largest partial regression coefficient (b = 0.40,
to tributed the

ECOLOGY
marine of including diversity (20) (Fig. This article contains supporting information online at
minimized the chance bacterium C-17, classified as Nitrosomonasof se- S2). P < 0.0001), with sediment moisture, nitrate concentration, plant
mental factors Pairwise community similaritythe sequences appear calcu- cover, salinity, and1073/pnas.1016308108/-/DCSupplemental.
quencing or PCR or dispersal limitation to β-diversity depends on
errors. Most (95%) of between the samples was air and water temperature contributing to
Jennifer relatedMartinya,1, Jonathan A. Nitrosospira-likePennc, Steven D. Allisona,d, and M. Claire Horner-Devinedistance con-
closely B. H. either based the the presence or absence of each OTU using smaller, but significant, partial regression coefficients (b e 0.09–
lated to on marine Eisenb, Kevin clade,
somonadales community similarity. Geographic =
a rarefied Sørensen’s index (4). Community similarity using this
sediments (e.g., ref. abundance-based 0.17, the 0.05) (Table 1). Because salt marsh bacteria may be
known to be abundant in estuarinehighly correlated with the19) or to tributed P < largest of California, Irvine, CAused a global ocean of
incidence index was Biology, and dDepartment of Earth System Science, University ocean currents, we also coefficient (b = 0.40,
partial regression 92697; bDepartment

ECOLOGY
a
Department of Ecology and classified as Nitrosomonas (20) (Fig. S2).
Evolutionary dispersing through
marine bacterium Sørensen index (Mantel test: ρvol. 108 P =no. 19 (21). P < 0.0001), with sediment moisture, nitrate(24), to estimate plant
C-17, May 10, 2011 | = 0.9239; | 0.0001)
7850–7854 |Ecology, University of California Davis Genome Center, Davis, CA 95616;circulation model (23), as applied previously concentration,
Evolution and PNAS | c www.pnas.org/
Center for Marine Biotechnology and Biomedicine, The Scripps
Pairwise community similarity between the samples was Jolla, CA 92093; and eSchool and timesandFishery Sciences, University between
A plot of community similarity San Diego, La calcu-
Institution of Oceanography, University of California atversus geographic distance cover, salinity,of Aquatic and hypothetical microbial cells of Washington,
for relative dispersal air of water temperature contributing to
Seattle, WA 98195 the presence or samples revealed that the Nitrosomonadales
lated based on each pairwise set of absence of each OTU using smaller, but significant, partial regression coefficientspoints 0.09–
each sampling location. Dispersal times between sampling (b =
Sunday, September 16, 12 display a significant, negative distance-decay curve (slope = −0.08, did not explain more variability in bacterial community similarity

Earth Microbiome Project


Microbial Range Maps


Things You Could Do
• Mississippi River: 2320 miles long


Things You Could Do
• Mississippi River: 2320 miles long
• 1 site / mile
• 3 samples / site
• 6960 samples
• rRNA PCR w/ barcodes
• metagenomics w/ barcodes
• Miseq Run:
• 30 million sequence reads
• 4310 sequences / sample
• Hiseq 2000
• 6 billion sequence reads
• 862,068 sequences / sample


Things You Could Do
• Mississippi River: 12,249,600 feet long
• 1 site / 500 feet
• 3 samples / site
• 73497 samples
• rRNA PCR w/ barcodes
• metagenomics w/ barcodes
• Miseq Run:
• 30 million sequence reads
• 408 sequences / sample
• Hiseq 2000
• 6 billion sequence reads
• 81,635 sequences / sample


What’s New in Phylotyping II

• Metagenomics avoids biases of rRNA
PCR

shotgun
sequence


Metagenomic Phylotyping
B
A

Cluster C

Just
B E. coli Humans
Phylogeny
A

Yeast
OTUs C

OTU2 OTU1

OTU1 OTU4
OTU3
OTU2

OTU3 E. coli Humans
OTU4 Yeast


Phylogenetic Challenge

??



Multiple approaches


Method 1: Each is an island


Method 1: Each is an island

• Build alignment, models, trees for full length seqs
• Analyze fragmented reads one at a time


STAP ss-rRNA Taxonomy Pip
Figure 1. A flow chart of the STAP pipeline.
doi:10.1371/journal.pone.0002566.g001

STAP database, and the query sequence is aligned to them using a
the CLUSTALW profile alignment algorithm [40] as described w
above for domain assignment. By adapting the profile alignment s
a
t
o
G
t

t

Each sequence
s
T
c

analyzed separately a
q
c
e
b

b
S
p
a
Figure 2. Domain assignment. In Step 1, STAP assigns a domain to t
each query sequence based on its position in a maximum likelihood d
tree of representative ss-rRNA sequences. Because the tree illustrated ‘
here is not rooted, domain assignment would not be accurate and s
reliable (sequence similarity based methods cannot make an accurate
s
assignment in this case either). However the figure illustrates an
important role of the tree-based domain assignment step, namely s
automatic identification of deep-branching environmental ss-rRNAs. d
doi:10.1371/journal.pone.0002566.g002 a

PLoS ONE | www.plosone.org 5

Wu et al. 2008 PLoS One

FigureSeptember 16, 12
Sunday, 1. A flow chart of the STAP pipeline.

AMPHORA

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

Phylotyping w/ Proteins

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

Method 2: Most in the Family



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??


Method 2: Most in family

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xxxxxxxxxxxxxx

One tree for those w/ overlap


rRNA in Sargasso Metagenome

Venter et al., Science
304: 66. 2004


RecA Phylotyping in Sargasso Data

304: 66. 2004


Weighted % of Clones

0
0.125
0.250
0.375
0.500
Al
ph
ap
ro
t eo
Be ba

ta ct
er
pr ia
ot
eo
G

304: 66. 2004
am b ac
m t er
ap ia
ro
Ep t eo
si ba
lo ct

np er
ro ia
eo t
De ba
lta ct
pr er
ot ia
eo
ba
C
EFG

ct
ya er
no ia
ba
ct
er
Fi ia
rm
ic
EFTu

ut
es
Ac
tin
ob
ac
te
ria
C
hl
HSP70

or
ob
i
C

Major Phylogenetic Group
FB
Sargasso Phylotypes

C
RecA

hl
or
of
le
xi
Sp
iro
ch
ae
te
s
RpoB

Fu
so
ba
De ct
in er
ia
oc
Sargasso Phylotyping

oc
cu
s-
rRNA

Th
Eu er
ry m
ar u
ch s
ae
C ot
a
re
na
rc
ha
eo
ta

STAP, QIIME, Mothur ss-rRNA Taxonomy Pip

Combine all into
one alignment

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

all of these bioinformatics steps together in one package. therefore, to invest a large amount of time and effort to
To this end, we have built an automated, user-friendly, get to that list of microbes. But now that current efforts
workflow-based system called WATERS: a Workflow for are significantly more advanced and often require com-

WATERs
the Alignment, Taxonomy, and Ecology of Ribosomal parison of dozens of factors and variables with datasets of
Sequences (Fig. 1). In addition to being automated and thousands of sequences, it is not practically feasible to
Page 2 of 14 simple to use, because WATERS is executed in the Kepler process these large collections "by hand", and hugely inef-
scientific workflow system (Fig. 2) it also has the advan- ficient if instead automated methods can be successfully
tage that it keeps track of the data lineage and provenance employed.
of data products [23,24]. Broadening the user base
Automation A second motivation and perspective is that by minimiz-
The primary motivation in building WATERS was to ing the technical difficulty of 16 S rDNA analysis through
minimize the technical, bioinformatics challenges that the use of WATERS, we aim to make the analysis of these
ic- arise when performing DNA sequence clustering, phylo- datasets more widely available and allow individuals with

A).
Check Build
sly Align
chimeras
Cluster
Tree
ers
nly
ed, Diversity
Assign Tree w/
statistics &
ed graphs
Taxonomy Taxonomy
ng
ge- Cytoscape
OTU table Unifrac
de- network ﬁles
he
a
nt Figure 1 Overview of WATERS. Schema of WATERS where white
ise boxes indicate "behind the scenes" analyses that are performed in WA-
he TERS. Quality control files are generated for white boxes, but not oth-
erwise routinely analyzed. Black arrows indicate that metadata (e.g.,
on
sample type) has been overlaid on the data for downstream interpre-
n- tation. Colored boxes indicate different types of results files that are
nd generated for the user for further use and biological interpretation.
Colors indicate different types of WATERS actors from Fig. 2 which
Figure 2 Screenshot of WATERS in Kepler software. Key features: the library of actors un-collapsed and displayed on the left-hand side, the input
eys were used: green, Diversity metrics, WriteGraphCoordinates, Diversity and output paths where the user declares the location of their input files and desired location for the results files. Each green box is an individual Kepler
graphs; blue, Taxonomy, BuildTree, Rename Trees, Save Trees; Create- actor that performs a single action on the data stream. The connectors (black arrows) direct and hook up the actors in a defined sequence. Double-
er) clicking on any actor or connector allows it to be manipulated and re-arranged.
Unifrac; yellow, CreateOtuTable, CreateCytoscape, CreateOTUFile;
16 white, remaining unnamed actors.
n-
as
chimeric sequences generated during PCR identifying
nto
tly Hartman et sets 2010. W.A.T.E.R.S.:as opera-
closely related al of sequences (also known a Workﬂow for the Alignment, Taxonomy, and Ecology
nc- of Ribosomal units or OTUs), removing redundant
tional taxonomic
Sequences. BMC Bioinformatics 2010, 11:317 doi:
sequences above a certain percent identity cutoff, assign-
6S 10.1186/1471-2105-11-317 each sequence or
ing putative taxonomic identifiers to
As
representative of a group, inferring a phylogenetic tree of
n-
the sequences, and comparing the phylogenetic structure

One Major Issue with rRNA

• Copy number varies greatly between taxa
• Can lead to significant errors in estimates
of relative abundance from numbers of
reads


Kembel Correction

Kembel, Wu, Eisen, Green. In press.
PLoS Computational Biology.
Incorporating 16S gene copy number
information improves estimates of
microbial diversity and abundance


Method 3: All in the family



A single tree with everything?


rRNA analysis
B
A

Cluster C

Just
B E. coli Humans
Phylogeny
A

Yeast
OTUs C

OTU2 OTU1

OTU1 OTU4
OTU3
OTU2

OTU3 E. coli Humans
OTU4 Yeast


PhylOTU Finding Meta

Figure 1. PhylOTU Workflow. Computational processes are represented as squares and databases are represented as cylinders in
workflow of PhylOTU. See Results section for details.
Sharpton TJ, Riesenfeld SJ, Kembel SW, Ladau J, O'Dwyer JP, Green JL, Eisen JA, Pollard KS. (2011)
doi:10.1371/journal.pcbi.1001061.g001
PhylOTU: A High-Throughput Procedure Quantifies Microbial Community Diversity and Resolves Novel
Taxa from Metagenomic used toPLoS Comput Biol 7(1): e1001061. doi:10.1371/journal.pcbi.1001061
alignment Data. build the profile, resulting in a multiple PD versus PID clustering, 2) to explore overlap betw
sequence alignment of full-length reference sequences and clusters and recognized taxonomic designations, and
Sunday, September 16, 12 metagenomic reads. The final step of the alignment process is a the accuracy of PhylOTU clusters from shotgun re

RecA, RpoB in GOS

GOS 1

GOS 2

GOS 3

GOS 4

Wu D, Wu M, Halpern A, Rusch DB, Yooseph S, et al. (2011) Stalking
the Fourth Domain in Metagenomic Data: Searching for, Discovering,
GOS 5
and Interpreting Novel, Deep Branches in Marker Gene Phylogenetic
Trees. PLoS ONE 6(3): e18011. doi:10.1371/journal.pone.0018011


Phylosift/ pplacer

Aaron Darling, Guillaume Jospin, Holly Bik, Erik Matsen, Eric
Lowe, and others

Phylosift

• Probabilistic Phylogenetic Ecology
• https://github.com/gjospin/PhyloSift
• http://phylosift.wordpress.com


Method 4: All in the genome


Multiple Genes?

A single tree with everything?


Kembel Combiner

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


typically used as a qualitative measure because duplicate s
quences are usually removed from the tree. However, the
test may be used in a semiquantitative manner if all clone

Kembel Combiner
even those with identical or near-identical sequences, are i
cluded in the tree (13).
Here we describe a quantitative version of UniFrac that w
call “weighted UniFrac.” We show that weighted UniFrac b
haves similarly to the FST test in situations where both a

FIG. 1. Calculation of the unweighted and the weighted UniFr
measures. Squares and circles represent sequences from two differe
environments. (a) In unweighted UniFrac, the distance between t
circle and square communities is calculated as the fraction of t
branch length that has descendants from either the square or the circ
environment (black) but not both (gray). (b) In weighted UniFra
branch lengths are weighted by the relative abundance of sequences
the square and circle communities; square sequences are weight
twice as much as circle sequences because there are twice as many tot
circle sequences in the data set. The width of branches is proportion
to the degree to which each branch is weighted in the calculations, an
gray branches have no weight. Branches 1 and 2 have heavy weigh
since the descendants are biased toward the square and circles, respe
tively. Branch 3 contributes no value since it has an equal contributio
from circle and square sequences after normalization.

Kembel SW, Eisen JA, Pollard KS, Green JL (2011) The Phylogenetic Diversity of Metagenomes. PLoS
ONE 6(8): e23214. doi:10.1371/journal.pone.0023214


Uses of Phylogeny
in Genomics and Metagenomics

Example 2:

Functional Diversity and
Functional Predictions


Phylogenomics
PHYLOGENENETIC PREDICTION OF GENE FUNCTION

EXAMPLE A METHOD EXAMPLE B

2A CHOOSE GENE(S) OF INTEREST 5

3A 1 3 4
2B 2
IDENTIFY HOMOLOGS 5
1A 2A 1B 3B 6

ALIGN SEQUENCES

1A 2A 3A 1B 2B 3B 1 2 3 4 5 6

CALCULATE GENE TREE

Duplication?

1A 2A 3A 1B 2B 3B 1 2 3 4 5 6

OVERLAY KNOWN
FUNCTIONS ONTO TREE

Duplication?

2A 3A 1B 2B 3B 1 2 3 4 5 6
1A

INFER LIKELY FUNCTION
OF GENE(S) OF INTEREST
Ambiguous
Duplication?

Species 1 Species 2 Species 3

Based on
1A 1B 2A 2B 3A 3B 1 2 3 4 5 6

ACTUAL EVOLUTION
(ASSUMED TO BE UNKNOWN) Eisen, 1998
Genome Res 8:
Duplication 163-167.


Diversity of Proteorhodopsins

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

Improving Functional Predictions

• Same methods discussed for phylotyping
improve phylogenomic functional
prediction for protein families
• Increase in sequence diversity helps too


Carboxydothermus sporulates

Wu et al. 2005 PLoS Genetics 1: e65.

Wu et al. 2005 PLoS Genetics 1: e65.

Characterizing the niche-space distributions of components NMF in Metagenomes

0 .1 0 .2 0 .3 0 .4 0 .5 0 .6 0 .2 0 .4 0 .6 0 .8 1 .0

Polyne sia Archipe la gos_ G S 0 4 8 a _ C ora l R e e f
India n O ce a n_ G S 1 2 0 _ O pe n O ce a n
Polyne sia Archipe la gos_ G S 0 4 9 _ C oa sta l
G a la pa gos Isla nds_ G S 0 2 6 _ O pe n O ce a n
G e ne ra l
C a ribbe a n S e a _ G S 0 1 5 _ C oa sta l
C a ribbe a n S e a _ G S 0 1 9 _ C oa sta l
India n O ce a n_ G S 1 1 4 _ O pe n O ce a n H igh
E a ste rn Tropica l Pa cific_ G S 0 2 3 _ O pe n O ce a n M e dium
India n O ce a n_ G S 1 1 0 a _ O pe n O ce a n
India n O ce a n_ G S 1 0 8 a _ La goon R e e f Low
C a ribbe a n S e a _ G S 0 1 8 _ O pe n O ce a n NA
G a la pa gos Isla nds_ G S 0 3 4 _ C oa sta l
C a ribbe a n S e a _ G S 0 1 7 _ O pe n O ce a n
India n O ce a n_ G S 1 4 8 _ F ringing R e e f
C a ribbe a n S e a _ G S 0 1 6 _ C oa sta l S e a
India n O ce a n_ G S 1 4 9 _ H a rbor
E a ste rn Tropica l Pa cific_ G S 0 2 2 _ O pe n O ce a n W a te r de pth
S ites

S a rga sso S e a _ G S 0 0 1 c_ O pe n O ce a n
G a la pa gos Isla nds_ G S 0 3 0 _ W a rm S e e p
G a la pa gos Isla nds_ G S 0 2 9 _ C oa sta l >4000m
G a la pa gos Isla nds_ G S 0 3 1 _ C oa sta l upwe lling
India n O ce a n_ G S 1 1 7 a _ C oa sta l sa m ple
2000!4000m
G a la pa gos Isla nds_ G S 0 2 8 _ C oa sta l 900!2000m
G a la pa gos Isla nds_ G S 0 3 6 _ C oa sta l 100!200m
Polyne sia Archipe la gos_ G S 0 5 1 _ C ora l R e e f Atoll
N orth Am e rica n E a st C oa st_ G S 0 1 4 _ C oa sta l 20!100m
N orth Am e rica n E a st C oa st_ G S 0 0 6 _ E stua ry 0!20m
E a ste rn Tropica l Pa cific_ G S 0 2 1 _ C oa sta l
N orth Am e rica n E a st C oa st_ G S 0 0 9 _ C oa sta l
N orth Am e rica n E a st C oa st_ G S 0 1 1 _ E stua ry
N orth Am e rica n E a st C oa st_ G S 0 0 5 _ E m baym e nt

Co Co Co Co Co

Chlorophyll
Salinity

Temperature

Water Depth
Sample Depth

Insolation
mp mp mp mp mp
on on on on on
en en en en en
t1 t2 t3 t4 t5

(a) (b) (c)

Functional biogeography of ocean microbes
Figure 3: a) Niche-space non-negative matrix
revealed through distributions for our ﬁve components (H T );Weitz,site-
w/ b) the Dushoff,
similarity matrix (HJiang environmental variables for the sites. The matrices Neches,
factorization ˆ ˆ T H); c) et al. In press PLoS
Langille, are
aligned so that the same row corresponds to the same site in each matrix. Sites are
One. Comes out 9/18. Levin, etc
ordered by applying spectral reordering to the similarity matrix (see Materials and
Methods). Rows are aligned across the three matrices.

Uses of Phylogeny
in Genomics and Metagenomics

Example 3:

Selecting Organisms for Study


GEBA

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

GEBA

THAT
IS
SO
LAMG10
http://www.jgi.doe.gov/programs/GEBA/pilot.html

How To Keep Up?

• IMG
• Genomes Online
• MicrobeDB
• http://github.com/mlangill/microbedb/
• Langille MG, Laird MR, Hsiao WW, Chiu TA, Eisen
JA, Brinkman FS. MicrobeDB: a locally
maintainable database of microbial genomic
sequences. Bioinformatics. 2012 28(14):1947-8.


Improving Phylotyping


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


Better Reference Tree

Morgan et al.
submitted

Improving Functional Predictions


Sifting Families
Representative
Genomes

B
A Extract
Protein
New
Genomes
Annotation

Extract
All v. All
Protein
BLAST
Annotation

Homology
Screen for
(MCL) C
Clustering
Homologs

SFams HMMs

Align &
Build
Sharpton et al. submitted Figure 1
HMMs


Zorro - Automated Masking
9.0

8.0

Distance to True Tree
7.0

6.0

5.0

4.0
200
3.0
no masking

ce to True Tree
2.0 zorro
1.0 gblocks
0.0
200 400 800 1600 3200
Sequence Length

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


Phylogenetic Contrasts


GEBA Lesson

We have still only scratched the
surface of microbial diversity


PD: All

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

Families/PD not uniform
31

6


GEBA uncultured
Number of SAGs from Candidate Phyla

406
1
OD1

OP1

OP3

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

Sample collections at 4 additional sites are underway.

Phil Hugenholtz

97


GEBA Lesson

Need Experiments from Across
the Tree of Life too


Conclusion


MICROBES


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


Jonathan Eisen @phylogenomics talk for #LAMG12

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