Eisen.lake arrowhead2010c

The Importance of History
(and other obsessions)

Jonathan A. Eisen
UC Davis

Talk for Lake Arrowhead Microbial
Genomes 2010 (#LAMG10)

Homework

• Do blastp search with other famous people
associated with Lake Arrowhead Meeting
• JEFFREYHMILLER
• SARAHPALIN and her relationship to fungi
B. fuckeliana
• see http://phylogenomics.blogspot.com/
2008/09/tracing-evolutionary-history-of-
sarah.html

Quotes 2004
• Space-time continuum of genes and genomes
• Gene sequences are the wormhole that allows
one to tunnel into the past
• The human mind can conceive of things with no
basis in physical reality
• Thoughts can go faster than the speed of light

Quotes 2006

• The human guts are a real milieu of stuff
• You better kiss everybody
• Microbes not only have a lot of sex, they have a
lot of weird sex
• This is how you do metagenomics on 50
dollars, and that’s Canadian dollars

Quotes 2008
• Antibiotics do not kill things, they corrupt them
• There comes a point in life when you have to bring
chemists into the picture
• The rectal swabs are here in tan color
• And there's Jeffrey Dahmer
• We are the environment. We live the phenotype.
• If I have time I will tell you about a dream
• A paper came out next year

Quotes 2010
• We have been using this word for many years without actually realizing it
was correct
• Another thing you need to know" pause "Actually you don't NEED to
know any of this
• "I have been inﬂuenced by Fisher Price throughout my life
• Don't take that away from us
• It takes 1000 nanobiologists to make one microbiologist
• I am going to wrap up as I hear the crickets chirping
• And we will bring out the unused cheese from yesterday
• In an engineering sense, the vagina is a simple plug ﬂow reactor
• This is going to be ironic coming from someone who studies circumcision
• A little bit about time, but I am going to spend a lot less time on time than
on space

Keywords I remember from 2010
• Penis
• Vagina
• Anthrax
• Acne
• Ulcer (multiple kinds)
• Global warming
• Antibiotic resistance
• Virulence

24

rRNA Tree of Life
Bacteria

Archaea

Eukaryotes

FIgure from Barton, Eisen et al.
“Evolution”, CSHL Press.
Based on tree from Pace NR, 2003.

Proteobacteria

2002 TM6
OS-K
Acidobacteria
• At least 40
Termite Group
OP8
phyla of
Nitrospira
Bacteroides
bacteria
Chlorobi
Fibrobacteres
Marine GroupA
WS3
Gemmimonas
Firmicutes
Fusobacteria
Actinobacteria
OP9
Cyanobacteria
Synergistes
Deferribacteres
Chrysiogenetes
NKB19
Verrucomicrobia
Chlamydia
OP3
Planctomycetes
Spriochaetes
Coprothmermobacter
OP10
Thermomicrobia
Chloroﬂexi
TM7
Deinococcus-Thermus
Dictyoglomus
Aquiﬁcae
Thermudesulfobacteria
Thermotogae
OP1 Based on Hugenholtz,
OP11 2002

2002
Proteobacteria
TM6
OS-K
• At least 40
Acidobacteria
Termite Group
OP8
phyla of
Nitrospira
Bacteroides
bacteria
Chlorobi
Fibrobacteres
Marine GroupA
• Genome
WS3
Gemmimonas sequences are
Firmicutes
Fusobacteria mostly from
Actinobacteria
OP9
Cyanobacteria
three phyla
Synergistes
Deferribacteres
Chrysiogenetes
NKB19
Verrucomicrobia
Chlamydia
OP3
Planctomycetes
Spriochaetes
Coprothmermobacter
OP10
Thermomicrobia
Chloroﬂexi
TM7
Deinococcus-Thermus
Dictyoglomus
Aquiﬁcae
Thermotogae
OP11 2002

2002
Proteobacteria
TM6
OS-K
• At least 40
Acidobacteria
Termite Group
OP8
phyla of
Nitrospira
Bacteroides
bacteria
Chlorobi
Fibrobacteres
Marine GroupA
• Genome
WS3
Gemmimonas sequences are
Firmicutes
Fusobacteria mostly from
Actinobacteria
OP9
Cyanobacteria
three phyla
Synergistes
Deferribacteres
Chrysiogenetes • Some other
NKB19
Verrucomicrobia
Chlamydia
phyla are only
OP3
Planctomycetes
Spriochaetes
sparsely
Coprothmermobacter
OP10
sampled
Thermomicrobia
Chloroﬂexi
TM7
Deinococcus-Thermus
Dictyoglomus
Aquiﬁcae
Thermotogae
OP11 2002

Why Increase Phylogenetic Coverage?
• Common approach within some eukaryotic
groups (FGP, NHGRI, etc)
• Many successful small projects to ﬁll in
bacterial or archaeal gaps
• Phylogenetic gaps in bacterial and archaeal
projects commonly lamented in literature
• Many potential beneﬁts

Proteobacteria
• NSF-funded TM6 • At least 40 phyla
OS-K
Tree of Life Acidobacteria
Termite Group of bacteria
OP8
Project Nitrospira
• Genome
Bacteroides
Chlorobi
• A genome Fibrobacteres
Marine GroupA
sequences are
from each of WS3
Gemmimonas mostly from
eight phyla Firmicutes
Fusobacteria three phyla
Actinobacteria
OP9
Cyanobacteria
Synergistes
• Some other
Deferribacteres
Chrysiogenetes phyla are only
NKB19
Verrucomicrobia
Chlamydia
sparsely sampled
OP3
Planctomycetes
Spriochaetes
• Solution I:
Coprothmermobacter
OP10 sequence more
Thermomicrobia
Chloroﬂexi
TM7
phyla
Deinococcus-Thermus
Dictyoglomus
Aquiﬁcae
Eisen & Ward, PIs Thermudesulfobacteria
Thermotogae
OP1
OP11

Proteobacteria
OS-K
OP8
Project Nitrospira
• Genome
Bacteroides
Chlorobi
Marine GroupA
sequences are
from each of WS3
Actinobacteria
OP9
Cyanobacteria
Synergistes
• Some other
Deferribacteres
NKB19
Verrucomicrobia
Chlamydia
sparsely sampled
OP3
Planctomycetes
Spriochaetes
• Still highly
Coprothmermobacter
OP10 biased in terms
Thermomicrobia
Chloroﬂexi
TM7
of the tree
Deinococcus-Thermus
Dictyoglomus
Aquiﬁcae
Thermotogae
OP1
OP11

Major Lineages of Actinobacteria
2.5 Actinobacteria
2.5.1 Acidimicrobidae
2.5.1 Acidimicrobidae 2.5.1.1 Unclassified
2.5.1.2 "Microthrixineae
2.5.1.1 Unclassified 2.5.1.3 Acidimicrobineae
2.5.1.3.1 Unclassified
2.5.1.2 "Microthrixineae 2.5.1.3.2 Acidimicrobiaceae
2.5.1.4 BD2-10
2.5.1.3 Acidimicrobineae 2.5.1.5 EB1017
2.5.2 Actinobacteridae
2.5.1.4 BD2-10 2.5.2.1 Unclassified
2.5.2.10 Ellin306/WR160
2.5.1.5 EB1017 2.5.2.11 Ellin5012
2.5.2.12 Ellin5034
2.5.2 Actinobacteridae 2.5.2.13 Frankineae
2.5.2.1 Unclassified 2.5.2.13.2 Acidothermaceae
2.5.2.13.3 Ellin6090
2.5.2.10 Ellin306/WR160 2.5.2.13.4 Frankiaceae

2.5.2.11 Ellin5012 2.5.2.13.5
2.5.2.13.6
Geodermatophilaceae
Microsphaeraceae

2.5.2.12 Ellin5034 2.5.2.13.7
2.5.2.14
Sporichthyaceae
Glycomyces

2.5.2.13 Frankineae 2.5.2.15
2.5.2.15.1
Intrasporangiaceae
Unclassified
2.5.2.14 Glycomyces 2.5.2.15.2
2.5.2.15.3
Dermacoccus
Intrasporangiaceae
2.5.2.15 Intrasporangiaceae 2.5.2.16
2.5.2.17
Kineosporiaceae
Microbacteriaceae
2.5.2.16 Kineosporiaceae 2.5.2.17.1
2.5.2.17.2
Unclassified
Agrococcus
2.5.2.17 Microbacteriaceae 2.5.2.17.3
2.5.2.18
Agromyces
Micrococcaceae
2.5.2.18 Micrococcaceae 2.5.2.19
2.5.2.2
Micromonosporaceae
Actinomyces
2.5.2.19 Micromonosporaceae 2.5.2.20
2.5.2.20.1
Propionibacterineae
Unclassified
2.5.2.2 Actinomyces 2.5.2.20.2
2.5.2.20.3
Kribbella
Nocardioidaceae
2.5.2.20 Propionibacterineae 2.5.2.20.4
2.5.2.21
Propionibacteriaceae
Pseudonocardiaceae
2.5.2.21 Pseudonocardiaceae 2.5.2.22
2.5.2.22.1
Streptomycineae
Unclassified
2.5.2.22 Streptomycineae 2.5.2.22.2
2.5.2.22.3
Kitasatospora
Streptacidiphilus
2.5.2.23 Streptosporangineae 2.5.2.23
2.5.2.23.1
Streptosporangineae
Unclassified
2.5.2.3 Actinomycineae 2.5.2.23.2
2.5.2.23.3
Ellin5129
Nocardiopsaceae
2.5.2.4 Actinosynnemataceae 2.5.2.23.4
2.5.2.23.5
Streptosporangiaceae
Thermomonosporaceae
2.5.2.5 Bifidobacteriaceae 2.5.2.3
2.5.2.4
Actinomycineae
Actinosynnemataceae
2.5.2.6 Brevibacteriaceae 2.5.2.5 Bifidobacteriaceae
2.5.2.6 Brevibacteriaceae
2.5.2.7 Cellulomonadaceae 2.5.2.7 Cellulomonadaceae
2.5.2.8 Corynebacterineae
2.5.2.8 Corynebacterineae 2.5.2.8.1 Unclassified
2.5.2.8.2 Corynebacteriaceae
2.5.2.9 Dermabacteraceae 2.5.2.8.3 Dietziaceae
2.5.2.8.4 Gordoniaceae
2.5.3 Coriobacteridae 2.5.2.8.5 Mycobacteriaceae
2.5.2.8.6 Rhodococcus
2.5.3.1 Unclassified 2.5.2.8.7 Rhodococcus
2.5.2.8.8 Rhodococcus
2.5.3.2 Atopobiales 2.5.2.9 Dermabacteraceae
2.5.3.3 Coriobacteriales 2.5.2.9.2 Brachybacterium
2.5.2.9.3 Dermabacter
2.5.3.4 Eggerthellales 2.5.3 Coriobacteridae
2.5.3.1 Unclassified
2.5.4 OPB41 2.5.3.2 Atopobiales
2.5.3.3 Coriobacteriales
2.5.5 PK1 2.5.3.4 Eggerthellales
2.5.4 OPB41
2.5.6 Rubrobacteridae 2.5.5 PK1
2.5.6 Rubrobacteridae
2.5.6.1 Unclassified 2.5.6.1 Unclassified
2.5.6.2 "Thermoleiphilaceae
2.5.6.2 "Thermoleiphilaceae 2.5.6.2.1 Unclassified
2.5.6.2.2 Conexibacter
2.5.6.3 MC47 2.5.6.2.3 XGE514
2.5.6.3 MC47
2.5.6.4 Rubrobacteraceae 2.5.6.4 Rubrobacteraceae

Proteobacteria
OS-K
OP8
Project Nitrospira
• Genome
Bacteroides
Chlorobi
Marine GroupA
sequences are
from each of WS3
Actinobacteria
OP9
Cyanobacteria
Synergistes
• Some other
Deferribacteres
NKB19
Verrucomicrobia
Chlamydia
sparsely sampled
OP3
Planctomycetes
Spriochaetes
• Same trend in
Coprothmermobacter
OP10 Archaea
Thermomicrobia
Chloroﬂexi
TM7
Deinococcus-Thermus
Dictyoglomus
Aquiﬁcae
Thermotogae
OP1
OP11

Proteobacteria
OS-K
OP8
Project Nitrospira
• Genome
Bacteroides
Chlorobi
Marine GroupA
sequences are
from each of WS3
Actinobacteria
OP9
Cyanobacteria
Synergistes
• Some other
Deferribacteres
NKB19
Verrucomicrobia
Chlamydia
sparsely sampled
OP3
Planctomycetes
Spriochaetes
• Same trend in
Coprothmermobacter
OP10 Eukaryotes
Thermomicrobia
Chloroﬂexi
TM7
Deinococcus-Thermus
Dictyoglomus
Aquiﬁcae
Thermotogae
OP1
OP11

Proteobacteria
OS-K
OP8
Project Nitrospira
• Genome
Bacteroides
Chlorobi
Marine GroupA
sequences are
from each of WS3
Actinobacteria
OP9
Cyanobacteria
Synergistes
• Some other
Deferribacteres
NKB19
Verrucomicrobia
Chlamydia
sparsely sampled
OP3
Planctomycetes
Spriochaetes
• Same trend in
Coprothmermobacter
OP10 Viruses
Thermomicrobia
Chloroﬂexi
TM7
Deinococcus-Thermus
Dictyoglomus
Aquiﬁcae
Thermotogae
OP1
OP11

Proteobacteria
• GEBA TM6
OS-K • At least 40 phyla
Acidobacteria
• A genomic Termite Group
OP8
of bacteria
encyclopedia Nitrospira
Bacteroides • Genome
Chlorobi
of bacteria and Fibrobacteres
Marine GroupA sequences are
archaea WS3
Firmicutes
Fusobacteria
Actinobacteria
three phyla
OP9
Cyanobacteria
Synergistes
• Some other
Deferribacteres
NKB19
Verrucomicrobia
Chlamydia sparsely sampled
OP3
Planctomycetes
Spriochaetes • Solution: Really
Coprothmermobacter
OP10
Thermomicrobia
Fill in the Tree
Chloroﬂexi
TM7
Deinococcus-Thermus
Dictyoglomus
Aquiﬁcae
Thermotogae
OP1
OP11

GEBA Pilot Project Overview
• Identify major branches in rRNA tree for
which no genomes are available
• Identify those with a cultured representative in
DSMZ
• DSMZ grew > 200 of these and prepped DNA
• Sequence and ﬁnish 100+ (covering breadth of
bacterial/archaea diversity)
• Annotate, analyze, release data
• Assess beneﬁts of tree guided sequencing
• 1st paper Wu et al in Nature Dec 2009

GEBA Pilot Project: Components
• Project overview (Phil Hugenholtz, Nikos Kyrpides, Jonathan Eisen,
Eddy Rubin, Jim Bristow, Tanya Woyke)
• 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, DSMZ, GBMF)

GEBA and Openness
• All data released as quickly as
possible w/ no restrictions to
IMG-GEBA; Genbank, etc
• Data also available in
Biotorrents (http://
biotorrents.net)
• Individual genome reports
published in OA “Standards in
Genome Sciences (SIGS)”
• 1st GEBA paper in Nature freely
available and published using
Creative Commons License
43

GEBA Lesson 1

rRNA Tree is Useful for Identifying
Phylogenetically Novel Organisms

44

Network of Life?
Bacteria

Archaea

Eukaryotes

Figure from Barton, Eisen et al.

PD of rRNA, Genome Trees Similar

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

GEBA Lesson 2

Phylogeny-driven genome selection
helps discover new genetic diversity

Network of Life?
Bacteria

Archaea

Eukaryotes

FIgure from Barton, Eisen et al.

Protein Family Rarefaction
Curves
• Take data set of multiple complete genomes
• Identify all protein families using MCL
• Plot # of genomes vs. # of protein families

Phylogenetic Distribution Novelty:
Bacterial Actin Related Protein
2"#3)&4&*&& !"#*)$*),+%
5"#$-.-6&0&1- !"#$%,$-%)(
7"#0(1.8-9& !"#$''+-+,',!
5"#:1,)*&$/0 !"#&$,%+)+-+ ! " #$%
!"#$%&'()*&& !"#$%&'(%()
(( +"#,-.(/01 !"#*+,**'+(
;"#01,&-*0 !"#%*+$--(
<"#$-.-3.1%&0 !"#%',&'-+)
') 2"#$&*-.-1 !"#$'(-%%+&$
="#$.1001 !"#-*$+$(&( ! &’ (
$++ >"#0$1,/%1.&0 !"#&$**+),)-!
*$ $++ ;"#01,&-*0 !"#*+,$*'(
'* 5"#:1,)*&$/0 !"#&$,%+%-%%
$++ 5"#$-.-6&0&1- !"#',&+$)*
! &’ )
?"#@-%1*)A10(-. !"#&%'%&*%*
$++ B"#A1%%/0# "#%*,-&*'(
)* 2"#*-)').@1*0 !"#*-&'''(+
5"#$-.-6&0&1- !"#',&&*&* ! &’ *
$++ ?"#@-%1*)A10(-. !"#$)),)*%,
$++ ;"#01,&-*0 !"#*+,$*),!
;"#)$C.1$-/@ !"#&&),(*((- + ! &’
5"#$-.-6&0&1- !"#$++-&%%!
), ."#,1(-*0 !"#$'-+*$((&! ! &’ ,
(( !"#(C1%&1*1 !"#$-,(%'+-!
(% 5"#$-.-6&0&1- !"#$,+$(,&
$++ 5"#:1,)*&$/0 !"#&$,%+-,(,! ! &’ -
-) ?"#4&0$)&4-/@ !"#''-+&%$-
)% ?"#@-%1*)A10(-. !"#$)),),%)
() 5"#$-.-6&0&1- !"#',&,$$%
$++ ?"#C1*0-*&&!"#&$-*$ $(&$ ! &’ .
$++ D"#01(&61 !"#$-&'*)%&+!
!"#(C1%&1*1!"#$-%$ $),) ! &’ /
?"#@-%1*)A1(-. !"#$((&+,*-
$++ <"#@/0$/%/0 !"#&&'&%'*(, ! &’ ( 0

+/*!

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

See also Guljamow et al. 2007 Current Biology.

GEBA Lesson 3

improves genome annotation

Most/All Functional Prediction Improves
w/ Better Phylogenetic Sampling
• 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
Kostas Natalia Thanos Nikos Iain
Mavrommatis Ivanova Lykidis Kyrpides Anderson

GEBA Lesson 4

Metadata and individual genome
papers important

SIGS
http://standardsingenomics.org/

GEBA Lesson 5

improves analysis of metagenome data

genomes
if no reference
• Assigning reads to
phylogenetic groups
using multiple genes
• Phylogenetic binning

• Phylogenetic ecology
- especially important
Weighted % of Clones
Al
ph
ap
ro
0
0.1250
0.2500
0.3750
0.5000

Be te Al
ta ob ph
G

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7

pr ac a
am ot te
m eo ria Be pro
ap ba
ro ct ta teo
D te er G p b
el ob ia
ta am rot ac
pr ac
Ep ot te
U si
lo eo ria m eo te
nc ba
ba ria
la np Ep ap
ss ro ct ct
ifi te er si rot
ed ob ia lo
Pr ac n eo eria
ot te De pr ba
eo ria
ba lta ote cte
Cy ct pr ob ria
an er
ob ia o a
ac C teo cte
Ch te ya b ri
ria
la no ac a
m b te
Ac yd
id ia
ob e Fi act ria
rm er
Ba act
ct er
ia
Ac ic ia
Uses of phylogenetic

er ut
Ac oi tin es
de
tin te ob
ob s a
ac
te C cte
ria hl ri
Aq or a
Pl ui
an fic ob
ct
om ae C i
yc FB
Sp et C
iro es hl
ch o
ae
te
Major Phylogenetic Group

Fi
Sp rof
rm s
ic
iro lex
i
Sargasso Phylotypes

ut
classiﬁcation in metagenomics

Ch es Fu cha
lo
ro De
U fle
so ete
nc xi in ba s
la Ch oc
ss lo ct
ifi ro oc
ed bi
er
Ba Ecus ia
ct ur -
er
ia yaTh
C rcherm
re
na aeous
frr

tsf

t
pgk

rplL
rplF

rplP

rplT
rplE
infC

rpsI
rplS
rplA
rplB

rplK
rplC

rpsJ

rc
rplN
rplD

rplM

rpsE

rpsS
rpsB

rpsK
rpsC
rpoB

rpsM
pyrG
nusA
dnaG

rpmA

smpB

ha a
eo
ta

genomes
if no reference
phylogenetic groups
using multiple genes
Limited

• Phylogenetic binning

• Phylogenetic ecology
- especially important
sampling
Weighted % of Clones
Al
ph
ap
ro
0
0.1250
0.2500
0.3750
0.5000

Be te Al
ta ob ph
G

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7

pr ac a
poor genomic

am ot te
m eo ria Be pro
ap ba
ro ct ta teo
D te er G p b
el ob ia
ta
• Assigning reads to in past

pr ac am rot ac
Ep ot te
U si
lo eo ria m eo te
nc ba
ba ria
la np Ep ap
ss ro ct ct
ifi te er si rot
ed ob ia lo
Pr ac n eo eria
ot te De pr ba
eo ria
ba lta ote cte
Cy ct pr ob ria
an er
ob ia o a
by

ac C teo cte
Ch te ya b ri
ria
la no ac a
m b te
Ac yd
id ia
ob e Fi act ria
rm er
Ba act
ct er
ia
Ac ic ia
Uses of phylogenetic

er ut
Ac oi tin es
de
tin te ob
ob s a
ac
te C cte
ria hl ri
Aq or a
Pl ui
an fic ob
ct
om ae C i
yc FB
Sp et C
iro es hl
ch o
ae
te
Major Phylogenetic Group

Fi
Sp rof
rm s
ic
iro lex
i
Sargasso Phylotypes

ut
classiﬁcation in metagenomics

Ch es Fu cha
lo
ro De
U fle
so ete
nc xi in ba s
la Ch oc
ss lo ct
ifi ro oc
ed bi
er
Ba Ecus ia
ct ur -
er
ia yaTh
C rcherm
re
na aeous
frr

tsf

t
pgk

rplL
rplF

rplP

rplT
rplE
infC

rpsI
rplS
rplA
rplB

rplK
rplC

rpsJ

rc
rplN
rplD

rplM

rpsE

rpsS
rpsB

rpsK
rpsC
rpoB

rpsM
pyrG
nusA
dnaG

rpmA

smpB

ha a
eo
ta

Metagenomic Analysis Improves
w/ Phylogenetic Sampling
• Small but real improvements in
–Gene identification / confirmation
–Functional prediction
–Binning
–Phylogenetic classification

Metagenomic Analysis Improves
w/ Phylogenetic Sampling
• Small but real improvements in
–Gene identification / confirmation
–Functional prediction
–Binning
–Phylogenetic classification
• But not a lot ...

GEBA Future 1

Need to adapt genomic and
metagenomic methods to make use of
GEBA data

Al
ph
ap
ro
Be te
ta ob
G

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
pr ac
am ot te
m eo ria
ap ba
ro ct
D te er
el ob ia
ta
pr ac
Ep ot te
U si
lo eo ria
nc ba
la np
ss ro ct
ifi te er
ed ob ia
Pr ac
ot te
eo ria
ba
Cy ct
an er
ob ia
ac
Ch te
ria
la
m
Ac yd
id ia
ob e
Ba act
ct er
er ia
Ac oi
de
tin te
ob s
ac
te
ria
Aq
Pl ui
an fic
ct
om ae
yc
Sp et

AMPHORA - each read on its own tree
iro es
ch
ae
Fi te
rm s
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ut
Ch es
Improves with better

lo
ro
U fle
nc
phylogenetic methods

la xi
ss Ch
ifi lo
ed ro
bi
Ba
ct
er
ia
Phylogenetic Binning Using AMPHORA
frr

tsf
pgk

rplL
rplF

rplP

rplT
rplE
infC

rpsI
rplS
rplA
rplB

rplK
rplC

rpsJ
rplN
rplD

rplM

rpsE

rpsS
rpsB

rpsK
rpsC
rpoB

rpsM
pyrG
nusA
dnaG

rpmA

smpB

Improving Phylogeny for
Metagenomic Reads
• Examples using reference trees
– AMPHORA (Wu and Eisen)
– PPlacer (Erik Matsen)
– FastTree (Morgan Price)
• Variants
– Use concatenated alignment of markers not just
individual genes (Steven Kembel)
– Apply to OTU identiﬁcation not just classiﬁcation
(Thomas Sharpton)
– CoBinning: look for linkage among fragments/genes
(Aaron Darling)

Al
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0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
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D te er
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pr ac
Ep ot te
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la np
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ifi te er
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Pr ac
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ac
Ch te
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la
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Ac yd
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Ba act
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Ac oi
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ac
gene families

te
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Aq
Pl ui
an fic
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om ae
yc
Sp et

iro es
ch
ae
Fi te
rm s
ic
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Improves with more

Ch es
lo
ro
U fle
nc xi
la Ch
ss lo
ifi ro
ed bi
Ba
ct
er
ia
frr

tsf
pgk

rplL
rplF

rplP

rplT
rplE
infC

rpsI
rplS
rplA
rplB

rplK
rplC

rpsJ
rplN
rplD

rplM

rpsE

rpsS
rpsB

rpsK
rpsC
rpoB

rpsM
pyrG
nusA
dnaG

rpmA

smpB

Identifying new markers

• Take all genomes
• All vs. all search
• Identify protein families
• For each family measure
–Evenness in copy number
–Universality
–Phylogenetic congruence with WGT
–Monophyly for superfamilies

Distances between gene trees and the AMPHORA concatenated genome tree
rpmA coaE
coaE rpmA
trmD rplL
rpsS rpsQ
radA rplR
rplD rplQ
tsf rpsH
frr smpB
ttf rpsO
rplR rplP
rplM rpsS
rplI rplV
rpsB rplT
rpsO rplO
mraW rpsP
rpsH rpsK
rplQ rplU
rplL tsf
rplT trmD
rplE rplS
rpsP ttf
rplC rpsI
rplV mraW
rplS rpsL
infC rpsG
rpsM rplM
rplO rplI
rplU pyrH
rpsL rpsM
rpsQ ruvA
guaA radA
rpsG purA
smpB rplK
priA rplD
rpsK infC
rplK rplC
serS rplE
rplA rplA
rplF frr
ruvA rplF
rpsC serS
rplN rplN
rplP guaA
rpsE ruvB
pyrH rpsB
rpsI rpsJ
secY rRNA16S
rpsJ secY
purA rplB
rplB priA
nusA rpsE
ruvB rpsC
rRNA16S nusA
0 1 2 3 4 5 6 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

NODAL distance SPLIT distance

AMPHORA marker Ribosomal protein Transcription/translation related protein DNA repair protein Protein of other function

Distance between the genome tree and 100 random trees (average ± standard deviation)

Identifying new phylogenetic
markers within phyla
• Take all genomes within a phylum
• All vs. all search
• Identify protein families
• For each family measure
–Evenness in copy number
–Universality
–Phylogenetic congruence with WGT
–Monophyly for superfamilies

Keep only the families with:
Universality * Evenness * monophyly >= 90*90*90

Phylogenetic group Genome Number Gene Number Maker Candidates

Archaea 62 145415 102

Actinobacteria 63 267783 136

Alphaproteobacteria 94 347287 142

Betaproteobacteria 56 266362 294

Gammaproteobacteria 126 483632 141

Deltaproteobacteria 25 102115 44

Epislonproteobacteria 18 33416 446

Bacteriodes 25 71531 179

Chlamydae 13 13823 561

Chloroflexi 10 33577 140

Cyanobacteria 36 124080 532

Firmicutes 106 312309 80

Spirochaetes 18 38832 72

Thermi 5 14160 727

Thermotogae 9 17037 646

Al
ph
ap
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G

0
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0.3
0.4
0.5
0.6
0.7
pr ac
am ot te
m eo ria
ap ba
ro ct
D te er
el ob ia
ta
pr ac
Ep ot te
U si
lo eo ria
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la np
ss ro ct
ifi te er
ed ob ia
Pr ac
ot te
eo ria
ba
Cy ct
an er
ob ia
ac
Ch te
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la
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Ac yd
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Ba act
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Ac oi
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Other needs?

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te
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Pl ui
an fic
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om ae
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Sp et

iro es
ch
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Fi te
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ut
Ch es
lo
ro
U fle
nc xi
la Ch
ss lo
ifi ro
ed bi
Ba
ct
er
ia
frr

tsf
pgk

rplL
rplF

rplP

rplT
rplE
infC

rpsI
rplS
rplA
rplB

rplK
rplC

rpsJ
rplN
rplD

rplM

rpsE

rpsS
rpsB

rpsK
rpsC
rpoB

rpsM
pyrG
nusA
dnaG

rpmA

smpB

Other Ways to Make Better Use
of GEBA Data
• Rebuild protein family models
• Experiments from across the tree needed
• Need better phylogenies, including HGT
• Improved tools for using distantly related
genomes in metagenomic analysis
• Better recording and sharing of metadata
about organisms

GEBA Future 2

The dark matter of the biological
universe

Phylogenetic Diversity:
Sequenced Bacteria & Archaea

From Wu et al. 2009

Phylogenetic Diversity with
GEBA

From Wu et al. 2009

Phylogenetic Diversity: Isolates

From Wu et al. 2009

Phylogenetic Diversity: All

From Wu et al. 2009

Fantasy analysis of # PFAMs
GEBA Genomes
PD/Genome
~0.1

PFAMs/Genome
~1000

PFAMs/PD
~10000

Total PFAMS
~10,000,000

From Wu et al. 2009

Conclusions

• Sequencing phylogenetically novel genomes
has many beneﬁts
• To obtain the most beneﬁts, we need to change
and adapt: computationally and
experimentally
• Most of the phylogenetic diversity of microbes
remains to be sampled
• Long live the Lake Arrowhead Microbial
Genomes meeting

Thanks
Institutions $$$$
JGI etc DOE
UC Davis NSF
DSMZ GBMF
TIGR

People
Dongying Wu
Phil Hugenholtz
Nikos Kyrpides FIgure from Barton, Eisen et al.
Hans-Peter Klenk “Evolution”, CSHL Press.

Eddy Rubin Based on tree from Pace NR, 2003.

Eisen.lake arrowhead2010c

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