Jonathan Eisen talk at Lake Arrowhead Microbial Genomics Mtg #LAMG10

The Importance of History
(and other obsessions)

Jonathan A. Eisen
UC Davis

Talk for Lake Arrowhead Microbial
Genomes 2010 (#LAMG10)

Wednesday, September 15, 2010

Social Networking in Science


Bacterial evolve


Evolution of Lake Arrowhead


Blast Peptide
LAKEARROWHEAD


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


2010


2008


2006


2004


No
2002

Wayback Machine


2002


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.

Compare PD in rRNA and WGT


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


Synapomorphies exist


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
pha
pr
ot
0
0.1250
0.2500
0.3750
0.5000

Be eo
Al
ta ba
ph
G

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7

am
pr
ot ct
er
a
m eo ia 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
pha
pr
ot
0
0.1250
0.2500
0.3750
0.5000

Be eo
Al
ta ba
ph
G

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7

pr a
poor genomic

am ot ct
er
m eo ia 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


Phylogenetic Binning Using AMPHORA
dnaG
0.7
frr
infC
0.6 nusA
pgk
pyrG
0.5

0.4
Improves with better rplA
rplB
rplC
rplD

0.3 phylogenetic methods rplE
rplF
rplK
rplL
0.2 rplM
rplN
rplP
0.1 rplS
rplT
rpmA
0 rpoB
rpsB

es
ia

es
s

s
ria

bi
ia

ia

om ae
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AMPHORA - each read on its own tree

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)

Jonathan Eisen talk at Lake Arrowhead Microbial Genomics Mtg #LAMG10

Jonathan Eisen talk at Lake Arrowhead Microbial Genomics Mtg #LAMG10

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Jonathan Eisen talk at Lake Arrowhead Microbial Genomics Mtg #LAMG10