6 November 2007 © ETH Zürich | Genevestigator Gene expression analysis and network discovery: Genevestigator Philip Zimmermann, Genevestigator Team, ETH

6 November 2007© ETH Zürich | Genevestigator

Gene expression analysis and network discovery:

Genevestigator

Philip Zimmermann, Genevestigator Team, ETH Zurich

6 November 2007 P. Zimmermann / ETH Zurich / [email protected] 2

Presentation flow

Gene networks – biological context

Microarray compendium: how, and what for?

Meta-profile analysis: concepts and validation

Genevestigator® V3

Data integration

Summary & conclusion


Presentation flow




Genevestigator® V3

Data integration



Gene networks - biological context

What is the interpretational value of a gene network derived by graphical modeling or correlation analysis?

a snapshot in time?

a snapshot in space?

an average trend?


Gene networks - biological context

From what experiment(s) was this network derived?

time-course?

cell culture, whole organism?

stimulus, drug response?

anatomy part?

stage of development?

genetic modification?


Context and dynamics of networks

Hypothesis: networks are dynamic and context-dependant

=> networks evolve!

=> networks may have different

functions in different contexts!

Question: how can we quantify the role of the context in shaping the network?


Context: the time-space-response dimensions

Time => time-course, development

Space => anatomy parts, intracellular localization

Response => response to external perturbations

=> response to modifications in the genome


Context and dynamics of networks

Modeling the time, space and response dimensions

requires: experiments testing time, space and response variables

storage of measurement data and its meta-data

developing analysis methods that incorporate these dimensions

(→ meta-profiles)


Presentation flow




Genevestigator® V3

Data integration


6 November 2007 P. Zimmermann / ETH Zurich / [email protected]

Analysis versus meta-analysis

Data storage

Data analysis

100 genes –what to do next?

10 billion datapoints –what to do next?

Microarrayexperiment


heterogenousdatasets

Data repositories

unsystematicor poor annotation

Data

Annotations

+meta-analysis

impossible!

?

=


Data warehouses

Dataqualitycontrol

+ordereddatasets

meta-analysis possible!=

systematicannotation

Expert annotationwith systematicontologies

anatomy

development

stimulus

mutation


Data quality control

RLE NUSE

Border elements Correlation matrix

Affy QC metrics

RNA degradation

Unprocessed values


Ontologies – example of Anatomy

Mouse / Rat: Edinburgh Mouse Atlas

Human: mapping to Mouse and Rat anatomy tree

Arabidopsis / Barley: terms from Plant Ontology

tree created by Genevestigator

Expert annotationwith systematicontologies

anatomy

development

stimulus

mutation


Ontologies – example of Development

Mouse: Theiler stages

Rat: Witschi stages

Human: Carnegie table

Arabidopsis: Boyes key


Meta-analysis tools

• Who is most interested to mine this data?

• Who can best interpret the results?

THE BIOLOGIST!Genevestigator® –a tool for biologists


Presentation flow




Genevestigator® V3

Data integration



Expression meta-profiles

[space] [time] [response] [response]


Data validation

Category type

Probe set

e.g. heart ventricle

e.g. Mm.23432

[space] [time]

[response]


Data validation

Category type

Probe set

e.g. heart ventricle

[space] [time]

[response]


Mouse anatomy meta-profiles [space]


Data validation

Category type

Probe sete.g. Mm.23432

[space] [time]

[response]


Transcription of Rnf33 has been shown to occuralready in the mouse oocyte but not beyond theeight-cell stage nor in adult tissues

Rnf33

Hoxa1 expression starts at E7.5 and begins to retreat caudally by day E8.5

hemopexin (hx), is known to be only lowly expressed in embryos and newborn mice and reaches it’s highest expression level not until the first year of age

Hoxa1

hemopexin

a – f: pre-natalg – l: post-natal


light-harvesting chlorophyll a/b binding protein (AT4G14690 )

protochlorophyllide reductase A (At5g54190 )


Presentation flow




Genevestigator® V3

Data integration



Development of Genevestigator®

14‘500 Affymetrix arrays (Nov 2007)

Human, mouse, rat, arabidopsis, barley

Metabolic and regulatory pathway maps

for mouse and arabidopsis

> 10‘000 registered users

> 500 citations in peer reviewed journals

AnatomyDevelopmentStimulusMutation

Microarray data

Public repositories

Genevestigator database

Curation & Quality control

Biological experiments

Application server

Client Java application

Genevestigator


Genevestigator® V3

Website Java Client Application

Database and Application Server Cluster


Toolsets and tools


[space] [time]

[response]




Biomarker Search toolset


Abiotic stresses and hormonal responses

salt (+)osmotic (+)

cold (+)

ABA (+)

2,4-Dglucose

salt (+)osmotic (+)

ABA (+)

norflurazon (-)mycorrhiza (-)

anoxia (-)hypoxia (-)

BL / H3BO3(+)

syringolin (-)cycloheximide (-)

H2O2 (-)

salt (-)osmotic (-)

---

ozone (-)genotoxic (-)

salt (+)drought (+)

MeJA (+)

syringolin (-)P. syringae (+)

ozone (+)B. cinerea (+)

hypoxia (-)

ethylene (+)

AVG (+)chitin (+)


[space] [time]

[response]


Biclustering

Searches subsets of genes

coexpressed across subsets

of conditions

BiMax algorithm Finds all maximal bicliques

[space] [time]

[response]


Example of a bicluster

36


ABA response

Beta-alanine

Starch / sucrose

Inositolphosphate

Cold response

Phenylalanine / TyrosineProline

ABA biosynthesis

[space] [time]

[response]


Presentation flow




Genevestigator® V3

Data integration



Biomarker search [time]

Genes expressed specifically

in seeds and germinating

seedlings

De-novo identification

of cis-regulatory elements


Biomarker search [space]

z = 18.2

z = 5.8

z = 5.4


Biomarker search [response]

„Supervised biclustering“ isoxaben (+)

norflurazon (-)

light (+)

nitrate_low (-)


Anatomy clustering and promoter analysis

Clusters of genes expressed specifically in:

cell suspension

petals

roots

seeds

stamen

xylem

z > 5.0


Development clustering and promoter analysis

Clusters of Arabidopsis genes expressed specifically at:

dev. stage 1

dev. stage 3

dev. stage 9

z > 5.0


Stimulus clustering and promoter analysis

„Supervised biclustering“ of stimulus meta-profiles:

cluster 1

cluster 2

cluster 4

cluster 5

cluster 7

z > 5.0


Data integration: transcriptome - proteome

cell

su

spen

sio

n

coty

led

on

s

flo

wer

s

leav

es

roo

ts

see

ds

cell suspension

cotyledons

flowers

leaves

roots

seeds

Transcripts

Pro

tein

s


Arabidopsis leaf transcripts and proteins

Protein quantification measure

Tra

ns

cri

pt

qu

an

tifi

ca

tio

n m

ea

su

re

Frequency

general background range for transcript quantification measure

proteins detected in leaves

proteins not detected in leaves but for whichthere is a probeset on the ATH1 array


Protein detection and transcript abundance

Fraction of „present“ transcriptsthat were detected onthe protein level

probe sets called “absent” on ATH1 (p >= 0.05)probe sets called “present” on ATH1 (p < 0.05)leaf proteins detected by peptide identification

0

500

1000

1500

2000

2500

3000

3500

4000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Transcript abundance measure (log2 signal)

Nu

mb

er o

f tr

ansc

rip

ts/p

rote

ins

leaf proteins detected

0

0.2

0.4

0.6

0.8

1

1.2

6 7 8 9 10 11 12 13 14 15


GO analysis

cell wallchloroplastcytosolERextracellularGolgi apparatusmitochondrianucleusother cellular componentsother cytoplasmic componentsother intracellular componentsother membranesplasma membraneplastidribosome

ATH1 array (control)

Proteins not detected but transcripts have high abundance ( >13 )

0

0.2

0.4

0.6

0.8

1

1.2

6 7 8 9 10 11 12 13 14 15

GO Cellular Component

n = 221 specific probesets with average signal in leaves >13


GO analysis

cell organization and biogenesisdevelopmental processesDNA or RNA metabolismelectron transport or energy pathwaysother biological processesother cellular processesother metabolic processesprotein metabolismresponse to abiotic or biotic stimulusresponse to stresssignal transductiontranscriptiontransport



0

0.2

0.4

0.6

0.8

1

1.2

6 7 8 9 10 11 12 13 14 15

GO Biological Process



GO analysis

0

0.2

0.4

0.6

0.8

1

1.2

6 7 8 9 10 11 12 13 14 15

GO Molecular Function


DNA or RNA bindinghydrolase activitykinase activitynucleic acid bindingnucleotide bindingother bindingother enzyme activityother molecular functionsprotein bindingreceptor binding or activitystructural molecule activitytranscription factor activitytransferase activitytransporter activity




Data integration – pathway analysis

Protein abundance

Tra

nscr

ipt a

bund

ance

Carotenoid biosynthesis

Phenylpropanoidmetabolism

Chlorophyll / Porphyrinmetabolism

Riboflavinmetabolism

Mevalonatebiosynthesis


Relative protein-to-transcript ratio

Calvin cycle

Fatty acidbiosynthesis

serine, glycine,cystein

starch and sucrosemetabolism


Relative protein-to-transcript ratio

Chlorophyll / Porphyrinmetabolism

Fatty acidbiosynthesis

Glycolysis / Gluconeogenesis

Purinemetabolism

Pyrimidinemetabolism


Proteomic and transcriptomic biomarkers

„Root-specific“expression

Search by scoring the proteomic dataset

Search by scoring the Genevestigator dataset


Proteomic and transcriptomic biomarkers

Search by scoring the proteomic dataset

Search by scoring the Genevestigator dataset


Presentation flow




Genevestigator® V3

Data integration



Summary and conclusions

Biological networks: importance of the biological context

Meta-profiles: context-driven analysis

Biological validation of meta-profiles and clusters

Genevestigator – a tool for biologists!

Data integration: challenging biological complexity


Experimentalcontext?

Organism?

Data type?

Modes ofinteractions?

Network dynamics?

Reproducibility?


Acknowledgements

ETH Zurich

Prof. Gruissem

Developer Team: Tomas Hruz, Oliver Laule, Stefan Bleuler, Philip Zimmermann

Gabor Szabo, Frans Wessendorp, Lukas Oertle, Dominique

Dümmler, Matthias Hirsch-Hoffmann


Thanks for your attention!

Documents

6 November 2007 © ETH Zürich | Genevestigator Gene expression analysis and network discovery: Genevestigator Philip Zimmermann, Genevestigator Team, ETH