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DESPRAD subproject
Alvis Brazma
EMBL-EBI
Hinxton, October 20, 2003
DESPRAD – Development and Establishment of Standards and Prototype Repository for Array Data
Participants
EBI UMC Utrecht University of Bergen RZPD Cambridge University EMBL Heidelberg University of Marseille (CIML) University of Madrid (CMB)
Three major sets of WPs:
Developing standards and an international infrastructure for microarray data sharing (WP1 – WP4)
Establishing a public repository for microarray data – ArrayExpress (WP4 – WP9)
Research in gene expression data analysis and gene networks (WP9 – WP12)
ArrayExpress goals
Serving as an archival repository for microarray data supporting publications
Providing easy access to microarray data in a structured and standardised format for research community
Facilitating the sharing of microarray designs and protocols
ArrayExpress approach
To collect the necessary information enabling the user to understand how to interpret the data
To try to represent the information in a structured way potentially allowing for automated analysis and mining
To work towards a community agreement to represent the microarry data in a standard way – founding of the MGED society
1. Standards
Founding the Microarray Gene Expression Data (MGED) society
Development of the standards
– MIMAE– MAGE– MGED ontology
Array scans
Spo
ts
Quantitations
Gen
es
Samples
Sharing microarray data – which data?
A
B
C
D
SamplesG
enes
Gene expression levels – problem 2
Sample annotations problem 1
Gene annotations
Gene expression matrix
Annotations
MGED Society
MGED 1, Hinxton, November 1999
MGED 2, Heidelberg, May 2000
MGED 3, Stanford University, April 2001
MGED 4, Boston, February 2002
MGED 5, Tokyo, September 2002
MGED 6, Aix-en-Provence, September 2003
MGED 7, Toronto, September 2004
-Microarray Gene Expression Data Society is an international organisation for facilitating the sharing of functional genomics and proteomics array data
Board of directors – EBI, Stanford, UCB, TIGR, Affymetrix, Rosetta,…
hybridisationlabelled
nucleic acidarray
RNA extract
Sample
Array design
hybridisationlabelled
nucleic acidarray
RNA extract
Sample
hybridisationlabelled
nucleic acidarray
RNA extract
Sample
hybridisationlabelled
nucleic acidarray
RNA extract
Sample
hybridisationlabelled
nucleic acidMicroarray
RNA extract
Sample
Experiment
Gene expression data matrix
normalization
integration
ProtocolProtocolProtocolProtocolProtocolProtocol
genes
The first database model - developed in collaboration with DKFZ in 1999
MGED standards - MIAME
Nature editorial
MAGE-MLMGED standards – MAGE-ML
The organisations and software supporting MAGE-ML include Affymetrix Agilent Biodiscovery (Imagene5.5) BASE (Open source project
coordinated at Lund) Iobion (Gene Traffic) Manchester University
(MAXDB) Molmine (J-Express) NCI NIEHS
Rosetta Biosoftware (Rosetta Resolver)
RZPD Sanger Institute LIMS (MIDAS) Silicon Genetics (GeneNet) Stanford University (SMD) TIGR (MADAM) UC at Berkeley University of Pennsylvania (RAD) UMC Utrecht
The organisations and software supporting MAGE-ML include Affymetrix Agilent Biodiscovery (Imagene5.5) BASE (Open source project
coordinated at Lund) Iobion (Gene Traffic) Manchester University
(MAXDB) Molmine (J-Express) NCI NIEHS
Rosetta Biosoftware (Rosetta Resolver)
RZPD Sanger Institute LIMS (MIDAS) Silicon Genetics (GeneNet) Stanford University (SMD) TIGR (MADAM) UC at Berkeley University of Pennsylvania (RAD) UMC Utrecht
Data in ArrayExpress
2002 2003 2004April September
1000
2000
3000
Hybs
February NovemberSeptember
6~100
~250
1172
~3000
ArrayExpress content (experiments)
Rattus norvegicus 10%
Homo sapiens 19%
Mus musculus 22%
Caenorhabditis elegans 8%
Arabidopsis thaliana 18%
Saccharomyces cerevisiae
8%
Schizosaccharomyces pombe
15%
By experiment
+1 drosophyla experiment
Submissions by labs (in hybs)
Hybs
272
12684
72
68
55
46
45
24 94
TIGR
Stanford
Novartis Foundation
UCF
Flanders
Sanger Institute
Ludwig Institute
Max Delbrück
EMBL
Others
Submissions by country (in experiments)
Germany28%
USA28%
UK26%
Switzerland2%
Canada2%Belgium
5%
Netherlands9%
Expression Profiler
(component interface)
SUBSELECT
CLUSTER 12
ArrayExpress web-page hits
2002 – 49 245
2003 – 274 983 (by 12 September)
ArrayExpress components
ArrayExpress
MIAMExpress- online
submission tool
MAGE-ML
Internet
www
MAGE-ML
Submissions Queries, Analysis
Large-scale microarray facilities
Expression Profiler
- online analysis tool
Smaller labs
Export to local analysis tools
MIAMExpress
Online since December 1, 2002– 2002 – 15 951 hits– 2003 – 112 871 hits by 12 September
So far ~20 submissions completed through MIAMExpress, i.e., about 25% of all experiments in ArrayExpress
MIAMExpress is open source software - installed in at least 15 labs (EMBL, RZPD, Leipzig, Leuven, Vancouver, VIB)
Tox-MIAMExpress – a specialised version for Toxicology
www
www
Array Manufacturers
(Affymetrix,Agilent)
LIMS(EMBL,TIGR)
Desktop Data Analysis software
ArrayExpress infrastructure
MAGE-ML retrieval
Queries
AccessSubmissions
Repository(Oracle)
ExpressionProfiler
MIAMExpress(MySQL)
ArrayExpress
Local databases
MIAMExpressLocal installations
(Cambridge,…)
Local databases(RZPD,Stanford)
www
MAGE-MLMAGE-ML pipelines
Query interface(Tomcat)
Hybs
300
272244
126
10142 23186 MIAMExpress
TIGR
RUBN
SMD
Sanger
RZPD
NASC
UMCU
Manual
Submissions by pipeline (in hybs)
ArrayExpress development
Simple queries (species, author, lab, array types, etc)
Hyperlinks to other databases
Repository(MAGE-OM
model)
sub
mis
sion
s
curation
Links back to the evidence
More complex queries (genes, expression levels, etc)
Database integration
Warehouse(simple
gene-centricmodel)
curation
Ensmart
SamplesG
enes
Gene expression levels
Sample annotations
Gene annotations
Gene expression data matrix
ArrayExpress development
Simple queries (species, author, lab, array types, etc)
Hyperlinks to other databases
Repository(MAGE-OM
model)
sub
mis
sion
s
curation
Summarised information about which gene is expressed where
Database integration
Gene Expression
Atlascuration
Links back to the evidence
More complex queries (genes, expression levels, etc)
Database integration
Warehouse(simple
gene-centricmodel)
curation
Ensmart
New in ArrayExpress
Password protected logins
Can be used to support anonymous refereeing of microarray papers
Discussions with Nature
2002 2003 2004
1000
2000
3000
4000
Hybs
Data growth in ArrayExpress
?
Distributed data collection
ArrayExpress
National microarray
centre
National microarray
centre
National microarray
centre
Smalllab
Stanford
TIGR
EMBL
Sanger
Smalllab
Smalllab
Smalllab
Smalllab
Smalllab
Smalllab
Smalllab
Data analysis tools
Expression profiler – complete redevelopment of the earlier tool – new interface, new functionality, XML based modularity – beta version will be ready on months 24
J-express – (developed in Bergen), talk by Inge Jonassen
Research
Microarray based gene network analysis – 2 publications out, 1 in print, 1 submitted
S. Pombe gene expression data analysis (in collaboration with the Sanger Institute) – publication in preparation
New algorithms for clustering and cluster comparison – 2 publications in preparation
Transcription factor binding network Chromatin IP experiments on a chip (ChiP on
chip) – Using microarrays for finding genomic (intragenic)
sequences (of length of few hundred bp) where a particular transcription factor is likely to bind
ChIP by Lee et al. (Science 2002) – binding site location data in yeast genome for 107 transcription factors (from about 250 yeast transcription factors in total)
Identified around 4500 binding locations
ChIP on chip network by Lee et al
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YGL152C
YGL182C
YGR147C
YGR184C
YGR241C
YGR243W
YHR026W
YHR039C-A
YHR040W
YIL022W
YIL023C
YIL075C
YIL108WYIL109C
YIR002C
YIR003W
YJL001W
YJL002C
YJR090C
YKL081W
YKL082C
YKL104C
YKL146W
YKL148C
YKR068C
YKR069W
YLL040C
YLR223C
YLR224W
YLR378C
YLR380W
YML001W
YML129C
YMR039C
YMR040W
YMR121C
YMR122W-A
YMR186W
YMR214W
YMR280C
YMR281W
YNL008C YNL090W
YNL091W
YNL161W
YNL169C
YNL171C
YNL262W
YNL263C
YNL284C
YNR011C
YNR012W
YNR039C
YNR040W
YOL004W
YOL005C
YOL006C
YOR149C
YOR150W
YOR187W
YOR335C
YOR336W
YPL180W
YPL182C
YPL204W
YPL206C
YPR025C
YPR026W
YPR052C
YPR053C
YPR055W
YPR067W
YPR074C
YPR076W
YPR163C
YPR164W
YPR181C
YPR182W
YHR006W
YDR215C
YFL002W-A
YFL002W-B
YFL017CYGR199W
YJL024C
YLR374C
YNL070W
YOR192C-A
YOR192C-B
YOR238W
YMR037C
Gene disruption network
A C
B D
A B Cgene B
gene C
gene D
gene A
Data for over 200 gene disruptions in Yeast
Hughes et al, Cell, 102 (2000)
Mutation network for S. Cerevisiae
Three networks in yeast
ChIP network (Lee et al)
Mutation network (Hughes et al)
In silico network – matching 38 experimentally known transcription factor binding sites (Pilpel et al) against yeast genome sequence
YGR086C
CCW6
SIC1
YLR194C
CHS1
ARO1
CPA2
ARG10
MET22
STE12FUS1
KAR4
STE2
GPA1
SST2
YAP1
GSH1
YLR460C
SWI5
ARG5
ECM40
LEU4 GCN4
HOM3
CLB2
MBP1
SCW10 CIS3MNN1
SWI4
GIC2
SWI6
YKL185W
YPL158C
YLR049C
PST1
YHR149CYBR070C
MNN5
SGA1
PCL1
PCL2
YER079W
YHR150W
YDR528W
YLR297W
YER128W
SWE1
YPR157WYER078C
PRY2 PLB3 SVS1ABF1
RNR1
HCM1
MCD1
YLR103C
DUN1
SMC3
RFA2
MUT5
SPT21
YLR104W
YJR030CPDS1
YNL313C YOX1
UFE1
YDR115W
CDC21
RAD27
PDS5
IRR1
DIN7
ERP3
YJL073W
GIN4
YPL267W
Intersection of the networks
Red – 39 arcs present in all networksGreen – arcs present in at least 2 networks and adjacent to one of SWI4, SWI6 or MBP1
How Chip-chip and disruption networks relate?
All genesAll genes
Transcription factors
Disrupted genes
tRegulation set of t
hEffectual set of h
All genesAll genes
Transcription factors
Disrupted genes
Regulation set of g
Effectual set of g
107
220
11
The overlap between the regulation and effectual sets are higher than expected only for 3 of 11 –
–STE 12 - pheramone response–GCN 4 - aa/purine starvation–SWI 5 - cell cycle
How Chip-chip and disruption networks relate?
All genes
Pairs of genes sometimes tend to have highly overlapping effects
All genes
Transcription factors
Disrupted genes
tRegulation set of t
h Effectual set of h
How to verify the possible relationships? protein-protein interaction (Y2H, cellzome,
etc.)
MIPS (C. v. Mering „reference set“)
Co-citation network (PubMed)
Overlapping target sets can be explained by p/p-interactions
known interaction
target set overlap small
target set overlap large
target set overlap large
predicted interaction
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity(false positive rate)s
en
sit
ivit
y(t
rue
po
sit
ive
ra
te)
mi2mi3mips
random
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity(false positive rate)
se
ns
itiv
ity
(tru
e p
os
itiv
e r
ate
)
ppi1ppi2
mipsrandom
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity(false positive rate)
se
ns
itiv
ity
(tru
e p
os
itiv
e r
ate
)
mi2mi3mips
random
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity(false positive rate)
se
ns
itiv
ity
(tru
e p
os
itiv
e r
ate
)
ppi1ppi2
mipsrandom
A) s1 and s2 from the same network
B) s1 and s2 from different networks
C) same vs different network-all confirmation methods combined
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity(false positive rate)
sen
sit
ivit
y(t
rue
po
sit
ive
ra
te)
mips-same random
all-diff
all-same
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity(false positive rate)s
en
sit
ivit
y(t
rue
po
sit
ive
ra
te)
mi2mi3mips
random
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity(false positive rate)
se
ns
itiv
ity
(tru
e p
os
itiv
e r
ate
)
ppi1ppi2
mipsrandom
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity(false positive rate)
se
ns
itiv
ity
(tru
e p
os
itiv
e r
ate
)
mi2mi3mips
random
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity(false positive rate)
se
ns
itiv
ity
(tru
e p
os
itiv
e r
ate
)
ppi1ppi2
mipsrandom
A) s1 and s2 from the same network
B) s1 and s2 from different networks
C) same vs different network-all confirmation methods combined
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity(false positive rate)
sen
sit
ivit
y(t
rue
po
sit
ive
ra
te)
mips-same random
all-diff
all-same
Predicted relationships
SPF1 ANP1
GAS1
HAP4
HAP3
TUP1
CYC8
SWI5
PDR1
HMLALPHA1
MCM1
SHE4
MBP1
SWI4
FKH1
YAP5
ACE2
SWI6
FKH2
NDD1
STE12
RAD57
ERG2
CLB2
YAP6
ROX1
IME4
MSN4
GAT3
RGM1
GCN4
BAS1
RTG1
VMA8
CUP5
AFG3
RSM18
MAC1
YMR293C
QCR2
BUD21 RPL8A
RPL12A
CEM1
UBR1
STE24ERG3
RRP6
CKB2
MSU1
CKA2
ARG5,6
SKN7 STB1
GAL4
FIL1
SST2
RAP1
FHL1
FKS1
MET4
RTS1
ADE2
TOP3
HOG1
DIG1
STE7
STE18 STE11
STE4
CUP9
PHD1
HMLALPHA2
YMR031W-A
YAP1
SSA3
SOK2
NRG1
CIN5
KIN3
YEL008W
YEL033W
YHL029C
BUD14
ERG28
RMD7
BUD22
AEP2
MET31
CAD1
HSF1
ARG81 ARG80
SMP1 CAT8
MIG1
SGS1
pheromoneresponse genes
cell cycle genes
WP13 – dissemination www.ebi.ac.uk/microarray, www.ebi.ac.uk/arrayexpress,
www.mged.org Talks in over 40 international conferences and
workshops At least 5 peer-reviewed publications (counting only the
ones with the major contribution from TEMBLOR) Contribution to two EMBO courses on microarray data
analysis Trainee students at the EBI A press-release in preparation
WP8.1 Formulate the minimum information about a microarray experiment (MIAME)
1, 4, 17, 18 21 0 24 Dds6
WP8.2 Definition of the data exchange format for microarray data (MAML)
1, 3, 17, 18, 19, 20
18 2 24 Dds7, Dds8, Dds9, Dds10
WP8.3 Participation in MGED and development of ontologies
1, 3, 4, 9, 17, 18, 19
51 0 36 Dds11, Dds12
WP8.4 Development of the data model and schema for database ArrayExpress
1, 3, 9, 17, 18, 19
34 4 24 Dds13
WP8.5 Database implementation 1, 3, 17 50 6 24 Dds14, Dds15, Dds16, Dds17
WP8.6 Development of data annotation and curation tools
1, 3, 4, 17, 18, 19
53 6 30 Dds18, Dds19
WP8.7 Population of the ArrayExpress with data from the partners and collaborators.
1, 3, 4, 9, 17-20
102 12 36 Dds20
WP8.8 Integration of ArrayExpress with other EBI databases and other Internet resources
1 30 12 36 Dds21
WP No. Description Partners Pers. Mths
Strt End Deliverables
WP8.9 Development and implementation of tools for accessing ArrayExpress
1, 3, 17, 18, 20
42 2 24 Dds22, Dds23, Dds24
WP8.10 Development of methods for exploiting gene expression data.
1, 3, 4, 9, 17-20
75 0 30 Dds25, Dds26
WP8.11 Integration of data analysis tools with ArrayExpress database and interfaces
1, 19, 20 30 12 36 Dds27, Dds28
WP8.12 Demonstration of analysis pipelines integrating database and analysis tools
1, 3, 4, 9, 17-20
49 12 36 Dds34, Dds35, Dds36, Dds31
WP8.13 Disseminate the results and stimulate the uptake of Desprad work
1, 4, 17, 19, 20
15 2 36 Dds31, Dds32, Dds29, Dds34, Dds37, Dds38
WP No. Description Partners Pers. Mths
Strt End Deliverables
Summary ArrayExpress public repository is up and running ahead
of the schedule, the number of submissions are growing and we already have ‘hits’ and requests from the users (though there is not very much data in yet)
The MIAME and MAGE-ML standards are established (and being finalised). Several TEMBLOR partners have implemented these standards and ArrayExpress is getting submissions
Data analysis tools are under development, prototypes are working and there is a number of peer-reviewed papers out
