Design of microarray experiments for genetical genomics studies 2006
Genomics Laboratory University Medical Center Utrecht... Microarray technology group microarray...
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Transcript of Genomics Laboratory University Medical Center Utrecht... Microarray technology group microarray...
Genomics LaboratoryUniversity Medical Center Utrecht
External controls & cell count
SP 15 60 180 360 min
Exit from stationary phaseGenes
SP 15 60 180 360 min
...
Microarray technology groupmicroarray production and use
Transcription regulation genome-widehow are genes regulated?
_
Bioinformatics groupdatamining & microarray analysis
Utrecht Genomics CenterUniversity Medical Center & Utrecht University
Technology-centered groups/facilitiesBioinformatics
Microarray technologyProteomics
SNP analysisSequencing
Research groupsTranscription regulation genome-wide: Holstege, Timmers
Signal Transduction: Bos, BurgeringDNA replication: van der Vliet
Genetics: WijmengaOncogenomics: Voest
Neurogenomics: Burbach
Temblor participation
WPs pm8.1 – 8.4 6
8.6 4 data annotation tools
8.7 10 populating ArrayExpress
8.9 6 mining ArrayExpress
8.10 6 mining expression data
8.12 4 practical demonstrations
Temblor participation
WPs pm8.1 – 8.4 6
8.6 4 data annotation tools
8.7 10 populating ArrayExpress
8.9 6 mining ArrayExpress
8.10 6 mining expression data
8.12 4 practical demonstrations
Current analysis pipeline
Scanner
Imagene QQCC GeneSpring GeNetLIMS
Gene descriptionsthrough flat files
TIFF images
Flat-files storageRDBMS
Quality ControlNormalization
MIAME compliancy enforcement
XML Database
Advanced analysisVisualization
User path
ArrayDesignMAGE-ML
ExperimentMAGE-ML
Store advanced analysisand visualization
MAGE-ML structure: Array Designs
Features(15552)
Reporters(6378)
Array Design(A-UMCU-1)
Array ManufacturersProtocol
ReporterGroupsGenes
Quality ControlsNormalization Controls
BioSequences(6370)
FeatureReporterMaps
DatabasesSGD
RefSeq
Zones(48)
MAGE-ML structure: Experiments
Experiment(E-UMCU-1)
Array Design(A-UMCU-1)
PhysicalBioAssay(hybridization)
MeasuredBioAssay(Raw data)
DerivedBioAssay(Normalized data)
DerivedBioAssay(Gene Expression Matrix)
Lextract Extract BioSample
BioSource
BioSampleTreatmentTreatment protocol
BioSampleTreatmentLabeling protocol
FeatureExtractionImage analysis protocol
parameters
BioAssayTreatmentHybridization and protocol
DataTransformationQuantitationTypeMapping
DataTransformationQuantitationTypeMapping
BioAssayMappingDesignElementMapping
Experimental Designself vs self, dye swap
Experimental FactorsNormalization controls
DescriptionNormalization
Replicates
Batch
Current analysis pipeline
Scanner
Imagene QQCC GeneSpring GeNetLIMS
Gene descriptionsthrough flat files
TIFF images
Flat-files storageRDBMS
Quality ControlNormalization
MIAME compliancy enforcement
XML Database
Advanced analysisVisualization
User path
ArrayDesignMAGE-ML
ExperimentMAGE-ML
Store advanced analysisand visualization
Data in ArrayExpress
4 experimentshuman cell line serum deprivationhuman cell line heat-shockyeast stationary-phase vs mid-log phaseyeast experimental procedure control experiments
2 array designsyeast 15552 featureshuman 19200 features
11 protocols
Including first fully MIAME compliant submission in MAGE-ML formatvan de Peppel et al., EMBO Reports, 2003
Data underway
Collection of Coelacie patients and controls (33 profiles)
Yeast stationary-phase time course (40 time points)
Yeast responses to copper excess and deprivation (64 time points)
Yeast mutant transcription factors (20 in duplicate)
Collection of head-neck tumor profiles (100 in duplicate)
& >25 projects being run through the microarray facility
Current analysis pipeline
Scanner
Imagene QQCC GeneSpring GeNetLIMS
Gene descriptionsthrough flat files
TIFF images
Flat-files storageRDBMS
Quality ControlNormalization
MIAME compliancy enforcement
XML Database
Advanced analysisVisualization
User path
ArrayDesignMAGE-ML
ExperimentMAGE-ML
Store advanced analysisand visualization
Future pipeline
Scanner
Imagene QQCC GeneSpring GeNet
LIMS
TIFF images
Flat-file storage
RDBMS
Quality ControlNormalization
RDBMS
Advanced analysisVisualization
User path
ArrayDesignMAGE-ML
ExperimentMAGE-ML
BASE
Store raw data in
temporary table
BioConductorR
RDBMS
Low level analysisFiltering
Normalization
Store advanced analysisand visualization
Gene descriptionsthrough Barcode
Raw data annotationMIAME compliancy
Future pipeline
Scanner
Imagene QQCC GeneSpring GeNet
LIMS
TIFF images
Flat-file storage
RDBMS
Quality ControlNormalization
RDBMS
Advanced analysisVisualization
User path
ArrayDesignMAGE-ML
ExperimentMAGE-ML
BASE
Store raw data in
temporary table
BioConductorR
RDBMS
Low level analysisFiltering
Normalization
Store advanced analysisand visualization
Gene descriptionsthrough Barcode
Raw data annotationMIAME compliancy
Temblor participation
WPs pm8.1 – 8.4 6
8.6 4 data annotation tools
8.7 10 populating ArrayExpress
8.9 6 mining ArrayExpress
8.10 6 mining expression data
8.12 4 practical demonstrations
Mining expression data
data tool ArrayExpress
mRNA coexpression na x x
data normalisation x x na
comparing expression-profiles x in progress
~400 expression profiles (time courses)
mRNA coexpression analysis
Find coregulated genes
Combine with other functional genomic data(eg protein interaction, protein localisation, phenotype etc.)
Use for: hypothesis verification, function prediction and data quality assesment
Kemmeren et al., Mol. Cell, 2002
~400 expression profiles (time courses)
mRNA coexpression analysis
Mining expression data
data tool ArrayExpress
mRNA coexpression na x x
data normalisation x x na
comparing expression-profiles x in progress
Microarray controls: subgrid layout
9 external normalization controls in duplicate
5 external ratio controls in duplicatenegative (x-hyb) controls
buffer spots and empty features
Testing external control normalisation
Normalization control spots
Gene spots
Van de Peppel et al., EMBO Reports, 2003
Mining expression data
data tool ArrayExpress
mRNA coexpression na x x
data normalisation x x na
comparing expression-profiles x in progress
med9
sin4
srb11
srb10
srb9
gal11
med3nut1
srb2srb5
med1
srb8 ylr358c
ylr322w
ylr261w
Comparing expression profiles
ylr
26
1w
ylr
35
8c
srb
11
srb
10
srb
9
srb
8
ylr
32
2w
Temblor participation
WPs pm8.1 – 8.4 6
8.6 4 data annotation tools
8.7 10 populating ArrayExpress
8.9 6 mining ArrayExpress
8.10 6 mining expression data
8.12 4 practical demonstrations
Meetings/courses 2003
Organisation/
Month City Country Institute Name of meeting or courseJan Amsterdam NL NKI Technology SeminarFeb Denver USA AAAS Microarrays and Functional GenomicsFeb Leiden NL MGC Yeast to ManMar Hinxton UK EBI Informatics and Analysis of Microarray DataMar Bertinoro Italy EGF 2nd Course in Comparative and Functional GenomicsMar Utrecht NL Astma Fonds Genomics & Proteomics: innovative technologies etcApr Veldhoven NL NAV Yearly meeting dutch anthrpogenetics societyApr Utrecht NL Hubrecht Utrecht BioinformaticsMay Prague CZ ESF Functional genomics and DiseaseMay Amsterdam NL VU YeasterdayJun Heidelberg DE EMBO/EMBL EMBO course microarraysJun Utrecht NL NVBMB Bioinformatics at the interfaceJul Sheffield UK Biochemical Soc. Unravelling nature's networkAug Utrecht NL EPS PhD SummerschoolSep Heidelberg DE EMBO-YIP-PhD YIP PhD courseSep Leuven Bel ESF Microarray data analysis and standardsOct Munich DE Transregio5 Chromatin
External controls & cell count
SP 15 60 180 360 min
Exit from stationary phaseGenes
SP 15 60 180 360 min
...
Microarray facilityDik van LeenenTony MilesMarian Groot-KoerkampJoop van Helvoort
Transcription regulationNynke van BerkumTheo BijmaJeroen van de PeppelNienke KettelarijMarijana RadonjicJean-Christophe AndrauPaul Roepman
_
BioinformaticsPatrick KemmerenHarm van BakelPhilip LijnzaadThessa Kockelkorn
Frank HolstegeUniversity Medical Center Utrecht, the Netherlands