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Genomics Laboratory University Medical Center Utrecht... Microarray technology group microarray production and use Transcription regulation genome-wide.

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Presentation on theme: "Genomics Laboratory University Medical Center Utrecht... Microarray technology group microarray production and use Transcription regulation genome-wide."— Presentation transcript:

1 Genomics Laboratory University Medical Center Utrecht... Microarray technology group microarray production and use Transcription regulation genome-wide how are genes regulated ? _ Bioinformatics group datamining & microarray analysis

2 Utrecht Genomics Center University Medical Center & Utrecht University Technology-centered groups/facilities Bioinformatics Microarray technology Proteomics SNP analysis Sequencing Research groups Transcription regulation genome-wide: Holstege, Timmers Signal Transduction: Bos, Burgering DNA replication: van der Vliet Genetics: Wijmenga Oncogenomics: Voest Neurogenomics: Burbach

3 Temblor participation WPs pm 8.1 – 8.4 6 8.6 4data annotation tools 8.710populating ArrayExpress 8.9 6mining ArrayExpress 8.10 6mining expression data 8.12 4practical demonstrations

4 Temblor participation WPs pm 8.1 – 8.4 6 8.6 4data annotation tools 8.710populating ArrayExpress 8.9 6mining ArrayExpress 8.10 6mining expression data 8.12 4practical demonstrations

5 Current analysis pipeline Scanner ImageneQQCC GeneSpring GeNetLIMS Gene descriptions through flat files TIFF images Flat-files storageRDBMS Quality Control Normalization MIAME compliancy enforcement XML Database Advanced analysis Visualization User path ArrayDesign MAGE-ML Experiment MAGE-ML Store advanced analysis and visualization

6 MAGE-ML structure: Array Designs Features (15552) Reporters (6378) Array Design (A-UMCU-1) Array ManufacturersProtocol ReporterGroups Genes Quality Controls Normalization Controls BioSequences (6370) FeatureReporterMaps Databases SGD RefSeq Zones (48)

7 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 BioSampleTreatment Treatment protocol BioSampleTreatment Labeling protocol FeatureExtraction Image analysis protocol parameters BioAssayTreatment Hybridization and protocol DataTransformation QuantitationTypeMapping DataTransformation QuantitationTypeMapping BioAssayMapping DesignElementMapping Experimental Design self vs self, dye swap Experimental Factors Normalization controls Description Normalization Replicates Batch

8 Current analysis pipeline Scanner ImageneQQCC GeneSpring GeNetLIMS Gene descriptions through flat files TIFF images Flat-files storageRDBMS Quality Control Normalization MIAME compliancy enforcement XML Database Advanced analysis Visualization User path ArrayDesign MAGE-ML Experiment MAGE-ML Store advanced analysis and visualization

9 Data in ArrayExpress 4 experiments human cell line serum deprivation human cell line heat-shock yeast stationary-phase vs mid-log phase yeast experimental procedure control experiments 2 array designs yeast 15552 features human 19200 features 11 protocols Including first fully MIAME compliant submission in MAGE-ML format van de Peppel et al., EMBO Reports, 2003

10 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

11 Current analysis pipeline Scanner ImageneQQCC GeneSpring GeNetLIMS Gene descriptions through flat files TIFF images Flat-files storageRDBMS Quality Control Normalization MIAME compliancy enforcement XML Database Advanced analysis Visualization User path ArrayDesign MAGE-ML Experiment MAGE-ML Store advanced analysis and visualization

12 Future pipeline Scanner ImageneQQCC GeneSpring GeNet LIMS TIFF images Flat-file storage RDBMS Quality Control Normalization RDBMS Advanced analysis Visualization User path ArrayDesign MAGE-ML Experiment MAGE-ML BASE Store raw data in temporary table BioConductor R RDBMS Low level analysis Filtering Normalization Store advanced analysis and visualization Gene descriptions through Barcode Raw data annotation MIAME compliancy

13 Future pipeline Scanner ImageneQQCC GeneSpring GeNet LIMS TIFF images Flat-file storage RDBMS Quality Control Normalization RDBMS Advanced analysis Visualization User path ArrayDesign MAGE-ML Experiment MAGE-ML BASE Store raw data in temporary table BioConductor R RDBMS Low level analysis Filtering Normalization Store advanced analysis and visualization Gene descriptions through Barcode Raw data annotation MIAME compliancy

14 Temblor participation WPs pm 8.1 – 8.4 6 8.6 4data annotation tools 8.710populating ArrayExpress 8.9 6mining ArrayExpress 8.10 6mining expression data 8.12 4practical demonstrations

15 Mining expression data data tool ArrayExpress mRNA coexpression naxx data normalisationxxna comparing expression-profilesxin progress

16 ~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

17 ~400 expression profiles (time courses) mRNA coexpression analysis

18 Mining expression data data tool ArrayExpress mRNA coexpression naxx data normalisationxxna comparing expression-profilesxin progress

19 Microarray controls: subgrid layout 9 external normalization controls in duplicate 5 external ratio controls in duplicate negative (x-hyb) controls buffer spots and empty features

20 Testing external control normalisation Normalization control spots Gene spots Van de Peppel et al., EMBO Reports, 2003

21 Mining expression data data tool ArrayExpress mRNA coexpression naxx data normalisationxxna comparing expression-profilesxin progress

22 med9 sin4 srb11 srb10 srb9 gal11 med3 nut1 srb2 srb5 med1 srb8 ylr358c ylr322w ylr261w Comparing expression profiles ylr261w ylr358c srb11srb10 srb9srb8 ylr322w

23 Temblor participation WPs pm 8.1 – 8.4 6 8.6 4data annotation tools 8.710populating ArrayExpress 8.9 6mining ArrayExpress 8.10 6mining expression data 8.12 4practical demonstrations

24 Meetings/courses 2003

25 ... Microarray facility Dik van Leenen Tony Miles Marian Groot-Koerkamp Joop van Helvoort Transcription regulation Nynke van Berkum Theo Bijma Jeroen van de Peppel Nienke Kettelarij Marijana Radonjic Jean-Christophe Andrau Paul Roepman _ Bioinformatics Patrick Kemmeren Harm van Bakel Philip Lijnzaad Thessa Kockelkorn Frank Holstege University Medical Center Utrecht, the Netherlands

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