1. GEO (Gene Expression Omnibus) Deepak Sambhara Georgia Institute of Technology 21 June, 2006

2. What is GEO? -A gene expression repository created by the NCBI -Located: http://www.ncbi.nlm.nih.gov/projects/geo - Supports data submissions, browsing, query and retrieval. - Organized on three levels: platforms, series, and samples

3. Why Use GEO? - Validating PADRE by invalidating public data - Thorough data for microarray experiments - Designing interface of MAGMA

4. Background and Significance -MIAME (Minimum Information About a Microarray Experiment) Compliant -Effort to help standardize publicly available data -http://www.mged.org/Workgroups/ MIAME/ MIAME CHECKLIST -Experimental Design -Samples used, extract preparation and labeling -Hybridization procedures and parameters -Measurement data and specifications - Array Design

5. QUERY Search - Search by Data Sets, Gene profiles, GEO Accession numbers, or GEO Blast -Can modify queries using search tabs on results page - Search tabs: limits, history, clipboard, and query translation E.g. Filter for only experiments with.CEL files

6. QUERY Results - Listed by relevance; sortable by: datasets, platforms and series -Up to 500 results per page; shows summary of experiment, can list by briefs, PubMed links etc. - If.CEL files exist, downloadable on results page. - Click GEO accession number to access experiment page

7. Browsing - Can browse by data sets (Result page with all experiments) or GEO Accessions -GEO Accessions browsed by Platforms, Samples, or Series

8. Demo http://www.ncbi.nlm.nih.gov/projects/geo GO TO

9. Search data sets for “cancer”

10. Download.CEL files Click GEO Accession link to access experiment

11. Take note of chip platform Find the corresponding.pdf document using PubMed IDs Take note of Classes, and number of arrays

12. Download DataSet file (Raw data) and Annotation file DataSet SOFT file list gene expression for all patients

13. Web-based analysis through Heirarchial Clustering, Value Distributions and t-tests

14. Can plot selected gene profiles using a region of interest box

15. Click value distribution for distribution of avg. gene expression values for outlier detection

16. One or two- tailed t-tests completed to compare two classes in the experiment Significance Levels can be adjusted from 0.001 to 0.100

17. Shows Probe Set ID’s found significant based on chosen class comparisons

18. Features PROSCONS - User-friendly interface - MIAME Compliant - Web based analysis - Raw data/Annotation files available - Vastly expansive/thorough compared to other microarray databases - GSE series/ GDS series differences - Must have PubMed ID -.CEL files not available for all datasets -.CEL files are individually zipped - No Quality Control Information