1. Developed at the Broad Institute of MIT and Harvard Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, and Mesirov JP. GenePattern 2.0. Nature Genetics 38 no. 5 (2006): pp500-501 GenePattern is supported by funding from the NIH

2. Today… Introduction to GenePattern –Why –What –How Demonstration Summary

3. Challenges Modern research methods follow a more integrative approach Tools are not available to biomedical researchers Tools are difficult to use Results difficult to interpret correctly

4. Purpose Create tools that are easily accessible to biomedical researchers Allows for a combination of multiple data sources and methods Allows for “reproducible research”

5. GenePattern 1.Offers a repository of analytic and visualization tools: Modules 2.Easy creation of complex methods from these tools: Pipelines 3.The rapid development and dissemination of new methods: Programming Environment

6. 1. Modules Point and click ~ 60 analysis modules (handout) Documentation Designed for Affymetrix data 14 different file extensions

7. 2. Pipelines Golub et al illustrates need Records the methods, parameters and data to ensure reproducibility Allows methods to be “chained” Published or create new Easily shared Assigns version numbers

8. 3. Programming environment Libraries allow transparent access to GenePattern modules from R, Matlab and Java Language independent mechanism to add new tools to the module repository Tools can be your own or public (e.g. from Bioconductor)

9. Functional Architecture Taken from Reich et al Nature Genetics 2006

10. Components 1.The GenePattern server 2.The Java Client 3.The Web Client

11. Software Architecture Reich et al Nature Genetics 2006

12. GenePattern Current version –Release: 2.0.1, Release date 3/2/2006 OS compatibility: –Windows: XP, 2000, 2003 –Mac: OS X 1.3.9 or later –Unix: Linux, Solaris, Tru64 Hardware requirements: –256MB RAM –500MB disk space

13. Demonstration http://www.broad.mit.edu/cancer/software/genepattern/

14. Gene Expression Analysis Four broad categories 1.Differential analysis/Marker selection 2.Prediction 3.Class discovery 4.Pathway analysis Data Formats Annotations

15. Proteomics SELDI, MALDI and LC-MS in mzXML format Quality assessment Peak detection Spectra comparison Proteomic analysis pipeline Data conversion

16. SNP analysis In alpha testing Uses high-density SNP microarray data Copy number alterations Loss of heterozygosity (LOH) detection

17. Data preprocessing and conversion Importing, exporting and file conversion Normalization, filtering and imputing ID conversion and annotation Row and column extraction, transpose, reorder and split data

18. Comparison of Selected Microarray Analysis Software Platforms Reich et al Nature Genetics 2006

19. Summary Has a few minor problems Is it something MIBLab can use? –Who is user? –What is it missing? Should be easily added

20. Sources Gould J, Getz G, Monti S, Reich M, Mesirov JP. Comparative Gene Marker Selection suite. Bioinformatics. 2006 May 18; Liefeld T, Reich M, Gould J, Zhang P, Tamayo P, Mesirov JP. GeneCruiser: a web service for the annotation of microarray data. Bioinformatics. 2005 Sep 15;21(18):3681-2. Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP. GenePattern 2.0. Nature Genetics 2006 May;38(5):500-1.