1. Gene Expression Analysis using Microarrays Anne R. Haake, Ph.D.

2. Figure by Lawrence Berkeley Lab Human Genome Center, Berkeley, California, USA

3. Post-Genomic Age ? A switch in focus from sequencing to understanding how genomes function

4. How do we relate gene identity to cell physiology, disease & drug discovery?  Functional Genomics =“ development and application of global (genome-wide or system-wide) experimental approaches to assess gene function by making use of the information and reagents provided by structural genomics”

5. Gene Expression Analysis What is gene expression? What can we learn from expression analysis? How is the analysis accomplished? What are the challenges for bioinformatics?

6. Gene Expression Flow of Information

7. Individual cells in an organism have the same genes (DNA) but…. It is the expression of thousands of genes and their products (RNA, proteins), functioning in a complicated and orchestrated way, that make that organism what it is.

8. Differential Gene Expression A Few Examples: Cell type specific -e.g. skin cell vs. brain cell Developmental stage -e.g. embryonic skin cell vs. adult skin cell Disease state -e.g. normal skin cell vs. skin tumor cell Environment-specific -e.g. skin cell untreated vs. treated drugs, toxins

9. What can we learn by analyzing complex patterns of gene expression? Classifications: for diagnosis, prediction… Cell-type, stage-specific, disease- related, treatment-related patterns of gene expression? Gene Networks/Pathways: Functional roles of genes in cellular processes? Gene regulation and gene interactions

10. Gene Networks

11. http://industry.ebi.ac.uk/~brazma/Genenets

12. Gene Expression Analysis Need efficient ways to study these complex patterns. 1) Techniques of Biochemistry/Molecular Biology Resolution of the patterns = expression data (RNA or protein) 2) Management of complex data sets 3) Mining of the data to gain useful information

13. Gene Expression Analysis High-Throughput Techniques Microarray or Gene Chip = cDNA arrays or oligo arrays (Affymetrix) Filter Arrays Differential Display SAGE

14. Gene Chip technology DNA microarrays = hundreds to thousands of different DNA sequences spotted onto glass microscope slide Compare binding (base-pairing) of two different sets of expressed gene sequences to the template DNA microarray Allows simultaneous analysis of thousands of genes: Is the gene expressed? At what level? *expression levels are relative

15. Flash Animation available at: http://www.bio.davidson.edu/Biology/Courses/genomics/chip/chip.html

16. The Full Yeast Genome on a Chip 6116 Yeast Genes 96 Intergenic regions + lots of control samples –Primers purchased from Research Genetics Total spots printed: 707,520 Total Arrays: 110 Actual Time to print: 52 hours Credits: Dr. Patrick O. Brown laboratory: pbrown@cmgm.stanford.edu pbrown@cmgm.stanford.edu

17. Outcomes of Microarray Analysis Size and complexity of the problem –Example: 20,000 genes from 10 samples under 20 different conditions -  4,000,000 pieces of data  challenges for Bioinformatics

18. Outcomes of Microarray Analysis Large, complex data sets Wide availability of technology large number of distributed databases Current state: data scattered among many independent sites (accessible via Internet) or not publicly available at all.

19. Current Problems Facing Bioinformatics Standardization & Quality Control In the Experiments (data quality at several levels) Management of the Data -Standardization of the databases -Public access to the databases Information from the Data -Need for data mining algorithms customized for gene expression analysis

20. Microarray Databases Need public repository with standardized annotation Issues : - difficulty in describing expression experiments; remember that measurements are relative (complicates comparisons) –Structure of the database itself –Internet-based tools for searching and using semantic context to allow comparisons

21. Public Microarry Repositories 4 Major Efforts: GeneX at US National Center for Genome Resources http://www.ncgr.org/research/genex/ ArrayExpress at European Bioinformatics Institute http://www.ebi.ac.uk/arrayexpress/

22. Public Repositories Stanford University Database http://genome- www4.stanford.edu/MicroArray/SMD/inde x.htmlhttp://genome- www4.stanford.edu/MicroArray/SMD/inde x.html

23. Public Repositories Gene Expression Omnibus at US National Center for Biotechnology Information Example at: http://www.ncbi.nlm.nih.gov/geo/query/ acc.cgi?acc=GSM39 http://www.ncbi.nlm.nih.gov/geo/query/ acc.cgi?acc=GSM39

24. Mining of the Expression Databases A gene expression pattern derived from a single microarray experiment is simply a snapshot (one experimental sample vs reference) Usually want to understand a process or changes in expression over a collection of samples  gene expression profile Example?

25. Mining of the Expression Databases General Approaches Raw data from multiple experiments converted to a gene expression matrix - Rows: Different genes - Columns: Different samples - Numerical values encoded by color (red=positive green=negative blue=n.a.)

26. Typical approach Look for similarities (or differences) in patterns e.g. Compare rows to find evidence for co-regulation of genes 1) Need ways to measure similarity (distance) among the objects being compared 2) Then, group together objects (genes or samples) with similar properties.

27. Cluster Analysis Partitions biological samples into groups based on their statistical behavior. - Unsupervised Analysis - Supervised Analysis: classification rules

28. Analytic Approaches Clustering Algorithms –Hierarchical –K-mean –Self-organizing maps –Others

29. Eisen et al. http://www.pnas.org/cgi/content/ full/95/25/14863

30. Success Story Gene Clustering Approach Yeast genome –Complete set of genes used to study diauxic shift time course –Cluster analysis of data identified group of genes with similar expression profiles –Upstream regulatory sites of these genes compared to identify transcription factor binding sites (see Brazma & Vilo reference)

31. Example-Sample Clustering Classification of cancers –Comparing 2 acute leukemias (AML and ALL) Biological/Clinical Problems: Previously, no single reliable test to distinguish Differ greatly in clinical course & response to treatments. http://waldo.wi.mit.edu/MPR/figures_ALL_AML.html

34. The prediction of a new sample is based on 'weighted votes' of a set of informative genes.

35. Analytic Approach: 1) Class discovery = classification by clustering of microarray data using tumors of known type  Found 1100 of 6817 genes correlated with class distinction 2) Formation of a class predictor = 50 most informative genes  class discovery of unknown tumors

37. Analytic Approaches Limitation of cluster analysis: similarity in expression pattern suggests co-regulation but doesn’t reveal cause-effect relationships Bayesian Networks –Represent the dependence structure between multiple interacting quantities (e.g. expression levels of genes) –gene interactions & models of causal influence Others? many

38. Check the Web: Free Software Available Some useful links: Expression Profiler http://ep.ebi.ac.uk/ GeneX (NCGR) http://genex.sourceforge.net/ www.ncgr.org/research/genex/other_tools.html http://www.kdnuggets.com/software/suites.html

39. Additional References: R. Ekins and F.W. Chu :Microarrays: their origins and applications. Trends in Biotechnology, 17: 217-218, 1999. Brazma et al., One-stop shop for microarray data. Nature 403: 699 – 700, 2000. Brazma A. and Vilo, J:Minireview. Gene expression data analysis. FEBS Letters, 480:17-24, 2000.