1. 1 FINAL PROJECT- Key dates 31.12 –last day to decided on a project * 11-10/1- Presenting a proposed project in small groups A very short presentation (Max 5 minutes) Title- Background Main question Major tools you are planning to use to answer the questions 1.3 Final submission

2. Gene Expression Analysis

3. Studying Gene Expression 1987-2010 3 Spotted microarray One channel microarray RNA profiling- Next Generation Sequencing

4. 4 Applications Identify gene function –Similar expression can infer similar function Find tissue/developmental specific genes –Different expression in different cells/tissues Find genes affected by different conditions –Different expression under different conditions Diagnostics –Different genes expression can indicate a disease state

5. 5 Different types of microarray technologies 1.Spotted Microarray Two channel cDNA microarrays. 2.DNA Chips One Channel microarrays ( Affymetrix, Agilent),

6. 6 http://www.bio.davidson.edu/Courses/genomics/chip/chip.html Microarray Experiment

7. 7 Experimental Protocol Two channel cDNA arrays 1.Design an experiment (probe design) 2. Extract RNA molecules from cell 3.Label molecules with fluorescent dye 4.Pour solution onto microarray –Then wash off excess molecules 5. Shine laser light onto array –Scan for presence of fluorescent dye 6. Analyze the microarray image

8. 8 Analyzing Microarray Images Original Image One gene or mRNA One tissue or condition

9. 9 Cy3Cy5 Cy3 Cy5 log 2 Cy3 The ratio of expression is indicated by the intensity of the color Red= High mRNA abundance in the experiment sample Green= High mRNA abundance in the control sample Transforming raw data to ratio of expression

10. 10 Cy3Cy5 Cy3 Cy5 log 2 Cy3 The ratio of expression is indicated by the intensity of the color Red= High mRNA abundance in the experiment sample Green= High mRNA abundance in the control sample Transforming raw data to ratio of expression

11. 11 Expression Data Format cold normal hot uch1 -2.0 0.0 0.924 gut2 0.398 0.402 -1.329 fip1 0.225 0.225 -2.151 msh1 0.676 0.685 -0.564 vma2 0.41 0.414 -1.285 meu26 0.353 0.286 -1.503 git8 0.47 0.47 -1.088 sec7b 0.39 0.395 -1.358 apn1 0.681 0.636 -0.555 wos2 0.902 0.904 -0.149 Conditions Genes / mRNAs

12. 12 One channel DNA chips Each sequence is represented by a probe set 1 probe set = N probes (Affymetrix 16 probes of length 25 mer). Unknown sequence or mixture (target) colored with on\e fluorescent dye. Target hybridizes to complimentary probes only The fluorescence intensity is indicative of the expression of the target sequence

13. 13 Affymetrix Chip

14. 14 Spotted arrays – oLonger probes (~70), more stable reactions oEasy to make in the lab (by reverse transcription) oHighly specific DNA chips oMore sensitive (higher density) oMore coverage oEnable more flexible designs (e.g differentially measuring splice variants) Pros and cons

15. 15 Designing probes for microarray experiments Probe on DNA chip is shorter than target –Choice of which section to hybridize Select a region which is unstructured –RNA folding, DNA stem-and-loop Choose region which is target-specific –Avoid cross-hybridization with other DNA Avoid regions containing variation –Minimize presence of mutation sites

16. 16 Probe Design Two main factors to optimize Sensitivity –Strength of interaction with target sequence –Requires knowledge of target only Specificity –Weakness of interaction with other sequences –Requires knowledge of ‘background’

17. 17 Sources of Inaccuracy Some sequences bind better than others –A–T versus G–C Low complexity sequences - Cross-hybridization Effects of experimental conditions –temperature

18. 18 Splicing Specific Microarrays Pre-mRNA mRNA Total transcript level +

19. 19 Microarray Analysis Unsupervised -Partion Methods K-means SOM (Self Organizing Maps) -Hierarchical Clustering Supervised Methods -Analysis of variance -Discriminate analysis -Support Vector Machine (SVM)

20. 20 Clustering Grouping genes together according to their expression profiles. Hierarchical clustering Michael Eisen, 1998 : Generate a tree based on similarity (similar to a phylogenetic tree) –Each gene is a leaf on the tree –Distances reflect similarity of expression –Internal nodes represent functional groups

21. Results of Clustering Gene Expression Limitations: –Hierarchical clustering in general is not robust –Genes may belong to more than one cluster

22. Clustering 22 Genes are clustered according to similar expression patterns Self Organizing Maps

23. 23 What can we learn from clusters with similar gene expression ?? Similar expression between genes –One gene controls the other in a pathway –Both genes are controlled by another –Both genes required at the same time in cell cycle –Both genes have similar function Clusters can help identify regulatory motifs –Search for motifs in upstream promoter regions of all the genes in a cluster

24. Normalized expression data from microarrays Experiment 1 Experiment 2 Experiment 3 Finding Regulatory Motifs Within Expression Clusters Search promoter regions for shared sequence motifs.

25. 25 EXAMPLE HnRNPA1 and SRp40 have a similar gene expression pattern in different tissues

26. Are they regulated by the same transcription factor ? 26 hnrnpA1 promoter SRp40 promoters Common motif 1. Extract their promoter regions 2. Find a common motif in both sequences (MEME) 3. Identify the transcription factor related to the motif

27. 27 How can we use microarray for diagnostics?

28. 28 How can microarrays be used as a basis for diagnostic ? patient 1 patient 2 patient 3 patient 4 patient 5 Gen1 +--++ Gen2 ++-+- Gen3 -+++- Gen4 +++-- Gen5 --+-+

29. Informative Genes 29 Differentially expressed in the two classes. Goal Identifying (statistically significant) informative genes

30. 30 How can microarrays be used as a basis for diagnostic ? patinet 1 patient 2 patient 4 patient 3 patient 5 Gen1 +-+-+ Gen3 -+++- Gen4 ++-+- Gen2 +++-- Gen5 ---++ Informative Genes

31. 31 Specific Examples Cancer Research Ramaswamy et al, 2003 Nat Genet 33:49-54 Hundreds of genes that differentiate between cancer tissues in different stages of the tumor were found. The arrow shows an example of a tumor cells which were not detected correctly by histological or other clinical parameters.

32. 32 Supervised approches for predicting gene function based on microarray data SVM would begin with a set of genes that have a common function (red dots), In addition, a separate set of genes that are known not to be members of the functional class (blue dots) are specified.

33. 33 Using this training set, an SVM would learn to discriminate between the members and non-members of a given functional class based on expression data. Having learned the expression features of the class, the SVM could recognize new genes as members or as non-members of the class based on their expression data. ?

34. 34 Using SVMs to diagnose tumors based on expression data Each dot represents a vector of the expression pattern taken from a microarray experiment. For example the expression pattern of all genes from a cancer patients.

35. 35 How do SVM’s work with expression data? In this example red dots can be primary tumors and blue are from metastasis stage. The SVM is trained on data which was classified based on histology. ? After training the SVM we can use it to diagnose the unknown tumor.

36. 36 Gene Expression Databases and Resources on the Web GEO Gene Expression Omnibus - http://www.ncbi.nlm.nih.gov/geo/ List of gene expression web resources –http://industry.ebi.ac.uk/~alan/MicroArray/ Another list with literature references –http://www.gene-chips.com/ Cancer Gene Anatomy Project –http://cgap.nci.nih.gov/ Stanford Microarray Database –http://genome-www.stanford.edu/microarray/