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BI420 – Introduction to Bioinformatics Gene Expression Analysis and Proteins Gabor T. Marth Department of Biology, Boston College [email protected] Gene expression Why study gene expression? Which genes are active • at different developmental stages? • in cells of different tissues? • at different time points in the same cell? • cells under different environmental conditions? • between normal and cancerous cells? Expression microarrays • Spotted cDNA arrays • Affymetrix GeneChips • Bubble jet / Ink jet arrays Microarray construction cDNA preparation Expression assay Microarray construction and use Extracting the dataData Extracting 200 10000 50.00 5.64 4800 4800 1.00 0.00 9000 300 0.03 -4.91 Cy3 Cy5 Cy 5 Cy5 Cy 3 log 2 Cy3 Genes Experiments Time course experiments Microarray data flow Microarray experiment Image Analysis Unsupervised Analysis – clustering Database Data Selection Supervised Analysis Normalization Networks Data Matrix Decomposition Normalization • balance fluorescent intensities of two dyes • adjust for differences in experimental conditions Normalization Unsupervised analysis – clustering • Why: if the expression pattern for gene B is similar to gene A, maybe they are involved in the same or related pathway • How: Re-order expression vectors in the data set so that similar patterns are together Self-organizing maps (SOMs) • SOMs result in gene partitions • genes are assigned to partitions containing similar genes • neighboring partitions are more similar to each other than they are to distant partitions Application: classification of cancers Thanks Expression informatics slides courtesy of: Olga Troyanskaya, Ph.D. Department of Computer Science Lewis-Sigler Institute for Integrative Genomics Princeton University Protein identification Protein separation by 2D gel eletrophoresis Protein identification mass spectrometry Protein function protein chips: identification of proteins that bind a certain chemical