Gene Expression Analysis and Proteins BI420 – Introduction to Bioinformatics Gene Expression Analysis and Proteins Gabor T. Marth Department of Biology, Boston College marth@bc.edu

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 data

Time course experiments

Microarray experiment Microarray data flow Microarray experiment Unsupervised Analysis – clustering Image Analysis Database Supervised Analysis Data Selection 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