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

2. Gene expression

3. 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?

4. Expression microarrays
Spotted cDNA arrays
Affymetrix GeneChips
Bubble jet / Ink jet arrays

5. Microarray construction

6. cDNA preparation

7. Expression assay

8. Microarray construction and use

9. Extracting the data

10. Time course experiments

11. Microarray experiment
Microarray data flow
Microarray experiment
Unsupervised
Analysis –
clustering
Image Analysis
Database
Supervised
Analysis
Data Selection
Normalization
Networks
Data Matrix
Decomposition

12. Normalization balance fluorescent intensities of two dyes
adjust for differences in experimental conditions

13. Normalization

14. 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

15. 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

16. Application: classification of cancers

17. Thanks Expression informatics slides courtesy of:
Olga Troyanskaya, Ph.D.
Department of Computer Science
Lewis-Sigler Institute for Integrative Genomics
Princeton University

18. Protein identification
Protein separation by 2D gel eletrophoresis

19. Protein identification
mass spectrometry

20. Protein function
protein chips: identification of proteins that bind a certain chemical