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Exploring Gene Expression Analysis: Techniques and Insights

Delve into gene expression analysis covering microarray construction, data analysis methods like clustering, and applications in cancer classification. Understand the importance of studying gene expression patterns in different biological contexts. Learn through examples and informative slides.

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Exploring Gene Expression Analysis: Techniques and Insights

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

  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. What are expression microarrays?

  5. Expression microarrays – “physical appearance”

  6. Microarray construction

  7. cDNA preparation

  8. Expression assay

  9. Expression microarray movie DNA microarray chip animation: http://www.bio.davidson.edu/Courses/genomics/chip/chip.html

  10. Chip readout – absolute expression and ratio

  11. Chip readout – relative transcription

  12. Chip readout – example

  13. Time course experiments Experiment: measuring gene expression as oxygen gets depleted in yeast grown in a closed container

  14. Time course data

  15. Data analysis – normalization • balance fluorescent intensities of two dyes • adjust for differences in experimental conditions

  16. Normalization

  17. Log2 transformation Double or half expression now has the same magnitude

  18. Clustering – intro • 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

  19. Clustering – numerical

  20. Clustering – visual

  21. Hierarchical clustering: pair-wise similarity

  22. Hierarchical clustering: cluster construction

  23. Clustering – large example

  24. Next two classes Chapter 7. Chapter 8.

  25. Application of microarrays: classification of cancers

  26. Microarrays to detect genome copy #

  27. Protein identification Protein separation by 2D gel eletrophoresis

  28. Protein identification mass spectrometry

  29. Protein function identification protein chips: identification of proteins that bind specific chemicals

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

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