1. Gene Expression Analysis
BI420 – Introduction to Bioinformatics
Gene Expression Analysis
Department of Biology, Boston College

2. 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?

3. Challenges In Measuring Expression
Calling Differential Expression
Challenges In Measuring Expression
Differential Expression
Is the difference in expression between the test and the control greater than the uncertainty in the measurement?

4. Gene expression naturally bounces around a lot

5. Methods For Measuring Gene Expression (all genes in the cell)
Microarrays (older, cheaper)
Sequenced based measurement RNA-Seq (replacing microarrays)

6. Expression microarray movie
DNA microarray chip animation:

7. What are expression microarrays?

8. Expression microarrays – “physical appearance”

9. cDNA preparation

10. Expression assay

11. Chip readout – absolute expression and ratio

12. Chip readout – relative transcription

13. Chip readout – example

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

15. Time course data

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

17. Normalization

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

19. 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

20. Clustering – numerical

21. Clustering – visual

22. Hierarchical clustering: pair-wise similarity

23. Hierarchical clustering: cluster construction

24. Clustering – large example

25. Application of microarrays: classification of cancers

26. RNA Seq

27. |-----------Annotated Gene-----------|
Measuring Gene Expression With RNA Seq
RNA Seq
ACCCAATTTTCTGAAAATATCCGTGTCTTCCAG
Align reads
Count the number of reads that align uniquely within the regions of annotated genes (Shotgun, Cap-Trap, SAGE)
| Annotated Gene |
Genome

28. You get something like this
Rep1 Rep2 Rep3
Gene A
Gene B
Gene C
Gene D
Gene E
*Skewed distribution
*Genes bounce around in replicates

30. Gene Ontology Enrichment
Ontology - An explicit formal specification of how to represent the objects, concepts and other entities
Gene Ontology- structured vocabulary used to describe aspects of the cell
Arranged in a hierarchy
Someone curates an ontology

31. Matlab example: Analyzing Gene Expression Profiles

32. Matlab example: Gene Ontology Enrichment in Microarray Data

33. Typical Steps in a Gene Expression Experiment
Isolate RNA from several biological replicates (See slides 3-4)
Microarray
Label
Hybridize to microarray
Image microrray
Normalize data
RNA-Seq
Sequence RNA
Align to genome
Count aligned reads
Normalize data between samples
Analysis – independent of measurement technique
Call differential gene expression
Clustering
Gene Ontology enrichment