Download Genome variation informatics: SNP discovery

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