Computational analysis of genome-wide expression data.ppt

Computational analysis of genome-wide expression data
Lecture overview
Microarray technology and applications
How the data is collected and what you get.
“High level” analysis methods: applied to the study of human a.
Supervised and unsupervised learning.
Feature selection.
Method for applying biological prior knowledge.
(If there is time) Further applications of the technology.
Review: gene expression
DNA  pre-mRNA  mRNA  protein
Many potential steps for regulation.
Many genes are differentially transcribed according to:
tissues
cell types
in various disease, physiological, and developmental states.
mRNA is easy to quantify using hybridization assays.
protein levels harder to measure in a high-throughput assay.
Microarrays
Thousands of small (20-200m) spots of DNA probes on a glass slide.
Use to measure gene expression (RNA) levels of 10,000-20,000+ genes in parallel.
Old way: Northern blots etc. let you measure one gene at a time.
Generally give only relative expression information.
Methods exist for getting absolute measurements (SAGE, calibrated arrays)
Yields a type of snapshot of the molecular state of the sample.
Applications for microarrays
Diagnosis: molecular ‘portraits’ of disease
Preventative medicine - early detection.
Personalized treatment.
Refined diagnosis and prognosis.
Disease/phenotype characterization: What genes are affected by condition X? (esp. complex traits)
Gene expression work elucidation: what happens to the expression of gene Y if you knock out gene X?
Mutation and polymorphism detection
Genome analysis: gene finding/structure determination
Sets a model for high-throughput technologies:
Protein arrays
Post-translational modification assay arrays
Protein interaction arrays
Why microarrays are of interest putational biologists
Can generate a lot of data very quickly (compared to what biologists usually deal with).
Many studies include 50 samples or more = ~1,000,000 data points.
Messier than sequence data (in interesting way