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Microarrays and Gene Expression Analysis

Microarrays and Gene Expression Analysis. Gene Expression Data. Microarray experiments Applications Data analysis Gene Expression Databases. DNA Microrray. A microarray is a tool for analyzing gene expression in genomic scale.

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Microarrays and Gene Expression Analysis

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  1. Microarrays and Gene Expression Analysis

  2. Gene Expression Data • Microarray experiments • Applications • Data analysis • Gene Expression Databases

  3. DNA Microrray A microarray is a tool for analyzing gene expression in genomic scale. The microarray consists of a small membrane or glass slide containing samples of many genes arranged in a regular pattern.

  4. DNA Microarrays • First introduced in 1987 • Still undergoing much development • Small piece of glass • Thousands/millions of cells • Each cell contains a DNA probe • Millions of copies bound to glass • Cells capture their reverse complement • Ordinary DNA/RNA base pair hybridization

  5. Chips or Microarrays Two types of microarray technologies • Spotted Microarray Traditionally called DNA microarrays. 2. Affymetrix-Developed at Affymetrix, Inc. Historically called DNA chips.

  6. Spotted Microarray • probe cDNA (50~5,000 bases long) is immobilized to a solid surface such as glass using robot spotting and exposed to a set of targets either separately or in a mixture.

  7. http://www.bio.davidson.edu/biology/courses/genomics/chip/chip.htmlhttp://www.bio.davidson.edu/biology/courses/genomics/chip/chip.html

  8. Experimental Protocol • Identify RNA/DNA sequences of interest • Design probes that are sequence-specific • Extract molecules from cell environment • Label molecules with fluorescent dye • Pour solution onto microarray • Then wash off excess molecules • Shine laser light onto array • Scan for presence of fluorescent dye

  9. One tissue or condition One geneor mRNA Microarray Images Original Image Summary

  10. Cy5 Cy3 Cy5 log2 Cy3 The ratio of expression is indicated by the intensity of the color Red= HighmRNA abundance in the experiment sample Green= High mRNA abundance in the control sample Cy3 Cy5

  11. Conditions Genes / mRNAs Expression Data Format normal hot cold uch1 -2.0 0.0 0.924 gut2 0.398 0.402 -1.329 fip1 0.225 0.225 -2.151 msh1 0.676 0.685 -0.564 vma2 0.41 0.414 -1.285 meu26 0.353 0.286 -1.503 git8 0.47 0.47 -1.088 sec7b 0.39 0.395 -1.358 apn1 0.681 0.636 -0.555 wos2 0.902 0.904 -0.149

  12. Microarray Applications • Identify genes whose function is related • Similar expression in group in many cases • Find genes expressed in specific tissues • Different expression in different cells • Find genes affected by environment • Different expression under different conditions • Distinguish different forms of a disease • Different expression in different patients

  13. Microarray ApplicationsSpecific Examples • Evolution Chimpanzees and Human genomes are 98.7 % identical and therefore phylogenetic analysis can not separated them into different species. Most differences between human and chimpanzees have been detected at the level of gene expression in the brain.

  14. Microarray ApplicationsSpecific Examples • Behaviour Gene Expression Profiles in the Brain Predict Behavior in Individual Honey Bees Charles W. Whitfield,1,2 Anne-Marie Cziko,1 Gene E. Robinson1,2*We show that the age-related transition by adult honey bees from hive work to foraging is associated with changes in messenger RNA abundance in the brain for 39% of 5500 genes tested. This result, discovered using a highly replicated experimental design involving 72 microarrays, demonstrates more extensive genomic plasticity in the adult brain than has yet been shown. Experimental manipulations that uncouple behavior and age revealed that messenger RNA changes were primarily associated with behavior. Individual brain messenger RNA profiles correctly predicted the behavior of 57 out of 60 bees, indicating a robust association between brain gene expression in the individual and naturally occurring behavioral plasticity.

  15. Microarray ApplicationsSpecific Examples Cancer Research Hundreds of genes that differentiate between cancer tissues in different stages of the tumor were found. These different stages were not detected by histological or other clinical parameters. Ramaswamy et al, 2003 Nat Genet 33:49-54

  16. Microarray Analysis GOALS Sample classification • What are the set of genes that differentiate between two or more groups of treatments Gene Classification - What is the set of genes that have the same expression profile along a set of treatments

  17. Microarray Analysis How do we answer them ? • Supervised Methods -Analysis of variance -Discriminate analysis -Support Vector Machine (SVM) • Unsupervised -Partion Methods K-means SOM (SelfOrganizing Maps -Hierarchical Clustering

  18. TO BE CONTINUED • Rest of the slides from Lecture12_05 where moved to Lecture13_05

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