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A Gene Coexpression Network for Global Discovery of Conserved Genetic Modules

A Gene Coexpression Network for Global Discovery of Conserved Genetic Modules. Joshua M. Stuart, Eran Segal, Daphne Koller, Stuart K. Kim Presented by Carri-Lyn Mead. Investigating Gene Function. Genome sequences for Human, Fly, Worm, Yeast DNA Microarrays Coregulated genes

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A Gene Coexpression Network for Global Discovery of Conserved Genetic Modules

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  1. A Gene Coexpression Network for Global Discovery of Conserved Genetic Modules Joshua M. Stuart, Eran Segal, Daphne Koller, Stuart K. Kim Presented by Carri-Lyn Mead

  2. Investigating Gene Function • Genome sequences for Human, Fly, Worm, Yeast • DNA Microarrays • Coregulated genes • Functionally related genes • Correlated expression patterns • Cross species comparison of gene expression

  3. To measure evolutionarily conserved coexpression on a genome-wide scale, create a gene coexpression network

  4. Step 1: Find Meta-genes • 6307 Total Meta-genes • 6591 human • 5180 worm • 5802 fly • 2434 yeast

  5. Step 2: Identify Meta-genes with correlated coexpression 3182 DNA Microarrays 1202 human 979 worm 155 fly 643 yeast

  6. Step 2: Identify Meta-genes with correlated coexpression • Pearson correlation of gene pairs • Rank genes by Pearson correlation • Generate P –value of rank configuration • P < 0.05 cutoff indicates coexpression • Link coexpressed meta-genes

  7. Gene Coexpression Network Result: • Network of 3416 metagenes • Connected by 22,163 expression interactions

  8. 3-D Terrain Map

  9. Component 5 • Strongly enriched for meta-genes involved in cell cycle processes • Contains 241 meta-genes • 110 previously known to be involved in cell cycle • 131 not previously known to be involved in cell cycle

  10. Testing Significance of Results • Rule out random pairs of meta-genes having significant coexpression interactions • Ensure broad and diverse microarray data • Test network stability with added noise

  11. Verify Results • Experimentally validate predicted gene functions • Select 5 meta-genes MEG1503 (snRNP protein involved in splicing) MEG342 (nucleoporin-interaction component) MEG4513 (novel protein, unknown function) MEG1192 (novel protein, unknown function) MEG1146 (novel protein, unknown function)

  12. Further Analyses • Single Species Networks vs Multi-species Network

  13. Further Analyses • Accuracy related to more data in Multi-species networks?

  14. Conservation of Genetic Modules

  15. Conclusions • Gene coexpression networks can be used as a powerful tool for generating hypotheses about genes whose functions are unknown. • Gene coexpression networks can be used to describe the evolution of genetic interactions. • Multi-species networks perform better than single species networks overall.

  16. Discussion Topics • What other model organisms would be useful to expand the multi-species network? • Would the multi-species network be as useful for species that are more closely related? • Gene orthology is based on protein sequence similarity. Does sequence conservation equate to conserved function? • Are 12 clusters of meta-genes sufficient to hypothesize function for 3416 metagenes? • How can gene function for genes without known orthologs be investigated?

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