Download A Gene Coexpression Network for Global Discovery of Conserved

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
 Functionally related genes
 Correlated expression patterns
► Cross
species comparison of gene
expression
To measure evolutionarily conserved
coexpression on a genome-wide
scale,
create a gene coexpression network
Step 1: Find Meta-genes
► 6307
► 6591
Total Meta-genes
human
► 5180 worm
► 5802 fly
► 2434 yeast
Step 2: Identify Meta-genes with
correlated coexpression
3182 DNA
Microarrays
1202 human
979 worm
155 fly
643 yeast
Step 2: Identify Meta-genes with
correlated coexpression
Pearson correlation of gene pairs
2. Rank genes by Pearson correlation
3. Generate P –value of rank configuration
4. P < 0.05 cutoff indicates coexpression
5. Link coexpressed meta-genes
1.
Gene Coexpression Network
Result:
► Network of 3416 metagenes
► Connected by 22,163 expression interactions
3-D Terrain Map
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
Testing Significance of Results
1.
2.
3.
Rule out random pairs of meta-genes
having significant coexpression
interactions
Ensure broad and diverse microarray data
Test network stability with added noise
Verify Results
► Experimentally
functions
validate predicted gene
 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)
Further Analyses
► Single
Species Networks vs Multi-species Network
Further Analyses
► Accuracy
related to more data in Multispecies networks?
Conservation of Genetic Modules
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.
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?