Functional genomics and inferring regulatory pathways with gene expression data.
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Transcript of Functional genomics and inferring regulatory pathways with gene expression data.
Functional genomics and inferring regulatory pathways with gene expression data
Principle of Epistasis Analysis
•Determines order of influence•Used to reconstruct pathways
Experimental Design:Single vs Double-Gene Deletions
Epistasis Analysis Using Microarrays to Determine the Molecular Phenotypes
Time series expression (0-24hrs) every 2hrs
Van Driessche et al. Epistasis analysis with global transcriptional phenotypes. Nature Genetics 37, 471 - 477 (2005)
Pathway Reconstruction
Expression data
Known pathway
Inferred pathway
Expression Profiling in 276 Yeast Single-Gene Deletion Strains
“The Rosetta Compendium”
• Only 19 % of yeast genes are essential in rich media, Giaever et. al. Nature (2002)
Clustered Rosetta Compendium Data
Gene Deletion Profiles Identify Gene Function and Pathways
Systematic phenotyping
yfg1 yfg2 yfg3
CTAACTC TCGCGCA TCATAATBarcode
(UPTAG):
DeletionStrain:
Growth 6hrsin minimal media
(how many doublings?)
Rich media
…
Harvest and label genomic DNA
Microarrays for functional genomics
Hillenmeyer M, et al., Science 2008
Explaining deletion effects
Relevant Relationships (that need to be explained)
• Rosetta compendium used
• 28 deletions were TF (red circles)
– 355 diff. exp. genes (white boxes)
– P < 0.005
– 755 TF-deletion effects (grey squiggles)
Evidence for pathway inferrence
• Step 1: Physical Interaction Network– Y2H, chIP-chip
• Step 2: Integrate state data – Measure variables that are a function of
the network (gene expression)– Monitor these effects after perturbing
the network (TF knockouts).
Inferring regulatory paths
=
=
Direct
Indirect
Annotate: inducer or repressor
OR
Annotate: Inducer or Repressor
Computational methods• Problem Statement:
– Find regulatory paths consisting of physical interactions that “explain” functional relationship
• Method: – A probabilistic inference approach
– Yeang, Ideker et. al. J Comp Bio (2004)
• To assign annotations• Formalize problem using a factor graph• Solve using max product algorithm
– Kschischang. IEEE Trans. Information Theory (2001)– Mathematically similar to Bayesian inference, Markov random
fields, belief propagation
Inferred Network Annotations
A network withambiguous annotation
Inferring Regulatory Role50/132 protein-DNA interactions had been confirmed in low-throughput assays (Proteome BioKnowledge Library)
Inferred regulatory roles (induction or repression) for 48 out of 50 of these interactions agreed with their experimentally determined roles.(96%, binomial p-value < 1.22 × 10-7)
Target experiments to one network region
Expression for: SOK2, HAP4 , MSN4 , YAP6
Expression of Msn4 targets
Average Z-score
Negative control
Expression of Hap4 targets
Yap6 targets are unaffected
Refined Network Model
• Caveats– Assumes target
genes are correct– Only models linear
paths– Combinatorial effects
missed– Measurements are
for rich media growth
Using this method of choosingthe next experiment
• Is it better than other methods?
• How many experiments?
• Run simulations vs:– Random– Hubs
Simulation results
# simulated deletions profiles used to learn a “true” network