Functional genomics and inferring regulatory pathways with gene expression data.
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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)
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).
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 with ambiguous annotation
Inferring Regulatory Role 50/132 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)
Which deletion experiments should we do next? A mutual information based score –For each candidate experiment (gene ) Variability of predicted expression profiles –Predict profile for each possible set of annotations –More variation = more information from experiment Reuse network inference algorithm to compute effect of deletion.