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