RECOMB SATELLITE MEETINGNEW-YORK, NOVEMBER 2010

GENIE – GEne Network Inference with Ensemble of trees

Van Anh Huynh-ThuDepartment of Electrical Engineering and Computer Science, Systems and Modeling, University of Liege, Belgium

Inference of GRNs Gene regulatory networks (GRNs) are

behind the scene players in gene expression

How do we determine the regulators of each gene?

Input:Gene expression data in different

conditions/time pointsA subset of the genes that contains all the

regulators (without GENIE accuracy plummets)

Underlying Model Every reverse engineering tool assumes

an underlying model GENIE assume that the GRN is a

Boolean network Therefore, the regulation of each gene is

a Boolean function

GENIE Strategy Outline Not to make strong assumptions about

the possible regulatory interactions (e.g. a strong assumption is linearity)

Treat time-series as static experiments Solve the problem for each gene

separately, and combine the results The final output is a ranking of potential

interactions in descending confidence

GENIE workflow

Tree-based Ensemble Methods A regulation function is a binary tree – at each

node a binary test according to a different regulator is performed

The prediction is at the leaf For each gene, randomly select a set of

samples and produce a tree from each one (the root is the single gene that splits K random conditions of the target best, and so on)

Rank the regulators according to their importance in the trees

Ranking of regulators

#S is the number of samples that reach the node N

#St (Sf) is the number of samples with output true

(false)

Var() is the variance of the output

In order to avoid bias towards highly variable genes, the

expression values are first normalized to unit variance

Best performer in DREAM5 network inference

The Genetic Landscape of the Cell

Charles BooneUniversity of Toronto, Donnelly Center

Synthetic Genetic Arrays

No growth

•Single mutant strand (query gene) is crossed with all other single mutants•Double mutants are selected•Currently done for budding yeast, e.coli and s.pombe

Genetic Interactions Positive interaction: The double knockout is

more viable than would be expected by the separate contributions of the single knockouts

Negative interaction: The double knockout is less viable than would be expected by the separate contributions of the single knockouts

They crossed ~1700 yeast single mutants with ~3,800 single mutants, and after filtering failures they got ~5.4 million double mutants

Yeast Interaction MapEdges are interactions that pass cutoff threshold (170,000)

Proximity in the layout is according to similarity in interaction profiles

Colored sets = GO enrichment

Proximity between clusters and related functions

Proximate clustersBoth require cytoskeleton genes

Zoom in on pathway

Red – NegativeGreen - Positive

Budding

Required for polarizationand growth

Cell division

Interactions between pathways and complexes were often monochromatic

Translation

Positive vs. negative interactions

Negative interactions are ~two times more prominentthan positive

No interaction

Degree distribution

Severe fitness defects in single mutants correlate with degree

Hubs are less numerous

Gene duplicates interact less

Correlation between degree and gene properties

Black - PPI

#morphological phenotypes

# chemical perturbations

unstable structure

Genetic interactions between cellular processes

Cell cycle is more buffered?

Hubs in the chemical interaction networks match hubs in GI network

DNA repair

Hydroxyurea blocks DNA synthesisErodoxin (new) similar to protein Folding-related gene

Single mutant + chemical = chemical interaction

Discovering Master Regulators of Alcohol Addiction

William ShinCenter for Computational Biology and BioinformaticsColumbia University

Rat Model of Alcohol Addiction

ControlAlcohol Self Administration

Alcohol Vapor Treatment(Chronic alcohol addiction)

ControlNon

DependentDependent

No Alcohol Vapor

Rat model of alcohol addictionAlcohol self-

administration (lever pressing)

Alcohol Intake during early withdrawal

Dependent(exposed to alcohol vapor )

Non-dependent(exposed to air)

Baseline

0

25

50

75

100

Alc

ohol

resp

ondi

ng (0

.5 h

r) *

Induction of alcohol-dependence

Identification of TF-target interactions Rat Brain regions were sliced and used as

microarray samples92 samples from Dependent, Non-Dependent,

Control Rats across 8 regions that are known as sites-of-action for of addictive drugs.

Applied ARACNE to this dataInformation-theory based (MI)Tests triplets of genes for indirect interactions

130,000 TF-target interactions in total

Screening of false positives

Targetsof TF1

TF1

TF2

THE MASTER REGULATORS ARE ENRICHED TFS NOT SHADOWED BY ANY OTHER

TF1 shadows TF2: TF2 appears enriched only because it shares common targets with TF1Targets

of TF2

Masters regulators in the Accumbens shell

Activity profile at different brain regions

siRNA validation has 50-75% success rate

NOT ALL TARGETS WERE TESTED YET