RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010
description
Transcript of RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010
![Page 1: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/1.jpg)
RECOMB SATELLITE MEETINGNEW-YORK, NOVEMBER 2010
![Page 2: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/2.jpg)
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
![Page 3: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/3.jpg)
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)
![Page 4: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/4.jpg)
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
![Page 5: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/5.jpg)
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
![Page 6: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/6.jpg)
GENIE workflow
![Page 7: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/7.jpg)
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
![Page 8: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/8.jpg)
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
![Page 9: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/9.jpg)
Best performer in DREAM5 network inference
![Page 10: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/10.jpg)
The Genetic Landscape of the Cell
Charles BooneUniversity of Toronto, Donnelly Center
![Page 11: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/11.jpg)
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
![Page 12: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/12.jpg)
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
![Page 13: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/13.jpg)
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
![Page 14: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/14.jpg)
Proximity between clusters and related functions
Proximate clustersBoth require cytoskeleton genes
![Page 15: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/15.jpg)
Zoom in on pathway
Red – NegativeGreen - Positive
Budding
Required for polarizationand growth
Cell division
Interactions between pathways and complexes were often monochromatic
Translation
![Page 16: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/16.jpg)
Positive vs. negative interactions
Negative interactions are ~two times more prominentthan positive
No interaction
![Page 17: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/17.jpg)
Degree distribution
Severe fitness defects in single mutants correlate with degree
Hubs are less numerous
![Page 18: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/18.jpg)
Gene duplicates interact less
![Page 19: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/19.jpg)
Correlation between degree and gene properties
Black - PPI
#morphological phenotypes
# chemical perturbations
unstable structure
![Page 20: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/20.jpg)
Genetic interactions between cellular processes
Cell cycle is more buffered?
![Page 21: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/21.jpg)
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
![Page 22: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/22.jpg)
Discovering Master Regulators of Alcohol Addiction
William ShinCenter for Computational Biology and BioinformaticsColumbia University
![Page 23: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/23.jpg)
Rat Model of Alcohol Addiction
ControlAlcohol Self Administration
Alcohol Vapor Treatment(Chronic alcohol addiction)
ControlNon
DependentDependent
No Alcohol Vapor
![Page 24: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/24.jpg)
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
![Page 25: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/25.jpg)
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
![Page 26: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/26.jpg)
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
![Page 27: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/27.jpg)
Masters regulators in the Accumbens shell
![Page 28: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/28.jpg)
Activity profile at different brain regions
![Page 29: RECOMB SATELLITE MEETING NEW-YORK, NOVEMBER 2010](https://reader036.fdocuments.in/reader036/viewer/2022062411/568166d1550346895ddae2c4/html5/thumbnails/29.jpg)
siRNA validation has 50-75% success rate
NOT ALL TARGETS WERE TESTED YET