Pathway analysis for genomics

1. Pathway
analysis
for
genomics
Gary
Bader
Oct.23.2012
–
FGED
Toronto

2. Microtubule
Cytoskeleton
Cell Projection
& Cell Motility
Cell Proliferation
Glycosylation
Adhesion
Regulation of GTPase
Kinase Activity/Regulation
CNS Development
Intellectual
Disability
Autism
GTPase/Ras
Signaling
Regulation of cell proliferation
Positive regulation of cell proliferation
Tyrosin kinase
Vasculature develepment
Palate develepment
Organ Morphogenesis
Behavior
Heart develepment
RHO Ras
Membrane
Kinase regulation
Cell Motility
(stricter cluster)
Centrosome
Nucleolus
Cell cycle
Regulation of
hormone levels
Aminoacid
derivative /
amine
metabolism
Synaptic vescicle maturation
Reelin pathway
LIS1 in neuronal
migration and
development
Negative
regulation
of cell cycle
cKIT
pathwaymTor
pathway
Zn finger
domain
Carboxyl
esterase
domain
Ras signaling GTPase regulator
Neuron
migration
Cell Motility
(stricter cluster)
Cell morphogenesis
Cell projection
organization
CNS
development
Brain
development
Neurite development
CNS neuron
differentiation
Axonogenesis
Projection neuron
axonogenesis
Cerebral cortex
cell migration
SMC flexible hinge domain
Urea and amine group metabolism
MHC-I
Zoom of CNS-Development
ID ID
ASDASD
Both
0%
12.5%
Enriched
in deletions
FDR
Known
disease genes
Enriched only
in disease genes
Node type (gene-set)
Edge type (gene-set overlap)
From disease genes
to enriched gene-sets
Between gene-sets
enriched in deletions
Between sets enriched in
deletions and in disease
genes or between disease
sets only
Pinto
et
al.
FuncBonal
impact
of
global
rare
copy
number
variaBon
in
auBsm
spectrum
disorders.
Nature.
2010
Jun
9.

3. CorrelaBon
to
CausaBon
• GWAS:
find
geneBc
markers
correlated
with
disease
–
powerful
approach,
but:
– genomics
reduces
staBsBcal
power
(>mulBple
tesBng
correcBon
with
>SNPs)
– rare
variants
=
more
samples
• Associate
pathways
to
increase
power
– Fewer
pathways,
organize
many
rare
variants
(damaging
the
system
causes
the
disease)
• Use
pathway
knowledge
to
idenBfy
potenBal
disease
causes

4. The Systems
Biology Pyramid
Cary, Bader, Sander, FEBS
Letters 579 (2005) 1815-20
Chris Sander, MSKCC
Cytoscape
cPath2
Pathway
Commons
BioPAX

5. hWp://pathguide.org
Vuk
Pavlovic
Sylva
Donaldson
>320
Pathway
Databases!
• Varied
formats,
representa;on,
coverage
• Pathway
data
extremely
difficult
to
combine
and
use

6. BioPAX
Pathway
Language
• Represent:
– Metabolic
pathways
– Signaling
pathways
– Protein-­‐protein,
molecular
interacBons
– Gene
regulatory
pathways
– GeneBc
interacBons
• Community
effort:
pathway
databases
distribute
pathway
informaBon
in
standard
format
www.biopax.org

7. Emek Demir
SBGN.org
BioCarta
BioPAX

8. Aim: Convenient Access to Pathway Information
Facilitate
creaBon
and
communicaBon
of
pathway
data
Aggregate
pathway
data
in
the
public
domain
Provide
easy
access
for
pathway
analysis
Long
term:
Converge
to
integrated
cell
map
hCp://pathwaycommons.org

9. Pathway
Commons:
cPath2
• hWp://www.pathwaycommons.org/pc2/
Emek Demir, Igor Rodchenkov, Chris Sander Ozgun Babur, Arman Aksoy,
Onur Sumer, Ethan Cerami, Ben Gross

11. Network
visualizaBon
and
analysis
UCSD,
ISB,
Agilent,
MSKCC,
Pasteur,
UCSF,
Unilever,
UToronto,
U
Texas
hWp://cytoscape.org
Pathway
comparison
Literature
mining
Gene
Ontology
analysis
AcBve
modules
Complex
detecBon
Network
moBf
search

12. Cytoscape
3
• Complete
re-­‐architecture:
OSGi
–
everything
is
an
app
• Enables
future
features:
– More
stable
and
powerful
APIs
– ScripBng,
macros,
recordable
history,
beWer
undo/redo
– Command
line
mode,
good
for
use
on
compute
clusters
– InteracBve
control
from
other
scripBng
languages
e.g.
R
• Fixing
bugs
and
porBng
plugins
• 3.0
beta
now
available
– Mirror
funcBonality
in
2.8
– Encourage
plugin
to
app
porBng
– hWp://www.cytoscape.org/cy3.html

14. 14
hWp://cytoscapeweb.cytoscape.org/
Chris;an
Tannus-­‐Lopes,
Max
Franz,
Yue
Dong

15. Compound
Nodes
Onur Sumer
Ugur Dogrusoz
Bilkent, Ankara
hCp://cytoscapeweb.cytoscape.org/demos/compound

16. Cytoscape.js:
HTML5
–
iPad
cytoscape.github.com/cytoscape.js/

17. Gene Function Prediction
hWp://www.genemania.org
Quaid Morris (Donnelly), Sara Mostafavi
Rashad Badrawi, Ovi Comes, Sylva Donaldson,
Max Franz, Christian Lopes, Farzana Kazi,
Jason Montojo, Harold Rodriguez, Khalid Zuberi
•
Guilt-­‐by-­‐associaBon
principle
•
Biological
networks
are
combined
intelligently
to
opBmize
predicBon
accuracy
•
Algorithm
is
more
fast
and
accurate
than
its
peers
Mostafavi
S
et
al.
Genome
Biol.
2008;9
Suppl
1:S4
Warde-­‐Farley
D
et
al.
Nucleic
Acids
Res.
2010
Jul;
8:W214-­‐20.

18. The
Factoid
Project
• Publishing
in
science
– Highly
inefficient
– Outdated
technology,
difficult
to
search
and
compute
• hWp://www.elseviergrandchallenge.com/
– Winner:
hWp://reflect.ws/
• Pathway
and
network
informaBon
database
curaBon
– Highly
inefficient
• The
factoid
project
Max
Franz,
Igor
Rodchenkov,
Ozgun
Babur,
Emek
Demir,
Chris
Sander

19. Pathway
and
Network
Analysis
1. Gene
set:
pathway
enrichment
analysis
2. Network:
network
regions
(modules),
regulaBon
3. Process
model:
classical
pathways
4. SimulaBon
model:
detailed
models
Mechanistic
Understanding
GenomeCoverage
Anduse

20. Increase Coverage and Depth
Cellular
Process
Representa;on
• Gene
set
• Network
• Process
model
• SimulaBon
model
• Analysis
methods
need
to
keep
up
Coverage
Depth
Depth
Data and analysis methods
Present
Future
Km=3.0×10−4

21. Acknowledgements
Bader
Lab
Domain
InteracBon
Team
Chris
Tan
Shirley
Hui
Shobhit
Jain
Brian
Law
Jüri
Reimand
Former:
David
Gfeller
Xiaojian
Shao
Funding
hWp://baderlab.org
www.GeneMANIA.org
Quaid Morris (Donnelly)
Rashad Badrawi, Ovi
Comes, Sylva
Donaldson,
Christian Lopes,
Farzana Kazi, Jason
Montojo, Sara Mostafavi,
Harold Rodriguez,
Khalid Zuberi
Gene;c
Intx,
Pathways:
Anastasia
Baryshnikova
Iain
Wallace
Magali
Michaut
Ron
Ammar
Daniele
Merico
Ruth
Isserlin
Vuk
Pavlovic
Igor
Rodchenkov
Pathway
Commons
Chris
Sander
Ethan
Cerami
Ben
Gross
Emek
Demir
Igor
Rodchenkov
Nadia
Anwar
Ozgun
Babur