Protein network analysis

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23 Οκτ 2013 (πριν από 3 χρόνια και 9 μήνες)

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Protein network analysis


Network motifs


Network clusters / modules


Co
-
clustering networks & expression


Network comparison

(species, conditions)


Integration of genetic & physical nets


Network visualization



www.cytoscape.org


OPEN SOURCE Java platform for
integration of systems biology data


Layout and query of
networks
(
physical, genetic, social, functional)


Visual and programmatic
integration of
network state
data
(attributes)


The ultimate goal is to provide
tools
to facilitate all aspects of
network assembly, annotation, and
use in biomedicine.


RECENT NEWS



Version 2.7
released
March 2010



Cytoscape

® Registered Trademark



The
Cytoscape

Consortium is a 501(c)3 non
-
for
-
profit in the State of California




Centerpiece of the new
National Resource for Network Biology
, $7 million from NCRR

Downloaded approximately 3000 times per month

Shannon et al.
Genome Research

2003

Cline et al.
Nature Protocols

2007

Cytoscape Plugin Usage Statistics

Integration of

networks and expression

Querying biological networks for “Active Modules”

Ideker et al.
Bioinformatics

(2002)

Interaction Database
Dump, aka “Hairball”

Active Modules

Color network nodes (genes/proteins) with:

Patient expression profile

Protein states

Patient genotype (SNP state)

Enzyme activity

RNAi phenotype

A scoring system for expression

activity


A

B

C

D

Scoring over multiple perturbations/conditions

Perturbations
/conditions

Searching for

active


pathways in a large network


Score subnetworks according to their overall amount of
activity



Finding the highest scoring subnetworks is NP hard, so
we use heuristic search algs. to identify a collection of
high
-
scoring subnetworks (local optima)



Simulated annealing and/or greedy search starting from
an initial subnetwork

seed




During the search we must also worry about issues such
as local topology and whether a subnetwork

s score is
higher than would be expected at random


Simulated Annealing Algorithm

Network regions
whose genes change
on/off or off/on
after knocking out
different genes

Initial Application to Toxicity:

Networks responding to DNA damage in yeast

Tom Begley and Leona Samson; MIT Dept. of Bioengineering


Systematic phenotyping of gene knockout strains in yeast


Evaluation of growth of each strain in the presence of MMS
(and other DNA damaging agents)



Sensitive



Not sensitive



Not tested


MMS sensitivity in ~25% of strains


Screening against a network of protein interactions…

Begley
et al.
,
Mol Cancer Res
, (2002)

Networks responding to DNA damage as revealed by

high
-
throughput phenotypic assays

Begley
et al.
,
Mol Cancer Res
, (2002)

Host
-
pathogen interactions regulating early stage HIV
-
1 infection


Genome
-
wide
RNAi

screens for genes required for infection utilizing a single cycle HIV
-
1
reporter virus engineered to encode
luciferase

and bearing the Vesicular
Stomatitis

Virus Glycoprotein (VSV
-
G) on its surface to facilitate efficient infection…

Sumit

Chanda

Project onto a large network of human
-
human
and human
-
HIV protein interactions

Network modules associated with infection

Konig

et al.
Cell
2008

Network
-
based classification

NETWORK
-
BASED CLASSIFICATION


Disease aggression

(Time from Sample Collection SC

to Treatment TX)

Chuang et al.
MSB
2007

Lee et al.
PLoS

Comp Bio
2008

Ravasi

et al.
Cell
2010

The Mammalian Cell Fate Map:

Can we classify tissue type using expression, networks,
etc
?

Gilbert Developmental
Biology 4
th

Edition

Interaction coherence within a tissue class

B

A

B

A

B

A

Endoderm

Mesoderm

Ectoderm (incl. CNS)

r = 0.9

r = 0.0

r = 0.2

Taylor et al.
Nature Biotech
2009

Protein interactions, not levels, dictate tissue specification

Functional Enrichment

Gene Set Enrichment Analysis
-

GSEA
-


::: Introduction.

MIT

Broad Institute


v 2.0 available since Jan 2007

v 2.0.1 available since Feb 16th 2007


Version 2.0 includes Biocarta, Broad Institute,

GeneMAPP, KEGG annotations and more...


Platforms: Affymetrix, Agilent, CodeLink, custom...

GSEA

(
Subramanian

et al.
PNAS. 2005.)

GSEA

applies

Kolmogorov
-
Smirnof

test
to

find

assymmetrical

distributions

for

defined


blocks of genes in
datasets

whole

distribution
.

Gene Set Enrichment Analysis
-

GSEA
-


::: Introduction.

Is this particular
Gene Set

enriched in my experiment?

Genes selected by researcher, Biocarta pathways, GeneMAPP sets,

genes sharing cytoband, genes targeted by common miRNAs

…up to you…



Dataset distribution

Number of genes

Gene Expression Level

Gene Set Enrichment Analysis
-

GSEA
-


::: Introduction.

::: K
-
S test

The Kolmogorov

Smirnov test is used to determine whether two underlying one
-
dimensional probability distributions differ, or whe
ther


an underlying probability distribution differs from a hypothesized distribution, in either case based on finite samples.


The one
-
sample KS test compares the empirical distribution function with the cumulative distribution functionspecified by the nu
ll hypothesis.

The main applications are testing goodness of fit with the normal and uniform distributions.


The two
-
sample KS test is one of the most useful and general nonparametric methods for comparing two samples, as it is sensitive

to differences


in both location and shape of the empirical cumulative distribution functions of the two samples.

Gene set 1 distribution

Gene set 2 distribution

ClassA ClassB

ttest cut
-
off

FDR<0.05

FDR<0.05

...testing genes independently...

Biological meaning?

Gene Set Enrichment Analysis
-

GSEA
-


::: Introduction.

Correlation

with

CLASS

-

+

ClassA ClassB

Gene

Set 1

ttest cut
-
off

Gene

Set 2

Gene

Set 3

Gene set 3

enriched in Class B

Gene set 2

enriched in Class A

Gene Set Enrichment Analysis
-

GSEA
-


::: Introduction.

Subramaniam
, PNAS 2005

NES

pval

FDR

Gene Set Enrichment Analysis
-

GSEA
-


::: Introduction.

The Enrichment Score :::

Benjamini
-
Hochberg

Network Alignment

Species 1 vs. species 2

Physical vs. genetic

Kelley et al.
PNAS

2003

Ideker & Sharan
Gen Res

2008

Cross
-
comparison of networks:

(1)

Conserved regions in the presence vs. absence of stimulus

(2)

Conserved regions across different species

Sharan et al.
RECOMB

2004

Scott et al.
RECOMB

2005

Sharan & Ideker
Nat. Biotech.

2006

Suthram et al.
Nature

2005

Conserved
Plasmodium

/
Saccharomyces

protein complexes

Plasmodium
-
specific

protein complexes

Suthram et al.
Nature
2005

La Count et al.
Nature
2005

Plasmodium:
a network apart?

Human vs. Mouse TF
-
TF Networks in Brain

Tim
Ravasi
, RIKEN
Consortium et al.
Cell
2010

Finding physical pathways to explain genetic interactions

Adapted from Tong
et al.
,
Science

2001

Genetic Interactions:



Classical method used to
map pathways in model
species



Highly analogous to

multi
-
genic interaction in
human disease and
combination therapy



Thousands are being
uncovered through
systematic studies


Thus as with other types, the
number of known genetic
interactions is

exponentially increasing…

Integration of genetic and physical interactions

160 between
-
pathway models

101 within
-
pathway models

Num interactions:

1,102 genetic

933 physical

Kelley and Ideker
Nature Biotechnology (
2005)

Systematic identification of


parallel pathway


relationships in
yeast

Unified Whole
Cell Model of
Genetic and
Physical
interactions

A dynamic DNA damage module map

Bandyopadhyay

et al.
Science
(2010)