Molecular

Systems

Biology

Article

number

doi

Citation

Molecular

Systems

Biology

EMBO

and

Nature

Publishing

Group

All

rights

reserved

REVIEW

Network-based

prediction

of

protein

function

Roded

Igor

and

Ron

Shamir

School

of

Computer

Science

Tel

Aviv

University

Tel

Aviv

Israel

Corresponding

author

School

of

Computer

Science

Tel

Aviv

University

Tel

Aviv

Israel

Tel.

Fax

E-mail

rshamir@tau.ac.il

These

authors

contributed

equally

to

this

work

Received

accepted

Functional

annotation

of

proteins

is

a

fundamental

problem

in

the

post-genomic

era

The

recent

availability

of

protein

interaction

networks

for

many

model

species

has

spurred

on

the

development

of

computational

methods

for

interpreting

such

data

in

order

to

elucidate

protein

function

In

this

review

we

describe

the

current

computational

approaches

for

the

task

including

direct

methods

which

propagate

functional

information

through

the

network

and

module-assisted

methods

which

infer

functional

modules

within

the

network

and

use

those

for

the

annotation

task

Although

a

broad

variety

of

interesting

approaches

has

been

developed

further

progress

in

the

field

will

depend

on

systematic

evaluation

of

the

methods

and

their

dissemination

in

the

biological

community

Molecular

Systems

Biology

March

doi

Subject

Categories

computational

methods

proteins

Keywords

data

integration

function

prediction

protein

interaction

networks

protein

modules

networks

with

nodes

representing

proteins

and

edges

representing

the

detected

PPIs

In

this

review

we

survey

the

growing

body

of

works

on

functional

annotation

of

proteins

via

their

network

of

interactions

-LRB-

summarized

in

Table

I

-RRB-

We

distinguish

two

types

of

approaches

-LRB-

Figure

-RRB-

direct

annotation

schemes

which

infer

the

function

of

a

protein

based

on

its

connections

in

the

network

and

module-assisted

schemes

which

first

identify

modules

of

related

proteins

and

then

annotate

each

module

based

on

the

known

functions

of

its

members

Naturally

the

presented

methods

and

the

emphasis

on

particular

ones

reflect

the

opinions

of

the

authors

Direct

methods

The

common

principle

underlying

all

direct

methods

for

functional

annotation

is

that

proteins

that

lie

closer

to

one

another

in

the

PPI

network

are

more

likely

to

have

similar

function

As

can

be

seen

in

Figure

there

is

an

evident

correlation

between

network

distance

and

functional

distance

that

is

the

closer

the

two

proteins

are

in

the

network

the

more

similar

are

their

functional

annotations

The

methods

described

below

differ

in

the

way

they

capture

and

exploit

this

correlation

In

the

following

we

denote

the

PPI

network

as

a

graph

G

-LRB-

V

E

-RRB-

-LRB-

see

Box

for

graph-theoretic

definitions

-RRB-

Neighborhood

counting

The

simplest

and

most

direct

method

for

function

prediction

determines

the

function

of

a

protein

based

on

the

known

function

of

proteins

lying

in

its

immediate

neighborhood

Schwikowski

et

al

-LRB-

-RRB-

predict

for

a

given

protein

up

to

three

functions

that

are

most

common

among

its

neighbors

Although

simple

and

effective

the

obvious

caveats

of

this

approach

are

that

associations

are

not

assigned

any

significance

values

and

the

full

topology

of

the

network

is

not

taken

into

account

in

the

annotation

process

Hishigaki

et

al

-LRB-

-RRB-

try

to

tackle

the

first

problem

by

computing

scores

for

function

assignment

In

detail

they

examine

the

n-neighborhood

of

a

protein

-LRB-

Box

-RRB-

For

a

protein

p

each

function

f

is

assigned

a

score

-LRB-

nf

ef

-RRB-

where

nf

is

the

number

of

proteins

in

the

n-neighborhood

of

p

that

have

the

function

f

and

ef

is

the

expectation

of

this

number

based

on

the

frequency

of

f

among

the

network

's

proteins

A

shortcoming

of

this

approach

is

that

within

the

n-neighborhood

proteins

at

different

distances

from

p

are

treated

in

the

same

way

Chua

et

al

-LRB-

-RRB-

try

to

tackle

the

second

problem

by

investigating

the

relation

between

network

distance

and

functional

similarity

They

focus

on

the

and

of

a

protein

and

devise

a

functional

similarity

score

that

gives

different

weights

to

proteins

according

to

their

distances

from

the

target

protein

Molecular

Systems

Biology

Introduction

The

past

decade

has

seen

a

revolution

in

sequencing

technologies

resulting

in

hundreds

of

sequenced

genomes

A

fundamental

challenge

of

the

post-genomic

era

is

the

interpretation

of

this

wealth

of

data

to

elucidate

protein

function

To

date

even

for

the

most

well-studied

organisms

such

as

yeast

about

one-fourth

of

the

proteins

remain

uncharacterized

-LRB-

Figure

-RRB-

Classical

computational

approaches

to

gene

annotation

collect

for

each

protein

a

set

of

features

characterizing

it

and

apply

machine-learning

algorithms

to

infer

annotation

rules

based

on

those

features

-LRB-

Pavlidis

et

al

-RRB-

The

newly

available

large-scale

networks

of

molecular

interactions

within

the

cell

have

made

it

possible

to

go

beyond

these

onedimensional

approaches

and

study

protein

function

in

the

context

of

a

network

In

particular

novel

high-throughput

technologies

for

protein

protein

interaction

-LRB-

PPI

-RRB-

measurements

-LRB-

Aebersold

and

Mann

Fields

-RRB-

have

created

large-scale

data

on

protein

interaction

across

human

and

most

model

species

These

data

are

commonly

represented

as

EMBO

and

Nature

Publishing

Group

Network-based

function

prediction

R

Sharan

et

al

Figure

Extent

of

annotation

of

proteins

in

model

species

For

each

species

the

charts

give

the

fractions

and

numbers

of

annotated

and

unannotated

proteins

according

to

the

three

ontologies

of

the

GO

annotation

The

numbers

are

based

on

the

Entrez

Gene

and

the

WormBase

databases

as

of

September

Graph

theoretic

methods

As

the

PPI

network

is

a

graph

it

is

natural

to

apply

graph

algorithms

for

its

functional

analysis

Two

main

approaches

have

been

suggested

in

this

context

cut-based

approaches

and

a

flow-based

algorithm

In

contrast

to

the

local

neighborhood

counting

methods

these

approaches

are

global

and

take

into

account

the

full

topology

of

the

network

Vazquez

et

al

-LRB-

-RRB-

aim

at

assigning

a

function

sv

to

each

unannotated

protein

v

so

as

to

maximize

the

number

of

edges

that

connect

proteins

-LRB-

unannotated

or

previously

annotated

-RRB-

assigned

with

the

same

function

Precisely

they

try

to

maximize

X

X

d

su

sv

hv

sv

u

v

where

is

the

set

of

edges

incident

on

two

unannotated

proteins

d

is

a

function

that

equals

if

x

y

and

otherwise

and

hv

-LRB-

f

-RRB-

denotes

the

number

of

neighbors

of

v

previously

annotated

with

function

f

The

first

term

in

the

optimization

criterion

concerns

unannotated

proteins

whereas

the

second

term

accounts

for

interactions

between

unannotated

and

previously

annotated

proteins

This

optimization

problem

which

generalizes

the

computationally

hard

problem

of

minimum

multiway

cut

-LRB-

Box

-RRB-

-LRB-

Dahlhaus

et

al

-RRB-

is

heuristically

solved

using

simulated

annealing

Karaoz

et

al

-LRB-

-RRB-

use

a

similar

approach

but

handle

one

function

at

a

time

Each

annotated

protein

v

receives

a

state

sv

that

equals

if

v

has

the

function

in

question

and

equals

otherwise

Next

an

assignment

of

and

states

to

P

unannotated

proteins

is

sought

so

as

to

maximize

su

sv

u

v

cut

problem

where

the

goal

is

to

assign

a

unique

function

to

all

unannotated

proteins

so

as

to

minimize

the

cost

of

edges

connecting

proteins

with

different

assignments

They

propose

an

integer

programming

reformulation

of

this

problem

which

allows

them

to

solve

the

problem

in

practice

-LRB-

on

the

yeast

PPI

network

-RRB-

by

applying

a

commercial

solver

-LRB-

CPLEX

-RRB-

Finally

Nabieva

et

al

-LRB-

-RRB-

also

suggest

a

flow-based

-LRB-

Box

-RRB-

approach

for

the

annotation

problem

As

they

note

that

the

maximum-cut-based

approaches

described

above

take

into

account

global

properties

of

the

network

but

do

not

reward

local

proximity

they

propose

a

novel

method

that

aims

at

considering

both

local

and

global

effects

They

handle

one

function

at

a

time

The

basic

idea

is

to

treat

each

protein

annotated

with

the

function

as

the

source

of

a

functional

flow

After

simulating

the

spread

over

time

of

this

functional

flow

through

the

network

each

unannotated

protein

is

assigned

a

score

for

having

the

function

based

on

the

amount

of

flow

it

received

during

the

simulation

Markov

random

field

A

number

of

probabilistic

approaches

to

the

annotation

problem

have

been

suggested

all

relying

on

a

Markovian

assumption

the

function

of

a

protein

is

independent

of

all

other

proteins

given

the

functions

of

its

immediate

neighbors

This

assumption

gives

rise

to

a

Markov

random

field

-LRB-

MRF

-RRB-

model

-LRB-

Box

-RRB-

initially

proposed

by

Deng

et

al

-LRB-

-RRB-

Deng

et

al

-LRB-

-RRB-

devise

an

MRF

model

in

which

the

probability

that

a

protein

v

is

assigned

with

a

certain

function

that

occurs

with

frequency

f

is

a

logistic

function

of

log

f

f

bN

v

a

N

v

N

v

N

v

where

a

and

b

are

model

parameters

and

N

-LRB-

v

-RRB-

and

N

-LRB-

v

-RRB-

are

the

numbers

of

neighbors

of

v

that

are

assigned

or

not

assigned

with

the

function

respectively

The

parameters

a

and

b

of

the

model

are

first

estimated

using

a

quasi-likelihood

method

and

then

Gibbs

sampling

is

used

for

inferring

the

functions

of

unannotated

proteins

Interestingly

as

pointed

out

by

Deng

et

al

-LRB-

-RRB-

setting

a

to

and

b

to

yields

the

exact

optimization

criterion

used

by

Karaoz

et

al

-LRB-

-RRB-

EMBO

and

Nature

Publishing

Group

The

advantage

of

this

formulation

is

that

a

partition

of

the

vertices

into

two

sets

only

is

sought

To

find

a

good

partition

Karaoz

et

al

-LRB-

-RRB-

apply

a

local

search

procedure

in

which

for

every

vertex

in

turn

-LRB-

until

convergence

-RRB-

the

state

of

the

vertex

is

changed

according

to

the

majority

of

the

states

of

its

neighbors

This

procedure

guarantees

a

solution

with

value

at

least

half

of

the

optimum

A

related

method

suggested

by

Nabieva

et

al

-LRB-

-RRB-

again

formulates

the

annotation

problem

as

a

minimum

multiway

Molecular

Systems

Biology

Network-based

function

prediction

R

Sharan

et

al

Table

I

A

summary

of

functional

annotation

methods

Direct

Neighborhood

based

Schwikowski

et

al

-LRB-

-RRB-

Hishigaki

et

al

-LRB-

-RRB-

Chua

et

al

-LRB-

-RRB-

Vazquez

et

al

-LRB-

-RRB-

Karaoz

et

al

-LRB-

-RRB-

Nabieva

et

al

-LRB-

-RRB-

Probabilistic

Integrating

multiple

data

sources

Deng

et

al

-LRB-

-RRB-

Letovsky

and

Kasif

-LRB-

-RRB-

Joshi

et

al

-LRB-

-RRB-

Deng

et

al

-LRB-

-RRB-

Lee

et

al

-LRB-

-RRB-

Lanckriet

et

al

-LRB-

-RRB-

Tsuda

et

al

-LRB-

-RRB-

Graph

theoretic

Letovsky

and

Kasif

-LRB-

-RRB-

also

use

an

MRF

model

Their

main

assumption

is

that

the

number

of

neighbors

of

a

protein

that

are

annotated

with

a

given

term

is

binomially

distributed

where

the

distribution

's

parameter

depends

on

whether

the

protein

has

that

function

or

not

They

employ

loopy

belief

propagation

-LRB-

Murphy

et

al

-RRB-

to

perform

inference

in

their

model

Integrating

multiple

information

sources

Several

authors

have

integrated

data

from

multiple

sources

for

the

annotation

task

The

approaches

differ

in

the

way

the

sources

are

combined

The

simplest

approach

treats

each

data

source

independently

Such

an

approach

for

function

prediction

was

first

introduced

by

Marcotte

et

al

-LRB-

-RRB-

in

a

different

context

Joshi

et

al

-LRB-

-RRB-

integrated

PPIs

genetic

interactions

and

coexpression

interactions

For

each

interaction

type

they

estimated

the

a

priori

probability

of

functional

association

given

an

interaction

of

this

type

By

treating

all

interactions

-LRB-

withinand

between-types

-RRB-

as

independent

of

one

another

they

combined

them

into

a

single

reliability

score

for

the

functional

association

of

a

protein

given

the

annotations

of

its

neighbors

A

second

approach

for

combining

multiple

data

types

constructs

a

joint

probabilistic

model

or

a

prediction

function

of

the

different

types

Deng

et

al

-LRB-

-RRB-

generalized

their

earlier

MRF

approach

to

allow

using

multiple

networks

in

the

annotation

process

and

applied

the

method

to

annotate

yeast

proteins

using

both

PPIs

and

genetic

interactions

-LRB-

Deng

et

al

Lee

et

al

-RRB-

Lanckriet

et

al

-LRB-

-RRB-

and

later

Tsuda

et

al

-LRB-

-RRB-

represent

each

data

type

using

a

matrix

of

kernel

similarity

values

These

matrices

are

then

combined

by

learning

optimal

relative

weights

for

the

different

kernels

Module-assisted

Based

solely

on

topology

General

methods

Bader

and

Hogue

-LRB-

-RRB-

Altaf-Ul-Amin

et

al

-LRB-

-RRB-

Sharan

et

al

-LRB-

-RRB-

Hierarchical

clustering-based

Arnau

et

al

-LRB-

-RRB-

Rives

and

Galitski

-LRB-

-RRB-

Maciag

et

al

-LRB-

-RRB-

Brun

et

al

-LRB-

-RRB-

Samanta

and

Liang

-LRB-

-RRB-

Graph

clusteringbased

Spirin

and

Mirny

-LRB-

-RRB-

King

et

al

-LRB-

-RRB-

Pereira-Leal

et

al

-LRB-

-RRB-

Przulj

et

al

-LRB-

-RRB-

Dunn

et

al

-LRB-

-RRB-

Bu

et

al

-LRB-

-RRB-

Enright

et

al

-LRB-

-RRB-

Adamcsek

et

al

-LRB-

-RRB-

Expanding

seed

complex

Asthana

et

al

-LRB-

-RRB-

Bader

-LRB-

-RRB-

Wu

and

Hu

-LRB-

-RRB-

Module-assisted

methods

The

prevalence

of

the

modularity

paradigm

in

molecular

cell

biology

has

led

to

an

extensive

use

of

modules

in

prediction

of

molecular

functions

By

functional

module

one

typically

means

a

group

of

cellular

components

and

their

interactions

that

can

be

attributed

to

a

specific

biological

function

-LRB-

Hartwell

et

al

-RRB-

Instead

of

predicting

functions

for

individual

genes

a

module-assisted

approach

attempts

to

first

identify

coherent

groups

of

genes

and

then

assign

functions

to

all

the

genes

in

each

group

The

module-assisted

methods

differ

mainly

in

their

module

detection

technique

Once

a

module

is

obtained

simple

methods

are

usually

used

for

function

prediction

within

the

module

For

example

every

function

shared

by

the

majority

of

the

module

's

genes

is

assigned

to

all

the

genes

in

the

module

Alternatively

a

hypergeometric

enrichment

P-value

is

computed

for

every

function

f

n

f

m

X

i

m

i

p

n

i

k

m

where

n

is

the

number

of

nodes

in

the

PPI

network

f

is

the

number

of

genes

in

the

network

annotated

with

the

function

and

m

is

the

module

size

The

functions

enriched

within

the

module

-LRB-

i.e.

obtaining

P-value

below

some

threshold

-RRB-

are

then

predicted

for

all

the

genes

in

the

module

Molecular

Systems

Biology

Integrating

gene

expression

data

Networks

active

in

a

specific

condition

Balazsi

et

al

-LRB-

-RRB-

de

Lichtenberg

et

al

-LRB-

-RRB-

Luscombe

et

al

-LRB-

-RRB-

Wachi

et

al

-LRB-

-RRB-

Jansen

et

al

-LRB-

-RRB-

Simonis

et

al

-LRB-

-RRB-

Tornow

and

Mewes

-LRB-

-RRB-

Zien

et

al

-LRB-

-RRB-

Ideker

et

al

-LRB-

-RRB-

Hanisch

et

al

-LRB-

-RRB-

Cabusora

et

al

-LRB-

-RRB-

Segal

et

al

-LRB-

-RRB-

Expression

analysis

of

known

pathways

Joint

module

identification

Integrating

diverse

genomic

data

Genetic

interactions

Phenotypic

profiles

Heterogeneous

data

sources

Kelley

and

Ideker

-LRB-

-RRB-

Haugen

et

al

-LRB-

-RRB-

Tanay

et

al

-LRB-

-RRB-

Tanay

et

al

-LRB-

-RRB-

The

last

column

represents

the

attributes

of

the

corresponding

algorithm

An

implementation

of

the

algorithm

with

a

graphical

user

interface

is

available

The

algorithm

uses

interaction

weights

based

on

confidence

levels

For

module-assisted

methods

the

method

can

naturally

produce

overlapping

modules

EMBO

and

Nature

Publishing

Group

Network-based

function

prediction

R

Sharan

et

al

Figure

Direct

versus

module-assisted

approaches

for

functional

annotation

The

scheme

shows

a

network

in

which

the

functions

of

some

proteins

are

known

-LRB-

top

-RRB-

where

each

function

is

indicated

by

a

different

color

Unannotated

proteins

are

in

white

In

the

direct

methods

-LRB-

left

-RRB-

these

proteins

are

assigned

a

color

that

is

unusually

prevalent

among

their

neighbors

The

direction

of

the

edges

indicates

the

influence

of

the

annotated

proteins

on

the

unannotated

ones

In

the

module-assisted

methods

-LRB-

right

-RRB-

modules

are

first

identified

based

on

their

density

Then

within

each

module

unannotated

proteins

are

assigned

a

function

that

is

unusually

prevalent

in

the

module

In

both

methods

proteins

may

be

assigned

with

several

functions

Yeang

et

al

Scott

et

al

-RRB-

Here

we

focus

on

the

former

as

the

latter

methods

are

currently

not

aimed

at

function

prediction

Detecting

functional

modules

from

network

topology

Several

works

focused

on

the

detection

of

functional

modules

based

solely

on

protein

interaction

data

Most

of

the

works

described

below

decompose

the

PPI

network

into

subnetworks

based

on

some

topological

properties

The

molecular

complex

detection

algorithm

-LRB-

MCODE

-RRB-

-LRB-

Bader

and

Hogue

-RRB-

consists

of

three

stages

vertex

weighting

complex

prediction

and

an

optional

post-processing

step

The

weighting

of

nodes

is

based

on

the

core

clustering

coefficient

-LRB-

Box

-RRB-

Bader

and

Hogue

-LRB-

-RRB-

propose

the

use

of

this

coefficient

instead

of

the

standard

clustering

coefficient

as

it

increases

the

weights

of

heavily

interconnected

graph

regions

while

giving

small

weights

to

the

less

connected

vertices

which

are

abundant

in

the

scale-free

protein

interaction

networks

Once

the

weights

are

computed

the

algorithm

traverses

the

weighted

graph

in

a

greedy

fashion

to

isolate

densely

connected

regions

The

post-processing

step

filters

or

adds

proteins

based

on

connectivity

criteria

MCODE

has

been

used

in

several

recent

publications

describing

EMBO

and

Nature

Publishing

Group

Figure

Correlation

between

protein

functional

distance

and

network

distance

X-axis

distance

in

the

network

Y-axis

average

functional

similarity

of

protein

pairs

that

lie

at

the

specified

distance

The

functional

similarity

of

two

proteins

is

measured

using

the

semantic

similarity

of

their

GO

categories

-LRB-

Lord

et

al

-RRB-

Module

finding

algorithms

can

be

divided

into

methods

using

solely

network

topology

information

and

methods

that

utilize

addition

data

sources

such

as

gene

expression

measurements

or

deletion

phenotypes

The

algorithms

vary

in

their

ability

to

detect

overlapping

modules

and

in

the

use

of

interaction

reliabilities

The

exact

definition

of

a

functional

module

also

varies

The

purpose

of

some

algorithms

is

the

detection

of

molecular

complexes

but

others

can

also

detect

sparser

structures

such

as

signaling

pathways

-LRB-

Steffen

et

al

Molecular

Systems

Biology

Network-based

function

prediction

R

Sharan

et

al

Box

Graph-theoretic

concepts

A

graph

is

a

pair

G

-LRB-

V

E

-RRB-

where

V

is

a

set

of

vertices

-LRB-

or

nodes

-RRB-

and

E

is

a

set

of

edges

connecting

pairs

of

vertices

In

PPI

networks

the

vertices

represent

proteins

and

the

edges

represent

interactions

The

distance

between

two

vertices

in

a

graph

is

the

number

of

edges

on

a

shortest

path

between

them

The

diameter

of

a

graph

is

the

maximum

distance

between

any

two

of

its

vertices

The

neighborhood

of

a

vertex

is

the

set

of

vertices

connected

to

it

The

n-neighborhood

of

a

vertex

is

the

set

of

vertices

whose

distance

from

it

is

at

most

n

A

clique

in

a

graph

is

a

fully

connected

subgraph

that

is

a

subgraph

in

which

every

two

vertices

are

connected

by

an

edge

The

degree

of

a

vertex

is

the

number

of

its

neighbors

A

cut

in

a

graph

is

a

partition

of

the

vertices

into

two

non-overlapping

sets

A

multiway

cut

is

a

partition

of

the

vertices

into

several

disjoint

sets

The

value

of

the

cut

is

the

number

of

edges

going

between

different

sets

Network

flow

Imagine

a

graph

as

a

network

of

interconnected

pipes

Suppose

water

gets

into

one

or

more

vertices

-LRB-

sources

-RRB-

from

the

outside

and

can

exit

the

network

at

certain

other

vertices

-LRB-

sinks

-RRB-

Then

it

will

spread

in

the

pipes

and

reach

other

nodes

until

it

exits

at

sinks

The

capacities

of

the

edges

-LRB-

i.e.

how

much

the

pipe

can

carry

per

unit

time

-RRB-

and

the

input

at

the

sources

determine

the

amount

of

flow

along

every

edge

-LRB-

i.e.

how

much

each

pipe

actually

carries

-RRB-

and

the

amount

exiting

at

each

sink

In

the

context

of

a

PPI

network

by

considering

proteins

that

have

a

certain

function

as

sources

and

simulating

flow

in

the

network

the

amount

of

flow

at

edges

and

sinks

can

be

used

to

annotate

additional

proteins

The

density

of

a

graph

is

the

fraction

of

edges

it

actually

has

out

of

all

possible

vertex

pairs

Hence

the

density

of

G

-LRB-

V

E

-RRB-

is

E

-LRB-

V

-LRB-

V

-RRB-

-RRB-

and

a

clique

graph

has

the

maximum

possible

density

that

is

The

clustering

coefficient

of

a

vertex

is

the

density

of

its

neighborhood

-LRB-

Watts

and

Strogatz

-RRB-

A

graph

is

called

a

k-core

if

the

minimal

degree

in

it

is

k.

Core

clustering

coefficient

For

a

parameter

k

the

core

clustering

coefficient

of

a

vertex

is

the

density

of

the

largest

k-core

of

its

immediate

neighborhood

-LRB-

Bader

and

Hogue

-RRB-

An

adjacency

matrix

of

a

graph

G

-LRB-

V

E

-RRB-

is

a

matrix

AjVj

jVj

faij

g

where

aij

if

and

only

if

vi

and

vj

are

neighbors

As

PPI

networks

usually

do

not

contain

loops

in

our

context

aii

A

graph

is

called

bipartite

if

its

vertices

can

be

partitioned

into

two

disjoint

sets

such

that

no

edge

connects

two

vertices

of

the

same

set

A

greedy

network

search

algorithm

is

subsequently

used

for

the

detection

of

high-scoring

modules

The

method

can

be

generalized

to

identify

modules

that

are

conserved

over

several

networks

Hierarchical

clustering-based

methods

Several

works

described

the

use

of

hierarchical

clustering

for

module

detection

A

key

decision

in

the

use

of

this

approach

is

the

selection

of

the

similarity

measure

between

protein

pairs

An

intuitive

similarity

metric

is

based

on

pairwise

distances

-LRB-

Box

-RRB-

between

proteins

in

the

network

One

of

the

problems

in

using

it

in

a

hierarchical

clustering

setting

is

that

the

distances

between

many

protein

pairs

are

identical

-LRB-

the

ties

in

proximity

problem

-RRB-

-LRB-

Arnau

et

al

-RRB-

Rives

and

Galitski

-LRB-

-RRB-

postulated

that

module

members

are

likely

to

have

similar

shortest

path

distance

profiles

They

therefore

used

hierarchical

clustering

on

the

all-pair

shortest

path

distances

matrix

and

outlined

modules

by

manual

inspection

Although

such

an

approach

is

impractical

for

genome-scale

networks

it

has

been

successfully

applied

to

focused

subnetworks

such

as

the

network

of

interactions

between

nuclear

proteins

and

a

regulatory

network

of

filamentation

in

Saccharomyces

cerevisiae

-LRB-

Rives

and

Galitski

-RRB-

A

more

sophisticated

version

of

this

approach

was

recently

applied

to

the

gene

expression

machinery

network

-LRB-

Maciag

et

al

-RRB-

The

authors

used

a

special

weighted

form

of

mutual

clustering

coefficient

-LRB-

Goldberg

and

Roth

-RRB-

for

quantifying

similarities

between

proteins

and

a

modified

version

of

the

k-means

algorithm

-LRB-

Hartigan

-RRB-

was

used

for

cluster

detection

Arnau

et

al

-LRB-

-RRB-

used

the

shortest

path

length

between

proteins

as

a

distance

measure

and

attempted

to

overcome

the

ties

in

proximity

problem

by

obtaining

multiple

equally

valid

hierarchical

clustering

solutions

with

a

random

choice

when

ties

are

encountered

The

fraction

of

the

solutions

in

which

the

protein

pair

was

clustered

together

was

then

used

as

a

similarity

measure

for

clustering

using

standard

hierarchical

algorithms

Additional

similarity

measures

proposed

for

using

in

a

hierarchical

clustering

setting

include

Czekanovski

Dice

distance

-LRB-

Brun

et

al

-RRB-

and

the

statistical

significance

of

the

number

of

common

interaction

partners

-LRB-

Samanta

and

Liang

-RRB-

mapping

of

large-scale

interaction

networks

-LRB-

LaCount

et

al

Rual

et

al

-RRB-

It

is

available

as

a

plug-in

for

the

Cytoscape

network

visualization

software

-LRB-

Shannon

et

al

-RRB-

Altaf-Ul-Amin

et

al

-LRB-

-RRB-

use

a

similar

approach

they

define

a

cluster

property

of

a

node

n

with

respect

to

a

cluster

C

as

the

number

of

edges

between

n

and

the

nodes

of

C

divided

by

that

number

averaged

over

the

nodes

of

C.

Starting

from

single

nodes

clusters

are

gradually

grown

as

long

as

the

cluster

property

of

the

added

nodes

and

the

density

of

the

cluster

both

exceed

a

certain

threshold

Sharan

et

al

-LRB-

-RRB-

proposed

the

NetworkBlast

algorithm

for

detecting

protein

modules

in

protein

interaction

networks

Each

candidate

set

of

proteins

is

assigned

a

likelihood

ratio

score

that

measures

its

fit

to

a

protein

complex

model

versus

the

chance

that

its

connections

arise

at

random

-LRB-

Box

-RRB-

EMBO

and

Nature

Publishing

Group

Graph

clustering

methods

Numerous

graph-clustering

algorithms

have

been

applied

to

the

graph

representing

the

binary

interactions

Spirin

and

Mirny

-LRB-

-RRB-

proposed

two

such

algorithms

The

first

algorithm

is

based

on

superparamagnetic

clustering

-LRB-

SPC

-RRB-

-LRB-

Blatt

et

al

-RRB-

and

the

second

is

a

Monte

Carlo

algorithm

maximizing

the

density

of

the

obtained

clusters

Both

algorithms

require

the

size

of

the

sought

clusters

as

input

The

SPC

algorithm

is

specifically

shown

to

perform

well

in

detecting

dense

structures

loosely

connected

to

other

areas

of

the

network

In

addition

to

detecting

protein

complexes

Spirin

and

Mirny

-LRB-

-RRB-

also

show

that

their

method

is

capable

of

detecting

sparsely

connected

functional

modules

such

as

the

MAPK

signaling

cascade

Molecular

Systems

Biology

Network-based

function

prediction

R

Sharan

et

al

Box

Function

prediction

using

the

MRF

method

The

Markov

random

field

-LRB-

MRF

-RRB-

model

provides

a

probabilistic

framework

for

simulating

the

mutual

influence

of

random

variables

via

a

neighborhood

system

Given

a

network

of

influence

the

state

of

any

random

variable

is

assumed

to

be

independent

of

all

other

random

variable

states

given

those

of

its

immediate

neighbors

In

the

function

prediction

setting

each

random

variable

corresponds

to

a

protein

and

its

states

correspond

to

certain

functional

annotations

The

joint

distribution

of

the

random

variables

can

be

shown

to

factorize

over

the

cliques

-LRB-

Box

-RRB-

of

the

network

-LRB-

Besag

-RRB-

That

is

the

probability

of

a

certain

assignment

of

discrete

states

x

-LRB-

y

xN

-RRB-

is

-LRB-

-RRB-

X

exp

f

H

x

g

exp

Hc

xc

Z

Z

p

x

where

N

is

the

total

number

of

variables

Z

is

a

normalizing

constant

C

is

the

set

of

all

cliques

in

the

network

Hc

is

a

potential

function

associated

with

clique

c

and

xc

is

the

assignment

of

states

to

the

members

of

c.

Inference

in

this

general

model

is

computationally

hard

hence

it

is

common

to

assign

potentials

to

all

cliques

of

size

greater

than

and

further

homogenize

the

model

by

associating

the

same

potential

function

with

all

cliques

of

the

same

size

For

such

a

homogeneous

second-order

MRF

we

have

H

x

X

xfvg

X

u

v

xfu

vg

Deng

et

al

-LRB-

-RRB-

treat

one

function

at

a

time

To

obtain

a

second-order

MRF

model

they

assume

that

the

probability

of

a

annotation

over

the

entire

network

is

proportional

to

exp

-LRB-

-RRB-

where

a

b

are

parameters

for

weighting

the

contributions

of

the

different

terms

and

Nij

is

the

number

of

interacting

pairs

with

assignment

i

j

-LRB-

unordered

-RRB-

Combining

the

a

priori

probability

of

an

assignment

with

s

which

depends

on

the

frequency

f

of

the

function

and

is

proportional

to

f

f

they

obtain

a

homogeneous

second-order

MRF

for

which

H

x

log

X

X

X

X

f

xfvg

b

xfug

xfvg

a

xfug

xfvg

xfvg

xfug

xfug

xfvg

f

u

v

u

v

u

v

Hence

the

probability

that

protein

v

is

assigned

with

the

function

given

the

annotations

of

its

neighbors

N

-LRB-

v

-RRB-

is

P

xfvg

v

logit

log

f

bN

v

a

N

v

N

v

N

v

f

where

N

-LRB-

v

i

-RRB-

is

the

number

of

neighbors

of

v

that

are

assigned

with

iA

-LCB-

-RCB-

and

logit

is

the

logistic

function

logit

-LRB-

x

-RRB-

-LRB-

e

x

-RRB-

Deng

et

al

-LRB-

-RRB-

estimate

the

two

parameters

of

the

model

using

a

quasi-likelihood

method

and

apply

Gibbs

sampling

to

infer

the

unknown

functional

annotations

Przulj

et

al

-LRB-

-RRB-

used

the

highly

connected

subgraphs

-LRB-

HCS

-RRB-

algorithm

-LRB-

Hartuv

and

Shamir

-RRB-

to

determine

complexes

in

PPI

data

A

highly

connected

subgraph

is

defined

as

a

subgraph

with

n

nodes

such

that

more

than

edges

must

be

removed

in

order

to

disconnect

it

It

can

be

shown

that

this

property

ensures

that

the

diameter

of

the

subgraph

-LRB-

Box

-RRB-

is

at

most

two

and

that

it

is

at

least

half

as

dense

as

a

clique

of

the

same

size

The

HCS

algorithm

finds

a

minimum

cut

-LRB-

Box

-RRB-

in

the

graph

and

uses

it

to

partition

the

graph

This

process

is

repeated

recursively

until

highly

connected

components

are

reached

The

restricted

neighborhood

search

clustering

-LRB-

RNSC

-RRB-

algorithm

proposed

by

King

et

al

-LRB-

-RRB-

partitions

the

node

set

of

the

network

into

clusters

based

on

a

cost

function

that

is

used

to

evaluate

the

partitioning

The

algorithm

starts

with

a

random

cluster

assignment

and

proceeds

by

reassigning

nodes

so

as

to

maximize

the

partition

's

score

In

addition

a

tabu

list

is

maintained

to

avoid

cycling

back

to

previously

explored

partitions

Finally

the

clusters

are

filtered

based

on

their

size

density

and

functional

homogeneity

The

Markov

clustering

-LRB-

MCL

-RRB-

algorithm

-LRB-

Enright

et

al

-RRB-

has

been

recently

proposed

for

complex

detection

in

PPI

data

Molecular

Systems

Biology

-LRB-

Krogan

et

al

-RRB-

The

algorithm

simulates

flow

on

the

PPI

graph

by

constructing

its

adjacency

matrix

-LRB-

Box

-RRB-

and

computing

its

successive

powers

to

increase

the

contrast

between

regions

with

high

flow

and

regions

with

a

low

flow

This

process

can

be

shown

to

converge

towards

a

partition

of

the

graph

into

high-flow

regions

corresponding

to

protein

complexes

separated

by

regions

of

no

flow

Additional

graph

clustering

techniques

applied

include

clique

percolation

-LRB-

Adamcsek

et

al

-RRB-

graph

flow

simulation

-LRB-

Pereira-Leal

et

al

-RRB-

edge-betweenness

clustering

-LRB-

Dunn

et

al

-RRB-

and

spectral

methods

-LRB-

Bu

et

al

-RRB-

Expansion

of

complex

seeds

In

contrast

to

finding

complexes

de

novo

in

the

protein

interaction

network

several

works

attempted

prediction

of

new

members

for

partially

known

protein

complexes

The

Complexpander

software

-LRB-

Asthana

et

al

-RRB-

receives

a

particular

core

set

of

proteins

and

produces

a

list

of

candidate

proteins

ranked

by

the

probability

of

membership

in

the

complex

This

approach

approximates

the

probability

that

a

EMBO

and

Nature

Publishing

Group

Network-based

function

prediction

R

Sharan

et

al

Box

Detecting

dense

subgraphs

using

maximum

likelihood

scoring

Maximum

likelihood-based

scoring

has

been

used

for

detecting

molecular

complexes

in

NetworkBlast

-LRB-

Sharan

et

al

-RRB-

and

for

integration

of

heterogeneous

data

through

biclustering

in

SAMBA

-LRB-

Tanay

et

al

-RRB-

For

a

given

subnetwork

H

this

technique

compares

the

probabilities

of

two

alternatives

-LRB-

i

-RRB-

H

has

dense

structure

-LRB-

the

subnetwork

model

-RRB-

and

-LRB-

ii

-RRB-

H

is

random

-LRB-

model

-RRB-

When

assigning

a

score

to

a

complex

in

the

subnetwork

model

every

possible

interaction

in

the

subnetwork

exists

with

some

high

probability

b

independently

of

other

protein

pairs

In

the

model

every

two

proteins

u

v

are

connected

with

probability

pu

v

that

depends

on

their

degrees

pu

v

can

be

estimated

by

generating

a

collection

of

random

networks

preserving

the

degree

of

every

protein

and

calculating

the

fraction

of

networks

in

which

an

interaction

between

u

and

v

exists

The

log-likelihood

ratio

of

the

two

alternatives

for

a

set

of

proteins

C

with

a

set

of

interactions

E

-LRB-

C

-RRB-

is

thus

X

u

v

C

given

protein

seed

in

a

network

without

edge

reliabilities

The

algorithm

first

identifies

a

core

by

a

heuristic

search

for

a

maximum

clique

containing

the

seed

and

then

expands

it

through

a

breadth-first-search

graph

traversal

-LRB-

Cormen

et

al

-RRB-

with

additional

vertices

such

that

it

will

meet

a

certain

community

criterion

Integrated

analysis

of

interactions

and

expression

profiles

Several

studies

have

integrated

PPI

data

with

diverse

additional

sources

to

infer

modular

structures

These

modular

structures

can

serve

as

potent

function

predictors

and

also

shed

light

on

the

interplay

between

the

different

sources

of

information

Microarray

technologies

-LRB-

Schena

et

al

-RRB-

which

measure

expression

levels

on

a

genome-wide

scale

are

currently

the

largest

source

of

high-throughput

genomic

information

A

natural

question

is

the

relation

between

transcription

pattern

similarity

of

a

pair

of

genes

and

the

existence

of

a

protein

interaction

between

their

products

As

shown

in

both

simple

and

complex

organisms

-LRB-

Ge

et

al

Hahn

et

al

-RRB-

genes

of

interacting

proteins

tend

to

share

similar

expression

patterns

Following

this

observation

the

use

of

both

information

sources

together

for

analysis

of

functional

modules

has

been

an

appealing

concept

adopted

by

many

research

groups

One

line

of

works

proposed

a

two-step

approach

extraction

of

a

group

of

genes

that

are

highly

expressed

in

a

certain

condition

and

then

analysis

of

the

topological

properties

of

PPI

networks

induced

by

these

genes

-LRB-

Luscombe

et

al

Balazsi

et

al

de

Lichtenberg

et

al

Wachi

et

al

-RRB-

Other

works

used

the

reverse

approach

by

analyzing

the

expression

coherence

of

known

pathways

or

complexes

-LRB-

Zien

et

al

Jansen

et

al

Tornow

and

Mewes

Simonis

et

al

-RRB-

Several

approaches

have

been

proposed

for

identifying

functional

modules

by

simultaneous

analysis

of

the

network

and

the

expression

data

Ideker

et

al

-LRB-

-RRB-

introduced

a

framework

for

identification

of

active

subnetworks

that

is

connected

regions

of

the

network

that

show

significant

changes

in

expression

over

a

particular

subset

of

the

conditions

This

method

uses

P-values

calculated

for

every

measurement

in

the

expression

data

to

derive

a

statistical

score

for

every

candidate

subnetwork

and

utilizes

simulated

annealing

to

search

for

high-scoring

subnetworks

A

similar

methodology

was

recently

employed

by

-LRB-

Cabusora

et

al

-LRB-

-RRB-

using

shortest-paths

algorithms

for

module

finding

The

co-clustering

methodology

-LRB-

Hanisch

et

al

-RRB-

uses

a

distance

function

that

combines

similarity

of

gene

expression

patterns

and

network

topology

The

network

distance

between

two

nodes

is

an

edge-weighted

version

of

their

topological

distance

in

the

network

The

expression

distance

is

based

on

the

Pearson

correlation

between

the

expression

patterns

The

two

distances

are

combined

into

a

similarity

score

using

a

logistic

function

and

hierarchical

clustering

is

applied

to

the

matrix

of

the

combined

distances

Segal

et

al

-LRB-

-RRB-

provided

a

probabilistic

formulation

in

which

a

module

is

a

group

of

genes

with

high

pairwise

similarities

and

with

a

significant

portion

of

the

possible

Molecular

Systems

Biology

L

C

log

X

b

b

log

pu

v

u

v

E

C

pu

v

In

case

edge

reliabilities

are

available

it

is

possible

to

incorporate

them

into

the

subnetwork

scoring

model

by

assessing

the

probability

of

the

available

observations

given

that

the

protein

pair

u

v

interacts

-LRB-

P

-LRB-

Ou

v

-RRB-

Tu

v

-RRB-

-RRB-

and

does

not

interact

-LRB-

P

-LRB-

Ou

v

-RRB-

Fu

v

-RRB-

-RRB-

The

log-likelihood

score

then

becomes

X

u

v

C

L

C

log

bP

Ou

v

jTu

v

b

P

Ou

v

jFu

v

pu

v

P

Ou

v

jTu

v

pu

v

P

Ou

v

jFu

v

In

the

SAMBA

algorithm

-LRB-

Tanay

et

al

-RRB-

a

bipartite

graph

represents

the

genomic

data

nodes

on

side

A

represent

genes

and

nodes

on

side

B

represent

different

properties

-LRB-

Figure

-RRB-

A

likelihood

ratio

is

used

to

score

a

potential

bicluster

-LRB-

A

B

-RRB-

by

summing

over

all

the

edges

between

A

and

B

X

u

v

A

B

L

C

log

X

b

b

log

pu

v

u

v

E

A

B

pu

v

Using

these

scores

optimization

algorithms

based

on

hashing

-LRB-

Tanay

et

al

-RRB-

and

local

search

-LRB-

Sharan

et

al

-RRB-

can

be

used

to

detect

highscoring

subnetworks

given

candidate

protein

is

co-complexed

with

the

core

by

the

probability

that

a

path

consisting

of

stable

protein

interactions

exists

between

the

candidate

and

a

member

of

the

core

The

algorithm

assigns

a

weight

to

each

pair

of

proteins

representing

the

probability

that

they

interact

directly

and

stably

These

weights

are

then

used

to

estimate

the

probability

that

two

proteins

are

connected

by

a

path

by

generating

a

collection

of

random

networks

and

counting

how

many

of

them

contain

a

short

path

between

the

protein

pair

For

a

similar

problem

an

algorithm

called

SEEDY

was

proposed

by

Bader

-LRB-

-RRB-

SEEDY

constructs

complexes

by

adding

proteins

to

a

given

seed

as

long

as

the

reliability

of

the

most

reliable

path

from

a

candidate

to

the

seed

does

not

fall

below

a

given

threshold

The

reliability

of

a

path

in

SEEDY

is

defined

as

the

product

of

the

reliabilities

of

its

edges

Wu

and

Hu

-LRB-

-RRB-

proposed

an

algorithm

that

detects

community

structures

-LRB-

Girvan

and

Newman

-RRB-

for

a

EMBO

and

Nature

Publishing

Group

Network-based

function

prediction

R

Sharan

et

al

A

Gene

expression

Complex

membership

Strong

confidence

in

complex

membership

with

protein

P

Medium

confidence

in

complex

membership

with

protein

P

B

Gene

g

Strong

Medium

Medium

Strong

induction

induction

repression

repression

Strong

binding

to

TF

T

Medium

binding

to

TF

T

High

Medium

sensitivity

sensitivity

High-confidence

Medium-confidence

interaction

interaction

TF

binding

Phenotypes

Protein

interactions

Genes

Properties

Figure

Integration

of

multiple

data

sources

using

the

SAMBA

framework

In

the

SAMBA

framework

-LRB-

Tanay

et

al

-RRB-

different

gene

characteristics

are

represented

by

properties

-LRB-

A

-RRB-

Quantitative

characteristics

such

as

gene

expression

levels

are

discretized

first

The

genes

and

the

properties

are

represented

by

nodes

in

a

bipartite

graph

-LRB-

B

-RRB-

where

edges

connect

genes

with

the

properties

they

have

The

SAMBA

algorithm

seeks

modules

consisting

of

a

subset

of

genes

and

a

subset

of

properties

such

that

these

subsets

are

densely

connected

in

the

graph

interactions

A

probabilistic

graphical

model

was

used

to

extract

a

pre-specified

number

of

modules

from

gene

expression

measurements

combined

with

a

protein

interaction

data

set

This

method

thus

produces

groups

of

genes

that

are

both

coexpressed

and

exhibit

a

dense

interaction

pattern

Integrated

analysis

of

interactions

and

diverse

genomic

data

Kelley

and

Ideker

-LRB-

-RRB-

described

the

first

module-assisted

integration

of

protein

interaction

data

with

genetic

interaction

data

Synthetic

lethality

and

synthetic

sickness

are

interactions

between

two

nonessential

genes

whose

combined

deletion

is

lethal

or

produces

a

severe

growth

effect

respectively

Kelley

and

Ideker

-LRB-

-RRB-

addressed

the

question

of

whether

genetic

interactions

occur

mostly

within

or

between

different

pathways

by

identifying

statistically

significant

modular

structures

in

the

combined

network

of

physical

-LRB-

protein

protein

and

protein

DNA

-RRB-

and

genetic

interactions

By

defining

pathways

as

dense

subnetworks

in

the

physical

interaction

network

Kelley

and

Ideker

-LRB-

-RRB-

show

that

genetic

interactions

occur

between

pathways

much

more

often

than

within

pathways

The

modular

structures

led

to

better

function

prediction

than

using

physical

interactions

alone

Another

source

of

rich

genomic

information

that

is

becoming

increasingly

available

is

the

collection

of

phenotypes

of

strains

containing

a

deletion

for

a

single

nonessential

gene

and

exposed

to

diverse

conditions

-LRB-

Brown

et

al

-RRB-

Haugen

et

al

-LRB-

-RRB-

measured

deletion

phenotypes

along

with

expression

profiles

in

the

arsenic

response

of

S.

cerevisiae

The

data

were

then

integrated

with

a

metabolic

network

and

a

physical

Molecular

Systems

Biology

network

using

the

ActiveModules

algorithm

-LRB-

Ideker

et

al

-RRB-

This

type

of

integration

was

used

to

reveal

proteins

central

to

the

arsenic

response

and

to

provide

network-based

explanations

to

the

differences

between

the

phenotypically

sensitive

pathways

and

the

differentially

expressed

ones

Tanay

et

al

-LRB-

-RRB-

described

an

integrative

framework

allowing

the

integration

of

protein

interaction

data

with

gene

expression

phenotypic

sensitivity

and

transcription

factor

-LRB-

TF

-RRB-

binding

using

the

SAMBA

biclustering

algorithm

-LRB-

Tanay

et

al

-RRB-

SAMBA

models

the

genomic

data

as

a

bipartite

graph

-LRB-

Box

-RRB-

where

nodes

on

one

side

represent

genes

and

that

on

the

other

side

represent

different

properties

derived

from

the

genomic

data

-LRB-

Figure

-RRB-

For

protein

interaction

data

a

property

represents

the

presence

of

an

interaction

with

a

specific

protein

A

gene

expression

experiment

is

represented

by

several

properties

corresponding

to

different

expression

level

ranges

A

TF

property

represents

the

binding

of

the

gene

promoter

by

the

TF

The

bipartite

graph

is

weighted

using

a

maximum

likelihoodbased

score

-LRB-

Box

-RRB-

Heavy

subgraphs

in

the

bipartite

graph

correspond

to

groups

of

genes

that

manifest

a

common

behavior

across

a

large

set

of

heterogeneous

experiments

This

approach

was

experimentally

shown

to

provide

accurate

function

prediction

and

was

later

extended

to

analyze

a

compendium

of

some

distinct

experiments

in

S.

cerevisiae

yielding

statistically

significant

modules

-LRB-

Tanay

et

al

-RRB-

Performance

comparison

The

availability

of

such

a

wide

range

of

methods

calls

for

a

comprehensive

comparison

among

them

Below

we

summarize

some

of

the

key

comparisons

reported

so

far

EMBO

and

Nature

Publishing

Group

Network-based

function

prediction

R

Sharan

et

al

Direct

methods

For

direct

methods

no

systematic

comparison

has

been

reported

so

far

but

some

information

on

the

performance

of

the

different

methods

can

be

gleaned

from

comparisons

made

in

the

annotation

studies

reviewed

above

one

of

which

we

describe

below

Several

measures

have

been

suggested

to

evaluate

the

quality

of

a

direct

annotation

method

-LRB-

see

e.g.

Deng

et

al

Nabieva

et

al

-RRB-

All

these

measures

are

close

variants

of

the

one

proposed

by

in

Deng

et

al

-LRB-

-RRB-

The

latter

is

based

on

measuring

the

precision

and

recall

of

an

annotation

computed

in

a

leave-one-out

setting

-LRB-

i.e.

the

known

annotation

of

a

single

protein

at

a

time

is

hidden

and

predicted

using

the

network

and

the

annotations

of

all

other

proteins

-RRB-

taking

into

account

multiple

annotations

per

protein

Specifically

let

ni

be

the

number

of

known

functions

for

protein

i

let

mi

be

the

number

of

predicted

functions

for

the

protein

when

hiding

its

true

annotations

and

let

ki

the

overlap

between

the

two

sets

The

precision

and

recall

of

the

predictions

are

defined

as

P

P

ki

ki

i

i

P

P

Precision

Recall

mi

ni

i

i

A

discussion

of

other

measures

that

are

applicable

if

the

Gene

Ontology

-LRB-

GO

-RRB-

annotation

is

used

and

of

weighted

matches

between

known

and

predicted

functions

based

on

their

positions

in

the

GO

hierarchy

-LRB-

Ashburner

et

al

-RRB-

appears

in

Deng

et

al

-LRB-

-RRB-

Chua

et

al

-LRB-

-RRB-

compared

several

schemes

including

neighborhood

counting

-LRB-

Schwikowski

et

al

-RRB-

the

method

of

Hishigaki

et

al

-LRB-

-RRB-

the

MRF

method

of

Deng

et

al

-LRB-

-RRB-

and

the

flow-based

method

of

Nabieva

et

al

-LRB-

-RRB-

-LRB-

To

avoid

possible

bias

we

do

not

report

here

on

the

method

of

Chua

et

al

-LRB-

-RRB-

which

was

also

included

in

the

comparison

-RRB-

Each

method

was

applied

to

the

MIPS

interaction

set

-LRB-

Mewes

et

al

-RRB-

using

the

GO

annotation

and

was

evaluated

using

the

precision

and

recall

measures

The

MRF

method

outperformed

the

others

by

a

significant

margin

whereas

the

other

three

methods

exhibited

similar

performance

These

results

were

consistent

across

the

three

main

MIPS

categories

cellular

role

biochemical

function

and

subcellular

localization

The

advantage

of

MRF

is

probably

owing

to

the

use

of

a

more

sophisticated

probabilistic

model

MCODE

and

MCL

As

the

authors

of

this

study

were

not

involved

in

the

development

of

any

of

the

algorithms

the

chances

of

inadvertent

evaluation

bias

are

low

Brohee

and

van

Helden

-LRB-

-RRB-

used

a

test

graph

constructed

by

representing

known

complexes

as

cliques

in

the

graph

and

generated

altered

graphs

by

random

addition

or

removal

of

edges

in

different

proportions

The

parameters

of

each

of

the

clustering

algorithms

were

then

tuned

based

on

this

data

set

using

a

statistic

combining

detection

sensitivity

and

specificity

In

addition

the

four

module-assisted

techniques

were

applied

to

six

PPI

graphs

formed

based

on

highthroughput

experiments

and

their

performance

was

evaluated

by

their

success

in

recovering

known

complexes

The

authors

found

that

the

MCL

algorithm

is

remarkably

robust

to

graph

alternations

MCL

had

the

best

performance

on

both

simulated

and

real

data

sets

whereas

RNSC

was

relatively

less

sensitive

to

suboptimal

parameters

MCODE

and

SPC

were

shown

to

be

clearly

inferior

under

most

conditions

The

comparative

analysis

also

highlighted

intrinsic

strengths

and

weaknesses

of

the

algorithms

MCODE

performed

best

on

random

data

by

detecting

the

fewest

false

positives

owing

to

its

ability

to

report

complexes

covering

only

part

of

the

network

rather

than

partitioning

all

the

nodes

into

complexes

SPC

tended

to

generate

mega-complexes

of

very

large

size

thus

obtaining

very

high

sensitivity

but

very

low

specificity

Notably

this

comparison

used

unweighted

networks

whereas

the

MCL

and

SPC

algorithms

can

deal

with

weighted

graphs

and

are

likely

to

give

better

performances

if

weights

are

assigned

to

reflect

the

reliability

of

the

interactions

-LRB-

PereiraLeal

et

al

-RRB-

Comparing

direct

and

module-assisted

methods

To

the

best

of

our

knowledge

no

systematic

comparison

of

network-based

function

prediction

covering

both

direct

and

module-assisted

methods

has

been

undertaken

to

date

In

a

simplistic

comparison

of

two

basic

methods

-LRB-

Figure

-RRB-

we

found

that

a

simple

neighbor-counting

method

has

a

higher

specificity

in

predicting

functions

when

compared

to

the

more

involved

module-assisted

MCODE

algorithm

This

may

be

explained

by

the

focus

of

MCODE

on

processes

imposing

subgraphs

with

a

dense

interaction

pattern

A

more

comprehensive

analysis

alongside

the

development

of

better

prediction

techniques

will

highlight

these

differences

Module-assisted

methods

One

of

the

obstacles

to

systematic

evaluation

of

the

different

module-assisted

methods

for

functional

annotation

is

the

lack

of

agreed

upon

technique

for

function

prediction

within

a

module

However

if

one

considers

a

module

as

a

functional

unit

whose

member

proteins

have

identical

functions

then

one

could

evaluate

a

module-assisted

method

by

the

fit

between

the

produced

modules

and

either

the

MIPS

complexes

catalog

-LRB-

Mewes

et

al

-RRB-

or

GO

categories

-LRB-

Ashburner

et

al

-RRB-

using

measures

similar

to

those

described

in

the

Direct

methods

section

A

systematic

quantitative

evaluation

of

four

module-assisted

clustering

algorithms

has

been

presented

recently

by

Brohee

and

van

Helden

-LRB-

-RRB-

RNSC

SPC

EMBO

and

Nature

Publishing

Group

Discussion

Efforts

for

network-based

function

prediction

have

been

going

on

for

over

years

now

since

the

introduction

of

molecular

techniques

capable

of

mapping

protein

interactions

on

a

genome-wide

scale

Despite

the

large

number

of

techniques

suggested

for

functional

annotation

using

networks

systematic

annotation

is

still

mostly

based

on

other

data

sources

such

as

sequence

homology

Several

goals

reviewed

below

have

to

be

accomplished

in

order

for

the

network-based

functional

annotation

tools

to

become

widely

used

Despite

the

large

number

of

different

algorithms

developed

for

both

direct

and

module-assisted

function

prediction

the

implementations

of

only

a

small

fraction

of

them

are

publicly

Molecular

Systems

Biology

Network-based

function

prediction

R

Sharan

et

al

Figure

Performance

comparison

of

a

direct

method

versus

a

moduleassisted

one

Two

receiver

operating

characteristic

-LRB-

ROC

-RRB-

curves

comparing

the

accuracy

of

a

neighborhood-counting

method

-LRB-

Schwikowski

et

al

-RRB-

and

of

the

MCODE

method

-LRB-

Bader

and

Hogue

-RRB-

in

predicting

GO

Biological

Process

annotations

using

a

PPI

network

obtained

from

BioGRID

-LRB-

Stark

et

al

-RRB-

A

ROC

curve

is

commonly

used

to

assess

prediction

performance

by

plotting

the

true

positive

rate

versus

the

false

positive

rate

when

varying

the

prediction

threshold

In

the

neighborhood-counting

variant

used

here

a

protein

is

assigned

with

a

function

if

the

hypergeometric

enrichment

P-value

for

the

function

in

the

protein

's

direct

neighborhood

is

below

a

certain

threshold

MCODE

clusters

were

obtained

using

the

Cytoscape

plug-in

with

the

node

score

cutoff

parameter

set

to

and

the

other

parameters

at

their

default

values

Using

MCODE

we

predict

a

function

for

a

protein

if

that

function

's

P-value

in

the

protein

's

module

is

below

a

certain

threshold

Each

ROC

curve

was

obtained

by

varying

the

threshold

Only

proteins

assigned

to

at

least

one

MCODE

cluster

were

used

in

the

analysis

for

both

methods

accessible

Only

a

handful

such

as

MCODE

-LRB-

Bader

and

Hogue

-RRB-

PRODISTIN

-LRB-

Baudot

et

al

-RRB-

CFinder

-LRB-

Adamcsek

et

al

-RRB-

and

NetworkBlast

-LRB-

-RRB-

are

currently

supported

with

a

graphical

interface

Networks

are

highly

visualizable

and

as

the

human

eye

is

better

in

pattern

detection

than

any

computer

a

good

graphical

interface

will

help

make

such

computational

tools

widely

used

by

the

biological

community

Although

the

field

has

advanced

considerably

in

recent

years

on

the

methodological

side

comprehensive

comparisons

of

the

plethora

of

available

annotation

methods

similar

to

that

performed

by

Brohee

and

van

Helden

-LRB-

-RRB-

are

greatly

in

need

Such

systematic

evaluation

efforts

were

recently

performed

in

other

fields

for

example

discovery

of

TF-binding

sites

-LRB-

Tompa

et

al

-RRB-

biclustering

of

expression

data

-LRB-

Prelic

et

al

-RRB-

and

protein

structure

prediction

-LRB-

Kryshtafovych

et

al

-RRB-

which

has

a

long

and

successful

history

of

community

evaluations

Owing

to

the

fundamental

differences

between

the

different

annotation

types

such

as

biological

process

and

molecular

complexes

it

is

clear

that

different

methods

are

best

suited

for

different

types

of

annotation

An

important

prerequisite

for

a

comprehensive

comparison

is

the

definition

of

golden

standards

for

functional

annotation

In

most

of

the

studies

described

here

the

MIPS

complexes

catalog

and

Gene

Ontology

were

used

as

a

benchmark

for

prediction

success

However

both

data

sets

are

currently

not

Molecular

Systems

Biology

hensive

and

some

annotations

are

found

in

one

but

not

in

the

other

Although

several

methods

described

above

use

diverse

functional

genomic

data

sources

they

are

still

greatly

outnumbered

by

methods

utilizing

only

the

network

topology

Owing

to

the

increasing

accessibility

of

microarray

technology

gene

expression

measurements

have

become

widely

available

for

diverse

conditions

across

species

As

of

August

almost

and

hybridization

samples

were

available

in

Gene

Expression

Omnibus

and

ArrayExpress

databases

respectively

This

huge

body

of

data

is

currently

poorly

exploited

by

integrative

annotation

methods

as

most

of

them

focus

on

expression

data

derived

in

a

single

study

Following

the

success

of

several

methods

integrating

expression

data

from

multiple

studies

-LRB-

Ihmels

et

al

Lee

et

al

Segal

et

al

Tanay

et

al

-RRB-

we

expect

that

techniques

based

on

large

compendia

of

expression

data

and

protein

interaction

networks

will

significantly

increase

the

accuracy

of

functional

annotation

Additional

large-scale

genomic

data

such

as

deletion

phenotypes

-LRB-

Brown

et

al

-RRB-

proteomic

measurements

-LRB-

Kislinger

et

al

-RRB-

and

protein

cellular

localization

-LRB-

Huh

et

al

-RRB-

can

also

be

used

in

an

integrative

framework

Data

of

a

high

diversity

and

dimensionality

have

been

integrated

using

biclustering

-LRB-

Tanay

et

al

-RRB-

and

kernel-based

methods

-LRB-

Lanckriet

et

al

-RRB-

This

review

focused

on

methods

aimed

at

genome-scale

functional

annotation

using

network

data

from

a

single

species

Several

additional

studies

developed

methods

for

detection

of

functional

modules

in

slightly

different

contexts

In

particular

specific

algorithms

were

developed

for

detection

of

molecular

complexes

from

lists

of

proteins

identified

in

biochemical

purification

experiments

rather

than

from

binary

interaction

networks

-LRB-

Krause

et

al

Hollunder

et

al

Scholtens

et

al

Gavin

et

al

-RRB-

Another

set

of

works

attempted

to

identify

evolutionarily

conserved

functional

modules

via

the

integration

of

networks

from

multiple

organisms

-LRB-

Kelley

et

al

Sharan

et

al

Campillos

et

al

Flannick

et

al

Gandhi

et

al

see

also

the

review

in

Sharan

and

Ideker

-RRB-

Which

methods

should

be

used

by

a

newcomer

to

the

field

As

mentioned

above

the

limited

information

about

the

comparative

performance

of

the

methods

presented

here

makes

it

difficult

to

decide

which

method

should

be

used

in

a

specific

setting

When

using

only

PPI

data

our

initial

and

limited

comparison

does

seem

to

indicate

that

direct

methods

are

currently

slightly

superior

to

module-assisted

ones

with

MRF

and

MCL

being

the

leading

techniques

for

direct

and

module-assisted

function

prediction

respectively

New

techniques

should

thus

be

compared

to

these

methods

to

prove

their

superiority

If

the

goal

is

actual

function

prediction

rather

than

methodological

improvement

the

use

is

mainly

limited

to

methods

that

are

implemented

as

a

tool

with

a

graphical

user

interface

or

available

as

a

web

server

-LRB-

Table

I

-RRB-

As

to

methods

integrating

multiple

data

sources

no

comparative

assessment

is

currently

available

When

using

interaction

networks

whether

as

a

sole

information

source

or

in

conjunction

with

other

data

sources

the

current

limitations

of

these

data

have

to

be

recognized

The

currently

available

protein

interaction

data

are

known

to

be

both

noisy

-LRB-

von

Mering

et

al

-RRB-

and

partial

-LRB-

Hart

et

al

-RRB-

In

addition

as

large-scale

interaction

mappings

are

EMBO

and

Nature

Publishing

Group

Network-based

function

prediction

R

Sharan

et

al

conducted

only

in

a

single

growth

condition

or

in

a

single

tissue

type

interaction

data

currently

lack

any

spatial

or

temporal

information

Clearly

for

some

functional

annotations

the

relevant

interactions

may

occur

only

under

specific

conditions

in

a

specific

time

point

In

addition

not

every

functional

aspect

of

the

protein

is

expected

to

be

manifested

in

its

interaction

pattern

Some

proteins

such

as

metabolic

enzymes

are

most

functional

on

their

own

without

the

need

for

cooperation

from

other

proteins

Despite

these

caveats

analysis

of

interaction

networks

is

a

young

promising

and

very

active

research

area

The

utilization

of

such

networks

for

function

prediction

is

just

one

of

a

plethora

of

possible

ways

by

which

this

rich

source

of

information

can

be

exploited

Although

techniques

for

network-based

function

prediction

have

been

continuously

improving

there

is

still

a

lot

of

room

for

improvement

both

in

terms

of

the

methodologies

and

in

terms

of

their

evaluation

We

expect

that

improved

more

accurate

methods

that

are

made

readily

accessible

to

the

biological

community

will

make

interaction

networks

a

prevalent

instrument

for

functional

annotation

among

their

many

other

important

uses

Acknowledgements

We

thank

Ken

Chua

for

providing

us

with

his

comparison

data

RS

was

supported

by

an

Alon

Fellowship

IU

is

a

fellow

of

the

Edmond

J

Safra

Bioinformatics

Program

at

Tel-Aviv

University

This

research

was

supported

by

a

grant

from

the

Israeli

Science

Foundation

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