Network  Biology  day  two  

Paolo  Tieri,  CNR,  Italy  Universidade  Federal  de  Minas  Gerais  

Belo  Horizonte,  Brazil  

2  Tools  and  Resources  

•  Resources  and  soAware  for  network  biology  – VisualizaEon  and  analysis  tools  – Databases  – Standards    

2  

•  Tools  for  visualizaEon  •  Tools  for  analysis  •  Tools  for  both  •  Standalone  •  web-­‐based  •  Plugins  (R,  matlab…)  •  Free  and  license  purchase  

Cytoscape  

•  Tomorrow!  

CellDesigner  •  hQp://www.celldesigner.org/  •  Structured  diagram  editor  for  drawing  gene-­‐regulatory  and  biochemical  networks  

•  hQp://www.celldesigner.org/models.html  model  repository  

yEd  •  hQp://www.yworks.com/en/products/yfiles/yed/    

•  Desktop  applicaEon  that  can  be  used  to  quickly  and  effecEvely  generate  high-­‐quality  diagrams  

Pajek  

•  hQp://pajek.imfm.si/doku.php?id=pajek  •  Pajek  (Slovene  word  for  Spider)  is  a  program  (Windows  only)  for  analysis  and  visualizaEon  of  large  networks  

•  Quite  powerful,  good  for  very  large  graphs  (fast),  not  very  used  in  biology  

Biolayout  3D  •  hQp://www.biolayout.org/  •  Designed  for  visualizaEon,  clustering,  exploraEon  and  analysis  of  very  large  network  graphs  in  two-­‐  and  three-­‐dimensional  space  derived  primarily,  but  not  exclusively,  from  biological  data  

•  hQp://youtu.be/pzyDC16YK14    

Circos  •  hQp://circos.ca/    •  soAware  package  for  visualizing  data  and  informaEon.  It  visualizes  data  in  a  circular  layout  —  this  makes  Circos  ideal  for  exploring  relaEonships  between  objects  or  posiEons    

hQp://youtu.be/y08gvSvoHxg  

If  you  are  curious:  hQp://youtu.be/M-­‐rTAr3pj5g  (54  mins)    

Hive  Plots  •  hQp://www.hiveplot.net/    •  A  scalable,  computaEonally  fast,  and  straight-­‐forward  network  visualizaEon  method  that  makes  possible  visual  interpretaEon  of  network  structure  and  evoluEon  hQp://youtu.be/1cKG-­‐VHIr8A    

PINA  Protein  InteracEon  Network  Analysis  

•  hQp://cbg.garvan.unsw.edu.au/pina/  •  integrated  plajorm  for  protein  interacEon  network  construcEon,  filtering,  analysis,  visualizaEon  and  management  

VisANT  

•  hQp://visant.bu.edu/  •  IntegraEve  network  plajorm  to  connect  genes,  drugs,  diseases  and  therapies  

NeAT  Network  Analysis  Tools  

•  hQp://rsat.ulb.ac.be/index_neat.html  •  Modular  computer  programs  specifically  designed  for  the  analysis  of  biological  network  

Netwalker  •  hQps://netwalkersuite.org/  •  desktop  applicaEon  for  funcEonal  analyses  of  large-­‐scale  genomics  datasets  within  the  context  of  molecular  network  

DAPPLE  Disease  AssociaEon  Protein-­‐Protein  Link  Evaluator  

•  hQps://www.broadinsEtute.org/mpg/dapple/dappleTMP.php    

•  DAPPLE  looks  for  significant  physical  connec7vity  among  proteins  encoded  for  by  genes  in  loci  associated  to  disease  according  to  protein-­‐protein  interacEons  reported  in  the  literature  

•  The  hypothesis  behind  DAPPLE  is  that  causal  geneEc  variaEon  affects  a  limited  set  of  underlying  mechanisms  that  are  detectable  by  protein-­‐protein  interac7ons  

IPA  Ingenuity  •  hQp://www.ingenuity.com/products/ipa  •  Understanding  of  complex  ‘omics  data  at  mulEple  levels  by  integraEng  data  from  a  variety  of  experimental  plajorms  and  providing  insight  into  the  molecular  and  chemical  interacEons,  cellular  phenotypes,  and  disease  processes  

•  hQp://youtu.be/_HDkjuxYRcY    

Gephi  •  hQps://gephi.github.io/  

•  Gephi  is  an  interacEve  visualizaEon  and  exploraEon  plajorm  for  all  kinds  of  networks  and  complex  systems,  dynamic  and  hierarchical  graphs  

•  hQp://player.vimeo.com/video/9726202    

Databases  

•  Different  from  online  tools,  but  more  and  more  are  offering  integrated  analysis  and  visualizaEon  tools  

Warnings  on  DB  usage  

•  Ambiguity  about  the  logic  behind  the  queries  the  user  is  allowed  to  formulate  (query  logic)  

•  inconsistency  among  the  iden7fiers  the  user  is  allowed  to  adopt  (Pathway  nomenclature  and  iden7fiers)  

•  a  database  may  simply  be  missing  the  relevant  pathway    

Thisjournalis

cThe

RoyalSocietyof

Chemistry

2013Mol.BioSyst.,2013,

9,2401--24072401

Cite

this:M

ol.BioSyst.,2013,9,2401

Sign

alling

path

way

datab

aseu

sability:lesso

ns

learned

PaoloTieri*

ab

andChristine

Nardini a

Background:issues

andlim

itationsrelated

toaccessibility,

understandabilityand

easeof

useof

signallingpathw

aydatabases

may

hamper

ordivert

researchw

orkflow,

leading,in

thew

orstcase,

tothe

generationof

confusingreference

framew

orksand

misinterpretation

ofexperim

entalresults.In

anattem

ptto

retrievesignalling

pathway

datarelated

toa

specificset

oftest

genes,w

equeried

andanalysed

theresults

fromsix

ofthe

major

curatedsignalling

pathway

databases:Reactom

e,Pathw

ay-Com

mons,K

EGG

,InnateDB,PID

,andW

ikipathways.Findings:although

we

expecteddiff

erences–

oftena

desirablefeature

forthe

integrationof

eachindividual

query,w

eobserved

variationsof

exceptionalm

agnitude,with

disproportionatequality

andquantity

ofthe

results.Some

ofthe

more

remarkable

dif-ferences

canbe

explainedby

thediverse

conceptualdesigns

andpurposes

ofthe

databases,the

typesof

datastored

andthe

structureof

thequery,

asw

ellas

bym

issingor

erroneousdescriptions

ofthe

searchprocedure.To

gobeyond

them

ereenum

erationof

theseproblem

s,w

eidentified

anum

berof

operationalfeatures,

inparticular

innerand

crosscoherence,

which,

oncequantified,

offerobjective

criteriato

choosethe

bestsource

ofinform

ation.Conclusions:insilico

biologyheavily

relieson

theinfor-

mation

storedin

databases.Toensure

thatcom

putationalbiology

mirrors

biologicalreality

andoffers

focusedhypotheses

tobe

experimentally

validated,coherence

ofdata

codificationis

crucialand

yethighly

underestimated.W

em

akepractical

recomm

endationsfor

theend-user

tocope

with

thecurrent

stateof

thedatabases

asw

ellas

forthe

maintainers

ofthose

databasesto

contributeto

thegoal

ofthe

fullenactment

ofthe

opendata

paradigm.

Backg

rou

nd

The

omic

revolution1

has

engen

dereda

num

berof

new

oppor-tun

itiesan

dch

allenges.

First,accessibility

todata

has

beengreatly

increased,

with

benefits

and

drawbacks

relatedto

dataavailability. 2

Second,

stemm

ing

fromaccessibility,

the

possibilityto

readsuch

datain

apractical

man

ner

isa

challen

ge.For

example,

standards

mustbe

defined

tosum

marise

the

largeam

ountofin

formation

contain

edin

the

omics

inth

eform

ofm

etadata.Th

esestan

-dards

areexpressed

as‘‘M

inim

alIn

formation

Standards’’

and

include

MIAM

E3

and

MIM

IX4

forexpression

dataan

dH

UPO

5

forprotein

s.Finally,data

organisation

iscrucialfor

re-usage,toallow

three

comm

onplace

tasksin

molecular

computation

albiology:(i)validate

novelresults,based

onexistin

gexperim

ents

(enh

ance

statisticalpow

er);(ii)

testan

dexplore

novel

dataan

alysesusin

gexistin

gexperim

ents

(enh

ance

the

biologicalbreadth

ofthe

findin

g);and

(iii)infer

additionalin

formation

by

integratin

gdiff

erent

sourcesof

data(see,

forexam

ple,th

eD

RYAD

initiative,

http://datadryad.org/).

Allof

these

tasksare

comm

only

performed

with

databases,w

hich

represent

the

most

directaccess

tobiological

data. 6

Data

integration

isth

em

ostrecen

tn

eedin

this

omic

revolutionan

dis

acrucial

stepfor

personalised

medicin

e.Patien

tsare

alsoa

complex

and

multifaceted

systemth

atm

ustbe

represented

bya

varietyof

heterogen

eousm

olecularsn

ap-sh

ots.In

deed,in

tegrationoccurs

atseveral

levels,in

cluding:

integrationof

homogeneous

studies,for

example,

transcriptionaldata

fromdiff

erentm

icroarrayplatform

sor

nextgeneration

sequencing(N

GS,

asan

example,

seeref.

7and

8);integration

ofheterogeneous

studies,for

example,

different

layersof

omics,

suchas

transcriptomic,

post-transcriptomic

andproteom

ic; 9–12

integrationand

reconstructionof

biologicalpathw

aysfrom

lowthroughput

experiments

13,14(see,

forexam

ple,the

definitionof

thepopular

KEG

Gdatabase

15);and,finally,overallintegrationof

theaforem

entionedstudies. 16

Indeed,

wh

ileth

efirst

two

typesof

integration

relyon

recently

defined

standards

(high

through

putdata

protocols),th

elatter

requirem

anual

curationof

previouslyexistin

gin

for-m

ation,

oftenstored

intextual

format,

and

only

recently

transformed

intosoft-m

odelsand

made

availablein

onlinerepos-

itoriesan

ddatabases. 17

Significan

teff

ortled

toth

ecreation

of

aKey

Laboratoryof

Com

putationalBiology,

CAS-M

PGPartner

Institutefor

Com

putationalBiology,

ShanghaiInstitutes

forBiological

Sciences,C

hineseAcadem

yof

Sciences,Yue

YangR

oad320,

Shanghai,P.

R.

China

bCN

R-IAC

Consiglio

Nazionale

delleR

icerche,Istitutoper

leApplicazioni

delC

alcolo,Viale

A.M

anzoni30,

Rom

a,Italy.

E-mail:

p.tieri@iac.cnr.it

Received21st

June2013,

Accepted

23rdJuly

2013

DO

I:10.1039/c3mb70242a

ww

w.rsc.o

rg/m

olecu

larbio

systems

Molecu

larBio

Systems

OPIN

ION

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Issue

•  Same  may  happen  in  PPI  databases  

Turinsky  et  al  Nat  Biotec  2011  

•  Despite  the  high  quality  of  single  curated  databases,  conflicEng  informaEon  sEll  persists    

•  Database  curators  should  devote  the  same  aQenEon  that  they  pay  to  the  molecular  informaEon  stored  in  their  database  to  the  descripEon  of  the  algorithms  and  hypotheses  behind  the  search  procedures    

•  USER!  Always  consciously  scru7nize  the  informa7on  available  to  perform  a  rigorous  choice  of  resources    

Pathguide  

•  hQp://pathguide.org/  •  Pathguide  contains  informaEon  about  547  biological  pathway  related  resources  and  molecular  interacEon  related  resources  

UniProt  Unified  Protein  Resource  

•  hQp://www.uniprot.org/    •  comprehensive,  high-­‐quality  and  freely  accessible  resource  of  protein  sequence  and  funcEonal  informaEon  

•  hQp://youtu.be/ado1r8IDm3U      

InnateDB  

•  hQp://www.innatedb.com/  •  Publicly  available  database  of  the  genes,  proteins,  experimentally-­‐verified  interacEons  and  signaling  pathways  involved  in  the  innate  immune  response  of  humans,  mice  and  bovines  to  microbial  infecEon  

KEGG  

•  hQp://www.genome.jp/kegg/  •  KEGG  is  a  database  resource  for  understanding  high-­‐level  funcEons  and  uEliEes  of  the  biological  system,  such  as  the  cell,  the  organism  and  the  ecosystem,  from  molecular-­‐level  informaEon,  especially  large-­‐scale  molecular  datasets  generated  by  genome  sequencing  and  other  high-­‐throughput  experimental  technologies    

KEGG  Pathway  

•  hQp://www.genome.jp/kegg/pathway.html    •  KEGG  PATHWAY:  mapping  is  the  process  to  map  molecular  datasets,  especially  large-­‐scale  datasets  in  genomics,  transcriptomics,  proteomics,  and  metabolomics,  to  the  KEGG  pathway  maps  for  biological  interpretaion  of  higher-­‐level  systemic  func7ons  

•  hQp://www.genome.jp/kegg/tool/map_pathway1.html    

REACTOME  

•  hQp://www.reactome.org/  •  Reactome  is  a  free,  open-­‐source,  curated  and  peer  reviewed  pathway  database  

•  The  goal  is  to  provide  intuiEve  bioinformaEcs  tools  for  the  visualizaEon,  interpretaEon  and  analysis  of  pathway  knowledge  to  support  basic  research,  genome  analysis,  modeling,  systems  biology  and  educaEon  

•  The  current  version  (v51)  of  Reactome  was  released  on  December  8,  2014  

PathwayCommons  

•  hQp://www.pathwaycommons.org/about/  •  Pathway  Commons  is  a  network  biology  resource  and  acts  as  a  convenient  point  of  access  to  biological  pathway  informaEon  collected  from  public  pathway  databases,  which  you  can  search,  visualize  and  download  

•  All  data  is  freely  available,  under  the  license  terms  of  each  contribuEng  database  

APID  Agile  Protein  InteracEon  DataAnalyzer  

•  hQp://bioinfow.dep.usal.es/apid/index.htm  •  interacEve  bioinformaEc  web-­‐tool  that  has  been  developed  to  allow  exploraEon  and  analysis  of  main  currently  known  informaEon  about  protein-­‐protein  interacEons    all  known  experimentally  validated  protein-­‐protein  interacEons  (BIND,  BioGRID,  DIP,  HPRD,  IntAct  and  MINT)integrated  and  unified  in  a  common  and  comparaEve  plajorm.  The  analyEcal  and  integraEve  effort  done  in  APID  provides  an  open  access  frame  where  are  unified  in  a  unique  web  applicaEon  

iRefWeb  

•  hQp://wodaklab.org/iRefWeb/  •  web  interface  to  a  broad  landscape  of  data  on  protein-­‐protein  interacEons  (PPI)  consolidated  from  major  public  databases  

•  reliability  of  an  interacEon  using  simple  criteria,  such  as  the  number  of  supporEng  publicaEons,  the  scale  of  the  corresponding  studies  (high-­‐  or  low-­‐throughput)  or  the  detecEon  methods  used  in  the  original  experiments  

DAVID  Database  for  AnnotaEon,  VisualizaEon  and  Integrated  Discovery  •  hQp://david.abcc.ncifcrf.gov/    •  comprehensive  set  of  funcEonal  annotaEon  tools  for  invesEgators  to  understand  biological  meaning  behind  large  list  of  genes  

Enrichr  

•  hQp://amp.pharm.mssm.edu/Enrichr/  •  integraEve  web-­‐based  and  mobile  soAware  applicaEon  that  includes  new  gene-­‐set  libraries,  an  alternaEve  approach  to  rank  enriched  terms,  and  various  interacEve  visualizaEon  approaches  to  display  enrichment  results  

HGNC  HuGO  Gene  Nomenclature  CommiQee    

•  hQp://www.genenames.org/cgi-­‐bin/symbol_checker    

•  the  only  worldwide  authority  that  assigns  standardised  nomenclature  to  human  genes  

STRING  

•  hQp://string-­‐db.org/    •  database  of  known  and  predicted  protein  interacEons.  The  interacEons  include  direct  (physical)  and  indirect  (funcEonal)  associaEons;  they  are  derived  from  four  sources:  

•  Genomic  Context  •  High-­‐throughput  Experiments  •  (Conserved)  Coexpression  •  Previous  Knowledge      

IntAct  

•  hQps://www.ebi.ac.uk/intact/  •  freely  available,  open  source  database  system  and  analysis  tools  for  molecular  interacEon  data.  All  interacEons  are  derived  from  literature  curaEon  or  direct  user  submissions  and  are  freely  available  

MINT  Molecular  INTeracEon  database  

•  hQp://mint.bio.uniroma2.it/mint/Welcome.do    

•  Database  focused  on  experimentally  verified  protein-­‐protein  interacEons  mined  from  the  scienEfic  literature  by  expert  curators  

BioGRID  

•  hQp://thebiogrid.org/  •  online  interacEon  repository  with  data  compiled  through  comprehensive  curaEon  efforts  

•  Data  from  44,686  publicaEons  for  812,935  raw  protein  and  geneEc  interacEons  from  major  model  organism  species    

GeneMANIA  

•  hQp://genemania.org/    •  GeneMANIA  finds  other  genes  that  are  related  to  a  set  of  input  genes,  using  a  very  large  set  of  funcEonal  associaEon  data.  AssociaEon  data  include  protein  and  geneEc  interacEons,  pathways,  co-­‐expression,  co-­‐localizaEon  and  protein  domain  similarity  

BioModels  

•  hQps://www.ebi.ac.uk/biomodels-­‐main/    •  repository  of  computaEonal  models  of  biological  processes.  Models  described  from  literature  are  manually  curated  and  enriched  with  cross-­‐references  

ArrayExpress  

•  hQps://www.ebi.ac.uk/arrayexpress/  •  database  of  funcEonal  genomics  experiments  that  can  be  queried  and  the  data  downloaded  

•  Gene  expression  data  from  microarray  and  high  throughput  sequencing  studies  

•  Experiments  are  submiQed  directly  to  ArrayExpress  or  are  imported  from  the  NCBI  GEO  database.  

IDconverter  

•  hQp://idconverter.iib.uam.es/  

Standards  

•  Standard  languages  to  enable  integraEon,  exchange,  visualisaEon  and  analysis  of  biological  pathways  at  the  molecular  and  the  cellular  level  

•  Biological  Pathway  Exchange  (BioPAX)  •  Systems  Biology  Markup  Language  (SBML)  •  Systems  Biology  Graphical  NotaEon  (SBGN)  •  Cell  Markup  Language  (CellML)  •  PSI-­‐MI  •  …  •  hQp://www.psidev.info/    

Standards  /  Guidelines  •  MIMIx  is  a  community  guideline  advising  the  user  on  how  to  fully  describe  a  molecular  interacEon  experiment  and  which  informaEon  it  is  important  to  capture  

•  Molecule  (unanmiguous  descripEon)  •  experiment  (to  capture  the  aspects  of  an  interacEon  experiment  which  are  necessary  to  classify  and  criEcally  assess  the  results  and  their  interpretaEon)  

•  interac7on,  including  both  qualitaEve  parameters  and  quanEEve  parameters,  for  example  dissociaEon  constants.  However,  this  data  is  oAen  not  available,  and  thus  MIMIx  only  requires  two  elements  for  the  descripEon  of  an  interacEon,  the  list  of  molecules  par1cipa1ng  in  the  interacEon,  characterised  as  above,  and  a  quality  assessment  

•  …  

Standards  /  Data  Formats  

•  The  Proteomics  Standards  IniEaEve  (PSI)  aims  to  define  community  standards  for  data  representa7on  in  proteomics  to  facilitate  data  comparison,  exchange  and  verificaEon  

•  The  PSI  MI  format  is  a  data  exchange  format  for  molecular  interacEons.  It  is  not  a  proposed  database  structure  

•  hQp://psidev.sourceforge.net/molecular_interacEons//rel25/doc/    

Standards  /  Controlled  Vocabulary  

•  The  Controlled  Vocabularies  (CVs)  of  the  Proteomic  Standard  IniEaEve  (PSI)  provide  a  consensus  annota7on  system  to  standardize  the  meaning,  syntax  and  formalism  of  terms  used  across  proteomics,  as  required  by  the  PSI  Working  Groups  

•  Each  PSI  working  group  develop  the  CVs  required  by  the  technology  or  data  type  it  aims  to  standardize  

•  [Term]  •  id:  MI:0001  •  name:  interacEon  detecEon  method  •  namespace:  PSI-­‐MI  •  def:  "Method  to  determine  the  interacEon."  [PMID:14755292]  •  subset:  Drugable  •  subset:  PSI-­‐MI_slim  •  synonym:  "interacEon  detect"  EXACT  PSI-­‐MI-­‐short  []  •  relaEonship:  part_of  MI:0000  !  molecular  interacEon  

•  [Term]  •  id:  MI:0002  •  name:  parEcipant  idenEficaEon  method  •  namespace:  PSI-­‐MI  •  def:  "Method  to  determine  the  molecules  involved  in  the  interacEon."  [PMID:14755292]  •  subset:  PSI-­‐MI_slim  •  synonym:  "parEcipant  detecEon"  EXACT  PSI-­‐MI-­‐alternate  []  •  synonym:  "parEcipant  ident"  EXACT  PSI-­‐MI-­‐short  []  •  relaEonship:  part_of  MI:0000  !  molecular  interacEon  

BioPAX  

•  hQp://www.biopax.org/index.html    •  Biological  Pathway  Exchange  (BioPAX)  is  a  standard  language  to  represent  biological  pathways  at  the  molecular  and  cellular  level  and  to  facilitate  the  exchange  of  pathway  data  

•  p.Eeri@iac.cnr.it  

•  3+6=9  •  But  so  does  4+5  •  So,  explore  ways  to  do  things  •  Find  yours  •  Respect  others’  way  •  ;-­‐)  thank  you  all!