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Reactome Reactome a pathways knowledgebase a pathways knowledgebase Imre Vastrik EMBL-European Bioinformatics Institute 6/10/2005

Reactome a pathways knowledgebase

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Reactome a pathways knowledgebase. Imre Vastrik EMBL-European Bioinformatics Institute 6/10/2005. The Plan. Why? How? What does it look like/what can you do with it?. From data to knowledge. Decrease in computational access. Insulin binds the insulin receptor, causing it to - PowerPoint PPT Presentation

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Page 1: Reactome a pathways knowledgebase

ReactomeReactomea pathways knowledgebasea pathways knowledgebase

Imre Vastrik

EMBL-European Bioinformatics Institute

6/10/2005

Page 2: Reactome a pathways knowledgebase

The PlanThe Plan

• Why?

• How?

• What does it look like/what can you do with it?

Page 3: Reactome a pathways knowledgebase

From data to knowledgeFrom data to knowledge

Decrease in computational access

Page 4: Reactome a pathways knowledgebase

Insulin binds the insulin receptor, causing it todimerise. The dimerised form the autophosphorylateson 6 cytoplasmic tyrosines. This phosphorylated form recruits the IRS adaptor....

Page 5: Reactome a pathways knowledgebase
Page 6: Reactome a pathways knowledgebase

Decrease in computational access

……and exhaustionand exhaustion

Page 7: Reactome a pathways knowledgebase
Page 8: Reactome a pathways knowledgebase
Page 9: Reactome a pathways knowledgebase

• Why?

• How?

• What does it look like/what can you do with it?

Page 10: Reactome a pathways knowledgebase

History of ReactomeHistory of Reactome

• Started as Genome Knowledgebase in spring 2001.• Aim: capture the knowledge of biological

experts in a form that could be searched and reasoned over electronically, and which could act as a connecting link between sequence records and primary biomedical literature.

• Initially tried to capture and standardise the language used to describe molecular processes.

• 2001/2002 realised that what we are trying to capture are reactions and pathways.

• Rebranded as Reactome June 2004.

Page 11: Reactome a pathways knowledgebase

plasma membrane [GO:0005886]

Cytosol[GO:0005829]

extracellular region[GO:0005576]

Reactome data modelReactome data model

Insulin

Insu

lin r

ece

pto

r

Insu

lin r

ece

pto

r

InsulinIn

sulin

re

cep

tor

Insu

lin r

ece

pto

r

Insu

lin r

ece

pto

r

Insu

lin r

ece

pto

r

P P

Insulin

IRS

Insulin

Insu

lin r

ece

pto

r

Insu

lin r

ece

pto

r

P P

AT

Px1

2

AD

Px1

2

Page 12: Reactome a pathways knowledgebase

Reactome data modelReactome data modelUniProt:P01308

UniProt:P06213

PMID:8276779PMID:8039601

PMID:11737239PMID:8276779PMID:7781591

ChEBI:2359 ChEBI:2342

IRS

-1

IRS

-2

DO

K1

UniProt :Q9Y4H2 UniProt :Q99704UniProt :P35568

plasma membrane [GO:0005886]

Insulin

Insu

lin r

ece

pto

r

Insu

lin r

ece

pto

r

InsulinIn

sulin

re

cep

tor

Insu

lin r

ece

pto

r

Insulin

Insu

lin r

ece

pto

r

Insu

lin r

ece

pto

r

P P Insu

lin r

ece

pto

r

Insu

lin r

ece

pto

r

P P

Insulin

IRS

AT

Px1

2

AD

Px1

2

Cytosol[GO:0005829]

extracellular region[GO:0005576]

transmembrane receptorprotein tyrosinekinase activity[GO:0004714]

Page 13: Reactome a pathways knowledgebase

plasma membrane [GO:0005886]

Reactome data modelReactome data model

Insulin

Insu

lin r

ece

pto

r

Insu

lin r

ece

pto

r

InsulinIn

sulin

re

cep

tor

Insu

lin r

ece

pto

r

Insulin

Insu

lin r

ece

pto

r

Insu

lin r

ece

pto

r

P P Insu

lin r

ece

pto

r

Insu

lin r

ece

pto

r

P P

Insulin

IRS

IRS

-1

IRS

-2

DO

K1

UniProt :Q9Y4H2 UniProt :Q99704UniProt :P35568

UniProt:P01308

UniProt:P06213

AT

Px1

2

AD

Px1

2

ChEBI:2359 ChEBI:2342

transmembrane receptorprotein tyrosinekinase activity[GO:0004714]

PMID:8276779PMID:8039601

PMID:11737239PMID:8276779PMID:7781591

Cytosol[GO:0005829]

extracellular region[GO:0005576]

Insulin signalling

Page 14: Reactome a pathways knowledgebase

Ambiguity of connection maps…Ambiguity of connection maps…A B

C

D

+ +

+

Do you need A & B or just A | B to get active C?

Page 15: Reactome a pathways knowledgebase

……is avoided by using states and is avoided by using states and reactionsreactions

A

C

C’

C’’

B D

D’

A

C

C’’

B

D

D’

A & B A | B

Page 16: Reactome a pathways knowledgebase

About mice and men…About mice and men…

human mouse rat human

PMID:5555 PMID:4444PMID:8976 PMID:3924

Page 17: Reactome a pathways knowledgebase

… … and how not to mix themand how not to mix them

human

PMID:5555 PMID:4444

mouse

rat

Direct evidence Direct evidence

Indirect evidence

Indirect evidence

PMID:8976

PMID:3924

Page 18: Reactome a pathways knowledgebase

Two FAQsTwo FAQs

• What about tissue specific reactions?– We annotate to the union of all possible reactions: gene

expression data gives the set of reactions feasible in a cell

• What about fine dynamic balances?– We only capture qualitative information. The

quantitative/model aspects has to be handled by ODEs/Kds and SBML like techniques. We can link to these resources, but they are out of scope for the moment

Page 19: Reactome a pathways knowledgebase

Reviewer

(external)

Curator

(staff)

Expert

(external)

Page 20: Reactome a pathways knowledgebase

Release cycleRelease cycle

Repository

ReleaseDB

Extract finished & reviewed topics

Computationally project pathways to other organisms

Add cross-references (Ensembl, Entrez Gene, MIM, KEGG,…)

www.reactome.org

Page 21: Reactome a pathways knowledgebase

Reactome in numbersReactome in numbers(release 15, 26/9/2005)(release 15, 26/9/2005)

Human:• Reactions 1524• Pathways 659• Proteins 1095• “Small molecules” 379• Complexes 982• Literature references 1408

• Interactions 19471

Page 22: Reactome a pathways knowledgebase

• Why?

• How?

• What does it look like/what can you do with it?

Page 23: Reactome a pathways knowledgebase

HSAMMU

ANA

BSU

ECO

SSO

MJA

PFA

DDI

ATH

ANI

SPO

SCE

CEL

DME

TNI

Homo sapiens

Schizosaccharomyces pombe

Mus musculus

Tetraodon nigroviridis

Drosophila melanogaster

Caenorhabditis elegans

Saccharomyces cerevisiae

Aspergillus nidulans

Arabidopsis thaliana

Dictyostelium discoideum

Plasmodium falciparum

Methanococcus jannaschii

Sulpholobus solfataricus

Escherichia coli

Bacillus subtilis

Anabaena

Page 24: Reactome a pathways knowledgebase

Human

Species 1

Species 2

Rules for orthology-based inferenceRules for orthology-based inference

• 75% of a complex must have orthologs

• Lineage specific paralogs are allowed

• All small molecules presumed to exist if reactions exist

• Otherwise every input, output, catalyst must be present

Page 25: Reactome a pathways knowledgebase

HSAMMU

ANA

BSU

ECO

SSO

MJA

PFA

DDI

ATH

ANI

SPO

SCE

CEL

DME

TNI

Finding lineage-specific deletionsFinding lineage-specific deletions++

++

--++--

----

++++++

----

------

--

??++

++

??

++

----

??

++?? --

------

--

Page 26: Reactome a pathways knowledgebase

4.4

3.7

4.9

10.2

26.1

9.0

4.0

9.1

26.1

14.7

24.5

8.3

6.7

5.1

0.2

20.2

26.2

26

18.6

13.9

26.1

43.4

43.7

38.1

44.2

39.5

53.1

60.1

74.4

92.4

ANA

BSU

ECO

SSO

MJA

PFA

DDI

ATH

SPO

ANI

SCE

CEL

DME

TNI

MMU

Lineage-specific deletion ratesLineage-specific deletion rates

Page 27: Reactome a pathways knowledgebase

Absent in cerevisiae and pombe, but Absent in cerevisiae and pombe, but present in aspergilluspresent in aspergillus

Lipid metabolism

Xenobiotic metabolism

Metabolism of amino acids Nucleotide

metabolism (transport)

Page 28: Reactome a pathways knowledgebase

Lineage Deletion ratesLineage Deletion rates

Trp Catabolism

Head or Tail

DNA Repair

Redundant Paths

Insulin Signalling

Pathway modules

Page 29: Reactome a pathways knowledgebase

Presence of “small molecules”Presence of “small molecules”

50.0

60.2

57.9

48.5

31.0

40.1

79.5

75.4

53.5

80.7

59.4

88.9

84.8

90.9

98.8

18.7

24.4

24.1

17.3

12.8

24.6

43.0

42.0

36.7

42.2

37.8

52.8

58.9

74.0

92.0

ANA

BSU

ECO

SSO

MJA

PFA

DDI

ATH

SPO

ANI

SCE

CEL

DME

TNI

MMU

perc_inferred

perc_compounds

Page 30: Reactome a pathways knowledgebase

Tissue expressionTissue expression

0.00E+00

5.00E-02

1.00E-01

1.50E-01

2.00E-01

2.50E-01

Pearson-0.16-0.14-0.12 -0.1-0.08-0.06-0.04-0.02

00.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22

Pearson Correlation

Frequency

NoneComplexReactionNeighbour

Data from HumanNovartis Affy scan

more correlated

Page 31: Reactome a pathways knowledgebase

Reactome at a glanceReactome at a glance• Catalogue of all possible reactions (topology)

in an organism - reactome• Authored by experts• Currently human orientated• Computational predictions to other species• Data & code freely available

(www.reactome.org/download):– MySQL database, SBML, BioPAX + specialised

datasets– Perl and Java APIs– Website mirror– Data entry tool

Page 32: Reactome a pathways knowledgebase

Cold Spring Harbor Laboratory European Bioinformatics Institute Gene Ontology Consortium

Lincoln SteinPeter D'EustachioLisa MatthewsGopal GopinathMarc GillespieGuanming Wu

Elizabeth NickersonMarcela Tello-RuizGeeta Joshi-Tope

Ewan BirneyImre VastrikEsther SchmidtBijay JassalBernard de BonoDavid Croft

Suzanna Lewis

Groups & PeopleGroups & People

NHGRI Grant # R01 HG002639EU STREP EMI-CDEBI Industry program

www:http://www.reactome.orge-mail: [email protected]