network building in cytoscape using the biogateway app · sparql query example. biogateway app...

Network building in Cytoscape using the BioGateway App

benefits of hiding the SPARQL from your eyes

Martin Kuiper, Stian Holmås

Semantic Systems Biology Group

Department of Biology

Norwegian University of Science and Technology

Trondheim, Norway

www.semantic-systems-biology.org

www.biogateway.eu

BioGateway App Presentation EBI-Hinxton Nov 2018

http://www.semantic-systems-biology.org/

http://www.biogateway.eu

http://images.google.com/url?q=http://www.q2s.ntnu.no/~ajith/ntnu-logo.png&usg=AFQjCNHBjhZlcZkA8_zuSLTFKvYnuKwbCQ

Mission of my group

• Develop tools and resources for ‘biologists’

• Provide knowledge in ‘one-stop shop’ for domain biologists

• Participate in systems biology projects

• Demonstrate the use of semantic systems biology


Some products

Provide knowledge in a ‘one-stop shop’ for domain biologists:

✓ Cell cycle control: Cell Cycle Ontology CCO

✓ Generic RDF triple store: BioGateway

✓ Gene regulation: Gene eXpression Knowledge Base GeXKB


BioGateway

• Uses Virtuoso Open Server

– Open Source software that can host a triple store

– Can build this from RDF files

– Has a DB backend

• Supports SPARQL* language which allows querying RDF data (graphs)

• Its syntax is similar to that of SQL

*http://www.w3.org/TR/rdf-sparql-query/

http://www.openlinksw.com/virtuoso/


Semantic Systems Biology

SPARQL query example


the average user experience


• Plugin for the popular network visualization tool Cytoscape

• Client – server communication with BioGateway

• Translates line-by-line questions into SPARQL

• Uses controlled vocabularies and identifiers for queries

• Supports this by an ‘autocomplete’ function

• Renders the result in the form of a network

• Allows further interactive network exploration and extension

• Full provenance and metadata

The BioGateway App

- operates within Cytoscape -


Cytoscape has apps for access to RDF


BioGateway Design- data sources -

Building blocks:

• protein-centric, hence built around the graph 'prot' (from Uniprot)

• ontologies (GO, SIO)

• OMIM

• NCBI Taxonomy

• 'bridge' information:

– 'prot2prot' (IntAct)

– 'prot2onto' (GOA)

– 'prot2phen' (currently only diseases, UniProt)

– 'tfac2gene' (TFactS, TRRUST, HTRI, Signor, IntAct, GOA, ExTRI)

• ExTRI: TF-TG information extracted from PubMed abstracts using a text

mining pipeline - collaboration NTNU/CNIO/BSC - 30.000 quality sentences

→ in our demo: only human data


BioGateway Design- URIs -

Identification schemes:

• external URIs are re-used as much as possible

• BGW-specific URIs are minted only for:

– protein classes

– classes and instances in the 'bridge' graphs

• all other classes (ontology terms, taxa etc.) and instances (e.g. genes,

proteins) are identified by external URIs


BioGateway design- data model -

Data model:

• Directed graph (not necessarily acyclic) - natural choice for networks

• 3 types of nodes:

– classes: entities in the domain of discourse, e.g. proteins, diseases, etc (URIs)

– instances: particular interpretations/views of entities, serves to introduce

metadata into the graphs

– attributes: qualities, quantities, etc (literals)

• Multiple types of edges (properties), semantically defined externally (URIs)

• Basic unit of information: subject - predicate - object, a.k.a. triple

• Triples are organized as graphs, 2 types of graphs in BGW:

– defining entities

– defining relations among entities, e.g. protein-protein interaction, TF-TG

interactions, etc.


BioGateway Design- data model -

Examples BGW 3 (beta):• Entity class

– http://rdf.biogateway.eu/prot/Q9Y6T7-2

• Entity instances– http://identifiers.org/ensembl/ENSP00000386066

– http://identifiers.org/refseq/NP_663733.1

• Bridge class– http://rdf.biogateway.eu/prot-obo/Q9Y6T7-2--GO_0004143

• Bridge instances– http://rdf.biogateway.eu/prot-obo/Q9Y6T7-2--GO_0004143#goa


BioGateway Design- new data model -

Examples BGW 3.0 (coming soon):• Entity class:- http://rdf.biogateway.eu/prot/9606/UPI0000033B9C

• Entity instances:- http://purl.obolibrary.org/obo/PR_Q9Y6T7-2

- http://identifiers.org/ensembl/ENSP00000386066

- http://identifiers.org/refseq/NP_663733.1

• Bridge class:- http://rdf.biogateway.eu/prot-obo/9606/UPI0000033B9C--GO_0004143

• Bridge instance:- http://rdf.biogateway.eu/prot-obo/9606/UPI0000033B9C--

GO_0004143#goa


Future developments

Publication plan:

Short-term (next months)

• publish BioGateway 3.0 and the App:

– human protein/gene network building

Medium term (before summer 2019):

• publish BioGateway 3.1

– ENSEMBL proteomes

Longer term (before end of 2019?)

• publish BioGateway 3.2:

– content from signaling databases (e.g. Signor)


Co-evolution of BioGateway and the App

• Translating App queries to SPARQL posed some constraints

on the BioGateway data model

• Proteins and genes are not really the same and need

deconvolution

• Regulatory networks need to be grounded on transcription

factor - target gene interactions, in the broadest sense:

include text mining results

• Such data in turn implicated further wishes for the App:

– Network inspection triggers hub and cluster-based user focus

on conspicuous nodes and relationships

• node expansion

• link provenance

• provenance checking: curation

• Mechanism to stimulate network-based community curation

BioGateway

backend

Cytoscape

App

user

feedback


Co-evolution of BioGateway and the App

• Translating App queries to SPARQL posed some constraints

on the BioGateway data model

• Proteins and genes are not really the same and need

deconvolution

• Regulatory networks need to be grounded on transcription

factor - target gene interactions, in the broadest sense:

include text mining results

• Such data in turn implicated further wishes for the App:

– Network inspection triggers hub and cluster-based user focus

on conspicuous nodes and relationships

• node expansion

• link provenance

• provenance checking: curation

• Mechanism to stimulate network-based community curation


www.biogateway.eu

http://www.biogateway.eu/

The BioGateway Infotriples of the type:

Protein X

Protein Set

Gene Y

Gene Set

Protein Y

Protein Set

Protein Y

Protein Set

molecularly interacts with

encodes

Protein X

Protein SetTerm Yannotated with GO term

Protein X

Protein SetGene Y

Gene Settranscriptionally regulates

Protein X

Protein SetDisease Y

Disease Setinvolved in

IntAct

GOA

TRRUST

TFactS

Signor

ExTRI

UniProt

OMIM

UniProt

Ensembl


The BioGateway App

- specifying a wish -


• Give me all the proteins annotated with GO term X

• Give me all the proteins annotated with GO term Y

• Select from this the subset that shows a physical interaction

• Identify the genes that encode this protein subset

• Identify the transcription factors that regulate these genes

• Show only those transcription factors that are themselves

annotated with GO term Y

The BioGateway App

- a list of wishes -


The BioGateway App

- a list of wishes -


The BioGateway App- the SPARQL way -

#QUERY <http://www.semantic-systems-biology.org/biogateway/endpoint>BASE <http://rdf.biogateway.eu/graph/>SELECT DISTINCT ?A ?B ?C ?D ?E WHERE {GRAPH <prot2onto> {

?A <http://purl.obolibrary.org/obo/RO_0002331> <http://purl.obolibrary.org/obo/GO_0016567> .?B <http://purl.obolibrary.org/obo/RO_0002331> <http://purl.obolibrary.org/obo/GO_0000082> .

}GRAPH <prot2prot> {

?A <http://purl.obolibrary.org/obo/RO_0002436> ?B .}GRAPH <gene> {

?C <http://semanticscience.org/resource/SIO_010078> ?A .}GRAPH <gene> {

?D <http://semanticscience.org/resource/SIO_010078> ?B .}GRAPH <tfac2gene> {

?E <http://purl.obolibrary.org/obo/RO_0002428> ?C .?E <http://purl.obolibrary.org/obo/RO_0002428> ?D .

}GRAPH <prot2onto> {

?E <http://purl.obolibrary.org/obo/RO_0002331> <http://purl.obolibrary.org/obo/GO_0000082> .}}FILTER(?A!=?C)FILTER(?A!=?B)FILTER(?A!=?E)FILTER(?A!=?D)FILTER(?C!=?B)FILTER(?C!=?E)FILTER(?C!=?D)FILTER(?B!=?E)FILTER(?B!=?D)FILTER(?E!=?D)

}


The BioGateway App- the result as Cytoscape network -


The BioGateway App


The BioGateway App

one transcription factor that

rules them all – G1/S

Set of genes coding for proteins related to G1/S and ubiquitination

Set of proteins related to G1/S and ubiquitination

GO term G1/S

GO term

ubiquitination


The BioGateway App

confidence level 1,0


The BioGateway App

confidence level 1


The BioGateway App

confidence level 0,5


How do we see this being used?- address various questions -

?

protein

or

gene

result

setnetwork

what does

it connect

to?

how are

they

connected?

Can I link my

network with

gene

regulation?

Did I miss

anything?

Can I enrich

with signaling

causality?

(2019)


How do we see this being used in workflows?

- possible uses -

protein

or genequery

list

network

(bulk)

query

queryimport

network

seed

network

expanded

network

other

Cytoscape

work

copy

type

network export

or merge


network building in cytoscape using the biogateway app · sparql query example. biogateway app...

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