Bio2RDF : A Semantic Web Atlas of post genomic knowledge about Human and Mouse

BIO2RDF : A Semantic Web Atlas of post genomic knowledge about

Human and Mouse

François Belleau, Nicole Tourigny, Benjamin Good and Jean Morissette

● Centre de Recherche du CHUL, Université Laval● Département d'informatique et de génie logiciel, Université Laval

Vaugondy, Louis XV geograph, view of the world in the 18th century

Google Map view of the world in the 21th century

Evry, June 27, 2008 CHUL research center Laval University 4

Outline

Introduction

− Problem definition− Proposed approach− The 4 rules of linked data− Related Work

Results

− Bio2RDF first knowledge map− Semantic ranking

Paget query demo with SPARQL

Future work and Conclusion


Problem definition

● The objective of data integration is to make data distributed over a number of distinct, heterogeneous databases accessible via a single interface [Davidson 1995].

● We already use global text search engine on the web (Google, Yahoo).

● There is many specialized integrated search tools in bioinformatics (NCBI Entrez, EBI search, KEGG GenomeNet).


What is known about « Paget disease» ?

but first ...

What is known about the mouse and human

genomes ?


Popular web search engines without semantic


Some Bioinformatics integrated search tools

● EMBL-EBI EB-eye search● KEGG GenomeNet● NCBI Entrez


EMBL-EBI search


NCBI Entrez life science searchacross databases


KEGG GenomeNet search


Bio2RDF search

What is known about Paget disease in the mouse and

human genomes ?


Proposed approach

● Apply the semantic web model to data integration in bioinformatics;

● Use a PageRank [Brin 1998] variation adapted to semantic graph, a method analog to Aleman-Meza group's work: the LinkRank;

● Adopt standard (RDF, OWL) and use existing software (Sesame, Virtuoso, PiggyBank).


Outline

Introduction


Results





Linked data 4 rules

http://www.w3.org/DesignIssues/LinkedData

http://www.w3.org/DesignIssues/LinkedData


Rule #1: Use URIs as names for things.● Using normalized identifier to name

concept is already a reality in biology domain.

● Hexokinase is GO:0004396● Definition :

− Catalysis of the reaction: ATP + D-hexose = ADP + D-hexose 6-phosphate.

● Synonym of EC:2.7.1.1


Rule #2 : Use HTTP URIs so that people can look up those names.● Derefencable URL ● The Banff Manifesto rule for URN

− urn:bm:public_namespace:private_identifier● Normalized URL according to Banff

Manifesto: http://bio2rdf.org/public_namespace:private_identifier

● http://bio2rdf.org/go:0004396

http://bio2rdf.org/public_namespace:private_identifier


Rule #3 When someone looks up a URI, provide useful information.

● http://bio2rdf.org/go:0004396 returns the RDF graph of this topic

http://bio2rdf.org/go:0004396


Rule #4 :Include links to other URIs so that they can discover more things.

●Openess Ratio > 0 (to be defined)


Outline

Introduction


Results





Related work

● DBPedia● YeastHub● UniProt● HCLS linked data● Bio2RDF architecture


Related work – Linked data map

http://wiki.dbpedia.org/Interlinking

http://wiki.dbpedia.org/Interlinking


Related work – Linked data map

● If we were to draw a map of the existing relations between linked data from bioinformatics database providers, what would it look like?

● Could we measure the amount of post genomic knowledge available related to a mouse or human genome sequence?

● Could it help answer the what is known question?


Related work – YeastHub


Related work – UniProt beta


Related work – HCLS demo


Bio2RDF architecture


Bio2RDF actual datasources loaded in the Atlas graph


Outline

Introduction


Results




What is known about human and mouse genome in 2008?


What is Bioinformatics linked data ?


http://bio2rdf.org/map

Bio2RDF linked data map is a first answer attempt

http://bio2rdf.org/map


Outline

Introduction


Results





Semantic Web Ranking

● Openess Ratio● Averange Link Rank● Semantic weight

Openess Ratio

Average Link Rank

Semantic Weight


The semantic mashup effect

OR = 0ALR = 2MeSH

OR = 1ALR = 1GeneID

OR = 0,5ALR = 1,5PubMed

mean OR = 0,5mean ALR = 1,5


The semantic mashup effect

OR = 0ALR = 2,3

MeSH

OR = 1ALR = 1GeneID

OR = 0,5ALR = 1,5PubMed

mean OR = 0,4mean ALR = 1,6

Bio2RDF statistics by datasource

Bio2RDF actual 30 datasources

MeSH : OR = 0

Pubmed: OR = 0,5

GeneID : OR = 1

Bio2RDF : OR = 0,630 datasources, 225 namespaces


Knowledge gain of 0,19

From 0,77 to 0,58


Bio2RDF Semantic Web Atlas in numbers

● 30 different datasources, 30 different namespaces

− go, geneid, uniprot, pubmed, pdb, reactome, omim, etc.

● 195 namespaces referencing non-rdfized datasource

− cog, genethon, tigr, cath, goa, etc.

● 8 millions topics● 65 millions triples● 973 Mo, size of N3 format compressed data

− http://bio2rdf.org/download/bio2rdf-atlas-080414.n3.gz

http://bio2rdf.org/download/bio2rdf-atlas-080414.n3.gz


Bio2RDF Semantic Web Atlas in statistics

● Openess Ratio (OR) of 0.58● Averange Link Rank (ALR) of 4.7● 8 millions topics are connected by 19 millions

relations within the graph● 58 % of URIs are referencing the open world

outside the graph● 19 % of knowledge gain because of the mashup

effect


Outline

Introduction


Results





Bio2RDF search demo with SPARQL

What is known about Paget disease in the mouse and

human genomes ?

Submitted athttp://bio2rdf.org:8890/sparql

http://bio2rdf.org:8890/sparql


Submit the SPARQL query to Virtuoso


SPARQL query in a URL

http://bio2rdf.org:8890/sparql?defaultgraphuri=&query=CONSTRUCT+%7B%0D%0A%3Fs1+%3Fp1+%3Fo1+.%0D%0A%3Fs1+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22rdfsyntaxns%23type%3E+%3Ftype+.+%0D%0A%3Fs1+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdfschema%23label%3E+%3Flabel.+%0D%0A%3Fs1+%3Chttp%3A%2F%2Fbio2rdf.org%2Fbio2rdf%23linkRank%3E+%3FlinkRank.+%0D%0A%7D%0D%0AWHERE+%7B%0D%0A%3Fs1+%3Fp1+%3Fo1+.+%0D%0A%3Fo1+bif%3Acontains+%22paget%22+.%0D%0A%3Fs1+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22rdfsyntaxns%23type%3E+%3Ftype+.+%0D%0A%3Fs1+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdfschema%23label%3E+%3Flabel.+%0D%0A%3Fs1+%3Chttp%3A%2F%2Fbio2rdf.org%2Fbio2rdf%23linkRank%3E+%3FlinkRank.+%0D%0A%7D%0D%0A%0D%0A%0D%0A%0D%0A&format=application%2Frdf%2Bxml&debug=on


View results in HTML


View results with Sesame


View results with Piggy Bank


Outline

Introduction


Results





Future works

● Create new rdfizer for public data source;● Build a community of users around the

Bio2RDF project (visit the Google group);● Connect more datasources to Bio2RDF by

building collaboration between research groups;

● Offer a public SPARQL endpoint based on Virtuoso server :

− http://bio2rdf.org:8890/sparql

http://bio2rdf.org:8890/sparql


Conclusion

Those devices in the hands of scientists have forged our understanding of nature.


Conclusion

We have started to map the knowledge space of biology, we have a first impression of what the bioinformatics nation looks like, the time has come to explore it, the time has come to build

the knowledgescope.


Acknowlegments

Jean MorissetteNicole TourignyBenjamin Good

Bioinformatics lab’s team at CHUL Research Center :Philippe Rigault

Marc-Alexandre Nolin

Thanks to the essential annotators and data providerand to developers of open source project :

Sesame, Virtuoso and PiggyBank.François Belleau was a recipient of a studentship from Génome Québec. This work have been financed in part by the Atlas of Genomic Profiles of SteroidAction, a Genome Canada project. BMG is funded by Pacific Century and University of British Columbia Graduate Fellowships.


http://bio2rdf.orgQuery the graph with SPARQL http://bio2rdf.org:8890/sparql

Download our software http://sourceforge.net/projects/bio2rdf/

Download the Atlas data in N3 format http://bio2rdf.org/download

Join our group http://groups.google.ca/group/bio2rdf

Contact us at [email protected]

Technology

Bio2RDF : A Semantic Web Atlas of post genomic knowledge about Human and Mouse