Bio2RDF: Towards A Mashup To Build Bioinformatics Knowledge System

Towards A M ashup To Build
Bioinformatics K nowledge System

François Belleau, M arc-Alexandre Nolin,
Nicole Tourigny, Philippe Rigault, Jean M orissette

Département d'informatique et de génie logiciel
Université Laval

Presentation Plan
K nowledge integration vision


Bio2RDF architecture


RDFization of knowledge


Normalization of U RI


Parkinson E xample Demo


Conclusion


Banff, May 8, 2007 CHUL research center - Laval University 2

From the RDF inventor :
quot;Wouldn't it be great if you were able to
organize all this information based on
your own terms, instead of based on the
application you use to access the
information ?” (1999)
Ramanathan V. Guha

From WikiPedia :
Mashup (web application hybrid)

A mashup is a website or application that
combines content from more than one source
into an integrated experience.(2007)


Sir Berners-L ee’s vision of semantic web
« The Semantic Web is not a separate
Web but an extension of the current
one, in which information is given well-
defined meaning, better enabling
computers and people to work in
cooperation. »
Scientific Americain, 2001
Tim Berners- Lee

http://www.w3.org/2006/Talks/0404-mit-tbl/


Bio2RDF starting vision at ISM B 2005

Too many knowledge sources


available for life science scientists
Too many formats (text, X M L ,


HTM L )
New source each day with


specialized tool or web interface
Integration problem recognized by


global community

T hanks to Chr istopher Baker, Eric
Neum ann, Kei Cheun g and
Johan ne Luciaono for their ideas.


The knowledge integration problem in
bioinformatics

From the BioPAX group(2004) From Carol Goble at ISW C 2005


Integration methods in bioinformatics
1) Davidson 1995
“Transform data to the federated database on
demand”

2) Köhler 2003
“In different databases the same things can be
given different names”

3) Stein 2003
“link integration, view integration and data
warehousing”


Data warehouse approaches

url

http://www.ncbi.nlm.nih.gov/Database/ http://www.genome.jp/dbget/dbget.links.html


Bio2RDF ’s approach
to knowledge integration :

“Solve the problem of kn owledge
in tegration in biology by applying
a sem antic web approach.”


Other semantic web projects


Bio2RDF ’s design rules

2. Convert document to RDF format;
3. U se of a triplestore technology (sesame,
virtuoso, oracle);
4. Normalize U RIs;
5. Build a mashup as needed to answer speciﬁc
question (elmo);
6. Query the mashup with SeRQL or SPARQL .

Bio2RDF ’s architecture

#1

#5

#4

#2

#3

#6


Bio2RDF ’s knowledge sources


RDF conversion statistics
Data
Numb er of RDF
sourc LSID example Size of data converted
documents
e
go go:0000001 22 961 507 963 321
kegg path:aae00010 35 257 1 038 593 137
14 292 8 902 205
kegg cpd:c00001
438 724 210 458 897
mgi mgi:96103
17 359 573 639 380
ncbi omim:100050
ncbi geneid:1 2 744 786 67 225 535 082
obo obo's 59 name spaces 279 720 216 007 267
pdb pdb:100d 34 421 16 309 651 935
4 177 176 29 453 203 064
uniprot uniprot:A0A0 00
5 020 2 844 058
uniprot enzyme:1.-.-.-
191 664 364 728 083
uniprot pubmed:100133
uniprot taxonomy :10 337 564 125 630 659
uniprot niref:UniRef100_A0A000
u 7 990 452 14 865 490 144
… … … …


OpenRDF ’s software
http://www.openrdf.org/


RDF of geneid:15275

rdf:about
•

rdfs:label
•

dc:identifier, title, created
•

bio2rdf:lsid
•

bio2rdf:url
•

bio2rdf:synonym
•

bio2rdf:xRef
•


RDFizer
To rdﬁze: T o convert existin g
docum ent in to RD F form at.

efetch rdﬁzer


How to rdﬁze

From HTM L pages (prosite:ps00101)
•
From X M L documents using X SLT
•
(path:mmu00010)
From X M L documents using X Path and
•
J STL (geneid:15275)
From direct SQL access
•
(ensembl:ensmusg00000025875 )
From RDF document (uniprot:p26838 )
•
From Text ﬁles (cpd:c00001)
•


1) prosite:ps00101 from html using a regex


2) Kegg’s path:mmu00010 from X M L using X SL


3) ensembl:ensmusg00000025875 from SQL


4) uniprot:p26838 from RDF using SeRQL


One reality, many names
Different namespace identifier
●

pubmed:11992264 vs pmid:11992264
Uppercase and lowercase
●

uniprot:p26838 vs uniprot:P26838
Version number
●

genbank:ac008393 vs genbank:ac008393.7
Total id length
●

go:0032283 vs go:32283


RDF izing docum ent is not enough
we also need norm alized URIs.

http:/ / bio2rdf.org/ namespace:id
http:/ / bio2rdf.org/ pubmed:11992264
http:/ / bio2rdf.org/ uniprot:p26838
http:/ / bio2rdf.org/ genbank:ac008393
http:/ / bio2rdf.org/ go:0032283

U RI Normalization rules
Different namespace identifier
●

We resolve namespace synonymy with a urlrewrite rule, for
example pubmed and pmid.
Uppercase and lowercase
●

We write every U RI in lowercase
Version number
●

A owl:sameAs predicate is use to link the different versions
of a document.
Total id length
●

A ﬁxed length is determine for id.


U rl Rewrite Filter
http://tuckey.org/urlrewrite/
< rule>
< from> ^/ search:(.*?)@pubmed< / from>
< to> / rdfizer/ ncbi-entrez2rdf.jsp?db= pubmed&query= $1< / to>
< / rule>
< rule>
< from> ^/ pubmed:(.*)< / from>
< to> / rdfizer/ ncbi-pubmed2rdf.jsp?id= $1< / to>
< / rule>
< rule>
< from> ^/ pmid:(.*)< / from>
< to> / rdfizer/ lsid-sameas2rdf.jsp?from= pmid:$1&to= pubmed:$1< / to>
< / rule>

< rule>
< from> ^/ (.*):(.*)< / from>
< to type= quot;redirectquot;> http:/ / bio2rdf.org/ $1:$2< / to>
< / rule>


U RL vs L SID
http:/ / bio2rdf.org/ uniprot:p26838
owl:sameAs
urn:lsid:uniprot.org:uniprot:p26838

http:/ / bio2rdf .org/ un ipr ot:p26838

http:/ / bi o2rdf .org/ ur n:lsid:uni pr ot.or g:unipr ot:p2 6838


Our method to answer question

T o answer a very specialized
question, we build a speciﬁ c
kn owledge base (the mash up
stored in a RDF triplestore)
and then query it wi th SeRQL.


Parkinson examples
1. What is the semantic network of
OMIM records describing Parkinson’s
disease?
2. Which MeSH terms are mostly cited
in Parkinson’s disease publications?
3. What genes related to Parkinson’s
disease are involved in pathways
according to Kegg ?


Time for demo !


The big everything about parkinson
http:/ / localhost:8080/ bio2rdf/ search:parkinson@omim
http:/ / localhost:8080/ bio2rdf/ search:parkinson@geneid
http:/ / localhost:8080/ bio2rdf/ search:parkinson@uniprot
http:/ / localhost:8080/ bio2rdf/ search:parkinson@kegg
http:/ / localhost:8080/ bio2rdf/ load:pubmed
http:/ / localhost:8080/ bio2rdf/ sameas:hsa-geneid
http:/ / localhost:8080/ bio2rdf/ learn:geneid
http:/ / localhost:8080/ bio2rdf/ load:cpd
http:/ / localhost:8080/ bio2rdf/ load:reactome
http:/ / localhost:8080/ bio2rdf/ load:biopax-xref
http:/ / localhost:8080/ bio2rdf/ load:chebi
http:/ / localhost:8080/ bio2rdf/ load:obo-xref
http:/ / localhost:8080/ bio2rdf/ sameas:keggcompound-cpd

1.700 K triples
97 M bytes in turtle format
in 90 minutes


Third exemple SeRQL query
What genes related to Parkinson’s disease are involved in
pathways according to Kegg ?
SELECT
GeneticDisorder-label, Gene-label, pathway-label
FROM
{GeneticDisorder} rdf:type {<http://bio2rdf.org/omim#GeneticDisorder>},
{GeneticDisorder} rdfs:label {GeneticDisorder-label},
{GeneticDisorder} <http://www.w3.org/2002/07/owl#sameAs> {sameAs},
{Gene} <http://bio2rdf.org/bio2rdf#xRef> {sameAs},
{Gene} rdfs:label {Gene-label},
{Gene2} <http://www.w3.org/2000/01/rdf-schema#seeAlso> {Gene},
{xobject} <http://bio2rdf.org/kegg#xobject> {Gene2},
{xentry1} <http://bio2rdf.org/kegg#xentry1> {xobject},
{pathway} <http://bio2rdf.org/kegg#xrelation> {xentry1},
{pathway} rdfs:label {pathway-label}
WHERE
GeneticDisorder-label like quot;*PARKINSON*quot;


Query result


Conclusion


Before Bio2RDF integration


Our main results

● RDF is a framework that enables a very simple
thing: scalability of the knowledge base complexity.

● The Bio2RDF project proposes to keep complexity
in the bioinformatics knowledge space under
control by applying this proven web semantic
approach.


Now with Bio2RDF semantic integration


Bio2RDF ’s vision of knowledge map


Bio2RDF ’s map of distributed
bioinformatics knowledge

http://bio2rdf.org/bio2rdf-2007-02.owl

M ap of semantic resource


M ontreal’s subway map


Bio2RDF ’s actual knowledge map


Achievement
Public data + open source software + rdf
technology + rdﬁzer + normalized U RIs =
Bio2RDF knowledge integration;
A bioinformatic-integration ontology wont exist if
it is not adopted by the community, bio2rdf.owl is
just a proposed starting point;
46 millions RDF documents are now available at
http:/ / bio2rdf.org.


Bio2RDF project provides open
source RDFizer to the community.
So much style need to be rdﬁzed, if
you are interested to contribute,
join us!

Now lets build the big knowledge
map of bioinformatics…

Final words

Please, tell Sir Tim Berners-L ee that he was right
‘semantic web in bioinformatics’ is a k ille r a p p
to illustrate all the potential of the semantic web.
And also, tell M ark W ilkinson that semantic web
in bioinformatics won’t be full of cr e e p s if we
organize it like we did…


Thanks
Jean M orissette
Nicole Tourigny
Philippe Rigault

Bioinformatics lab’s team at CHU L Research Center

M any open source communities
(OpenRDF, Simile’s project, Tomcat, J STL and many more)

W 3C Bio-RDF G roup

G énome Québec
G énome Canada

Visit http://bio2rdf.org
Download http://sourceforge.net/projects/bio2rdf/

Discover http://bio2rdf.org/bio2rdf-2007-02.owl

Contact us at bio2rdf@gmail.com

Bio2RDF: Towards A Mashup To Build Bioinformatics Knowledge System

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