1. Predicting Druglikeness and Toxicity from Integrated Data and Services on the Life Science Semantic Web 1 Michel Dumontier, Ph.D. Associate Professor of Bioinformatics, Department of Biology, School of Computer Science, Institute of Biochemistry, Carleton University Professeur Associé, Département d’informatique et de génielogiciel, Université Laval Ottawa Institute of Systems Biology Ottawa-Carleton Institute of Biomedical Engineering 2011-EBI-Industry-SW::Dumontier
2. Is caffeine a drug-like molecule? Is acetaminophen toxic? 2 2011-EBI-Industry-SW::Dumontier
3. Finding the right information to answer a question is hard and sometimes requires a sophisticated workflow 3 2011-EBI-Industry-SW::Dumontier
5. What if we could answer a question by automatically building a knowledge base using both data and services? 5 2011-EBI-Industry-SW::Dumontier
6. The Semantic Web is a web of knowledge. 6 It is about standards for publishing, sharing and querying knowledge drawn from diverse sources It enables the answering of sophisticated questions 2011-EBI-Industry-SW::Dumontier
10. determine whether caffeine satisfies the requirements of being ‘drug like’ 7 2011-EBI-Industry-SW::Dumontier
11. Lipinski Rule of Five Rule of thumb for druglikeness (orally active in humans) (4 rules with multiples of 5) mass of less than 500 Daltons fewer than 5 hydrogen bond donors fewer than 10 hydrogen bond acceptors A partition coefficient value between -5 and 5 We need a more formal (machine understandable) description of a ‘drug-like molecule’ which specifies values for chemical descriptors 8 2011-EBI-Industry-SW::Dumontier
12. ontology as a strategy to formally represent knowledge 9 2011-EBI-Industry-SW::Dumontier
13. The Web Ontology Language (OWL) Has Explicit Semantics Can therefore be used to capture knowledge in a machine understandable way 10 2011-EBI-Industry-SW::Dumontier
14. Semanticscience Integrated Ontology (SIO) OWL2 ontology 900+ classes covering basic types (physical, processual, abstract, informational) with an emphasis on biological entities 169 basic relations (mereological, participatory, attribute/quality, spatial, temporal and representational) axioms can be used by reasoners to generate inferences for consistency checking, classification and answering questions about life science knowledge embodies emerging ontology design patterns specifies the representation of knowledge dereferenceable URIs searchable in the NCBO bioportal Available at http://semanticscience.org/ontology/sio.owl 11 2011-EBI-Industry-SW::Dumontier
16. The Chemical Information Ontology (CHEMINF) 100+ chemical descriptors 50+ chemical qualities Relates descriptors to their specifications, the software that generated them (along with the running parameters, and the algorithms that they implement) Contributors: Nico Adams, Leonid Chepelev, Michel Dumontier, Janna Hastings, EgonWillighagen, Peter Murray-Rust, Cristoph Steinbeck 13 http://semanticchemistry.googlecode.com 2011-EBI-Industry-SW::Dumontier
17. Molecular structure can be represented using a SMILES string, which is a common representation of the chemical graph 14 Cn1cnc2n(C)c(=O)n(C)c(=O)c12 ball & stick model for caffeine SMILES string for caffeine 2011-EBI-Industry-SW::Dumontier
18. Lipinski Rule of Five Empirically derived ruleset for druglikeness (4 rules with multiples of 5) mass of less than 500 Daltons fewer than 5 hydrogen bond donors fewer than 10 hydrogen bond acceptors A partition coefficient value between -5 and 5 A formal description using OWL: 15 2011-EBI-Industry-SW::Dumontier
19. What we then need are services that will consume SMILES strings and annotate the molecule with the required chemical descriptors 16 then we can reason about whether it satisfies the drug-likeness definition 2011-EBI-Industry-SW::Dumontier
20. Semantic Automated Discovery and Integration http://sadiframework.org SADI is a framework to create Semantic Web services using OWL classes as service inputs and outputs Mark Wilkinson, UBC Michel Dumontier, Carleton University Christopher Baker, UNB 17 2011-EBI-Industry-SW::Dumontier
21. Create code stubs using the ontology Publish the ontology to a web-accessible location http://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl Make sure that the class names are resolvable (easy when using the hash notation) http://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#smiles-molecule http://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#logp-molecule http://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#hbdc-molecule http://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#hdba-molecule http://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#lipinksi-druglike-molecule Download/checkout the code http://sadiframework.org Run the code generator (Java, Perl, python) specify the URIs that correspond to input and output types Implement the functionality We used the Chemistry Development Kit (CDK) to implement 4 services 18 2011-EBI-Industry-SW::Dumontier
22. Responds to a GET operation by providing the service description in RDF conforms to Feta (BioMoby, myGrid) 19 curl http://cbrass.biordf.net/logpdc/logpc <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:j.0="http://www.mygrid.org.uk/mygrid-moby-service#" > <rdf:Descriptionrdf:about=""> <j.0:hasServiceDescriptionText>no description</j.0:hasServiceDescriptionText> <j.0:hasServiceNameText rdf:datatype="http://www.w3.org/2001/XMLSchema#string">logpc</j.0:hasServiceNameText> <j.0:hasOperation rdf:resource="#operation"/> <rdf:typerdf:resource="http://www.mygrid.org.uk/mygrid-moby-service#serviceDescription"/> </rdf:Description> <rdf:Descriptionrdf:about="#input"> <j.0:objectType rdf:resource="http://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#smilesmolecule"/> <rdf:typerdf:resource="http://www.mygrid.org.uk/mygrid-moby-service#parameter"/> </rdf:Description> <rdf:Descriptionrdf:about="#operation"> <j.0:outputParameter rdf:resource="#output"/> <j.0:inputParameter rdf:resource="#input"/> <rdf:typerdf:resource="http://www.mygrid.org.uk/mygrid-moby-service#operation"/> </rdf:Description> <rdf:Descriptionrdf:about="#output"> <j.0:objectType rdf:resource="http://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#alogpsmilesmolecule"/> <rdf:typerdf:resource="http://www.mygrid.org.uk/mygrid-moby-service#parameter"/> </rdf:Description> </rdf:RDF> 2011-EBI-Industry-SW::Dumontier
23. Responds to a POST containing service input with a service output in RDF 20 The query is in RDF: <rdf:RDFxmlns="http://semanticscience.org/sadi/ontology/caffeine.rdf#" xmlns:so="http://semanticscience.org/sadi/ontology/lipinskiserviceontology.owl#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:sio="http://semanticscience.org/resource/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#"> <so:smilesmoleculerdf:about="http://semanticscience.org/sadi/ontology/caffeine.rdf#m"> <sio:SIO_000008 rdf:resource = "http://semanticscience.org/sadi/ontology/caffeine.rdf#msmiles"/> </so:smilesmolecule> <sio:CHEMINF_000018 rdf:about = "http://semanticscience.org/sadi/ontology/caffeine.rdf#msmiles"> <sio:SIO_000300 rdf:datatype="xsd:string">Cn1cnc2n(C)c(=O)n(C)c(=O)c12</sio:SIO_000300> </sio:CHEMINF_000018> </rdf:RDF> The response is in RDF: <rdf:Descriptionrdf:about="http://semanticscience.org/sadi/ontology/caffeine.rdf#mdalogp"> <rdf:typerdf:resource="http://semanticscience.org/resource/CHEMINF_000251"/> <j.0:SIO_000300 rdf:datatype="http://www.w3.org/2001/XMLSchema#double">-0.4311000000000006</j.0:SIO_000300> </rdf:Description> 2011-EBI-Industry-SW::Dumontier
24. 61 Chemical Semantic Web Services these and an increasing number of semantic web services are registered at http://sadiframework.org/registry/services/ 21 2011-EBI-Industry-SW::Dumontier
26. 23 Semantic Health and Research Environment SHARE is an application that execute (SPARQL) queries as workflows over SADI Services 2011-EBI-Industry-SW::Dumontier
27. “Reckoning”dynamic discovery of instances of OWL classes through synthesis and invocation of a Web Service workflow capable of generating data described by the OWL class restrictions, followed by reasoning to classify the data into that ontology 24 2011-EBI-Industry-SW::Dumontier
32. Resource Description Framework (RDF) Allows one to talk about anything Uniform Resource Identifier (URI) can be used as entity names Bio2RDF specifies the naming convention http://bio2rdf.org/uniprot:P05067 is a name for Amyloid precursor protein http://bio2rdf.org/omim:104300 is a name for Alzheimer disease uniprot:P05067 omim:104300 29
33. Life Science Dataset Registry Coordinates Naming Provides stable URI patterns for records and the entities they describe. Directory Service ~1500 datasets & dozens of resolvers. Discovery Service Registry links entities to records and their representations (RDF/XML, HTML, etc) and provider (Bio2RDF, Uniprot) Redirection Service Automatic redirection to data provider document 30 Stanford : 22-04-2010
34. Bio2RDF is now serving over 40 billion triples of linked biological data 31 2011-EBI-Industry-SW::Dumontier
35. Bio2RDF is a framework to create and provision linked data networks 32 Francois Belleau, Laval University Marc-Alexandre Nolin, Laval University Peter Ansell, Queensland University of Technology Michel Dumontier, Carleton University
36. Bio2RDF is part of a growing web of linked data 33 “Linking Open Data clouddiagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/” 2011-EBI-Industry-SW::Dumontier
37. something you can lookup or search for with rich descriptions 34 2011-EBI-Industry-SW::Dumontier
38. 35 SPARQL is the newcool kid on the query block SQLSPARQL 2011-EBI-Industry-SW::Dumontier
43. Benefits Data remains distributed – as the internet was meant to be! Data is not “exposed” as a SPARQL endpoint greater provider-control over computational resources Service invocation is straightforward and matchmaking by reasoning about ontology-based input/output descriptions 40 2011-EBI-Industry-SW::Dumontier
44. Is acetaminophen toxic? Classical approaches involve decision trees or machine learning over validated data. Algorithms are often proprietary, even by the regulatory agencies Issues around which data was used, and what the informative parameters are, and how easily can new information affect the outcomes? 41 2011-EBI-Industry-SW::Dumontier
45. OWLED2011 : Large-Scale Boolean Feature Based Trees as OWL ontologies 42 2011-EBI-Industry-SW::Dumontier
47. Summary Semantic Web technologies offer tantalizing ability to create and share data and services for drug discovery Bio2RDF provides linked life science data SADI provides a framework to provide semantic web services SHARE allows us to simultaneously query and reason about data and services represented using RDF/OWL Expressive ontologies can be used to make toxicity decisions transparent 44 2011-EBI-Industry-SW::Dumontier
48. 45 Acknowledgements CHEMINF Group Leo Chepelev Janna Hastings EgonWillighagen Nico Adams Bio2RDF: Peter Ansell, Francois Belleau, Allison Callahan, Jacques Corbeil, Jose Cruz-Toledo, Alex De Leon, Steve Etlinger, James Hogan, Nichealla Keath, Jean Morissette, Marc-Alexandre Nolin, Nicole Tourigny, Philippe Rigault and, Paul Roe SADI: Christopher Baker, Melanie Courtot, Jose Cruz-Toledo, Steve Etlinger, Nichealla Keath, Artjom Klein, Luke McCarthy, Silvane Paixao, Ben Vandervalk, Natalia Villanueva-Rosales, Mark Wilkinson Toxicity Group Leo Chepelev Dana Klassen 2011-EBI-Industry-SW::Dumontier
