Scientific Lenses over Linked Data An approach to support multiple integrated views
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The document discusses scientific lenses as a method to enhance drug discovery by integrating various data sources like ChemSpider, DrugBank, and ChEMBL for comparative analysis of compounds. It covers the architecture of the Open PHACTS platform, the concept of identity resolution for chemical compounds, and the complexities in data representation due to variations like stereoisomers. It emphasizes the need for flexible data linking and specifies that the equivalence of chemical data can depend on the context of the inquiry.
![Mapping Generation
✔
ops:OPS437281
has_stereoundefined_parent
[ci:CHEMINF_000456]
ops:OPS380297
is_stereoisomer_of
[ci:CHEMINF_000461]
ops:OPS380292
Other relationships
• has part
• is tautomer of
• uncharged counterpart
• isotope
…
16 October 2014 Scientific Lenses – A. J. G. Gray 21](https://image.slidesharecdn.com/sci-lenses-bolzano-141015153821-conversion-gate01/85/Scientific-Lenses-over-Linked-Data-An-approach-to-support-multiple-integrated-views-22-320.jpg)
![Lenses: Under the hood
GRAPH <http://rdf.chemspider.com> {
cw:979b545d-f9a9 cheminf:logd ?logd .
?iri cheminf:logd ?logd .
FILTER (?iri = cw:979b545d-f9a9 ||
?iri = cs:2157 ||
?iri = chembl:1280 ||
?iri = db:db00945 )
}
GRAPH <http://…
Q, L1 Q’
Query
Expander
Service
Identity
Mapping
Service
(BridgeDB)
Mappings
Profiles
cw:979b545d-f9a9, L1
[cw:979b545d-f9a9,
cs:2157,
chembl:1280,
db:db00945]
• Can also be achieved through UNION
• IMS call adds overhead
16 October 2014 Scientific Lenses – A. J. G. Gray 29](https://image.slidesharecdn.com/sci-lenses-bolzano-141015153821-conversion-gate01/85/Scientific-Lenses-over-Linked-Data-An-approach-to-support-multiple-integrated-views-30-320.jpg)
Scientific Lenses over Linked Data An approach to support multiple integrated views
- 1. Scientific Lenses over Linked Data An approach to support multiple integrated views Alasdair J G Gray A.J.G.Gray@hw.ac.uk alasdairjggray.co.uk @gray_alasdair
- 2. Open PHACTS Use Case “Let me compare MW, logP and PSA for launched inhibitors of human & mouse oxidoreductases” Chemical Properties (Chemspider) Launched drugs (Drugbank) Human => Mouse (Homologene) Protein Families (Enzyme) Bioactivty Data (ChEMBL) … other info (Uniprot/Entrez etc.) 16 October 2014 Scientific Lenses – A. J. G. Gray 1
- 3. Discovery Platform Apps Method Calls Domain API Drug Discovery Platform Interactive responses Production quality integration platform 16 October 2014 Scientific Lenses – A. J. G. Gray 2
- 4. App Ecosystem An “App Store”? Explorer Explorer2 ChemBioNavigator Target Dossier Pharmatrek Helium MOE Collector Cytophacts Utopia Garfield SciBite KNIME Mol. Data Sheets PipelinePilot scinav.it Taverna http://www.openphactsfoundation.org/apps.html 16 October 2014
- 5. API Hits April 2013 – March 2014: 15.8m April 2014 – Sept 2014: 14m Total: 29.8 million 16 October 2014 Scientific Lenses – A. J. G. Gray 4
- 6. Linked Data API Drug Target Pathway Disease (1.4) https://dev.openphacts.org/ 16 October 2014 Scientific Lenses – A. J. G. Gray 5
- 7. Open PHACTS Data Source Initial Records Triples Properties ChEMBL 1,481,473 304,360,749 77 DrugBank 19,628 517,584 74 UniProt 564,246 405,473,138 82 ENZYME 6,187 73,838 2 ChEBI 40,575 1,673,863 2 GeneOntology 38,137 2,447,682 26 GOA 661,232 1,765,622,393 15 ChemSpider 1,361,568 215,193,441 23 ConceptWiki 2,828,966 4,291,131 1 WikiPathways 946 1,949,074 34 16 October 2014 Scientific Lenses – A. J. G. Gray 6
- 8. Dataset Descriptions in the Open Pharmacological Space 14 January 2013 Being replaced by W3C HCLS community profile http://tiny.cc/hcls-datadesc-ed OPS Dataset Descriptions – A. J. G. Gray 7
- 9. OPS Discovery Platform Linked Data API (RDF/XML, TTL, JSON) Semantic Workflow Engine VoID Nanopub Db Data Cache (Virtuoso Triple Store) Domain Specific Services Identity Resolution Service Chemistry Registration Normalisation & Q/C Identifier Management Service Indexing Core Platform “Adenosine receptor 2a” P12374 EC2.43.4 CS4532 VoID Db VoID Nanopub Db VoID Db VoID Nanopub Public Content Commercial Public Ontologies User Annotations Apps
- 10. Multiple Identities Andy Law's Third Law “The number of unique identifiers assigned to an individual is never less than the number of Institutions involved in the study” http://bioinformatics.roslin.ac.uk/lawslaws/ GB:29384 P12047 X31045 16 October 2014 Scientific Lenses – A. J. G. Gray 9 Are these the same thing?
- 11. Gleevec®: Imatinib Mesylate Imatinib Imatinib MesylateMesylate YLMAHDNUQAMNNX-UHFFFAOYSA-N ChemSpider Drugbank PubChem 16 October 2014 Scientific Lenses – A. J. G. Gray 10
- 12. Gleevec®: Imatinib Mesylate Imatinib Are these records the same? It depends upon your task! Imatinib MesylateMesylate YLMAHDNUQAMNNX-UHFFFAOYSA-N ChemSpider Drugbank PubChem 16 October 2014 Scientific Lenses – A. J. G. Gray 11
- 13. Genes == Proteins? BRCA1: Chromosome 17 Breast cancer type 1 susceptibility protein http://en.wikipedia.org/wiki/File:Protei n_BRCA1_PDB_1jm7.png http://en.wikipedia.org/wiki/File:BRCA1 _en.png 16 October 2014 Scientific Lenses – A. J. G. Gray 12
- 14. Genes == Proteins? BRCA1: Chromosome 17 Breast cancer type 1 susceptibility protein http://en.wikipedia.org/wiki/File:Protei n_BRCA1_PDB_1jm7.png http://en.wikipedia.org/wiki/File:BRCA1 _en.png Are these records the same? It depends upon your task! 16 October 2014 Scientific Lenses – A. J. G. Gray 13
- 15. Example Use Cases I need to perform an analysis, give me details of the active compound in Gleevec. Which targets are known to interact with Gleevec? 16 October 2014 Scientific Lenses – A. J. G. Gray 14
- 16. Structure Lens I need to perform an analysis, give me Strict Relaxed Analysing Browsing skos:exactMatch (InChI) Scientific Lenses – A. J. G. Gray 15 16 October 2014 details of the active compound in Gleevec.
- 17. Name Lens Which targets are known to interact Strict Relaxed Analysing Browsing skos:closeMatch (Drug Name) skos:exactMatch (InChI) skos:closeMatch (Drug Name) Scientific Lenses – A. J. G. Gray 16 16 October 2014 with Gleevec?
- 18. What is a Scientific Lens? A lens defines a conceptual view over the data Specifies operational equivalence conditions Consists of: Identifier (URI) Title (dct:title) Description (dct:description) Documentation link (dcat:landingPage) Creator (pav:createdBy) Timestamp (pav:createdOn) Equivalence rules (bdb:linksetJustification) 16 October 2014 Scientific Lenses – A. J. G. Gray 17
- 19. Lens Effects: Ibuprofen Ibuprofen consists of two equally active stereoisomers. • Stereoisomers not always represented in data Users wish to retrieve information for any stereoisomer. CHEMBL427526 CHEMBL521 CHEMBL175 16 October 2014 Scientific Lenses – A. J. G. Gray 18
- 20. Default Lens Ibuprofen consists of two equally active stereoisomers. • Stereoisomers not always represented in data Users wish to retrieve information for any stereoisomer. 16 October 2014 Scientific Lenses – A. J. G. Gray 19
- 21. Stereoisomer Lens Ibuprofen consists of two equally active stereoisomers. • Stereoisomers not always represented in data Users wish to retrieve information for any stereoisomer. 16 October 2014 Scientific Lenses – A. J. G. Gray 20
- 22. Mapping Generation ✔ ops:OPS437281 has_stereoundefined_parent [ci:CHEMINF_000456] ops:OPS380297 is_stereoisomer_of [ci:CHEMINF_000461] ops:OPS380292 Other relationships • has part • is tautomer of • uncharged counterpart • isotope … 16 October 2014 Scientific Lenses – A. J. G. Gray 21
- 23. Initial Connectivity Datasets 37 Linksets 104 Links 7,096,712 Justifications 7 16 October 2014 Scientific Lenses – A. J. G. Gray 22
- 24. Compound Information Scientific Lenses – A. J. G. Gray 23 16 October 2014
- 25. Proceed with Caution! 16 October 2014 Scientific Lenses – A. J. G. Gray 24
- 26. Co-reference Computation Rules ensure Unrestricted transitivity within conceptual type Restrict crossing conceptual types Based on justifications Provenance captured 0..* 0..* 0..* 0..1 0..1 16 October 2014 Scientific Lenses – A. J. G. Gray 25
- 27. Initial Connectivity Datasets 37 Linksets 104 Links 7,096,712 Justification s 7 16 October 2014 Scientific Lenses – A. J. G. Gray 26
- 28. Inferred Connectivity Datasets 37 Linksets 883 Links 17,383,846 Justifications 7 16 October 2014 Scientific Lenses – A. J. G. Gray 27
- 29. BridgeDb 16 October 2014 Scientific Lenses – A. J. G. Gray 28
- 30. Lenses: Under the hood GRAPH <http://rdf.chemspider.com> { cw:979b545d-f9a9 cheminf:logd ?logd . ?iri cheminf:logd ?logd . FILTER (?iri = cw:979b545d-f9a9 || ?iri = cs:2157 || ?iri = chembl:1280 || ?iri = db:db00945 ) } GRAPH <http://… Q, L1 Q’ Query Expander Service Identity Mapping Service (BridgeDB) Mappings Profiles cw:979b545d-f9a9, L1 [cw:979b545d-f9a9, cs:2157, chembl:1280, db:db00945] • Can also be achieved through UNION • IMS call adds overhead 16 October 2014 Scientific Lenses – A. J. G. Gray 29
- 31. Experiment Is it feasible to use a stand-off mapping service? Base lines (no external call): “Perfect” URIs Linked data querying Expansion approaches (external service call): FILTER by Graph UNION by Graph C. Y. A. Brenninkmeijer, C. A. Goble, A. J. G. Gray, P. T. Groth, A. Loizou, S. Pettifer: Including Co-referent URIs in a SPARQL Query. COLD 2013. http://ceur-ws.org/Vol-1034/BrenninkmeijerEtAl_COLD2013.pdf
- 32. “Perfect” URI Baseline WHERE { GRAPH <chemspider> { cs:2157 cheminf:logp ?logp . } GRAPH <chembl> { chembl_mol:m1280 cheminf:mw ?mw . } } 16 October 2014 Scientific Lenses – A. J. G. Gray 31
- 33. Linked Data Baseline WHERE { GRAPH <chemspider> { cs:2157 cheminf:logp ?logp . } GRAPH <chembl> { ?chemblid cheminf:mw ?mw . } cs:2157 skos:exactMatch ?chemblid . } 16 October 2014 Scientific Lenses – A. J. G. Gray 32
- 34. Queries Drawn from Open PHACTS API: 1. Simple compound information (1) 2. Compound information (1) 3. Compound pharmacology (M) 4. Simple target information (1) 5. Target information (1) 6. Target pharmacology (M) 16 October 2014 Scientific Lenses – A. J. G. Gray 33
- 35. Queries Drawn from Open PHACTS API: 1. Simple compound information (1) 2. Compound information (1) 3. Compound pharmacology (M) 4. Simple target information (1) 5. Target information (1) 6. Target pharmacology (M) 16 October 2014 Scientific Lenses – A. J. G. Gray 34
- 36. Data: 167,783,592 triples Mappings: 2,114,584 triples Lenses: 1 Experiment Data 16 October 2014 Scientific Lenses – A. J. G. Gray 35
- 38. Average execution times 0.018
- 40. Explorer Screenshot 16 October 2014 Scientific Lenses – A. J. G. Gray 45
- 41. Explorer Screenshot 16 October 2014 Scientific Lenses – A. J. G. Gray 46
- 42. Conclusions Scientific data is complex and messy Requires flexibility in linking Equivalence depends upon context Lenses provide support for operation equivalence Chemical structures support automatic computing of links with justification 16 October 2014 Scientific Lenses – A. J. G. Gray 47
- 43. Acknowledgements Royal Society of Chemistry Colin Batchelor Karen Karapetyan Jon Steele Valery Tkachenko Antony Williams University of Manchester Christian Brenninkmeijer Ian Dunlop Carole Goble Steve Pettifer Robert Stevens Swiss Institute for Bioinformatics Christine Chichester European Bioinformatics Institute Mark Davies Anna Gaulton John Overington University of Vienna Daniela Digles Maastricht University Chris Evelo Andra Waagmeester Egon Willighagen VU University of Amsterdam Paul Groth Antonis Loizou Connected Discovery Lee Harland 16 October 2014 Scientific Lenses – A. J. G. Gray 48
- 44. Questions Alasdair J G Gray A.J.G.Gray@hw.ac.uk alasdairjggray.co.uk @gray_alasdair Open PHACTS pmu@openphacts.org openphacts.org @open_phacts
Editor's Notes
- #3: 1 of 83 business driver questions Took a team of 5 experienced researchers 6 hours to manually gather the answer
- #4: A platform for integrated pharmacology data Relied upon by pharma companies Public domain, commercial, and private data sources Provides domain specific API Making it easy to build multiple drug discovery applications: examples developed in the project
- #6: Actively being used Since launch (April 2013): 30million hits
- #7: Linked data API: multiple response formats (JSON, RDF, XML, CSV …) 3scala deployment, extensive memcaching Public dataset Provenance of data returned in response
- #8: Hosted on beefy hardware; data in memory (aim)
- #9: Specifies MIM checklist Reuses terms from VoID, PAV, DCTerms, PROV predicates
- #10: Import data into cache API calls populate SPARQL queries Integration approach Data kept in original model Data cached in central triple store API call translated to SPARQL query Query expressed in terms of original data Queries expanded by IMS to cover URIs of original datasets
- #11: Concept appears in multiple datasets, each with its own identifier This talk is about supporting the multiple identities that exist Rather than define a single approach, we want to support the use of multiple identifiers
- #12: Example drug: Gleevec Cancer drug for leukemia Lookup in three popular public chemical databases Different results Chemistry is complicated, often simplified for convenience Data is messy!
- #13: Are these records the same? It depends on what you are doing with the data! Each captures a subtly different view of the world Chemistry is complicated, often simplified for convenience Data is messy!
- #14: Do genes == proteins? Different conceptual types: gene and protein Biological data is complicated simplified for convenience ---- But if you’re saying why genes=proteins you may also want to be prepared for questions of when genes!=proteins. Splice variation is a common example, n the FAS receptor: http://en.wikipedia.org/wiki/Alternative_splicing#Exon_definition:_Fas_receptor there is one gene but it can be made into two distinct proteins - which have different biological effects), so you can obviously mix bio data that shouldnt be mixed by integrating these two functions on the same ID. [We currently dont handle this well in OPS] And the most used example here, the ghrelin gene is transcribed into a protein which is cleaved in two to form two completely different hormones, ghrelin and obestatin, which do very different things. But come from the same gene http://en.wikipedia.org/wiki/Ghrelin#Synthesis_and_variants
- #15: Often used as a shortcut for retrieval: BRCA1 easier to remember and type! Require the ability to equate them in the IMS ---- But if you’re saying why genes=proteins you may also want to be prepared for questions of when genes!=proteins. Splice variation is a common example, n the FAS receptor: http://en.wikipedia.org/wiki/Alternative_splicing#Exon_definition:_Fas_receptor there is one gene but it can be made into two distinct proteins - which have different biological effects), so you can obviously mix bio data that shouldnt be mixed by integrating these two functions on the same ID. [We currently dont handle this well in OPS] And the most used example here, the ghrelin gene is transcribed into a protein which is cleaved in two to form two completely different hormones, ghrelin and obestatin, which do very different things. But come from the same gene http://en.wikipedia.org/wiki/Ghrelin#Synthesis_and_variants
- #16: Analysis requires precise knowledge of the form of the compound across datasets Targets is a search activity, some likely to be mis-entered We use lenses to change the links between the data
- #17: Interested in physiochemical properties of Gleevec
- #18: Interested in biomedical and pharmacological properties sameAs != sameAs depends on your point of view Links relate individual data instances: source, target, predicate, reason. Links are grouped into Linksets which have VoID header providing provenance and justification for the link.
- #19: Lens enables certain relationships and disables others Alters links between the data
- #21: Default lens matches structures Only get data back associated with the structure entered with Really want all information about Ibuprofen Need a different lens
- #23: Validate structure: Source data is messy! Identify common problems: Charge imbalance Stereochemistry Compute physiochemical properties Identify related properties based on structure 17 relationship types
- #24: Can enter with IDs from any of the supported datasets
- #25: Platform extracts data from certain datasets These need to be connected Here there is no issue in computing transitive as they are all the same compound based on InChI key Would compute the full set of links
- #26: Insulin Receptor Issue when linking through PDB due to the way that proteins are crystalised
- #28: Can enter with IDs from any of the supported datasets
- #29: These are 1.3 figures In 1.4 130 raw linksets with 6,985,278 links 40,802 computed linksets with 25,584,293 links
- #30: Implementation available IMS takes query and expands URIs
- #31: Query with URIs Extract URIs Find equivalents under a certain lens (Isolates lens behaviour) Expand query Optimise based on context
- #35: Result size in brackets
- #36: Orange are actual OPS queries
- #37: Subset of the OPS data
- #38: Linked data approach performs badly with query 6 due to the query construction Name being bound to the chemical structure returned
- #39: Focus on other queries In general expansion is slower than base lines Worst case delta: 0.01842 (under 20ms) Human perception is 0.050 to 0.2 (50 -200ms)
- #46: Focus on query 6 No linked data as it performed very poorly on this query Size of result obliterates external call cost
- #48: Pharmacology count 2370 3044