Building linked data large-scale chemistry platform - challenges, lessons and solutions
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Chemical databases have been around for decades, but in recent years we have observed a qualitative change from rather small in-house built proprietary databases to large-scale, open and increasingly complex chemistry knowledgebases. This tectonic shift has imposed new requirements for database design and system architecture as well as the implementation of completely new components and workflows which did not exist in chemical databases before. Probably the most profound change is being caused by the linked nature of modern resources - individual databases are becoming nodes and hubs of a huge and truly distributed web of knowledge. This change has important aspects such as data and format standards, interoperability, provenance, security, quality control and metainformation standards. ChemSpider at the Royal Society of Chemistry was first public chemical database which incorporated rigorous quality control by introducing both community curation and automated quality checks at the scale of tens of millions of records. Yet we have come to realize that this approach may now be incomplete in a quickly changing world of linked data. In this presentation we will talk about challenges associated with building modern public and private chemical databases as well as lessons that we have learned from our past and present experience. We will also talk about solutions for some common problems.
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Building linked data large-scale chemistry platform - challenges, lessons and solutions
- 1. Building linked-data, large-scale chemistry platform: challenges, lessons and solutions Valery Tkachenko, Alexey Pshenichnov, Aileen Day, Colin Batchelor, Peter Corbett Royal Society of Chemistry ACS Spring 2016 San Diego, CA March 13th 2016
- 2. ChemSpider – 2007 - 2011 OpenPHACTS – 2011 - 2014 Chemistry Data Platform – 2014 - …
- 3. • 45 million chemicals and growing • Data sourced from >500 different sources • Crowdsourced curation and annotation • Ongoing deposition of data from our journals and our collaborators • A structure centric hub for web-searching
- 4. ChemSpider
- 5. Chemical vendors and datasources
- 6. ChemSpider
- 8. Literature and patents references
- 10. Spectra
- 11. Multimedia
- 12. Tagging
- 13. ChemSpider - Summary • Simple, flattish data model • InChI as a primary identifier • Linked by synonyms • Linked by “ExtId” • Standard searches (identity, substructure, similarity) • Very little semantics
- 14. Open PHACTS Mission: Integrate Multiple Research Biomedical Data Resources Into A Single Open & Sustainable Access Point OpenPHACTS: 2011-2014
- 15. info@openphactsfoundation.org @Open_PHACTS Open PHACTS Practical Semantics OpenPHACTS GlaxoSmithKline – Coordinator Universität Wien – Managing entity Technical University of Denmark University of Hamburg, Center for Bioinformatics BioSolveIT GmBH Consorci Mar Parc de Salut de Barcelona Leiden University Medical Centre Royal Society of Chemistry Vrije Universiteit Amsterdam Novartis Merck Serono H. Lundbeck A/S Eli Lilly Netherlands Bioinformatics Centre Swiss Institute of Bioinformatics ConnectedDiscovery EMBL-European Bioinformatics Institute Janssen Esteve Almirall OpenLink Scibite The Open PHACTS Foundation Spanish National Cancer Research Centre University of Manchester Maastricht University Aqnowledge University of Santiago de Compostela Rheinische Friedrich-Wilhelms-Universität Bonn AstraZeneca Pfizer
- 16. Why is it so hard to…. Competitors? What’s the structure? Are they in our file? What’s similar? What’s the target?Pharmacology data? Known Pathways? Working On Now? Connections to disease? Expressed in right cell type? IP?
- 17. @gray_alasdair Big Data Integration 18
- 18. OpenPHACTS Discovery Platform Nanopub Db VoID Data Cache (Virtuoso Triple Store) Semantic Workflow Engine Linked Data API (RDF/XML, TTL, JSON) Domain Specific Services Identity Resolution Service Chemistry Registration Normalisation & Q/C Identifier Management Service Indexing CorePlatform P12374 EC2.43.4 CS4532 “Adenosine receptor 2a” VoID Db Nanopub Db VoID Db VoID Nanopub VoID Public Content Commercial Public Ontologies User Annotations Apps 21 October 2014 Scientific Lenses – A. J. G. Gray 19
- 19. Gleevec®: Imatinib Mesylate 21 October 2014 Scientific Lenses – A. J. G. Gray 20 DrugbankChemSpider PubChem Imatinib MesylateImatinib Mesylate YLMAHDNUQAMNNX-UHFFFAOYSA-N
- 20. Scientific Lenses – A. J. G. Gray 21 skos:exactMatch (InChI) Strict Relaxed Analysing Browsing Structure Lens 21 October 2014 I need to compute an analysis, give me details of the active compound in Gleevec.
- 21. Commercial ibuprofen is a racemic mixture containing the same proportion of two chiral forms. Both chiral forms are equally active. Typically, the user will wish to retrieve info for any stereoisomer. CHEMBL427526 CHEMBL521 CHEMBL175 Lens Effects: Ibuprofen 21 October 2014 Scientific Lenses – A. J. G. Gray 22
- 22. Commercial ibuprofen is a racemic mixture containing the same proportion of two chiral forms. Both chiral forms are equally active. Typically, the user will wish to retrieve info for any stereoisomer. Default Lens 21 October 2014 Scientific Lenses – A. J. G. Gray 23
- 23. Commercial ibuprofen is a racemic mixture containing the same proportion of two chiral forms. Both chiral forms are equally active. Typically, the user will wish to retrieve info for any stereoisomer. Stereoisomer Lens 21 October 2014 Scientific Lenses – A. J. G. Gray 24
- 24. Mapping Generation 21 October 2014 Scientific Lenses – A. J. G. Gray 25 ops:OPS437281 ✔ ops:OPS380297 has_stereoundefined_parent [ci:CHEMINF_000456] ops:OPS380297 is_stereoisomer_of [ci:CHEMINF_000461] Other relationships • has part • is tautomer of • uncharged counterpart • isotope …
- 26. Explorer Screenshot 21 October 2014 Scientific Lenses – A. J. G. Gray 27
- 27. Explorer Screenshot 21 October 2014 Scientific Lenses – A. J. G. Gray 28
- 28. OpenPHACTS - Summary • Principal difference – inter-domain links • More complex, but still structure-centric data model • Ontological relationships introduced • Chemical Lenses – new type of search
- 29. Chemistry Data Platform – 2014 - …
- 30. Dimensions and complexity of science
- 32. RSC Archive – since 1841
- 33. Digitally Enabling RSC Archive
- 34. ChemSpider Synthetic Pages Compounds Reaction Analytical Data Text and References
- 35. RSC Databases RSC Compounds RSC Reactions RSC Spectra RSC Crystals RSC Polymers RSC Materials RSC Assays RSC Algorithms RSC Models …and on…
- 36. Compounds domain
- 38. Data quality issue and CVSP – Robochemistry – Proliferation of errors in public and private databases • ChemSpider • PubChem • DrugBank • KEGG • ChEBI/ChEMBL – Automated quality control system
- 39. Chemistry Validation and Standardization Platform
- 40. Chemistry Validation and Standardization Platform
- 41. Reactions domain
- 45. Chemistry Data Platform - Summary • Simplified models within domain • Domains are described with its own models with embedded semantics • No proper domain-specific identifiers • Extensive quality control – CVSP (DOI 10.1186/s13321-015-0072-8)
- 46. There is no way back
Hinweis der Redaktion
- Remember this, some of these questions are easier to answer than others
- 17
- Open PHACTS was developed to support the key questions of drug discovery Business questions have been at the heart of Open PHACTS and have driven the development of the platform Mx/psa, how calculated who did it? Mash up. With your data too, - top layer join together but need them all commercial Data provided by many publishers Originally in many formats: relational, SD files and RDF Worked closely with publishers Data licensing was a major issue Over 5 billion triples – 14 datasets & growing Hosted on beefy hardware; data in memory (aim) Extensive memcaching Pose complex queries to extract data
- 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
- Example drug: Gleevec Cancer drug for leukemia Lookup in three popular public chemical databases Different results
- Interested in physiochemical properties of Gleevec
- Validate structure: Source data is messy! Identify common problems: Charge imbalance Stereochemistry Compute physiochemical properties Identify related properties based on structure 17 relationship types
- Pharmacology count 2370 3044
- What about science and chemistry in particular?
- Information typically associated with reactions