Data Integration in a Big Data Context: An Open PHACTS Case Study
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This document discusses data integration challenges in a big data context using the Open PHACTS case study. Open PHACTS aims to integrate multiple biomedical data resources into a single open access point. It has developed a cloud-based production level system that provides semantic web-based APIs to access integrated data on diseases, tissues, targets, compounds and pathways. The system addresses issues like identity resolution, data quality, provenance and licensing to enable complex queries across diverse data sources.
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Data Integration in a Big Data Context: An Open PHACTS Case Study
- 1. Data Integration in a Big Data Context Open PHACTS Case Study Alasdair J G Gray A.J.G.Gray@hw.ac.uk alasdairjggray.co.uk @gray_alasdair
- 2. Big Data @gray_alasdair Big Data Integration 2 Volume Velocity Variety Veracity http://i.kinja-img.com/gawker-media/image/upload/lvzm0afp8kik5dctxiya.jpg
- 3. 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.) “Let me compare MW, logP and PSA for launched inhibitors of human & mouse oxidoreductases” @gray_alasdair Big Data Integration 3
- 4. Open PHACTS Mission: Integrate Multiple Research Biomedical Data Resources Into A Single Open & Free Access Point @gray_alasdair Big Data Integration 4
- 5. Literature PubChem Genbank Patents Databases Downloads Data Integration Data Analysis Firewalled Databases Repeat @ each company x A single, shared solution. Funded under • IMI: 2011-14 • ENSO: 2014-16 Pre-competitive Data @gray_alasdair Big Data Integration 5
- 6. http://dx.doi.org/10.1016/j.websem.2014.03.003 • Cloud-Based “Production” Level System. • Secure & Private • Guided By Business Questions • Uses Semantic Web Technology • Provides REST-ful API http://dx.doi.org/10.1016/j.drudis.2013.05.008 Discovery Platform @gray_alasdair Big Data Integration 6
- 7. Scientific Results http://ceur-ws.org/Vol- 1114/Demo_Dunlop.pdf http://dx.doi.org/10.1016/j.drudis.2014.11.006 http://dx.doi.org/10.1002/minf.v31.8 http://dx.doi.org/10.1371/journal.pone.0115 460 @gray_alasdair Big Data Integration 7
- 8. OPS Discovery Platform @gray_alasdair Big Data Integration 8 Drug Discovery Platform Apps Domain API Interactive responses Production quality integration platform Method Calls Standard Web Technologies
- 9. App Ecosystem @gray_alasdair An “App Store”? Explorer Explorer2 ChemBioNavigator Target Dossier Pharmatrek Helium MOE Collector Cytophacts Utopia Garfield SciBite KNIME Mol. Data Sheets PipelinePilot scinav.it Taverna Big Data Integration 9https://www.openphacts.org/2/sci/apps.html
- 10. http://chembionavigator.com ChemBio Navigator @gray_alasdair Big Data Integration 10
- 11. @gray_alasdair Big Data Integration 11
- 12. @gray_alasdair Big Data Integration 12
- 13. API Hits @gray_alasdair Big Data Integration 13 0 10 20 30 40 50 60 Jan 2013 Feb 2013 Mar 2013 Apr 2013 May 2013 June 2013 July 2013 Aug 2013 Sept 2013 Oct 2013 Nov 2013 Dec 2013 Jan 2014 Feb 2014 Mar 2014 Apr 2014 May 2014 June 2014 July 2014 Aug 2014 Sept 2014 Oct 2014 Nov 2014 Dec 2014 Jan 2015 Feb 2015 Mar 2015 Apr 2015 May 2015 June 2015 NoofHits Millions Month Public launch of 1.2 API 1.3 API 1.4 API 1.5 API
- 14. OPS 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 @gray_alasdair Big Data Integration 14
- 15. Open PHACTS Data @gray_alasdair Big Data Integration 15
- 16. John Wilbanks consulted for us A framework built around STANDARD well-understood Creative Commons licences – and how they interoperate Deal with the problems by: Interoperable licences Appropriate terms Declare expectations to users and data publishers One size won‘t fit all requirements Data Licensing (Or Lack Of!)
- 17. API: Complex Interactions @gray_alasdair Big Data Integration 17 Disease Tissue Target Compound Pathway
- 18. STANDARD_TYPE UNIT_COUNT ---------------- ------- AC50 7 Activity 421 EC50 39 IC50 46 ID50 42 Ki 23 Log IC50 4 Log Ki 7 Potency 11 log IC50 0 STANDARD_TYPE STANDARD_UNITS COUNT(*) ------------------ ------------------ -------- IC50 nM 829448 IC50 ug.mL-1 41000 IC50 38521 IC50 ug/ml 2038 IC50 ug ml-1 509 IC50 mg kg-1 295 IC50 molar ratio 178 IC50 ug 117 IC50 % 113 IC50 uM well-1 52 ~ 100 units >5000 types Implemented using the Quantities, Units, Dimension, Types Ontology (http://www.qudt.org/) Quantitative Data Challenges @gray_alasdair Big Data Integration 18
- 19. Quality Assurance @gray_alasdair Big Data Integration 19
- 20. P12047 X31045 GB:29384 Identity Mapping @gray_alasdair Big Data Integration 20 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/
- 21. Gleevec®: Imatinib Mesylate @gray_alasdair Big Data Integration 21 DrugbankChemSpider PubChem Imatinib MesylateImatinib Mesylate YLMAHDNUQAMNNX-UHFFFAOYSA-N
- 22. Gleevec®: Imatinib Mesylate @gray_alasdair Big Data Integration 22 DrugbankChemSpider PubChem Imatinib MesylateImatinib Mesylate YLMAHDNUQAMNNX-UHFFFAOYSA-N Are these records the same? It depends upon your task!
- 23. Big Data Integration 23 skos:exactMatch (InChI) Strict Relaxed Analysing Browsing Structure Lens @gray_alasdair I need to perform an analysis, give me details of the active compound in Gleevec.
- 24. Big Data Integration 24 skos:closeMatch (Drug Name) skos:closeMatch (Drug Name) skos:exactMatch (InChI) Strict Relaxed Analysing Browsing Name Lens @gray_alasdair Which targets are known to interact with Gleevec?
- 25. Data Provenance @gray_alasdair Big Data Integration 26
- 26. Data Provenance @gray_alasdair Big Data Integration 27
- 27. dev.openphacts.org @gray_alasdair Big Data Integration 29
- 29. Open PHACTS Approach 1. Know your audience Web developers 2. Understand your use cases Prioritised business questions 3. Identify access pathways Identify data Identify connections Implement API @gray_alasdair Big Data Integration 31
- 30. Questions Alasdair J G Gray A.J.G.Gray@hw.ac.uk alasdairjggray.co.uk @gray_alasdair Open PHACTS contact@openphacts.org openphacts.org @open_phacts @gray_alasdair Big Data Integration 32
Editor's Notes
- Deriving value from the data Volume: More data than you can process – relative term; complexity of processing Velocity: Data constantly being generated Variety: Multiple sources, formats, models Veracity: Accuracy of the data Open PHACTS: Not dealt with Velocity, although it is a challenge for us
- 1 of 83 business driver questions Took a team of 5 experienced researchers 6 hours to manually gather the answer Start of the project couldn’t be answered by a computer system 6 months in 30s with prototype now subsecond
- Pharma are all accessing, processing, storing & re-processing external research data Big waste of resources No competitive advantage OPS: 29 partners including many major pharma
- 83 questions ranked and top 20 taken as target
- 18 of top 20
- 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
- Not just in-house apps
- Actively being used for different purposes Public launch April 2013 Averaging 20 million hits a month from the start of 2015 38 million in the last 30 days Heavy usage from pharma, academia, and biotech 500+ registered users
- Import data into cache Integration approach Data kept in original model but cached centrally API call translated to SPARQL query Query expressed in terms of original data Queries expanded by IMS to cover URIs of original datasets
- 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 3 billion triples – 12 datasets Hosted on beefy hardware; data in memory (aim) Extensive memcaching Pose complex queries to extract data
- Interactions needed to satisfy use cases Gradually added additional types of data and interactions
- No standard units Even in curated sources! Feedback issues to data providers
- Validation & Standardization Platform Developed by Royal Society of Chemistry http://bit.ly/NZF5VB
- 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!
- 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!
- Interested in physiochemical properties of Gleevec
- 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.
- Open for anybody API grouped into theme areas Two phase interaction: Resolve thing to identifier Retrieve data about the identifier
- Sustainability
- API -> queries