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Spreading Semantics Over Biology

Spreading Semantics Over Biology. Phillip Lord Newcastle University. Overview. Conclusions Data Integration in ComparaGRID Annotation in CARMEN and CISBAN Computing with Semantics The future. Conclusions. Thin Semantics is Good More Semantics is Better Shared Semantics is Wonderful.

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Spreading Semantics Over Biology

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  1. Spreading Semantics Over Biology Phillip Lord Newcastle University

  2. Overview • Conclusions • Data Integration in ComparaGRID • Annotation in CARMEN and CISBAN • Computing with Semantics • The future

  3. Conclusions • Thin Semantics is Good • More Semantics is Better • Shared Semantics is Wonderful

  4. Key Problems • Scalability • Both in technology and processes • Usability • Autonomy

  5. Methods for Data Integration • Combining data from multiple, autonomous data sources. • TAMBIS • ontology driven mediation of querying • EcoCyc • ontology driven schema for warehousing • BioPAX • ontology defined interchange format. • More recently, ComparaGRID

  6. ComparaGRID • 6 Investigators • 5 Researchers Roslin Newcastle Manchester Cambridge John Innes NCYC • Commenced: 2003

  7. ComparGRID’s Problem Domain

  8. 12181 acatttctac caacagtgga tgaggttgtt ggtctatgtt ctcaccaaat ttggtgttgt 12241 cagtctttta aattttaacc tttagagaag agtcatacag tcaatagcct tttttagctt 12301 gaccatccta atagatacac agtggtgtct cactgtgatt ttaatttgca ttttcctgct 12361 gactaattat gttgagcttg ttaccattta gacaacttca ttagagaagt gtctaatatt 12421 taggtgactt gcctgttttt ttttaattgg gatcttaatt tttttaaatt attgatttgt 12481 aggagctatt tatatattct ggatacaagt tctttatcag atacacagtt tgtgactatt 12541 ttcttataag tctgtggttt ttatattaat gtttttattg atgactgttt tttacaattg 12601 tggttaagta tacatgacat aaaacggatt atcttaacca ttttaaaatg taaaattcga 12661 tggcattaag tacatccaca atattgtgca actatcacca ctatcatact ccaaaagggc 12721 atccaatacc cattaagctg tcactcccca atctcccatt ttcccacccc tgacaatcaa 12781 taacccattt tctgtctcta tggatttgcc tgttctggat attcatatta atagaatcaa Many Model Organism Databases

  9. Data Models, Model Data

  10. Databases and Knowledge database domain ontology Sequence Molecule Representation DNA SequenceRepresentation SequenceRecord id length seqString S_hasID S_hasLength S_hasSeqStr

  11. The Fluxion Stack Raw data Syntax Semantics Aggregation Raw data JDBC OWL OWL integrator Trans service Pub service data query

  12. The difficulties • The Cost of Integration • building ontologies is often hard • The Cost of Managing Change • biological knowledge tends to undergo a lot of flux • The Scalabilty of Expressive Ontologies.

  13. Getting the Semantics Upfront • Instead of annotating heterogenous data sources after the event, why not do so upfront? • Originators of the data are likely to understand it best. • Spreads the cost among those contributing.

  14. Engineering and Physical Sciences Research Council CARMENCode, Analysis, Repository and Modelling for e-Neurosciencewww.carmen.org.uk

  15. Consortium & Profile • £4M over 4 years • 20 Investigators Stirling St. Andrews Newcastle York Manchester Sheffield Leicester Cambridge Warwick Imperial Plymouth • Commenced 1st October 2006

  16. Virtual Laboratory for Neurophysiology • Enabling sharing and collaborative exploitation of data, analysis code and expertise that are not physically collocated

  17. The need for clear metadata • Most neurosciences data is relative simple in structure • But often contextually complex • Sometimes associated with behavioural features

  18. How do we represent… In silico Analysis Derived data Laboratory Experiments

  19. Functional Genomics Experiment (FuGE) • Model of common components in science investigations, such as materials, data, protocols, equipment and software. • Provides a framework for capturing complete laboratory workflows, enabling the integration of pre-existing data formats.

  20. Re-use Brain anatomy BIRNLex, FMA Taxonomy NCBI Taxonomy CARMEN Sample preparation sepCV

  21. What we need – lab based Age/stage development Subject preparation CARMEN Experiment process Subject training Subject stimulus Equipment Subject task

  22. What we need – In silico Data structures File formats CARMEN Algorithms Statistics Software

  23. Align with OBI Ontology for Biomedical Investigations • Aims to provide an ontology for the life sciences • Consortium to 15 communities from crop science to neuroscience • CARMEN will align and contribute to OBI

  24. The Difficulties • Even with a lot of pre-existing work there is a lot to describe • OBI has 15 communities involved in it Bio-ImagingJeff GretheBiomedical Informatics Research Network (BIRN) Coordinating CenterUniversity of California, San DiegoWinter 2007 Daniel RubinRadiological Society of North America (RSNA)National Center for Biomedical Ontology at Stanford Medical Informatics and the Department of Radiology, Stanford UniversityWinter 2007 Bill BugBiomedical Informatics Research Network (BIRN)Laboratory of Bioimaging and Anatomical Informatics, in the Department of Neurobiology and Anatomy, Drexel University College of MedicineSpring 2006 Cellular AssaysStefan Wiemann DKFZ   Clinical InvestigationsJennifer FostelClinical Trial OntologyNIEHS, National Institute for Environmental Health SciencesSpring 2004 Tina Hernandez-Boussard  Department of Genetics, Stanford Medical SchoolFall 2007 Crop SciencesRichard BruskiewichGeneration Challenge ProgrammeIRRI   ElectrophysiologyFrank GibsonCARMENSchool of Computing Science, Newcastle UniversitySpring 2007 Environmental OmicsNorman Morrison NERC Environmental Bioinformatic Centre and School of Computer Science, The University of ManchesterSpring 2004 Flow CytometryRyan BrinkmanISAC and FICCSBritish Columbia Cancer Research Center and University of British Columbia in the Department of Medical Genetics , Vancouver, BC, CanadaSpring 2004 Genomics/MetagenomicsDawn FieldGenome CatalogueNERC Centre for Ecology and HydrologyWinter 2005 Tanya GrayWinter 2005 ImmunologyRichard ScheuermannImmPort, FICCS, BioHealthBaseUniversity of Texas Southwestern Medical Center, in in Department of Pathology and Division of Biomedical InformaticsSpring 2006 Bjoern PetersImmune Epitope Database and Analysis ResourceLa Jolla Institute for Allergy and ImmunologySpring 2006 In Situ Hybridization and ImmunohistochemistryEric DeutschMISFISHIE    MetabolomicsSusanna SansoneMSI, The European Bioinformatics Institute EBI-EMBL, NET ProjectSpring 2004 Daniel SchoberSpring 2006 NeuroinformaticsBill BugBiomedical Informatics Research Network (BIRN)Laboratory of Bioimaging and Anatomical Informatics, in the Department of Neurobiology and Anatomy, Drexel University College of MedicineSpring 2006 Frank GibsonCARMENSchool of Computing Science, Newcastle UniversitySpring 2007 NutrigenomicsPhilippe Rocca-SerraRSBIThe European Bioinformatics Institute EBI-EMBL, NET ProjectSpring 2004 PolymorphismTina Hernandez-BoussardPharmGKBDepartment of Genetics, Stanford Medical SchoolWinter 2006Fall 2007ProteomicsSusanna SansonePSIThe European Bioinformatics Institute EBI-EMBL, NET ProjectSpring 2004 Daniel SchoberSpring 2006 Luisa MontecchiThe European Bioinformatics Institute EBI-EMBLSpring 2006 Chris Taylor   Trish Whetzel  Spring 2004 Frank GibsonSchool of Computing Science, Newcastle UniversitySpring 2007 ToxicogenomicsJennifer FostelToxicogenomicsNIEHS, National Institute for Environmental Health SciencesSpring 2004 Susanna SansoneRSBI The European Bioinformatics Institute EBI-EMBL, NET ProjectSpring 2004 TranscriptomicsSusanna SansoneMGED The European Bioinformatics Institute EBI-EMBL, NET ProjectSpring 2004 Philippe Rocca-SerraSpring 2004 Trish Whetzel  Spring 2004 Chris StoeckertDepartment of Genetics and Center for Bioinformatics, University of PennsylvaniaSpring 2004 Gilberto FragosoNCI Center for BioinformaticsSpring 2004 Joe White     Helen ParkinsonThe European Bioinformatics Institute EBI-EMBLSpring 2004 Mervi Heiskanen     Liju FanOntology Workshop, LLC, Columbia, MD, USASpring 2004 Helen CaustonImperial CollegeSpring 2004

  25. Information Extraction • More semantics is better? • How do we get extract the information? http://en.wikipedia.org/wiki/Image:Brain_090407.jpg

  26. Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN)

  27. ‘Folate’+ - + - ‘MMS’ - - + + Identification of novel interactions between nutrition and damage using automated yeast screening and analysis Screen mutants for sensitivity to damage/nutrition * * * Robot Robot • Data curation. • Functional analysis. • Interactions with in silico • programme. Reference set of 5,000 mutant strains

  28. CISBAN dataflow

  29. Data Entry with SYMBA http://symba.sourceforge.net/

  30. Data Entry with SYMBA

  31. CARMEN and CISBAN • We can provide more semantics upfront • This should make data more explicit • If we still need to integrate it should be easier. • Like much of biology, these projects are largely using structural simple, non-SW based technologies. • This is a lot of effort to go to; what do we hope to gain?

  32. Yeast Hub YeastHub: a semantic web use case for integrating data in the life sciences domain Kei-Hoi Cheung, Kevin Y. Yip, Andrew Smith, Remko deKnikker, Andy Masiar and Mark Gerstein doi:10.1093/bioinformatics/bti1026

  33. A rapturous reception • So the general idea is take a bunch of data, convert it to RDF, dump it into a RDF triple store […] to discover interesting things ? • http://www.nodalpoint.org/user/greg • Putting a lot of RDF in a bucket isn’t integration. Not unless the RDF is the same schema and using the same concepts • Carole Goble, University of Manchester

  34. A thin layer of semantics. • Inverse Document Frequency is a method for classifying documents; rare words carry more information than common ones. • In this case, YeastHub has a common semantics describing the type of document. • “protein” or “sequence” occurs a lot in Uniprot, but less in the bulk corpus • Rather than treating all documents equally, they use IDF twice. • Leveraging Biological Identifier Relationships and Related Documents to Enhance Information Retrieval for Proteomics -- Smith et al., 10.1093/bioinformatics/btm452 – Bioinformatics

  35. Thin Semantics • The semantics of YeastHub is not deep. • But even a thin layer of semantics is useful. • If we modify our technologies to use it. • A large part of library sciences has been encoded in 15 tags – Dublin Core

  36. Using Ontology to Classify Members of a Protein Family • Katy Wolstencroft (Bioinformatics) • Daniele Turi (Instance Store) • Phil Lord (myGrid) • Lydia Tabernero (Protein Scientist) • Matt Horridge, Nick Drummond et al (Protégé OWL) • Andy Brass and Robert Stevens (Bioinformatics)

  37. The Protein Phosphatases • A large superfamily of proteins • Motifs determine a protein’s place within the family • Recognising that motifs imply class membership is normally manual • Can these be captured in an ontology?

  38. Phosphatase Functional Domains Andersen et al (2001) Mol. Cell. Biol.21 7117-36

  39. Definition of Tyrosine Phosphatase Class TyrosineRreceptorProteinPhosphatase EquivalentTo: Protein That • (contains atLeast-1 ProteinTyrosinePhosphataseDomain and • contains 1 TransmembraneDomain

  40. ClassifyingProteins >uniprot|Q15262|PTPK_HUMAN Receptor-type protein-tyrosine phosphatase kappa precursor (EC 3.1.3.48) (R-PTP-kappa). MDTTAAAALPAFVALLLLSPWPLLGSAQGQFSAGGCTFDDGPGACDYHQDLYDDFEWVHV SAQEPHYLPPEMPQGSYMIVDSSDHDPGEKARLQLPTMKENDTHCIDFSYLLYSQKGLNP GTLNILVRVNKGPLANPIWNVTGFTGRDWLRAELAVSSFWPNEYQVIFEAEVSGGRSGYI AIDDIQVLSYPCDKSPHFLRLGDVEVNAGQNATFQCIATGRDAVHNKLWLQRRNGEDIPV……….. InterPro Codify Translate Instance Store Reasoner

  41. Results • Human phosphatases have been classified using the system • The ontology system refined classification - DUSC contains zinc finger domain characterised and conserved – but not in classification - DUSA contains a disintegrin domain previously uncharacterised – evolutionarily conserved • We have automated a part of the scientific process • We have defined our domain model in a computational form • We have collected some data • We have let the reasoner test whether the model fits the data • The semantics here are deeper with YeastHub, which allow us to reason

  42. Summary • Ontologies have been used in life sciences for data integration • Increasingly, are being used to describe the data early in the scientific process • Even thin semantics can be exploited for information retrieval • Richer semantics allows more use of computational inference

  43. Richer Expressivity • There are applications of more expressive semantics • Can we move to from specific software, to generic software with specific knowledge models • But, scalability and usability remain the bottleneck

  44. Industrialisation • Semantics in the life sciences is moving from small to large scale • building ontologies has now become very committee driven • we don’t understand ontology engineering as we do software engineering • Encapsulation, modularisation, continuous integration.

  45. Future • ComparaGRID has semantics describing schema which means data integration can happen on-the-fly. • Death to data warehouses! • CARMEN and CISBAN are gathering semantically enriched data in the first place. An End to Integration! • Semantics during dissemination • Knowledge for All.

  46. Acknowledgements The ComparaGRID consortium is Madhuchhanda Bhattacharjee, Richard Boys, Tony Burdett, Rob Davey, Jo Dicks, David Marshall, Andy Law, Phillip Lord, Trevor Paterson, Matthew Pocock, Peter Rice, Ian Roberts, Robert Steven, Paul Watson, Darren Wilkinson and Neil Wipat, Andy Gibson CISBAN is Tom Kirkwood (PI), Thomas von Zglinicki (PI), David Lydall (PI), Anil Wipat (PI), Stephen Addinall (Research Associate), Suzanne Advani (Technician), Kim Clugston (Research Associate), Sharon Denley (PA to Professor Tom Kirkwood), Amanda Greenall (Research Associate), Jennifer Hallinan (Research Associate), Dominic Kurian (Research Associate), Conor Lawless (Research Associate), Guiyuan Lei (Research Associate), Allyson Lister (Research Associate), Mandy Maddick (Research Associate), Satomi Miwa (Research Associate), Glyn Nelson (Research Associate), Bob Nicholson (Superintendent), Sharon Oljslagers (Technician), Joao Passos (Research Associate), Carole Proctor (Research Associate), Daryl Shanley (Research Associate), Oliver Shaw (Research Associate), Donna Stark (Research Secretary), Laura Steedman (Technician), Joyce Wang (Technician), Darren Wilkinson (Professor of Stochastic Modelling)

  47. TheUniversity OfSheffield CARMEN Acknowledgements Professor Colin Ingram, Professor Jim Austin, Professor Leslie Smith,Professor Paul WatsonDr. Stuart Baker,Professor Roman Borisyuk, Dr. Stephen Eglen, Professor Jianfeng Feng, Dr. Kevin Gurney, Dr. Tom JacksonDr. Marcus Kaiser, Dr. Phillip Lord, Dr. Paul Overton, Dr. Stefano Panzeri, Dr. Rodrigio Quian Quiroga, Dr. Simon Schultz, Dr. Evelyne Sernagor, Dr. V. Anne Smith, Dr. Tom Smulders Professor Miles Whittington, Christoph Echtermeyer, Martyn Fletcher, Frank Gibson, Mark Jessop Dr. Bojian Liang, Juan Martinez-Gomez, Dr. Chris Mountford, Agah Ogungboye, Georgios Pitsilis, Dr. Daniel Swan University ofSt Andrews

  48. Holiday Pictures

  49. Questions?

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