Spreading Semantics Over Biology
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Transcript of 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
Key Problems
• Scalability– Both in technology and processes
• Usability
• Autonomy
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
ComparaGRID
Roslin
Newcastle
Cambridge
John InnesNCYC
Manchester
• 6 Investigators
• 5 Researchers
• Commenced: 2003
ComparGRID’s Problem Domain
Many Model Organism Databases
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Data Models, Model Data
domain ontologydatabase
Databases and Knowledge
SequenceRecord
Sequence
S_hasID S_hasSeqStrS_hasLength
Molecule
DNA SequenceRepresentation
Representation
seqStringlengthid
The Fluxion Stack
Raw data
Rawdata
Pubservice
Transservice
integrator
query
data
AggregationSemanticsSyntax
JDBC OWL OWL
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.
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.
CARMENCode, Analysis, Repository and Modelling for e-Neuroscience
www.carmen.org.uk
Engineering and Physical Sciences Research Council
Consortium & Profile
Stirling
St. Andrews
Newcastle
York
Sheffield
Cambridge
ImperialPlymouth
Warwick
Leicester
Manchester
• £4M over 4 years
• 20 Investigators
• Commenced 1st October 2006
Virtual Laboratory for Neurophysiology
• Enabling sharing and collaborative exploitation of data, analysis code and expertise that are not physically collocated
The need for clear metadata
• Most neurosciences data is relative simple in structure
• But often contextually complex
• Sometimes associated with behavioural features
How do we represent…
LaboratoryExperiments
In silico Analysis
Derived data
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.
Re-use
CARMEN
Brain anatomy
BIRNLex, FMA
Taxonomy
NCBI Taxonomy
Sample preparation
sepCV
What we need – lab based
CARMEN
Age/stage development Subject preparation
Subject training
Subject task
Experiment process
Equipment Subject stimulus
What we need – In silico
CARMEN
File formats Data structures
Statistics Algorithms
Software
Align with OBI
• 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
Ontology for
Biomedical Investigations
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
Information Extraction
• More semantics is better?
• How do we get extract the information?
http://en.wikipedia.org/wiki/Image:Brain_090407.jpg
Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN)
Robot
Reference set of 5,000 mutant strains
‘Folate’ + - + -‘MMS’ - - + +
• Data curation.• Functional analysis.• Interactions with in silico programme.
***
Robot
Screen mutants for sensitivity to damage/nutrition
Identification of novel interactions between nutrition and damage using automated yeast screening and analysis
CISBAN dataflow
Data Entry with SYMBA
http://symba.sourceforge.net/
Data Entry with SYMBA
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?
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
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
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
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
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)
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?
Phosphatase Functional Domains
Andersen et al (2001) Mol. Cell. Biol. 21 7117-36
Definition of Tyrosine Phosphatase
Class TyrosineRreceptorProteinPhosphataseEquivalentTo: Protein That- (contains atLeast-1 ProteinTyrosinePhosphataseDomain and
- contains 1 TransmembraneDomain
Classifying Proteins
>uniprot|Q15262|PTPK_HUMAN Receptor-type protein-tyrosine phosphatase kappa precursor (EC 3.1.3.48) (R-PTP-kappa).
MDTTAAAALPAFVALLLLSPWPLLGSAQGQFSAGGCTFDDGPGACDYHQDLYDDFEWVHVSAQEPHYLPPEMPQGSYMIVDSSDHDPGEKARLQLPTMKENDTHCIDFSYLLYSQKGLNPGTLNILVRVNKGPLANPIWNVTGFTGRDWLRAELAVSSFWPNEYQVIFEAEVSGGRSGYIAIDDIQVLSYPCDKSPHFLRLGDVEVNAGQNATFQCIATGRDAVHNKLWLQRRNGEDIPV………..
InterPro
Instance Store
Reasoner
Translate
Codify
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
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
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
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.
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.
AcknowledgementsThe 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)
CARMEN AcknowledgementsProfessor Colin Ingram, Professor Jim Austin, Professor Leslie Smith, Professor Paul Watson Dr. Stuart Baker,Professor Roman Borisyuk, Dr. Stephen Eglen, Professor Jianfeng Feng, Dr. Kevin Gurney, Dr. Tom Jackson Dr. 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
TheUniversity OfSheffield
Holiday Pictures
Questions?