Phillip Lord
Newcastle University

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

• Thin Semantics is Good
• More Semantics is Better
• Shared Semantics is Wonderful

• Scalability
– Both in technology and processes
• Usability
• Autonomy

– 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 Investigators
• 5 Researchers
Manchester
Roslin
Newcastle
Cambridge
John Innes
NCYC
• Commenced: 2003

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

database
Sequence
SequenceRecord
S_hasID S_hasLength S_hasSeqStr domain ontology
Representation Molecule
DNA SequenceRepresentation id length seqString

Raw data
Raw data
JDBC
Syntax
Pub service
OWL
Semantics
Trans service
OWL
Aggregation integrator data query

• 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.

• 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.

www.carmen.org.uk
Engineering and Physical
Sciences Research Council

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

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

• Most neurosciences data is relative simple in structure
• But often contextually complex
• Sometimes associated with behavioural features

In silico Analysis
Derived data
Laboratory
Experiments

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.

Brain anatomy
BIRNLex, FMA
CARMEN
Sample preparation sepCV
Taxonomy
NCBI Taxonomy

Age/stage development
Subject preparation
Experiment process CARMEN Subject training
Subject stimulus Equipment
Subject task

Data structures
File formats
CARMEN
Algorithms Statistics
Software

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

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 is a lot to describe
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 • OBI has 15 communities involved in it
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

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

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

http://symba.sourceforge.net/

• 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?

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

• 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

• 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

• 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

• 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)

Andersen et al (2001) Mol. Cell. Biol. 21 7117-36

Class TyrosineRreceptorProteinPhosphatase
EquivalentTo: Protein That
- (contains atLeast-1
ProteinTyrosinePhosphataseDomain and
- contains 1 TransmembraneDomain

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

• 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

• 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

• 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

• 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.

• 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.

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)

Professor 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
The
University
Of
Sheffield
University of
St Andrews