The Neuroscience Information FrameworkMaking Resources Discoverable for the ComputationalNeuroscience CommunityJeffrey S. ...
The Neuroscience Information Framework: Discovery andutilization of web-based resources for neurosciencehttp://neuinfo.org...
Brief History of NIF• Outgrowth of Society for Neuroscience NeuroinformaticsCommittee– Neuroscience Database Gateway: a ca...
The Problem• Over 2000 databases have been identifiedthrough NIF– Researchers can’t visit them all– Most content from thes...
NIF uniquely provides access to thelargest registry of neuroscienceresources available on the webDateDataFederationDataFed...
Guiding principles of NIF• Builds heavily on existing technologies (open source tools andontologies)• Information resource...
http://neuinfo.orgA Quick Tour of the NIF
Domain Enhanced Search for NeuroscienceNIF now searches more than 55 databases with informationneuronal descriptions, neur...
Ontology Based Search Refinement
Diverse Database ContentNeuroMorpho.orgNeuronDB
Concept-based search• Search Google: GABAergic neuron• Search NIF: “GABAergic neuron”– NIF automatically searches for type...
Concept-based search
Use of Ontologies within NIF• Controlled vocabulary for describing type of resourceand content– Database, Image, Parkinson...
http://neurolex.orgBuilding the NIF Ontologies
Modular OntologiesNIFSTDNSFunctionMoleculeInvestigationSubcellularAnatomyMacromolecule GeneMoleculeDescriptorsTechniquesRe...
Anatomy Cell TypeCellularComponentSmallMoleculeNeuro-transmitterTransmembraneReceptorGABA GABA-RTransmitterVesicleTerminal...
NIF Cell• NIF has made significant enhancements to itscell ontology– Expanded neuron list– Generated neuronal classificati...
Neurolex Wikihttp://neurolex.org•NIF has posted itsvocabularies in Wiki form(Semantic MediaWiki)•Simplified interface foro...
NeuroLex and NeuroML“There was further discussion of how to define specifictypes of morphological groups such as apical de...
http://neuinfo.orgProviding communityaccess
Access at various levels…• A search portal (link to NIF advanced search interface) for researchers,students, or anyone loo...
http://wholebraincatalog.orgIntegration of NIFservices and ontologies
WBC and Simulation VisualizationDemonstrates theneurogenesissimulation drivenby the model ofAimone et al.,2009 from theGag...
WBC and NeuroConstructhttp://www.neuroml.org/tool_support.phpA network model of the cerebellar granule cell layer which ca...
NIF cardsSimple tool for linking searchresults to other sources ofinformationNIF literature results display for “Cerebellu...
Providing Semantic ContentRDF data / SPARQL Queries
The NIF Team• Maryann Martone, UCSD-PI• Jeff Grethe, UCSD-Co PI• Amarnath Gupta, UCSD-Co-PI• Ashraf Memon, UCSD, Project M...
The Neuroscience Information Framework: Making Resources Discoverable for the Computational Neuroscience Community
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The Neuroscience Information Framework: Making Resources Discoverable for the Computational Neuroscience Community

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Jeffery Grethe
July 30, 2010
OCNS July 24th – 30th 2010, San Antonio, Texas

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The Neuroscience Information Framework: Making Resources Discoverable for the Computational Neuroscience Community

  1. 1. The Neuroscience Information FrameworkMaking Resources Discoverable for the ComputationalNeuroscience CommunityJeffrey S. Grethe, Ph. D.Co-Principal Investigator, NIFCenter for Research in Biological SystemsUniversity of California, San DiegoOCNS 2010Workshop on Methods in Neuroinformatics
  2. 2. The Neuroscience Information Framework: Discovery andutilization of web-based resources for neurosciencehttp://neuinfo.org UCSD, Yale, Cal Tech, George Mason, Washington Univ A portal for finding andusing neuroscienceresources A consistent frameworkfor describing resources Provides simultaneoussearch of multiple typesof information,organized by category Supported by anexpansive ontology forneuroscience Utilizes advancedtechnologies to searchthe “hidden web”
  3. 3. Brief History of NIF• Outgrowth of Society for Neuroscience NeuroinformaticsCommittee– Neuroscience Database Gateway: a catalog of neurosciencedatabases• “Didn’t I fund this already?”– Over 2500 databases are on-line; no one can go to them all• “Why can’t I have a Google for neuroscience”– “Easy”, comprehensive, pervasive• Phase I-II: Funded by a broad agency announcement from theNIH Neuroscience Blueprint– Feasibility• Current phase: Started Sept 2008How can we provide a consistent and easy to implementframework for those who are providing resources, eg., data,and those looking for these data and resources➤ Both humans and machines
  4. 4. The Problem• Over 2000 databases have been identifiedthrough NIF– Researchers can’t visit them all– Most content from these resources not easily foundthrough standard search engines– Even more structured content on the web• Databases provide domain specific views of data– NIF provides a snapshot of information in a simple tounderstand form that can be further explored in thenative database– Providing a biomedical science based semanticframework for resource description and search
  5. 5. NIF uniquely provides access to thelargest registry of neuroscienceresources available on the webDateDataFederationDataFederationRecords Catalog Web IndexLiteratureCorpusNIFVocabulary9/2008 5 60,420* 388 113,458 67,000 18,884†7/2009 18 4,393,744* 1,605 497,740 101,627 17,0865/2010 55 23,228,658 2,871 1,184,261All(PubMed) 53,023% yearlyincrease 205 429 79 138 181% overallincrease 1,000 38,345 640 944 210* Numbers for initial sources were generated by examining current source content† First year of NIF contract involved re-factoring of ontology
  6. 6. Guiding principles of NIF• Builds heavily on existing technologies (open source tools andontologies)• Information resources come in many sizes and flavors• Framework has to work with resources as they are, not as we wishthem to be– Federated system; resources will be independently maintained– Developed for their own purpose with different levels of resources• No single strategy will work for the current diversity of neuroscienceresources• Trying to design the framework so it will be as broadly applicable aspossible to those who are trying to develop technologies• Interface neuroscience to the broader life science community• Take advantage of emerging conventions in search, semanticweb, linked dataand in building web communities
  7. 7. http://neuinfo.orgA Quick Tour of the NIF
  8. 8. Domain Enhanced Search for NeuroscienceNIF now searches more than 55 databases with informationneuronal descriptions, neuronal morphology, connectivity, chemicalcompounds…
  9. 9. Ontology Based Search Refinement
  10. 10. Diverse Database ContentNeuroMorpho.orgNeuronDB
  11. 11. Concept-based search• Search Google: GABAergic neuron• Search NIF: “GABAergic neuron”– NIF automatically searches for types ofGABAergic neuronsTypes ofGABAergicneurons
  12. 12. Concept-based search
  13. 13. Use of Ontologies within NIF• Controlled vocabulary for describing type of resourceand content– Database, Image, Parkinson’s disease• Entity-mapping of database and data content• Data integration across sources• Search: Mixture of mapped content and string-basedsearch– Different parts of NIF use the vocabularies in different ways– Utilize synonyms, parents, children to refine search– Increasing use of other relationships and logical inferencing• Generation of semantic content (i.e. RDF, LinkedData)
  14. 14. http://neurolex.orgBuilding the NIF Ontologies
  15. 15. Modular OntologiesNIFSTDNSFunctionMoleculeInvestigationSubcellularAnatomyMacromolecule GeneMoleculeDescriptorsTechniquesReagent ProtocolsCellInstrumentsNSDysfunctionQualityMacroscopicAnatomyOrganismResource• Single inheritancetrees with minimalcross domain andintradomainproperties• Orthogonal:Neuroscientistsdidn’t like toomany choices• Human readabledefinitions (notcomplete yet)• Set of expanded vocabularies largely imported from existingterminological resources• Adhere to ontology best practices as we understood them• Built from existing resources when possible• Standardized to same upper ontology: BFO• Encoded in OWL DL• Provides mapping to source terminologies• Provides synonyms, lexical variants, abbreviations
  16. 16. Anatomy Cell TypeCellularComponentSmallMoleculeNeuro-transmitterTransmembraneReceptorGABA GABA-RTransmitterVesicleTerminal AxonBoutonPresynapticdensityPurkinjeCellNeuronDentateNucleusNeuronCNSCpllection ofDeep CerebellarNucleiPurkinjeCell LayerDentateNucleusCytoarchitecturalPart ofCerebellar CortexExpressed inLocated in“Bridge files”
  17. 17. NIF Cell• NIF has made significant enhancements to itscell ontology– Expanded neuron list– Generated neuronal classifications based onneurotransmitter, brain region, molecules,morphology, circuit role– Recommended standard naming convention– Is working with the International NeuroinformaticsCoordinating Facility through the PONS (program inontologies for neural structures) program• Creating Knowledge base for neuronal classification basedon properties
  18. 18. Neurolex Wikihttp://neurolex.org•NIF has posted itsvocabularies in Wiki form(Semantic MediaWiki)•Simplified interface forontology construction andrefinement•Custom forms for neuronsand brain regions•Semantic linking betweencategory pages•Significant knowledge base•Curation  NIFSTD
  19. 19. NeuroLex and NeuroML“There was further discussion of how to define specifictypes of morphological groups such as apical dendrites,basal dendrites, axons, etc. Several options includehaving predefined names for common types or linking toontologies that define these types. We suggest addingtags or rdf for metadata that provide NeuroLex ontologyids to groups. We propose to begin with simple tags, andwhen a tag is present, one should assume it indicates “isa”. If more complicated semantic information is needed,we can use rdf in a way that is similar to SBML.”NeuroML Development Workshop 2010http://www.neuroml.org/files/NeuroMLWorkshop2010.pdf
  20. 20. http://neuinfo.orgProviding communityaccess
  21. 21. Access at various levels…• A search portal (link to NIF advanced search interface) for researchers,students, or anyone looking for neuroscience information, tools, data ormaterials.• Access to content normally not indexed by search engines, i.e, the "hiddenweb”• Tools for resource providers to make resources more discoverable, e.g.,ontologies, data federation tools, vocabulary services• Tools for promoting interoperability among databases• Standards for data annotation• The NIFSTD ontology covering the major domains of neuroscience, e.g.,brain anatomy, cells, organisms, diseases, techniques• Services for accessing the NIF vocabulary and NIF tools• Best practices for creating discoverable and interoperable resources• Data annotation services: NIF experts can enhance your resource throughsemantic tagging• NIF cards: Easy links to neuroscience information from any web browser• Ontology services: NIF knowledge engineers can help create or extendontologies for neuroscience
  22. 22. http://wholebraincatalog.orgIntegration of NIFservices and ontologies
  23. 23. WBC and Simulation VisualizationDemonstrates theneurogenesissimulation drivenby the model ofAimone et al.,2009 from theGage lab at theSalk Institutewithin the WholeBrain Cataloghttp://www.youtube.com/watch?v=1YzfXv4yNzg
  24. 24. WBC and NeuroConstructhttp://www.neuroml.org/tool_support.phpA network model of the cerebellar granule cell layer which can be fullyexpressed as a Level 3 NeuroML file. Visualised in the Whole Brain Catalog(left), and neuroConstruct (right)http://wiki.wholebraincatalog.org/wiki/Running_Simulations
  25. 25. NIF cardsSimple tool for linking searchresults to other sources ofinformationNIF literature results display for “Cerebellum”; concepts in NIF ontologies highlighted and linked to more information through NIFknowledge basehttp://nifcards.neuinfo.org/nifstd/anatomical_structure/birnlex_1489.html
  26. 26. Providing Semantic ContentRDF data / SPARQL Queries
  27. 27. The NIF Team• Maryann Martone, UCSD-PI• Jeff Grethe, UCSD-Co PI• Amarnath Gupta, UCSD-Co-PI• Ashraf Memon, UCSD, Project Manager• Anita Bandrowski, UCSD, NIF Curator• Fahim Imam, UCSD, Ontology Engineer• David Van Essen, Wash U, Co-PI• Erin Reid, Wash U• Gordon Shepherd, Yale, Co-PI• Perry Miller, Yale• Luis Marenco, Yale• Rixin Wang, Yale• Paul Sternberg, Cal Tech, Co-PI• Hans Michael-Muller, Cal Tech• Arun Ragarajan, Cal Tech• Giorgio Ascoli, George Mason,Co-PI• Sridevi Polavaram, GeorgeMason• Vadim Astakhov, UCSD• Andrea Arnaud-Stagg, UCSD• Lee Hornbrook, UCSD• Jennifer Lawrence, UCSD• Irfan Baig, UCSD student• Anusha Yelisetty, UCSDstudent• Timothy Tsui, UCSD student• Chris Condit, UCSD• Xufei Qian, UCSD• Larry Liu, UCSD

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