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NIFSTD and NeuroLex: A Comprehensive Ontology Development Based on Multiple Biomedical Ontologies and Community Involvement
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NIFSTD and NeuroLex: A Comprehensive Ontology Development Based on Multiple Biomedical Ontologies and Community Involvement


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Fahim Imam

Fahim Imam

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  • A critical component of Neuroscience Information Framework project (, NIF Standard (NIFSTD) is a set of modular ontologies covering a comprehensive set of neuroscience terminologies. This highlight the key features of NIFSTD and how NIF uses it to enable an effective concept-based search against a diverse collection of neuroscience resources over the web. Closely follows the best practices of Open Biological Ontology (OBO) community, And is standardized to the same upper level ontologies for biomedical sciences and promotes easy extensionNIFSTD is designed to collate existing neuroscience terminologies into a coherent set of orthogonal and interoperable modules. Relies on existing ontologies as the initial building block e.g., CHEBI, GO, PRO, OBI etc.
  • Within NIF-Cell module, along with other neurons, we have cerebellum neuron and we say that it’s a neuron. We also assert other things that are known about this type of neuron.
  • We have recently released NIFSTD v.1.8 ( where the key feature is the inclusion of various cross-domain bridge modules. These modules contain necessary restrictions along with a set of defined classes to infer useful classification of neurons and molecules. These classifications include neurons in terms of their soma locations in different brain regions (e.g., Hippocampal neurons, Cerebellum neurons), neurons by their neurotransmitter (e.g., GABAergic neuron) and circuit roles (e.g., intrinsic neurons), classification of molecules and chemicals by their molecular roles (e.g., Drug of abuse, Neurotransmitter). We keep the logical restrictions and definitions on required set of classes assigned in a separate bridge file so that the core universal hierarchies in different modules like NIF-Cell, NIF-Molecule, or NIF-Anatomy are open to easy extensions for broader communities without worries about specific, NIF-centric views. It keep the modularity principles intact and useful. You can always exclude the bridging module just to focus on the core module and build your own restrictions that you think is appropriate for your application.
  • <owl:importsrdf:resource=""/> <owl:importsrdf:resource=""/> <owl:importsrdf:resource=""/> <owl:importsrdf:resource=""/> <owl:importsrdf:resource=""/>
  • Having the defined classes enabled us to have useful concept-based queries through the NIF search interface. For example, while searching for ‘GABAergic neuron’, the system recognizes the term as ‘defined’ from the ontology, and looks for any neuron that has GABA as a neurotransmitter (instead of the lexical match of the search term) and enhances the query over those inferred list of neurons.
  • One of the largest roadblocks that we encountered was the lack of tools for the neuroscience community to contribute their knowledge into a formal ontology like NIFSTD. NIF has created NeuroLex (, a semantic wiki interface for the domain experts as an easy entry point to the NIFSTD contents. It has been extensively used in the area of neuronal cell types where NIF is working with a group of neuroscientists to create a comprehensive list of neurons and their properties. While the properties in NeuroLex are meant for easier interpretation, the restrictions in NIFSTD are more rigorous and based on standard OBO-RO relations.We organize our known knowledge within NeuroLex i.e., what can we say about a concept e.g., Cerebellum Purkeje neuron is a neuron whose soma resides within Cerebellum brain region, it has_role projection neuron role, it has GABA as a Neurotransmitter and so on.
  • Ontologies available as OWL file, RDF and through Web Services Bioportal ( of ontologies for biomedical research199 ontologies (including NIFSTD)Contains many mappingsProvides annotation servicesINCF Program on Ontologies for Neural StructuresNeuronal Registry Task ForceDescription of neural propertiesStructural LexiconDescription of properties across scales
  • To initiate the process of finding the overlaps between NIFSTD and DO, I have extracted a list of possible terms along with their IDs into the attached spreadsheet. The extraction process was automatic and based on maximum lexical similarity between the term lebels or synonymous terms (based on BioPortal mapping script). There are some obviouse non-sensical mapping that needs to be excluded from the list. Here are few things to consider:1. The term 'Cancer' refers to an Organism in NIFSTD and therefore should not be mapped with DO's 'Cancer'.2. Some of the terms in DO matches with GO, PATO, SO, and NIF-Anatomy. We need to decide on those as to where should they belong natively, or even if those mappings are even valid. Some of the term labels in DO requires some revision e.g., 'paralysed' is a PATO quality; while it's synonym from DO did match with NIFSTD's 'Paralysis', I'm not sure why 'paralyzed' should be the label.3. Some of the terms from NIFSTD maps with multiple terms with DO due to their synonymous similariuty. We need to go over them and decide the distinctions. For example, NIFSTD's  'Depressive Disorder' maps with all four of the following terms from DO:          DOID: endogenous depression        DOID: melancholia        DOID: neurotic depression        DOID: unipolar depressionSeems like the synonyms for the term in NIFSTD are not valid and therefore be corrected.
  • The NIF project provides an example of how ontologies can be used to enhance search and data integration across diverse resources. As the project moves forward, we are using NIFSTD to build an increasingly rich knowledgebase for neuroscience that integrates with the larger life science community
  • Transcript

    • 1. NIF: A COMPREHENSIVE ONTOLOGY FORNEUROSCIENCE & PRACTICAL GUIDE FORDATA-ONTOLOGY INTEGRATIONMaryann E. MARTONE, Fahim IMAM, Anita Bandrowski,Stephen LARSON, Georgio ASCOLI, Gordon SHEPHERD, Jeffery S. GRETHE,Amarnath GUPTAUniv. of California, San Diego, CA; George Mason Univ., Fairfax, VA; Yale Univ.,New Haven, CTFebruary 8, 2011Funded in part by the NIH NeuroscienceBlueprint HHSN271200800035C via NIDA.NEUROSCIENCE INFORMATION FRAMEWORKNIFSTD Ontologies 1
    • 2. NIF last yearNIFSTD Ontologies 2
    • 3. NIF today• ~30M data records from 68 databases,NIF registry (3600 software tools,databases etc) + full text publicationsearch– Focus of development now is onintegration of data with literature– Better search of data (SKOS?)• Annotation of data, now automated, willbecome slightly more manual (we will assertthe contents of columns that match parts ofontologies)NIFSTD Ontologies 3
    • 4. NIF: DISCOVER AND UTILIZE WEB-BASEDNEUROSCIENCE RESOURCES A portal to finding andusing neuroscienceresources A consistent frameworkfor describing resources Provides simultaneoussearch of multiple typesof information, organizedby category NIFSTD Ontology, acritical component Enables concept-based searchUCSD, Yale, Cal Tech, George Mason, Harvard MGHSupported by NIH BlueprintNIFSTD Ontologies 4
    • 5. NIF ‘dips’ into the lexicon for generalsearches like ‘cerebellum’ or‘ontology,’ where users can contributeknowledge, but bring data into thelexicon by using a application thatcalls our web services
    • 6. NIF today• Ontology-based search– Search requires all search terms: synonyms/acronyms/lexical variation– Added gene: and other : searches are coming (toxin: drug:)– Application logic: String match to multiple ontology terms = bring backall (e.g., striatum and caudate putamen)– Collapse duplicate classes by bridge files: same as relationship (Fahim)– Heavy use of defined classes (GABAergic neuron, hippocampal neuron,drug of abuse etc)
    • 7. One problemNIF LAMHDIBioportalNIFSTD Ontologies 7
    • 8. Cow example?• Description of nose vs. tail:which is more valid?• Should they point to the sameentity?• Is a mapping file the right placeto keep the knowledge that classA is related to class B, or shouldwe assert sameness withMireot?vs.
    • 9. NIF STANDARD ONTOLOGIES (NIFSTD)• Set of modular ontologies– Covering neuroscience relevantterminologies– Comprehensive 50,000+ distinctconcepts + synonyms• Expressed in OWL-DL language• Closely follows OBO communitybest practices– As long as they seem practical• Avoids duplication of efforts– Standardized to the same upper levelontologies, e.g.,– Basic Formal Ontology (BFO), OBORelations Ontology (OBO-RO),Phonotypical Qualities Ontology (PATO)– Relies on existing community ontologiese.g., CHEBI, GO, PRO, OBI etc.9NIF Standard Ontologies• Modules cover orthogonal domaine.g. , Brain Regions, Cells, Molecules,Subcellular parts, Diseases,Nervous system functions, etc.Bill Bug et al.NIFSTD Ontologies 9
    • 10. ABOUT ONTOLOGY• “Explicit specification of conceptualization”- Tom Gruber• Organizing the concepts involved in a domaininto a hierarchy and• Precisely specifying how the concepts are‘related’ with each other (i.e., logical axioms)• Explicit knowledge are asserted but implicitlogical consequences can be inferred– A powerful feature of an ontology10NIF Standard OntologiesNIFSTD Ontologies 10
    • 11. Class name Asserted necessary conditionsCerebellum Purkinje cell 1. Is a ‘Neuron’2. Its soma lies within Purkinje cell layer of cerebellar cortex’3. It has ‘Projection neuron role’4. It uses ‘GABA’ as a neurotransmitter5. It has ‘Spiny dendrite quality’Class name Asserted defining (necessary & sufficient) expressionCerebellum neuron Is a ‘Neuron’ whose soma lies in any part of the ‘Cerebellum’ or‘Cerebellar cortex’Principal neuron Is a ‘Neuron’ which has ‘Projection neuron role’, i.e., a neuronwhose axon projects out of the brain region in which its soma liesGABAergic neuron Is a ‘Neuron’ that uses ‘GABA’ as a neurotransmitterONTOLOGY – ASSERTED KNOWLEDGE11NIF Standard OntologiesNIFSTD Ontologies 11
    • 12. NIFSTD CURRENT VERSION12NIF Standard Ontologies• Key feature: Includes a set useful defined conceptsto have inferred classifications of asserted conceptsNIFSTD Ontologies 12
    • 13. NIFSTD BRIDGE FILESNIF-MoleculeNIF-AnatomyNIF-CellNIF-SubcellularNIFSTDNIF-Neuron-BrainRegion-Bridge.owlAllows people to assert their own restrictions in a different bridge filewithout worrying about NIF-specific view of the restriction on core modules.Cross-module relations amongclasses are assigned in a separatebridging module.NIF-Neuron-NT-Bridge.owlBridge
    • 14. CONCEPT-BASED SEARCH• Search Google: GABAergic neuron• Search NIF: GABAergic neuron– NIF automatically searches for types ofGABAergic neuronsTypes of GABAergicneuronsNIFSTD Ontologies 14
    • 15. NIFSTD AND NEUROLEX WIKI• Semantic wiki platform• Provides simple forms forstructured knowledge• Can add concepts,properties• Generate hierarchieswithout having to learncomplicated ontology tools• Good teaching tool forprinciples behindontologies• Community can contribute– Each term gets its own uniqueIDNIF Standard Ontologies15Stephen D. Larson et al.NIFSTD Ontologies 15
    • 16. ACCESS TO SHARED ONTOLOGIES• NIFSTD is available as– OWL Format– RDF and SPARQL Endpoint• Specific contents through web services–• Available through NCBO Bioportal– Repository of biomedical ontologies– 199 ontologies including NIFSTD– Provides annotation and mapping services–• INCF Program on Ontologies forNeural Structure– Neuronal Registry Task Force: Description ofneural properties– Structural Lexicon: Description of structuresacross scalesNIF Standard Ontologies 16NIFSTD Ontologies 16
    • 17. NIF Standard Ontologies 17Domain External Source Import/AdaptNIFSTDModuleOrganismtaxonomyNCBI Taxonomy, GBIF, ITIS, IMSR, Jackson Labs mouse catalog; . Specificallythe taxonomy of model organisms in common use by neuroscientistsAdapt NIF-OrganismMolecules IUPHAR ion channels and receptors, Sequence Ontology (SO); pending: NCBI,NCBI Entrez Protein, NCBI RefSeq, NCBI Homologene; NIDA drug lists, ChEBI,and Protein Ontology (PRO)AdaptIUPHAR;import PRONIF-MoleculeNIF-ChemicalSub-cellular Sub-cellular AnatomyOntology (SAO). Extracted cell parts and subcellularstructures from SAO-CORE . Soon to be importing GO Cellular Component withmappingImport NIF-SubcellularCell CCDB, NeuronDB, . terminologies; pending: OBO CellOntologyAdapt NIF-CellGross Anatomy NeuroNames extended by including terms from BIRN, SumsDB,,etc;Multi-scale representation of Nervous System Mac Macroscopic anatomyAdapt NIF-GrossAnatomyNervous systemfunctionSensory, Behavior, Cognition terms from NIF, BIRN,, MeSH, andUMLSAdapt NIF-FunctionNervous systemdysfunctionNervous system disease from MeSH, NINDS terminology; pending: OMIM Adapt/Import NIF-DysfunctionPhenotypicqualitiesPATO Imported as part of the OBO foundry core Import NIF-QualityInvestigation:reagentsOverlaps with molecules above, especially RefSeq for mRNA, ChEBI, Sequenceontology; pending: Protein Ontologyimport NIF-InvestigationInvestigation:instruments,protocols, plansBased on Ontology for Biomedical Investigation (OBI ) to include entities forbiomaterial transformations, assays, data collection, data transformations.Adapt NIF-InvestigationInvestigation:resource typeNIF, OBI, NITRC, Biomedical Resource Ontology (BRO) Adapt NIF-ResourceBiologicalProcessGene Ontology’s (GO) biological process in whole Import NIF-BioProcessNIFSTD EXTERNAL COMMUNITY SOURCESNIFSTD Ontologies 17
    • 18. • So Far..– Overlaps are detected and mappings were carefullycurated– Included a bridging module that asserts equivalenciesbetween NIF-Dysfunction and DOID• We could MIREOT DOID Classes as well• Drawback was loosing NIF’s annotation properties.• Having the bridgeing module allowed us to have contentsfrom both ontologies and to keep the mappings as well. (Didthe same with NIF-Subcellular and GO-Cell Component)• Collaborating on Mental Disorder - Addiction/Substance related disorder with DOID group• Taking a look at Barry Smith’s paper on Foundations for arealist ontology of mental diseaseNIF Standard Ontologies 18NIFSTD AND DOID COLLABORATION
    • 19. WORKING TO INCORPORATE COMMUNITY• NeuroPsyGrid–• NDAR Autism Ontology–• Disease Phenotype Ontology–• Cognitive Paradigm Ontology (CogPO)–• Neural ElectroMagnetic Ontologies (NEMO)– http://nemo.nic.uoregon.edu19NIF Standard OntologiesNIFSTD Ontologies 19
    • 20. SUMMARY AND CONCLUSIONS• NIF project with NIFSTD is an example of howontologies can be used to enhance search anddata integration across diverse resources• NIFSTD continues to create an increasingly richknowledgebase for neuroscience integrating withother life science community• NIF encourages the use of community ontologiesfor resource providers20NIF Standard OntologiesNIFSTD Ontologies 20
    • 21. Some questions:• If someone asserts sameness should that betreated differently by others?– How would we know? Should there be a tool thatwould search these assertions?• Can a lexicon be used as a set of base classes foruse in ontology building?– We took this approach with nervous system cells byadding properties, then asserted hierarchies:• GABAergic neuron• Cerebellum neuron• Intrinsic neuronNIFSTD Ontologies 21
    • 22. Even more questions?• If a term has no definition, then should it existin the lexicon?• Do tests belong in ontologies?NIFSTD Ontologies 22
    • 23. • NIFSTD Ontologies• NeuroLex Wiki• Neuroscience Information Framework(NIF)http://neuinfo.org23NIF Standard OntologiesNIFSTD Ontologies 23