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Neuroscience Information Framework Ontologies: Nerve cells in Neurolex and NIFSTD
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Neuroscience Information Framework Ontologies: Nerve cells in Neurolex and NIFSTD


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Maryann Martone …

Maryann Martone
Cell Ontology Workshop, Jackson Laboratories, Bar Harbor, ME
May 18-20, 2010

Published in: Technology, Education

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  • 1. Neuroscience InformationFramework Ontologies:Nerve cells in Neurolexand NIFSTDMaryann MartoneUniversity of California, San Diego
  • 2. The Neuroscience Information Framework: Discoveryand utilization of web-based resources for neurosciencehttp://neuinfo.orgUCSD, Yale, Cal Tech, George Mason, Washington UnivSupported by NIH Blueprint A portal for finding andusing neuroscienceresources A consistent frameworkfor describingresources Provides simultaneoussearch of multiple typesofinformation, organizedby category Supported by anexpansive ontology forneuroscience Utilizes advancedtechnologies to searchthe “hidden web”
  • 3. Modular ontologies for neuroscience NIF covers multiple structural scales and domains of relevance to neuroscience Incorporated existing ontologies where possible; extending them for neuroscience where necessary Normalized under the Basic Formal Ontology: an upper ontology used by the OBO Foundry Single inheritance Cross-domain relationships are being built in separate filesNIFSTDNSFunctionMolecule InvestigationSubcellularAnatomyMacromolecule GeneMoleculeDescriptorsTechniquesReagent ProtocolsCellInstrumentsBill BugNSDysfunctionQualityMacroscopicAnatomyOrganismResource
  • 4. Ontologies imported/used byNIF Gene Ontology Biological Process ChEBI (Mireot) PATO PRO (Bridge) BIRNLex OBI (Bridge) Disease Ontology (Mireot) Foundational Model of Anatomy (Mireot) Gene Ontology Cellular Component (Bridge) Neuronames, BAMS (Mapped) Cell Ontology (don’t use)Practical limitations imposed by tools and expertise; constantly addressingchallenges involved in using community ontologies that are evolving inproduction information systems
  • 5. NIF Cell Identity: Unique identifier nlx_neuron_nt_090803 Asserted hierarchy: Unique types of neuron Uniqueness assured by concatenating brain region with cell type Hippocampus CA1 pyramidal cell Neocortex layer 2/3 pyramidal cell Standard naming convention Major brain region, subregion, distinguishing characteristics, cell Bridge files: Cross module relations A set of properties that define it, e.g., part of, has neurotransmitter, has role Properties assigned at level of part of neuron where appropriate (brain region, molecule) Others at level of cell class: spiny, physiological Logical restrictions for defined classes: Necessary and sufficient conditions to identify members ofthat class GABAergic neuron is any member of class neuron has neurotransmitter GABAGordon Shepherd, Giorgio Ascoli, Kei Cheung, Maryann Martone, Fahim Imam, Stephen Larson
  • 6. CerebellumPurkinje cellsomaCerebellumPurkinje celldendriteCerebellumPurkinje cellaxonCerebellum granulecell layerCerebellumPurkinje cell layerCerebellummolecular layerHaspartHaspartHaspartIs part ofIs part ofIs part ofShared building blocks: Modular ontologiesjoined in bridge filesCalbindinCerebellumPurkinje neuronCerebellar cortexHas partHas partHas partIP3receptorNIF MoleculeNIF AnatomyNIF Subcellular
  • 7. DefinedClasses Neuron by brain region NIF Cell, NIF Subcellular, NIF Anatomy Hippocampus neuron is a type of neuron has part soma is part of any part ofhippocampus Neuron by molecule NIF Cell, NIF molecule Neurotransmitter GABAergic neuron By molecular constituent Parvalbumin-containing neuron Neuron by role Circuit role Principal neuron vs intrinsic neuron Functional role Sensory neuron, motor neuron Neuron by morphological quality Spiny neuron Pyramidal neuronSometimes use OBOrelations, sometimes short cuts that canbe expressed in OBO relations
  • 8. String vs concept based search
  • 9. Currently ~250 proposed classes
  • 10. Neuron qualities
  • 11. What can account for signalshere?Neurons are highly ramifying and polarized cells
  • 12. Properties assigned at level of part ofneuron
  • 13. Community contributions: Neurolex Semantic Wiki Good teaching tool for thepower of more formalsemantics Knowledge base easier toview, index and navigate Lighter weight and morehuman friendly than moreformal ontologies and tools Build knowledge from basiclexical elements and a fewrelationships Categories are linkedthrough explicit properties Currently over 10,000category pages Use relationship shortcutsthat can be expressed inOBO relations Working with internationalgroup of neuroscientists tocontribute content coveringdifferent domains anddevelop new content Stephen Larson and INCF
  • 14. Detailed properties Custom form based interface Olfactory bulb (main) mitral cell New version just about to be released References for each attribute Meant to be used by anyone Curators (me) go through and translate Translated into NIFSTD once finalized Some short cut relations translated e.g., soma located in = neuron has part soma is part of somebrain region
  • 15. Inferring the Mesoscale The NIFSTD is expressed inOWL (Web Ontology Language) Supports reasoning and inference Through integration with otherontologies covering grossanatomy and molecularentities, we are working tocreate inferences across scales Analyze locally; infer globally If there’s an axon terminal, thenthere must be an axon…Stephen Larson
  • 16. Desiderata 1000’s of neuron types; one group can’t do them all Early efforts all concentrate on same cell types (easyones) INCF has opportunity to coordinate different groups so wecan aggregate effort Standard set of properties and standard syntax forlogical definitions Trying to base them on REL, but we take shortcuts forpractical reasons Relation to GO function