NIF: A COMPREHENSIVE ONTOLOGY FOR NEUROSCIENCE & PRACTICAL GUIDE FOR DATA-ONTOLOGY INTEGRATION Maryann E. MARTONE, Fahim IMAM, Anita Bandrowski, Stephen LARSON, Georgio ASCOLI, Gordon SHEPHERD, Jeffery S. GRETHE, Amarnath GUPTA Univ. of California, San Diego, CA; George Mason Univ., Fairfax, VA; Yale Univ., New Haven, CT February 8, 2011 Funded in part by the NIH Neuroscience Blueprint HHSN271200800035C via NIDA. NEUROSCIENCE INFORMATION FRAMEWORK NIFSTD Ontologies neuinfo.org 1
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N IF : A C OMPREHENSIVE O NTOLOGY FOR N EUROSCIENCE & P RACTICAL G UIDE FOR D ATA -O NTOLOGY I NTEGRATION Maryann E. MARTONE, Fahim IMAM, Anita Bandrowski,
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NIF: A COMPREHENSIVE ONTOLOGY FOR NEUROSCIENCE & PRACTICAL GUIDE FOR DATA-
ONTOLOGY INTEGRATIONMaryann E. MARTONE, Fahim IMAM, Anita Bandrowski,
Stephen LARSON, Georgio ASCOLI, Gordon SHEPHERD, Jeffery S. GRETHE, Amarnath GUPTA
Univ. of California, San Diego, CA; George Mason Univ., Fairfax, VA; Yale Univ.,
New Haven, CT
February 8, 2011Funded in part by the NIH Neuroscience Blueprint HHSN271200800035C via NIDA.
NEUROSCIENCE INFORMATION FRAMEWORK
NIFSTD Ontologies neuinfo.org 1
NIF last year
NIFSTD Ontologies neuinfo.org 2
NIF today
• ~30M data records from 68 databases, NIF registry (3600 software tools, databases etc) + full text publication search– Focus of development now is on
integration of data with literature– Better search of data (SKOS?)• Annotation of data, now automated, will
become slightly more manual (we will assert the contents of columns that match parts of ontologies)
NIFSTD Ontologies neuinfo.org 3
NIF: DISCOVER AND UTILIZE WEB-BASED NEUROSCIENCE RESOURCES
A portal to finding and using neuroscience resources
A consistent framework for describing resources
Provides simultaneous search of multiple types of information, organized by category
NIFSTD Ontology, a
critical component Enables concept-based search
UCSD, Yale, Cal Tech, George Mason, Harvard MGH
Supported by NIH Blueprint
NIFSTD Ontologies neuinfo.org 4
NIF ‘dips’ into the lexicon for general searches like ‘cerebellum’ or ‘ontology,’ where users can contribute knowledge, but bring data into the lexicon by using a application that calls our web services
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
back all (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)
One problemNIF LAMHDI
Bioportal
NIFSTD Ontologies neuinfo.org 7
Cow example?• Description of nose vs. tail:
which is more valid?• Should they point to the same
entity?• Is a mapping file the right place
to keep the knowledge that class A is related to class B, or should we assert sameness with Mireot?
vs.
NIF STANDARD ONTOLOGIES (NIFSTD)
• Set of modular ontologies – Covering neuroscience relevant
Subcellular parts, Diseases, Nervous system functions, etc.
Bill Bug et al.
NIFSTD Ontologies neuinfo.org 9
NIF Standard Ontologies 10
ABOUT ONTOLOGY
• “Explicit specification of conceptualization” - Tom Gruber
• Organizing the concepts involved in a domain into a hierarchy and
• Precisely specifying how the concepts are ‘related’ with each other (i.e., logical axioms)
• Explicit knowledge are asserted but implicit logical consequences can be inferred – A powerful feature of an ontology
NIFSTD Ontologies neuinfo.org 10
NIF Standard Ontologies 11
Class name Asserted necessary conditions Cerebellum 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 neuron
whose axon projects out of the brain region in which its soma lies GABAergic neuron Is a ‘Neuron’ that uses ‘GABA’ as a neurotransmitter
ONTOLOGY – ASSERTED KNOWLEDGE
NIFSTD Ontologies neuinfo.org 11
NIF Standard Ontologies 12
NIFSTD CURRENT VERSION
• Key feature: Includes a set useful defined concepts to have inferred classifications of asserted concepts
NIFSTD Ontologies neuinfo.org 12
NIFSTD BRIDGE FILES
NIF-Molecule
NIF-Anatomy
NIF-CellNIF-
Subcellular
NIFSTD
NIF-Neuron-BrainRegion-Bridge.owl
Allows people to assert their own restrictions in a different bridge file without worrying about NIF-specific view of the restriction on core modules.
Cross-module relations among classes are assigned in a separate
– NIF automatically searches for types of GABAergic neurons
Types of GABAergic neurons
NIFSTD Ontologies neuinfo.org 14
NIF Standard Ontologies
NIFSTD AND NEUROLEX WIKI
• Semantic wiki platform• Provides simple forms for
structured knowledge• Can add concepts,
properties• Generate hierarchies
without having to learn complicated ontology tools
• Good teaching tool for principles behind ontologies
• Community can contribute– Each term gets its own unique
ID
15
Stephen D. Larson et al.
NIFSTD Ontologies neuinfo.org 15
NIF Standard Ontologies 16
ACCESS TO SHARED ONTOLOGIES
• NIFSTD is available as– OWL Format http://ontology.neuinfo.org – RDF and SPARQL Endpoint
• Specific contents through web services – http://ontology.neuinfo.org/ontoquest.html
• Available through NCBO Bioportal– Repository of biomedical ontologies– 199 ontologies including NIFSTD– Provides annotation and mapping services– http://bioportal.bioontology.org/
• INCF Program on Ontologies for Neural Structure– Neuronal Registry Task Force: Description of neural
properties– Structural Lexicon: Description of structures across
NCBI Taxonomy, GBIF, ITIS, IMSR, Jackson Labs mouse catalog; . Specifically the taxonomy of model organisms in common use by neuroscientists
Adapt NIF-Organism
Molecules 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)
Adapt IUPHAR; import PRO
NIF-MoleculeNIF-Chemical
Sub-cellular Sub-cellular AnatomyOntology (SAO). Extracted cell parts and subcellular structures from SAO-CORE . Soon to be importing GO Cellular Component with mapping
Gross Anatomy NeuroNames extended by including terms from BIRN, SumsDB, BrainMap.org, etc;Multi-scale representation of Nervous System Mac Macroscopic anatomy
Adapt NIF-GrossAnatomy
Nervous system function
Sensory, Behavior, Cognition terms from NIF, BIRN, BrainMap.org, MeSH, and UMLS
Adapt NIF-Function
Nervous system dysfunction
Nervous system disease from MeSH, NINDS terminology; pending: OMIM
Adapt/Import
NIF- Dysfunction
Phenotypic qualities
PATO Imported as part of the OBO foundry core Import NIF-Quality
Investigation: reagents
Overlaps with molecules above, especially RefSeq for mRNA, ChEBI, Sequence ontology; pending: Protein Ontology
import NIF-Investigation
Investigation: instruments, protocols, plans
Based on Ontology for Biomedical Investigation (OBI ) to include entities for biomaterial transformations, assays, data collection, data transformations.
Gene Ontology’s (GO) biological process in whole Import NIF-BioProcess
NIFSTD EXTERNAL COMMUNITY SOURCES
NIFSTD Ontologies neuinfo.org 17
NIF Standard Ontologies 18
• So Far..– Overlaps are detected and mappings were carefully curated– Included a bridging module that asserts equivalencies
between 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 contents from
both ontologies and to keep the mappings as well. (Did the 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 a realist ontology of mental disease