NIFSTD and NeuroLex: A Comprehensive Ontology Development Based on Multiple Biomedical Ontologies and Community Involvement
•
2 likes•1,457 views
Fahim Imam
Recommended
Recommended
More Related Content
What's hot
What's hot (16)
Viewers also liked
Viewers also liked (20)
Similar to NIFSTD and NeuroLex: A Comprehensive Ontology Development Based on Multiple Biomedical Ontologies and Community Involvement
Similar to NIFSTD and NeuroLex: A Comprehensive Ontology Development Based on Multiple Biomedical Ontologies and Community Involvement (20)
More from Neuroscience Information Framework
More from Neuroscience Information Framework (11)
Recently uploaded
Recently uploaded (20)
NIFSTD and NeuroLex: A Comprehensive Ontology Development Based on Multiple Biomedical Ontologies and Community Involvement
- 1. 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
- 2. NIF last year NIFSTD Ontologies neuinfo.org 2
- 3. 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
- 4. 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
- 5. 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
- 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 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)
- 7. One problem NIF LAMHDI Bioportal NIFSTD Ontologies neuinfo.org 7
- 8. 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.
- 9. NIF STANDARD ONTOLOGIES (NIFSTD) • Set of modular ontologies – Covering neuroscience relevant terminologies – Comprehensive 50,000+ distinct concepts + synonyms • Expressed in OWL-DL language • Closely follows OBO community best practices – As long as they seem practical • Avoids duplication of efforts – Standardized to the same upper level ontologies, e.g., – Basic Formal Ontology (BFO), OBO Relations Ontology (OBO-RO), Phonotypical Qualities Ontology (PATO) – Relies on existing community ontologies e.g., CHEBI, GO, PRO, OBI etc. 9NIF Standard Ontologies • Modules cover orthogonal domain e.g. , Brain Regions, Cells, Molecules, Subcellular parts, Diseases, Nervous system functions, etc. Bill Bug et al. NIFSTD Ontologies neuinfo.org 9
- 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 10NIF Standard Ontologies NIFSTD Ontologies neuinfo.org 10
- 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 neurotransmitter 5. It has ‘Spiny dendrite quality’ Class name Asserted defining (necessary & sufficient) expression Cerebellum 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 11NIF Standard Ontologies NIFSTD Ontologies neuinfo.org 11
- 12. NIFSTD CURRENT VERSION 12NIF Standard Ontologies • Key feature: Includes a set useful defined concepts to have inferred classifications of asserted concepts NIFSTD Ontologies neuinfo.org 12
- 13. NIFSTD BRIDGE FILES NIF- Molecule NIF- Anatomy NIF-Cell NIF- 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 bridging module. NIF-Neuron-NT-Bridge.owl Bridge
- 14. CONCEPT-BASED SEARCH • Search Google: GABAergic neuron • Search NIF: GABAergic neuron – NIF automatically searches for types of GABAergic neurons Types of GABAergic neurons NIFSTD Ontologies neuinfo.org 14
- 15. 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 NIF Standard Ontologies 15 Stephen D. Larson et al. NIFSTD Ontologies neuinfo.org 15
- 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 scales NIF Standard Ontologies 16 NIFSTD Ontologies neuinfo.org 16
- 17. NIF Standard Ontologies 17 Domain External Source Import/ Adapt NIFSTD Module Organism taxonomy 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-Molecule NIF-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 Import NIF-Subcellular Cell CCDB, NeuronDB, NeuroMorpho.org . terminologies; pending: OBO Cell Ontology Adapt NIF-Cell 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. Adapt NIF- Investigation Investigation: resource type NIF, OBI, NITRC, Biomedical Resource Ontology (BRO) Adapt NIF-Resource Biological Process Gene Ontology’s (GO) biological process in whole Import NIF- BioProcess NIFSTD EXTERNAL COMMUNITY SOURCES NIFSTD Ontologies neuinfo.org 17
- 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 NIF Standard Ontologies 18 NIFSTD AND DOID COLLABORATION
- 19. WORKING TO INCORPORATE COMMUNITY • NeuroPsyGrid – http://www.neuropsygrid.org • NDAR Autism Ontology – http://ndar.nih.gov • Disease Phenotype Ontology – http://openccdb.org/wiki/index.php/Disease_Ontology • Cognitive Paradigm Ontology (CogPO) – http://wiki.cogpo.org • Neural ElectroMagnetic Ontologies (NEMO) – http://nemo.nic.uoregon.edu 19NIF Standard Ontologies NIFSTD Ontologies neuinfo.org 19
- 20. SUMMARY AND CONCLUSIONS • NIF project with NIFSTD is an example of how ontologies can be used to enhance search and data integration across diverse resources • NIFSTD continues to create an increasingly rich knowledgebase for neuroscience integrating with other life science community • NIF encourages the use of community ontologies for resource providers 20NIF Standard Ontologies NIFSTD Ontologies neuinfo.org 20
- 21. Some questions: • If someone asserts sameness should that be treated differently by others? – How would we know? Should there be a tool that would search these assertions? • Can a lexicon be used as a set of base classes for use in ontology building? – We took this approach with nervous system cells by adding properties, then asserted hierarchies: • GABAergic neuron • Cerebellum neuron • Intrinsic neuron NIFSTD Ontologies neuinfo.org 21
- 22. Even more questions? • If a term has no definition, then should it exist in the lexicon? • Do tests belong in ontologies? NIFSTD Ontologies neuinfo.org 22
- 23. • NIFSTD Ontologies http://ontology.neuinfo.org • NeuroLex Wiki http://neurolex.org • Neuroscience Information Framework (NIF) http://neuinfo.org 23NIF Standard Ontologies NIFSTD Ontologies neuinfo.org 23
Editor's Notes
- A critical component of Neuroscience Information Framework project (http://neuinfo.org), 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 (http://ontology.neuinfo.org) 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="http://ontology.neuinfo.org/NIF/BiomaterialEntities/NIF-Neuron-NT-Bridge.owl"/> <owl:importsrdf:resource="http://ontology.neuinfo.org/NIF/BiomaterialEntities/NIF-Neuron-NT-Bridge_Inferred.owl"/> <owl:importsrdf:resource="http://ontology.neuinfo.org/NIF/BiomaterialEntities/NIF-Neuron-BrainRegion-Bridge.owl"/> <owl:importsrdf:resource="http://ontology.neuinfo.org/NIF/BiomaterialEntities/NIF-Neuron-SomaLocation-Bridge.owl"/> <owl:importsrdf:resource="http://ontology.neuinfo.org/NIF/BiomaterialEntities/NIF-Neuron-BR-Inferred.owl"/>
- 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 (http://neurolex.org), 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 Serviceshttps://confluence.crbs.ucsd.edu/display/NIF/OntoQuestMain.NCBO Bioportal (http://bioportal.bioontology.org/)Repository 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