NIF Functionalities and Capabilities

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NIF Functionalities and Capabilities

• Maryann E. Martone, Ph. D.

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NIF Technical Team

• Perry Miller, Yale
• Luis Marenco, Yale
• Yuli Li, Yale
• Arun Rangarajun, Cal Tech
• Hans-Michael Muller, Cal Tech
• Sridevi Polavaram, George Mason
• Jeff Grethe, UCSD
• Brian Sanders, UCSD
• Vadim Astakhov, UCSD
• Amarnath Gupta, UCSD
• Xufei Qian, UCSD
• Chris Condit, UCSD
• Bill Bug, UCSD
• Maryann Martone, UCSD

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NIF v1.0

• Provides unique system for discovery of diverse
  neuroscience resources from a single interface
• Databases and Knowledge bases
• Service provider animals, antibodies
• Portals
• Data in databases
• Literature
• Provides mechanisms for rapid integration of new
  resources with minimal effort
• Provides a practical yet advanced set of
  functions for resource discovery
• Flexible framework that adapts to domain
  evolution
• Customized for neuroscience
• Works with existing resources while providing
  means for better resources
• Built upon a strong semantic framework that lays
  groundwork for deep data integration and mining
  of neuroscience resources
• Most comprehensive vocabulary for neuroscience
  that links to other domains (60,000 terms)
• Builds upon existing NIH investments in
  infrastructure
• Databases
• BIRN
• Ontologies

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Current NIF resources

• NIF Registry
• 400 web resources annotated by humans with NIF
  vocabularies
• NIF Neuroscience Web
• Custom web index built using open source web
  tools (Nutch) from the NIF registry
• Neuroscience literature
• 15,000 articles, full text indexed using
  Textpresso tool
• NIF Data Federation
• Web accessible databases registered to NIF
  mediator for deep content query
• Limited number proof of concept
• Other portals
• Existing web resources that are themselves
  portals to resources
• Science.gov

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Demonstration of NIF System in ActionSingle
search for multiple resourcesQ1 Parkinsons
DiseaseQ2 Neurodegenerative Disease
NIF Prototype Interface
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Use Cases and Test Queries

• What software instruments are available for
  analyzing learning and memory functions?
• What databases provide phenotypic information for
  gene mutations in mouse, fly and c.elegans?
• What genes have been identified for (associated
  with) retinitis pigmentosa? Stargardts disease,
  glaucoma?
• What brain banks provide specimens of Batten
  tissue?
• For Parkinsons disease, what tissue and cell
  lines are available?
• Are there databases of chemical agents for
  visualizing the nervous system?
• What data base will give me information on the
  receptors and channels expressed in cortical
  neurons?
• I was just at a Blueprint Workshop for which a
  recommendation was provision of
  services/information about antibodies for studies
  in the nervous system. An NIH staffer said NIH
  already supports one such facility, but now I
  cant recall who said that and I want to locate
  that facility.
• I want to do some studies which need cells marked
  for GeneX so they can be visualized. Where can
  I find mutant mice with such markers?
• Ive heard that some national labs of the
  government provide analytical services. Does any
  provide nuclear magnetic resonance analysis
  services?
• I want to compare expression of the genes in the
  hypothalamus at different stages of mouse
  development. What data bases provide that
  information?
• What database will show me all of the genes known
  to be expressed in the nucleus accumbens?
• Analogous behavioral tests between mice and rats
• human disease and identified/suspected genetic
  etiology and enzyme/protein variation per gene
  variance and characteristic/diagnostic behavior
• What mouse astroglial cell lines are available
  for studying astrocyte-neuron metabolic
  interactions
• What databases list biomarkers of neurotoxicity?

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Locating relevant resources

• What brain banks provide specimens of Batten
  tissue?
• For Parkinsons disease, what tissue and cell
  lines are available?
• Are there databases of chemical agents for
  visualizing the nervous system?
• Where can I find phenotypic information on mutant
  mice?
• Where can I find software for learning and memory
  tests?

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Locating Specific Resources

• Where can I find mutant mice with Gene X?
• Where can I find antibodies against Protein Y?
• Deeper exposure of resource content
• Where can I find antibodies against
• K channels
• Slo K channels

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Getting Answers

• NIF Data Federation
• Builds upon data mediation tools developed by
  BIRN and Yale
• Tools for mapping database content to NIF
  vocabularies
• concept-based queries
• What calcium channels are found in Purkinje
  neurons?

http//soma.med.yale.edu8080/qi/query.do
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Web for Neuroscience

• Building upon the NIF Framework, we used open
  source Web tools to create a custom web index for
  Neuroscience
• Nutch, Lucene
• Web index was built from NIF Registry
• Ranking metrics and clustering can be customized
  for neuroscience applications

Q knockout
Amarnath Gupta, UCSD
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Registering a Resource to NIF

• Level 1
• NIF Registry high level descriptions from NIF
  vocabularies supplied by human curators
• Level 2
• Access to deeper content mechanisms for query
  and discovery
• Automated registration
• Self reporting resources Luis Marenco, Yale
  University
• Level 3
• Direct query of web accessible database
• Semantic registration
• Builds upon work in data mediation in BIRN and
  Yale

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Linking Neuroscience Resources Entrez-NIF Broker
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Building the NIF Vocabularies

• NIF Basic
• Daniel Gardner workshops with neuroscientists
  to obtain sets of terms that are useful for
  neuroscientists
• NIFSTD (NIF Standardized)
• Bill Bug built a set of expanded vocabularies
  using the structure of the BIRNLex
• Provides enhanced coverage of domains in NIF
  Basic
• More granularity
• Provides synonyms, lexical variants,
  abbreviations
• Provides coverage of additional domains through
  importing existing resources, e.g., molecules
• Currently 60,000 terms
• Encoded in OWL/RDF machine readable,
  machine-based reasoning
• Provides mapping to source terminologies,
  including NIF Basic

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Benchmarks and Testing

• Benchmarks are most commonly used to assess
  performance (i.e. the speed of a system).
• For NIF, benchmarks must also relate to the
  content as this is a core driver of the user
  experience
• How fast can you find a relevant answer?
• How easy is it to use?
• System has evolved based on internal testing over
  the past few months
• NIF project team at NIH
• Sample queries and use cases
• NIF advisory committee
• David Van Essen, Huda Akil, Doug Bowden, Rob
  Williams
• SFN demonstrations
• January 15th Site will be released for beta
  testing
• Tutorials and documentation
• Means for user feedback

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User Interface

Portal to Neuroscience on the Web
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About NIF
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Help / FAQ

• a structure, such as
• brain
• visual system
• putamen
• area 24 of Brodmann
• stellate cell
• dopamine receptor
• substance P

• a method or tool, such as
• PET
• dissection
• cresyl stain
• brain atlas
• tunnel microscopy
• spectroscopy
• monoclonal antibody

• a function, such as
• working memory
• color vision
• parkinsonian tremor
• fMRI activation
• evoked potential
• receptor binding
• gene expression

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Make a Website Accessible via NIF
Prototype courtesy of Dr. Doug Bowden
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Summary

• NIF delivers a modular infrastructure for
  resource discovery and integration
• Built upon NIH investments in infrastructure
• Practical yet advanced
• Components may be re-used in multiple contexts
• Provides guidelines for resource creation to
  optimize discovery and integration
• Portal for query across multiple sources relevant
  to Neuroscience
• Information landscape of neuroscience
• Extensible and configurable
• Framework is applicable to other scientific
  domains
• Built upon a strong semantic foundation
• Utilizes community standards and provides
  neuroscience extensions
• Human and machine readable

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Future Directions

• NIF v1.0
• Prototype features to production
• Population of resource registry, data federation
  and vocabularies
• Interface refinement
• Marketing and deployment
• Journals, Disease Foundations, INCF are
  interested
• Lays the foundation for future development
• NIF v2.0
• More automated methods of discovery and updates
• Richer semantics into ontology and query
  functions more automated reasoning
• Richer integration between NIF sources
• Additional interfaces, e.g., spatially-based
• Deep data integration and data mining across
  neuroscience

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Deliverables NIF v1.0

• NIF terminologies
• Basic (XML) and enhanced (OWL)
• Human and machine readable
• NIF terminology services
• NIF resources
• NIF registry
• Human curated listing of Neuroscience relevant
  resources on the web
• Textpresso text archive
• Literature archive indexed according to NIF
  terminologies from neuroscience-related journals
• NeuroMorpho.org
• Human curated database of over 3000 reconstructed
  neurons
• NIF data federation prototype
• Deep query of web-accessible databases (6 so
  far)
• Tools for registration and vocabulary mapping
• Query interfaces for NIF resources

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NIFv1.0 Provides

• Catalog of Neuroscience Resources, annotated with
  controlled vocabulary
• Means to register and query web resources with
  very different degrees of structure and
  capabilities
• Hidden web, i.e., content in databases not
  accessible to search engines
• Neuroscience literature Full-text indexing and
  data mining using Textpresso
• Existing portals and internet resources
• Multiple ways to find information
• Interface for searching across multiple types of
  resources with single query
• Flexible framework that adapts to domain
  evolution
• Tools for creating discoverable resources
• Strong semantic foundation for data integration
• NIF Vocabularies 60,000 terms assembled from
  existing resources and workshops covering many
  neuroscience domains
• Diseases, cell types, brain anatomy, ion channels
  and receptors, taxonomy, techniques, datatypes,
  resource types, behavioral paradigms

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Building NIFSTD

• OBO Foundry principles and best practices
• NIFSTD is built from a set of modular ontologies
• Anatomy Neuronames (via BIRNLex)
• Taxonomy NCBI taxonomy (via BIRNLex)
• Molecule IUPHAR PDPS Ki SwissProt (neuro)
• Cell NIF (Senselab, Neuromorpho, CCDB)
• Subcellular anatomy GO SAO
• Disease MESH/UMLS NINDS OMIM (neuro)
• Resource descriptors NIF, NITRC, NCBC, OBI
• Technique NIF Ontology for Biomedical
  Investigation (OBI)
• Behavior NIF, BIRN, BrainMap
• Attributes PATO
• Each is mapped to a unique identifier
• Single inheritance with minimal assignment of
  properties
• Each file is imported separately, but integrated
  through the Basic Formal Ontology into a single
  vocabulary
• Imported using manual, semi-automated and
  automated means
• Degree of intervention dependent on the
  vocabulary
• At this point, large degree of manual
  intervention is often necessary
• Link back to source ID is maintained

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NIF Vocabularies

• NIF terminologies provide a shared vocabulary for
  annotation of neuroscience data
• NIF terminologies provide the shared semantics
  for accessing resources and data through the NIF
  interface
• Semantic enrichment of terms to enable more
  targeted and meaningful queries
• Ultimately, NIF terminologies are critical for
  data and database interoperability