# Presentation: 245696

## NIF Functionalities and Capabilities

- Maryann E. Martone, Ph. D.

## 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

## 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

## 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

## Demonstration of NIF System in ActionSingle search for multiple resourcesQ1:  Parkinsons DiseaseQ2:  Neurodegenerative Disease

- _[NIF Prototype Interface](http://nif.sdsc.edu:8080/edu.sdsc.daks.nif.nifgwt/nifgwt.html)_

**Notes:**
Diverse resources

## 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? Stargardt’s disease, glaucoma?
- What brain banks provide specimens of Batten tissue?
- For Parkinson’s 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 can’t 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? **
- I’ve 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?

## Locating relevant resources

- What brain banks provide specimens of Batten tissue?
- For Parkinson’s 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?

**Notes:**
Batten brain bank
Parkinson cell tissue
Contrast agent
Mutant mouse phenotype

## 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

**Notes:**
Query at high level not sufficient for most content
NeuroMab resource:  antibodies to many proteins
Annotated to receptor, channel but not individuals

## 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.](http://soma.med.yale.edu:8080/qi/query.do)__[yale](http://soma.med.yale.edu:8080/qi/query.do)__[.](http://soma.med.yale.edu:8080/qi/query.do)__[edu](http://soma.med.yale.edu:8080/qi/query.do)__[:8080/](http://soma.med.yale.edu:8080/qi/query.do)__[qi](http://soma.med.yale.edu:8080/qi/query.do)__[/query.do](http://soma.med.yale.edu:8080/qi/query.do)_

**Notes:**
Existing resources
No additional re-engineering
Utilize functions already developed by the resource to explore data
Will be demonstrated in detail by David Van Essen
Calcium channel Purkinje neuron (expand terms)

## 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

- Amarnath Gupta, UCSD

**Q:  knockout**

## 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

## Linking Neuroscience Resources:  Entrez-NIF Broker

## 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

## 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

## User Interface

**Portal to Neuroscience on the Web**

** ****Search**

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

**About NIF**

**Send Feedback**

**Help / FAQ**

**Enter a topic of interest in the box above.**

**New Search**

**Make a  Website Accessible via NIF**

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

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

- _Advanced Search_

- Prototype courtesy of Dr. Doug Bowden

## 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

**Notes:**
Practical:  Built on capabilities of todays resources but was built with an eye toward extensibility

Allows this to occur in an incremental fashion to ensure that neuroscientists will find useful information 

## 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

## 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

## 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

## 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
- Encoded in OWL/RDF

## Slide 21

## 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