Jeffery Grethe July 30, 2010 OCNS July 24th – 30th 2010, San Antonio, Texas

1. The Neuroscience Information Framework
Making Resources Discoverable for the Computational
Neuroscience Community
Jeffrey S. Grethe, Ph. D.
Co-Principal Investigator, NIF
Center for Research in Biological Systems
University of California, San Diego
OCNS 2010
Workshop on Methods in Neuroinformatics

2. The Neuroscience Information Framework: Discovery and
utilization of web-based resources for neuroscience
http://neuinfo.org UCSD, Yale, Cal Tech, George Mason, Washington Univ
 A portal for finding and
using neuroscience
resources
 A consistent framework
for describing resources
 Provides simultaneous
search of multiple types
of information,
organized by category
 Supported by an
expansive ontology for
neuroscience
 Utilizes advanced
technologies to search
the “hidden web”

3. Brief History of NIF
• Outgrowth of Society for Neuroscience Neuroinformatics
Committee
– Neuroscience Database Gateway: a catalog of neuroscience
databases
• “Didn’t I fund this already?”
– Over 2500 databases are on-line; no one can go to them all
• “Why can’t I have a Google for neuroscience”
– “Easy”, comprehensive, pervasive
• Phase I-II: Funded by a broad agency announcement from the
NIH Neuroscience Blueprint
– Feasibility
• Current phase: Started Sept 2008
How can we provide a consistent and easy to implement
framework for those who are providing resources, eg., data,
and those looking for these data and resources
➤ Both humans and machines

4. The Problem
• Over 2000 databases have been identified
through NIF
– Researchers can’t visit them all
– Most content from these resources not easily found
through standard search engines
– Even more structured content on the web
• Databases provide domain specific views of data
– NIF provides a snapshot of information in a simple to
understand form that can be further explored in the
native database
– Providing a biomedical science based semantic
framework for resource description and search

5. NIF uniquely provides access to the
largest registry of neuroscience
resources available on the web
Date
Data
Federation
Data
Federation
Records Catalog Web Index
Literature
Corpus
NIF
Vocabulary
9/2008 5 60,420* 388 113,458 67,000 18,884†
7/2009 18 4,393,744* 1,605 497,740 101,627 17,086
5/2010 55 23,228,658 2,871 1,184,261
All
(PubMed) 53,023
% yearly
increase 205 429 79 138 181
% overall
increase 1,000 38,345 640 944 210
* Numbers for initial sources were generated by examining current source content
† First year of NIF contract involved re-factoring of ontology

6. Guiding principles of NIF
• Builds heavily on existing technologies (open source tools and
ontologies)
• Information resources come in many sizes and flavors
• Framework has to work with resources as they are, not as we wish
them to be
– Federated system; resources will be independently maintained
– Developed for their own purpose with different levels of resources
• No single strategy will work for the current diversity of neuroscience
resources
• Trying to design the framework so it will be as broadly applicable as
possible to those who are trying to develop technologies
• Interface neuroscience to the broader life science community
• Take advantage of emerging conventions in search, semantic
web, linked dataand in building web communities

7. http://neuinfo.org
A Quick Tour of the NIF

8. Domain Enhanced Search for Neuroscience
NIF now searches more than 55 databases with information
neuronal descriptions, neuronal morphology, connectivity, chemical
compounds…

9. Ontology Based Search Refinement

10. Diverse Database Content
NeuroMorpho.org
NeuronDB

11. Concept-based search
• Search Google: GABAergic neuron
• Search NIF: “GABAergic neuron”
– NIF automatically searches for types of
GABAergic neurons
Types of
GABAergicneuron
s

12. Concept-based search

13. Use of Ontologies within NIF
• Controlled vocabulary for describing type of resource
and content
– Database, Image, Parkinson’s disease
• Entity-mapping of database and data content
• Data integration across sources
• Search: Mixture of mapped content and string-based
search
– Different parts of NIF use the vocabularies in different ways
– Utilize synonyms, parents, children to refine search
– Increasing use of other relationships and logical inferencing
• Generation of semantic content (i.e. RDF, Linked
Data)

14. http://neurolex.org
Building the NIF Ontologies

15. Modular Ontologies
NIFSTD
NS
Function
Molecule
Investigatio
n
Subcellular
Anatomy
Macromolecule Gene
Molecule
Descriptors
Techniques
Reagent Protocols
Cell
Instruments
NS
Dysfunctio
n
Quality
Macroscopic
Anatomy
Organis
m
Resource
• Single inheritance
trees with minimal
cross domain and
intradomain
properties
• Orthogonal:
Neuroscientists
didn’t like too
many choices
• Human readable
definitions (not
complete yet)
• Set of expanded vocabularies largely imported from existing
terminological resources
• Adhere to ontology best practices as we understood them
• Built from existing resources when possible
• Standardized to same upper ontology: BFO
• Encoded in OWL DL
• Provides mapping to source terminologies
• Provides synonyms, lexical variants, abbreviations

16. Anatomy Cell Type
Cellular
Component
Small
Molecule
Neuro-
transmitter
Transmembrane
Receptor
GABA GABA-R
Transmitter
Vesicle
Terminal Axon
Bouton
Presynaptic
density
Purkinje
Cell
Neuron
Dentate
Nucleus
Neuron
CNS
Cpllection of
Deep Cerebellar
Nuclei
Purkinje
Cell Layer
Dentate
Nucleus
Cytoarchitectural
Part of
Cerebellar Cortex
Expressed in
Located in
“Bridge files”

17. NIF Cell
• NIF has made significant enhancements to its
cell ontology
– Expanded neuron list
– Generated neuronal classifications based on
neurotransmitter, brain region, molecules,
morphology, circuit role
– Recommended standard naming convention
– Is working with the International Neuroinformatics
Coordinating Facility through the PONS (program in
ontologies for neural structures) program
• Creating Knowledge base for neuronal classification based
on properties

18. Neurolex Wiki
http://neurolex.org
•NIF has posted its
vocabularies in Wiki form
(Semantic MediaWiki)
•Simplified interface for
ontology construction and
refinement
•Custom forms for neurons
and brain regions
•Semantic linking between
category pages
•Significant knowledge base
•Curation  NIFSTD

20. NeuroLex and NeuroML
“There was further discussion of how to define specific
types of morphological groups such as apical dendrites,
basal dendrites, axons, etc. Several options include
having predefined names for common types or linking to
ontologies that define these types. We suggest adding
tags or rdf for metadata that provide NeuroLex ontology
ids to groups. We propose to begin with simple tags, and
when a tag is present, one should assume it indicates “is
a”. If more complicated semantic information is needed,
we can use rdf in a way that is similar to SBML.”
NeuroML Development Workshop 2010
http://www.neuroml.org/files/NeuroMLWorkshop2010.pdf

21. http://neuinfo.org
Providing community
access

22. Access at various levels…
• A search portal (link to NIF advanced search interface) for researchers,
students, or anyone looking for neuroscience information, tools, data or
materials.
• Access to content normally not indexed by search engines, i.e, the "hidden
web”
• Tools for resource providers to make resources more discoverable, e.g.,
ontologies, data federation tools, vocabulary services
• Tools for promoting interoperability among databases
• Standards for data annotation
• The NIFSTD ontology covering the major domains of neuroscience, e.g.,
brain anatomy, cells, organisms, diseases, techniques
• Services for accessing the NIF vocabulary and NIF tools
• Best practices for creating discoverable and interoperable resources
• Data annotation services: NIF experts can enhance your resource through
semantic tagging
• NIF cards: Easy links to neuroscience information from any web browser
• Ontology services: NIF knowledge engineers can help create or extend
ontologies for neuroscience

23. http://wholebraincatalog.org
Integration of NIF
services and ontologies

24. WBC and Simulation Visualization
Demonstrates the
neurogenesis
simulation driven
by the model of
Aimone et al.,
2009 from the
Gage lab at the
Salk Institute
within the Whole
Brain Catalog
http://www.youtube.com/watch?v=1YzfXv4yNzg

25. WBC and NeuroConstruct
http://www.neuroml.org/tool_support.php
A network model of the cerebellar granule cell layer which can be fully
expressed as a Level 3 NeuroML file. Visualised in the Whole Brain Catalog
(left), and neuroConstruct (right)
http://wiki.wholebraincatalog.org/wiki/Running_Simulations

26. NIF cardsSimple tool for linking search
results to other sources of
information
NIF literature results display for “Cerebellum”; concepts in NIF ontologies highlighted and linked to more information through NIF
knowledge base
http://nifcards.neuinfo.org/nifstd/anatomi
cal_structure/birnlex_1489.html

27. Providing Semantic Content
RDF data / SPARQL Queries

28. The NIF Team
• Maryann Martone, UCSD-PI
• Jeff Grethe, UCSD-Co PI
• Amarnath Gupta, UCSD-Co-PI
• Ashraf Memon, UCSD, Project Manager
• Anita Bandrowski, UCSD, NIF Curator
• Fahim Imam, UCSD, Ontology Engineer
• David Van Essen, Wash U, Co-PI
• Erin Reid, Wash U
• Gordon Shepherd, Yale, Co-PI
• Perry Miller, Yale
• Luis Marenco, Yale
• Rixin Wang, Yale
• Paul Sternberg, Cal Tech, Co-PI
• Hans Michael-Muller, Cal Tech
• Arun Ragarajan, Cal Tech
• Giorgio Ascoli, George Mason,
Co-PI
• Sridevi Polavaram, George
Mason
• Vadim Astakhov, UCSD
• Andrea Arnaud-Stagg, UCSD
• Lee Hornbrook, UCSD
• Jennifer Lawrence, UCSD
• Irfan Baig, UCSD student
• Anusha Yelisetty, UCSD
student
• Timothy Tsui, UCSD student
• Chris Condit, UCSD
• Xufei Qian, UCSD
• Larry Liu, UCSD