The Neuroscience Information FrameworkMaking Resources Discoverable for the Computational

Neuroscience Community

Jeffrey S. Grethe, Ph. D.

Co-Principal Investigator, NIFCenter for Research in Biological Systems

University of California, San Diego

OCNS 2010Workshop on Methods in Neuroinformatics

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”

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

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

NIF uniquely provides access to the largest registry of neuroscience resources available on the web

DateData

Federation

Data Federation

Records Catalog Web IndexLiterature

CorpusNIF

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 1,184,261All

(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

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 data and in building web communities

http://neuinfo.org

A Quick Tour of the NIF

Domain Enhanced Search for Neuroscience

NIF now searches more than 55 databases with information neuronal descriptions, neuronal morphology, connectivity, chemical compounds…

Ontology Based Search Refinement

Diverse Database Content

NeuroMorpho.org

NeuronDB

Concept-based search• Search Google: GABAergic neuron• Search NIF: “GABAergic neuron”

– NIF automatically searches for types of GABAergic neurons

Types of GABAergic

neurons

Concept-based search

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)

http://neurolex.org

Building the NIF Ontologies

Modular Ontologies

NIFSTD

NS Function

MoleculeInvestigatio

nSubcellular Anatomy

Macromolecule Gene

Molecule Descriptors

Techniques

Reagent Protocols

Cell

Instruments

NS Dysfunctio

nQualityMacroscopic

AnatomyOrganis

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

Anatomy Cell TypeCellular

ComponentSmall

Molecule

Neuro-transmitter

TransmembraneReceptor

GABA GABA-R

TransmitterVesicle

Terminal AxonBouton

Presynapticdensity

PurkinjeCell

Neuron

Dentate NucleusNeuron

CNS

Cpllection of Deep Cerebellar

Nuclei

PurkinjeCell Layer

DentateNucleus

CytoarchitecturalPart of

Cerebellar Cortex

Expressed in

Located in

“Bridge files”

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

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

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 2010http://www.neuroml.org/files/NeuroMLWorkshop2010.pdf

http://neuinfo.org

Providing community access

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

http://wholebraincatalog.org

Integration of NIF services and ontologies

WBC and Simulation VisualizationDemonstrates 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

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

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/anatomical_structure/birnlex_1489.html

Providing Semantic Content

RDF data / SPARQL Queries

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