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

The Neuroscience Information Framework: Discovery and utilization of web-based resources for neuroscience 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 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 Date Data Federation Data Federation RecordsCatalogWeb Index Literature Corpus NIF Vocabulary 9/ ,420* ,45867,00018,884 † 7/ ,393,744* 1,605497,740101,62717,086 5/ ,228,658 2,8711,184,261 All (PubMed)53,023 % yearly increase % overall increase1,000 38, * 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

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)

Building the NIF Ontologies

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 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

Anatomy Cell Type Cellular Component Cellular Component Small Molecule Small Molecule Neuro- transmitter Neuro- transmitter Transmembrane Receptor Transmembrane Receptor GABA GABA-R Transmitter Vesicle Transmitter Vesicle Terminal Axon Bouton Terminal Axon Bouton Presynaptic density Presynaptic density Purkinje Cell Purkinje Cell Neuron Dentate Nucleus Neuron Dentate Nucleus Neuron CNS Cpllection of Deep Cerebellar Nuclei Cpllection of Deep Cerebellar Nuclei Purkinje Cell Layer Purkinje Cell Layer Dentate Nucleus Dentate Nucleus Cytoarchitectural Part of Cerebellar Cortex Cytoarchitectural Part 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 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

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

wholebraincatalog.org Integration of NIF services and ontologies

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

WBC and NeuroConstruct 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)

NIF cards Simple 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 cal_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