Maryann Martone Seminar for Johnson and Johnson Pharmaceutical Research and Development, Ambler, PA April 30, 2010

1. The Neuroscience Information
Framework
Establishing a practical semantic framework for
neuroscience
Maryann Martone, Ph. D.
University of California, San Diego

2. NIF Team
Amarnath Gupta, UCSD, Co Investigator
Jeff Grethe, UCSD, Co Investigator
Gordon Shepherd, Yale University
Perry Miller
Luis Marenco
David Van Essen, Washington University
Erin Reid
Paul Sternberg, Cal Tech
Arun Rangarajan
Hans Michael Muller
Giorgio Ascoli, George Mason University
Sridevi Polavarum
Anita Bandrowski, NIF Curator
Fahim Imam, NIF Ontology Engineer
Karen Skinner, NIH, Program Officer
Lee Hornbrook
Kara Lu
Vadim Astakhov
Xufei Qian
Chris Condit
Stephen Larson
Sarah Maynard
Bill Bug
Karen Skinner, NIH

3. What does this
mean?
•3D Volumes
•2D Images
•Surface meshes
•Tree structure
•Ball and stick models
•Little squiggly lines
Data People
Information systems

4. The Neuroscience Information Framework: Discovery and
utilization of web-based resources for neuroscience
http://neuinfo.org
UCSD, Yale, Cal Tech, George Mason, Washington Univ
Supported by NIH Blueprint
 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”

5. Where do I find…
• Data
• Software tools
• Materials
• Services
• Training
• Jobs
• Funding opportunities
• Websites
• Databases
• Catalogs
• Literature
• Supplementary
material
• Information portals
...And how many are there?

6. NIF in action

7. Query expansion: Synonyms
and related concepts
Boolean queries
Data sources
categorized by
“data type” and
level of nervous
system
Simplified views of
complex data
sources
Tutorials for using
full resource when
getting there from
NIF
Hippocampus OR “CornuAmmonis” OR
“Ammon’s horn”

8. NIF searches across multiple sources of
information
•NIF data federation
•Independent
databases
registered with NIF
or through web
services
•NIF registry: catalog
•NIF Web: custom web
index (work in
progress)
•NIF literature:
neuroscience-centered
literature corpus
(Textpresso)

9. Guiding principles of NIF
• Builds heavily on existing technologies (open source tools)
• 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
– Very few use standard terminology or map to ontologies
• 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 and in
building web communities

10. 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; DISCO protocol
Level 3
Direct query of web accessible database
Automated registration
Mapping of database content to NIF
vocabulary by human

11. The NIF Registry
•Very, very simple
model
•Annotated with NIF
vocabularies
•Resource type
•Organism
•Reviewed by NIF
curators

12. Level 2: Updates and deeper integration
• DISCO involves a collection of files that reside on each participating
resource. These files store information describing:
- attributes of the resource, e.g., description, contact person,
content of the resource, etc. -> updates NIF registry
- how to implement DISCO capabilities for the resource
• These files are maintained locally by the resource developers and are
“harvested” by the central DISCO server.
• In this way, central NIF capabilities can be updated automatically as
resources evolve over time.
• The developers of each resource choose which DISCO capabilities their
resource will utilize
Luis Marenco, MD, Rixin Wang, PhD, Perry L. Miller, MD, PhD, Gordon
Shepherd, MD, DPhil
Yale University School of Medicine

13. DISCO Level 2 Interoperation
• Level 2 interoperation is designed for resources that
have only Web interfaces (no database API).
• Different resources require different approaches to
achieve Level 2 interoperation. Examples are:
1. CRCNS - requires metadata tagging of Web pages
2. DrugBank - requires directed traversal of Web
pages to extract data into a NIF data repository
3. GeneNetwork - requires Web-based queries to
achieve “relational-like” views using “wrappers”

14. DrugBank Example
The DrugBank Web interface showing data
about a specific drug (Phentoin).

15. DrugBank Example (continued)
This DISCO Interoperation file specifies how to extract data from the DrugBank
Web interface automatically.

16. DrugBank Example (continued)
A NIF user views data retrieved from DrugBank in response to a query in a
transparent, integrated fashion.

17. Level 3
• Deep query of federated databases with
programmatic interface
• Register schema with NIF
– Expose views of database
– Map vocabulary to NIFSTD
• Currently works with relational and XML
databases
– RDF capability planned for NIF 2.5 (April 2010)
• Works with NIF registry: databases also
annotated according to data type and biological
area

18. Integrated views and gene search

19. Is GRM1 in cerebral cortex?
• NIF system allows easy search over multiple sources of information
• Well known difficulties in search
• Inconsistent and sparse annotation of scientific data
• Many different names for the same thing
• No standards for data exchange or annotation at the semantic level
– Lack of standards in data annotation require a lot of human investment in
reconciling information from different sources
Allen Brain Atlas
MGD
Gensat

20. Cerebral Cortex
Atlas Children Parent
Genepaint Neocortex, Olfactory cortex (Olfactory
bulb; piriform cortex), hippocampus
Telencephalon
ABA Cortical plate, Olfactory areas,
Hippocampal Formation
Cerebrum
MBAT (cortex) Hippocampus, Olfactory, Frontal,
Perirhinal cortex, entorhinal cortex
Forebrain
MBL Doesn’t appear
GENSAT Not defined Telencephalon
BrainInfo frontal lobe, insula, temporal lobe,
limbic lobe, occipital lobe
Telencephalon
Brainmaps
Entorhinal, insular, 6, 8, 4, A SII 17,
Prp, SI
Telencephalon

21. Modular ontologies for neuroscience
 NIF covers multiple structural scales and domains of relevance to neuroscience
 Incorporated existing ontologies where possible; extending them for neuroscience where necessary
 Normalized under the Basic Formal Ontology: an upper ontology used by the OBO Foundry
 Based on BIRNLex: Neuroscientists didn’t like too many choices
 Cross-domain relationships are being built in separate files
 Encoded in OWL-DL, but also maintained in a Wiki form, a relational database form and any other way it is needed
NIFSTD
NS FunctionMolecule Investigation
Subcellular
Anatomy
Macromolecule Gene
Molecule Descriptors
Techniques
Reagent Protocols
Cell
Instruments
Bill Bug
NS Dysfunction Quality
Macroscopic
Anatomy
Organism
Resource

22. How are ontologies used?
• Search: query expansion
– Synonyms
– Related classes
– “concept based queries”
• Annotation:
– Resource categorization
– Entity mapping
• Ranking of results
– NIF Registry; NIF Web

23. Concept-based search: Entity mapping
Brodmann area 3 Brodmann.3
Synonyms and explicit mapping of database content help smooth over
terminology differences and custom terminologies

24. Concept-based query: GABAergic neuron
•Simple search will not return examples
of GABAergic neurons unless the data
are explicitly tagged as such
•Too many possible classifications to get
them all by keywords
•Classes are logically defined in NIF
ontology
•i.e., a GABAergic neuron is any
neuron that uses GABA as a
neurotransmitter

25. Building NIF ontologies: Balancing act
• Different schools of thought as to how to build vocabularies and
ontologies
• NIF is trying to navigate these waters, keeping in mind:
– NIF is for both humans and machines
– Our primary concern is data
– We have to meet the needs of the community
– We have a budget and deadlines
• Building ontologies is difficult even for limited domains, never
mind all of neuroscience, but we’ve learned a few things
– Reuse what’s there: trying to re-use URI’s rather than map when possible
– Make what you do reusable: adopt best practices where feasible
• Numerical identifiers, unique labels, single asserted simple hierarchies
– Engage the community
– Avoid “religious” wars: separate the science from the informatics
– Start simple and add more complexity
• Create modular building blocks from which other things can be built

26. Ontologies, etc
Mike Bergman

27. What we’ve learned
• Strategy: Create modular building blocks that can be knit
into many things
– Step 1: Build core lexicon (NeuroLex)
• Classes and their definitions
• Simple single inheritance and non-controversial hierarchies
• Each module covers only a single domain
• Understandable by an average human
– Step 2: NIFSTD: standardize modules under same upper ontology
• OBO compliant in OWL
– Step 3: Create intra-domain and more useful hierarchies using properties and
restrictions
– Brain partonomy
– Step 4: Bridge two or more domains using a standard set of relations
– Neuron to brain region
– Neuron to molecule, e.g., GABAergic neuron

28. Neurolex
• More human centric
• More stable class structure
– Ontologies take time; many versions are retired-not good for information
systems that are using identifiers
• Synonyms and abbreviations were essential for users
• Can’t annotate if they can’t find it
• Can’t use it for search if they can’t find it
• Facilitates semi-automated mapping
• Contains subsets of ontologies that are useful to neuroscientists
– e.g., only classes in Chebi that neuroscientists use
• Wanted the community to be able to see it and use it
– Simple understandable hierarchies
• Removed the independent continuants, entity etc
• Used labels that humans could understand
– Even if they were plural (meningesvsmeninx)

30. Reclassification based on logical definitions: GABA neuron is any member of
class neuron that has neurotransmitter GABA
NIF Bridge File
•NIF Cell
•NIF Molecule
•Define a set of
properties that relate
neuron classes to
molecule
classes, e.g.,
•Neuron
•Has
neurotransmitter
•Purkinje cell is a
Neuron
•Purkinje cell has
neurotransmitter
GABA

31. Maintaining multiple versions
• NIF maintains the NIF vocabularies in different
forms for different purposes
– Neurolex Wiki: Lexicon for community review and
comment
– NIFSTD: set of modular OWL files normalized
under BFO and available for download
– NCBO Bioportal for visibility and mapping services
– Ontoquest: NIF’s ontology server
• Relational store customized for OWL ontologies
• Materialized inferred hierarchies for more efficient
queries

32. NeuroLex Wiki
http://neurolex.org Stephen Larson
“The human interface”
Semantic media wiki

33. NIF Architecture
Gupta et al., Neuroinformatics, 2008 Sep;6(3):205-17

34. Summary
• NIF has tried to adopt a flexible, practical approach to assembling,
extending and using community ontologies
– We use a combination of search strategies: string based, lexicon-based,
ontology-based
– We believe in modularity
– We believe in starting simple and adding complexity
– We believe in single asserted hierarchies and multiple inferred hierarchies
– We believe in balancing practicality and rigor
• NIF is working through the International Neuroinformatics
Coordinating Facility (INCF) to engage the community to help build
out the Neurolex and start adding additional relations
– Neuron registry task force
– Structural lexicon

35. Where do we go from here?
• NIF 2.5
– Automatic expansion
of logically defined
terms
• More services
– NIF vocabulary
services are available
now
• More content
• More, more, more NIF Blog
NIF is learning lessons in practical data integration

36. Musings from the NIF…
•No single approach, technology, philosophy, tool, platform
will solve everything
•The value of making resources discoverable is not
appreciated
•Developing resources (tools, databases, data) that are
interoperable at this point is an act of will
•Decisions can be made at the outset that will make it easier or harder to
integrate
•We build resources for ourselves and our constituents, not
automated agents
•We get mad when commercial providers don’t make their products
interoperable
•Many times the choice of terminology is based on expediency or
who taught you biology rather than deep philosophical differences
•Ontologies, terminologies, lexicons, thesauri
•Developing a semantic framework on top of unstable resources is
difficult
•Need to adopt a flexible approach

37. NIF Evolution
V1.0: NIF
NIF 1.5+++++
Then Now Later