The document discusses neuroscience ontologies created by the Neuroscience Information Framework (NIF). It describes how NIF incorporates existing ontologies and extends them for neuroscience as needed. NIF includes modular ontologies covering multiple scales including molecules, cells, anatomy, and functions. Key ontologies discussed include NIFSTD, Neurolex, and bridging files that link related concepts across ontologies. Examples are provided of how neuron classes are defined based on attributes such as brain region, molecular constituents, and roles.

1. Neuroscience Information
Framework Ontologies:
Nerve cells in Neurolex
and NIFSTD
Maryann Martone
University of California, San Diego

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
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”

3. 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
 Single inheritance
 Cross-domain relationships are being built in separate files
NIFSTD
NS
Function
Molecule Investigation
Subcellular
Anatomy
Macromolecule Gene
Molecule
Descriptors
Techniques
Reagent Protocols
Cell
Instruments
Bill Bug
NS
Dysfunction
Quality
Macroscopic
AnatomyOrganism
Resource

4. Ontologies imported/used by
NIF
 Gene Ontology Biological Process
 ChEBI (Mireot)
 PATO
 PRO (Bridge)
 BIRNLex
 OBI (Bridge)
 Disease Ontology (Mireot)
 Foundational Model of Anatomy (Mireot)
 Gene Ontology Cellular Component (Bridge)
 Neuronames, BAMS (Mapped)
 Cell Ontology (don’t use)
Practical limitations imposed by tools and expertise; constantly addressing
challenges involved in using community ontologies that are evolving in
production information systems

5. NIF Cell
 Identity: Unique identifier
 nlx_neuron_nt_090803
 http://ontology.neuinfo.org/NIF/BiomaterialEntities/NIF-Neuron-NT-
Bridge.owl#nlx_neuron_nt_090803
 Asserted hierarchy: Unique types of neuron
 Uniqueness assured by concatenating brain region with cell type
 Hippocampus CA1 pyramidal cell
 Neocortex layer 2/3 pyramidal cell
 Standard naming convention
 Major brain region, subregion, distinguishing characteristics, cell
 Bridge files: Cross module relations
 A set of properties that define it, e.g., part of, has neurotransmitter, has role
 Properties assigned at level of part of neuron where appropriate (brain region, molecule)
 Others at level of cell class: spiny, physiological
 Logical restrictions for defined classes: Necessary and sufficient conditions to identify members of
that class
 GABAergic neuron is any member of class neuron has neurotransmitter GABA
Gordon Shepherd, Giorgio Ascoli, Kei Cheung, Maryann Martone, Fahim Imam, Stephen Larson

6. Cerebellum
Purkinje cell
soma
Cerebellum
Purkinje cell
dendrite
Cerebellum
Purkinje cell
axon
Cerebellum granule
cell layer
Cerebellum
Purkinje cell layer
Cerebellum
molecular layer
Has
part
Has
part
Has
part
Is part of
Is part of
Is part of
Shared building blocks: Modular ontologies
joined in bridge files
Calbindin
Cerebellum
Purkinje neuron
Cerebellar cortex
Has part
Has part
Has part
IP3
receptor
NIF Molecule
NIF Anatomy
NIF Subcellular

7. DefinedClasses
 Neuron by brain region
 NIF Cell, NIF Subcellular, NIF Anatomy
 Hippocampus neuron is a type of neuron has part soma is part of any part of
hippocampus
 Neuron by molecule
 NIF Cell, NIF molecule
 Neurotransmitter
 GABAergic neuron
 By molecular constituent
 Parvalbumin-containing neuron
 Neuron by role
 Circuit role
 Principal neuron vs intrinsic neuron
 Functional role
 Sensory neuron, motor neuron
 Neuron by morphological quality
 Spiny neuron
 Pyramidal neuron
Sometimes use OBO
relations, sometimes short cuts that can
be expressed in OBO relations

8. String vs concept based search

10. Currently ~250 proposed classes

11. Neuron qualities

12. What can account for signals
here?
Neurons are highly ramifying and polarized cells

13. Properties assigned at level of part of
neuron

14. Community contributions: Neurolex Semantic Wiki
 Good teaching tool for the
power of more formal
semantics
 Knowledge base easier to
view, index and navigate
 Lighter weight and more
human friendly than more
formal ontologies and tools
 Build knowledge from basic
lexical elements and a few
relationships
 Categories are linked
through explicit properties
 Currently over 10,000
category pages
 Use relationship shortcuts
that can be expressed in
OBO relations
 Working with international
group of neuroscientists to
contribute content covering
different domains and
develop new content
http://neurolex.org Stephen Larson and INCF

15. Detailed properties
 Custom form based interface
 Olfactory bulb (main) mitral cell
 New version just about to be released
 References for each attribute
 Meant to be used by anyone
 Curators (me) go through and translate
 Translated into NIFSTD once finalized
 Some short cut relations translated
 e.g., soma located in = neuron has part soma is part of some
brain region

16. Inferring the Mesoscale
 The NIFSTD is expressed in
OWL (Web Ontology Language)
 Supports reasoning and inference
 Through integration with other
ontologies covering gross
anatomy and molecular
entities, we are working to
create inferences across scales
 Analyze locally; infer globally
 If there’s an axon terminal, then
there must be an axon…
Stephen Larson

17. Desiderata
 1000’s of neuron types; one group can’t do them all
 Early efforts all concentrate on same cell types (easy
ones)
 INCF has opportunity to coordinate different groups so we
can aggregate effort
 Standard set of properties and standard syntax for
logical definitions
 Trying to base them on REL, but we take shortcuts for
practical reasons
 Relation to GO function