ICBO2009NeuroLexPosterx - Download NIF Ontologies

schoolmistInternet και Εφαρμογές Web

22 Οκτ 2013 (πριν από 3 χρόνια και 9 μήνες)

86 εμφανίσεις



Stephen D. Larson, Sarah M. Maynard,
Fahim

Imam, Maryann E.
Martone



Dept
Neuroscience, UCSD, San Diego,
CA

There is a growing need to accumulate neuroscience data for sharing and merging within the neuroscience
community, in a machine readable format (Bloom F, 2006).



Observations are made by neuroscientists but not always formally recorded for other individuals to analyze
or expand upon.



Experiments are not standardized and tend to generate complex data types that are not amenable to
efficient storage in a database or in computation (
Martone

et al., 2004).

INTRODUCTION

THE CELLULAR KNOWLEDGE BASE:
DESCRIBING INSTANCES AND MERGING
ACROSS SCALES

2D and 3D morphological data of nerve cells and their relationships within cellular
microdomains

are described as
instances of the SAO.

This allows the SAO instances to be intermediaries between structures imaged at different spatial scales and
stored in different data formats:

Anatomical Location
General

Neostriatum

Anatomical Location
Specific

Nucleus Accumbens

CCDB ID

ACC1_2ma

Is Spiny (T/F)

T

Is Spiny (T/F)

T

Length

18.67

m

Is Spiny (T/F)

T

Length

1.39

m

Surface Area

1.09

m
2

Volume

0.04

m
3

Length

1.36

m

Surface Area

1.25

m
2

Volume

0.06

m
3

SUBCELLULAR ANATOMY ONTOLOGY
(SAO)

NeuroLex.org


A NIF Standard Ontology
-
based semantic wiki for neuroscience

THE SMART ATLAS

RECLASSIFICATION, SYNTHESIS, AND

REASONING
WITH SAO INSTANCES

We

constructed

some

OWL

classes

that

reclassify

the

neuron

cell

types

based

on

their

properties

assigned

by

the

SAO
.



We

classified

neurons

based

on

neurotransmitter,

morphological

type

or

presence

of

spines

simply

by

defining

using

OWL

and

Protégé

that

these

categories

ought

to

include

any

cell

which

had

the

main

property

of

that

category

(e
.
g
.
,

that

the

neuron

was

known

to

use

glutamate

or

GABA

as

a

neurotransmitter,

etc)
.




After

defining

these

categories,

we

used

the

open

source

ontology

reasoner

Pellet

(
Sirin

et

al,

2007
)

to

transform

the

flat

version

of

the

SAO

neuron

type

hierarchy

(A)

into

the

inferred

hierarchy

(B)
.



The

inferred

hierarchy

demonstrates

that

a

cell

like

the

a

Medium

Spiny

cell

is

both

spiny

and

GABAergic

while

a

Dentate

Gyrus

granule

cell

can

be

classified

as

spiny,

glutamatergic
,

and

granule

at

the

same

time
.



Any

arbitrary

reclassification

may

be

performed

using

the

combinations

of

properties

that

suits

the

purpose

of

the

user
.

Since

the

inferred

hierarchy

is

not

written

back

to

the

ontology,

this

allows

us

to

maintain

a

hierarchy

with

single

parents

in

the

authored

version

of

the

ontology,

while

still

allowing

cells

to

exist

in

multiple

inferred

categories
.

There is rising interest in using ontologies to formalize knowledge
within a domain for exchange among the neuroscience community
and incorporation into the semantic web and existing groups, e.g.,
Neuroscience Information Framework (NIF), National Center for
Biomedical Ontologies (NCBO), and Biomedical Information
Resource Network (BIRN).




Ontology
:” an explicit, formal representation of terms and their
relationships within a particular domain that is both: 1) human
interpretable and 2) machine readable


We are creating neuroscience
-
specific ontologies such as the
Subcellular Anatomy Ontology

(SAO) to formalize knowledge in
the neuroscience field for classifying and describing scientific
observations at the subcellular level.


The SAO takes the view that the cell should provide the rallying
point for information integration in biological tissues.


Thus, the SAO
starts with the cell and models how cell parts, including molecules,
fit into coarser levels of anatomy.


This view contrasts with the
approaches of many ontologies that start at the level of gross
anatomy and traverse down to the level of the cell e.g., the
Foundational Model of Anatomy (FMA);
Rosse

and
Mejino
, (2003)
and BAMS;
Bota

et al., (2005).



REFERENCES


Bloom F (2006). Prying Open the Black Box, Science, 314(5796):17.

Bota
, M., Dong, H.W., Swanson, L.W. (2005). Brain architecture management system.
Neuroinformatics

3, 15
-
48.

Grenon
, P. (2003). BFO in a nutshell: a bi
-
categorial

axiomatization

of BFO and comparison with DOLCE. IFOMIS, ISSN


1611
-
4019).

Grenon
, P., Smith, B., Goldberg, L. (2004). Biodynamic ontology: applying BFO in the biomedical domain. Stud Health
Technol

Inform 102, 20
-
38.

Martone

ME, Gupta A, and
Ellisman

MH (2004). E
-
Neuroscience: Challenges and Triumphs in Integrating Distributed Data from Molecules to Brains,
Nat
Neurosci
, 7(5): 467
-
72.

Rosse

C,
Mejino

JL Jr. (2003) A reference ontology for biomedical informatics: the Foundational Model of Anatomy. J Biomed Inform. 36, 478
-
500
.

Sirin
, E.,
Parsia
, B.,
Grau
, B.C,
Kalyanpur
, A. and Katz, Y. (2007). Pellet: a practical OWL
-
DL
reasoner
. Journal of Web Semantics 5.

Zhang, W., Zhang, Y.,
Zheng
, H., Zhang, C.,
Xiong
, W.,
Olyarchuk
, J.G., Walker, M.,
Xu
, W., Zhao, M., Zhao, S., Zhou, Z., Wei, L. (2007).


SynDB
: a synapse protein database based on synapse ontology.


Nucleic Acids Res 35, D737
-
41.

JINX: IMAGE ANNOTATION FOR ELECTRON
TOMOGRAPHIC DATA

Challenge: How do we get people to describe data according to the SAO?

Instances can be added via Jinx, a segmentation tool that allows microscopic image data to be ontologically annotated.

The SAO has been incorporated in Jinx, a manual segmentation tool at the National Center for Microscopy and Imaging
Research (NCMIR), so the annotation process follows the current workflow and does not require any extra work for the
user.

During segmentation in Jinx, as objects of interest are identified in each slice throughout a
tomographic

volume, rather
than supplying their own object name identifiers, users select entities from a pick list defined by the SAO.

Users can define whether a given instance is either independent or
part of
,
related to
, or
contacts with

another entity,
e.g., Mitochondrion_0000 is part of Synaptic_Bouton_0000.

The grand challenge of
neuroinformatics

is the creation of systems
that seamlessly integrate data across spatial and temporal scales
such that information, for example, about white matter bundles
derived from diffusion tensor
imagin

is analyzable in context with
electrophysiological data recorded from the neurons whose axons
make up the bundles.




The difficulties in performing this type of integration from data alone is
illustrated on the left, which shows an
intracellularly

injected medium
spiny neuron from the mouse nucleus
accumbens
, imaged using
correlated light and electron microscopy.


At each level, different types
of visualization and analytical tools are applied to extract meaningful
content, e.g., the branching structure of the dendritic tree, the surface
area of dendritic spines. But the knowledge required to relate these
different data representations and analysis results resides in the
scientist who understands the relationship among these different data
types and biological objects.


The SAO describes neurons,
glia
,
multicellular

microdomains

and their associated functional compartments,
cellular components, and molecular constituents.


As a way to keep epistemological distinctions clear, we adopted as an organizing framework the Basic Formal
Ontology version 1.0 (BFO 1.0;
Grenon

et al., 2003)


The structure/function dichotomy is expressed in the BFO
through the division of all possible entities into continuants (objects, qualities, sites, etc.) and
occurrents

(processes, temporal intervals).


A continuant is an entity in the world that endures through time (
Grenon

et al.,
2004).


Examples of continuants are basic cell structures such as mitochondria and nuclei, as well as lumens and
membranes.


On the other hand, an
occurrent

refers to a process, event, activity, or change.


Examples include the
cell cycle phases, cell secretion, and motility.


The BFO further divides continuants into dependent and
independent continuants.


An independent continuant is an entity that exists irrespective of its relationship to
anything else, e.g., cell, organism.


A dependent continuant is an entity that inheres in an independent continuant,
e.g., color, age.


Created using Protégé (http://protege.stanford.edu) and built as a concept hierarchy linked by relationships such
as “
is a
” and “
has part
,” e.g., “neuron is a cell; cell has part nucleus.”


The SAO is not meant to encode rules that define a canonical cell, but rather provides the framework to describe a
particular instance of a
neuroepithelial

cell based on its properties.


The SAO was built in the Web Ontology Language (OWL) format (http://www.w3.org/TR/owl
-
features)

a W3
standard, an extension of RDF, and compatible with the growing semantic web.

Tomographic

Reconstruction:
Medium Spiny
Neuron Dendrite,
ACC1_2ma (from
CCDB)

Medium

Spiny Neuron

Dendrite

Dendritic

Branch

Dendritic

Spine 1

Dendritic

Spine 32



Instance of Medium Spiny Neuron Dendrite and Its Functional
Compartments

has

Regional

part

has

Regional

part

has

Regional

part

has

Regional

part

properties

properties

properties

properties

properties

Querying gene expression patterns
using the smart atlas and spatial
histogram.

The user defines a spatial location
and the type and/or intensity of
signal to be returned.

In this case, three different types of
image data were returned for lhx5:
protein labeling (top), radioactive in
situ hybridization (middle) and gene
specific cell fill (bottom). The latter
two images are from GENSAT.

We have reassembled data instances in the cases of the chemical synapse (left) and the Node of
Ranvier

(right). The synapse is modeled using the
object aggregate and site classes.



We created an aggregate object consisting of a pre
-
synaptic part, a post
-
synaptic part and a
junctional

part, similar to the Synapse Ontology of Zhang
et al., (2007) and then localize them to the synaptic site.



Each of these parts have cell components, e.g., synaptic vesicles, located within them that define the extents of these parts
, i
.e., the pre
-
synaptic part
is the part of the
presynaptic

structure (axon terminal, dendrite or soma) containing synaptic vesicles.



Through the relationships encoded in the SAO, we can restrict the definition of the synapse to that part of the cellular stru
ctu
re where certain
structures, e.g., synaptic vesicles, or molecules are localized.

Internode

Axon

Juxtaparanode

Axon

Paranode

Axon

Node of

Ranvier

Axon

Internode

Juxtaparanode

Paranode

Node of

Ranvier

Subplasma
-

lemmal


Coating

Voltage

Gated

Na Channel

K

Channel

has
component

is location of

has molecular
constituent

is location of

is location of

site

adjacent to

site

adjacent to

site

adjacent to

Oligodendrocyte

Paranodal

Termination

is location of

Peripheral

Astrocyte

Process

Astrocyte

is
location
of

is location
of

Oligodendrocyte

Compact

Myelin

is location of

Axon

is regional part of

Oligodendrocyte

is regional part
of

is regional part of

is regional part of

is regional part of

restriction

non restriction

Key:

has molecular
constituent

Axon_0000
is_part_of

Glomerulus_0000

Clathrin_Coated_Vesicle_0000
is_part_of

Dendrite_0002

Synaptic_Bouton_0002
contacts_with

Dendrite_0001

Synaptic_Bouton_0002
is_part_of

Glomerulus_0000

Sub
-
surface_Cisternae_0000
is_part_of

Axon_0000

Mitochondrion_0000
is_part_of

Synaptic_Bouton_0000

Dendritte_0002
is_part_of

Glomerulus_0000

Astrocyte_Process_0000
is_related_to

Dendrite_0000

Cellular
Knowledge
Base

Obtain data (CCDB)

2d image

3d volume

1

Segment objects (Jinx)

2

Instance: relationships recorded

3

Instance is
stored

4

Surface rendering
generated

5

View relationships

Workflow: Ontology
-
based Annotation

SAO
-
guided annotation

Axon_0000

Dendrite_0002

Cell_Body

0001

Clathrin_Coated
_

Vesicle_0000

Mitochondrion

0001

Synaptic_Bouton

0000

Mitochondrion

0000

Cristae_0000

Synaptic_Bouton

0002

Dendrite_0000

Mitochondrion

0004

Vacuole_0000

Myelin_Sheath

0000

related to

part of

root

contacts with

Dendrite_0001

Synaptic_Bouton

0002

SER_0000

Glomerulus_0000

Astrocytic
_

Process_0000

Lysosome

0000

Compact_Myelin

0000

Sub
-
surface_

Cisternae_0000

Chemical

Synapse

Synaptic

Site

Post
-
synaptic

Density

Population (of

Synaptic Vesicle)

Pre
-
synaptic

Active Zone

Component

Asymmetrical

Pre
-
synaptic

Active Zone

Synaptic

Vesicle

Pre
-
synaptic

Site

Synaptic

Cleft

Post
-
synaptic

Site

has aggregate

part

has aggregate

part

located in

has property

located in

located in

is population of

located in

contains site

contains site

contains site

contains site

has aggregate

part

In this example, we performed a straightforward rule
-
based reasoning task, inferring the
presence of a connection between two brain areas from the presence of a single synapse in
an electron micrograph.


Prior to reasoning, Synapse_1 has two intercellular junction compartments, Pre
-
synaptic_compartment_1 and Post
-
synaptic_compartment_1. Applying the first two
Algernon rules, it is discovered that the pre
-
synaptic compartment is related to
Dendritic_Spine_1.


Using rules that infer the presence of neurons from axon terminals, and a corresponding set
of rules for dendritic spines, the synapse can be directly associated with the two neurons
that participate in that synapse, through the properties Pre
-
synaptic_Neuron

and Post
-
synaptic_Neuron
.


If two neurons share a synapse, then there is a connection between those neurons. If those
neurons are in different brain areas, then those areas have a connection between them.
Since the neurons that participated in Synapse_1 have already been identified, and their
locations are known through
has_BAMS_location

relations, the third rule can make an
explicit connection statement about the brain areas that the neurons are found within.


Through a methodology like this, many different kinds of data can be brought together
across scales

((:instance "
sao:Chemical_Synapse
" ?a)


(:instance "
sao:Post
-
synaptic_Compartment
" ?b)


("
sao:intercellular_Junction_Compartment
" ?a ?b)


(:instance "
sao:Neuron_Compartment
" ?c)


("
sao:is_Intracellular_Junction_Compartment_Of
" ?b ?c)


(:instance "
sao:Neuron
" ?d)


("
sao:is_Compartment_Of
" ?c ?d)


(
has_Post
-
synaptic_Neuron

?a ?d))

((:instance "
sao:Chemical_Synapse
" ?a)


(:instance "
sao:Pre
-
synaptic_Compartment
" ?b)


("
sao:intercellular_Junction_Compartment
" ?a ?b)


(:instance "
sao:Axon_Terminal
" ?c)


("
sao:is_Intracellular_Junction_Compartment_Of
" ?b ?c)


(:instance "
sao:Axon
" ?d)


("
sao:has_Compartment
" ?d ?c)


(:instance "
sao:Neuron
" ?e)


("
sao:has_Compartment
" ?e ?d)


(
has_Pre
-
synaptic_Neuron

?a ?e))

((:instance "
sao:Chemical_Synapse
" ?a)


(
has_Pre
-
synaptic_Neuron

?a ?b)


(
has_Post
-
synaptic_Neuron

?a ?c)


(
has_BAMS_Location

?b ?d)


(
has_BAMS_Location

?c ?e)


(:add
-
instance (?f "
bams:Connection_Statement
")


(:name ?f "") ("
bams:reference
" ?f "Inferred")


("
bams:sending_Structure
" ?f ?d)


("
bams:receiving_Structure
" ?f ?e)


(
example_Synapse

?f ?a)))

Algernon
-
J Rules used to produce inference
diagrammed above. (http://algernon
-
j.sourceforge.net/)