The Joy of Ontology

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The Joy of Ontology

• Suzanna Lewis
• SMI Colloquium
• April 20th, 2006

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Sections

• Why make an ontology
• What is an ontology
• How to create an ontology
• Logically
• Technically
• Organizationally
• National Center for Biomedical Ontology
• Case study Phenotypes, our current work on OBD

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Why make an ontology?

• What is the motivation?

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The Problem(s) with data

• Inaccessibility of widely distributed data
• Over abundance of information
• Speed and performance
• Interpreting the data syntactically
• Interpreting the data semantically

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We started the GO

• To develop a shared language adequate for the
  annotation of molecular characteristics across
  organisms.
• To agree on a mutual understanding of the
  definition and meaning of any word used. and thus
  to support cross-database queries.
• To provide database access via these common terms
  to gene product annotations and associated
  sequences.

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Annotation of Yeast Microarray Clusters Using GO
GENE PROCESS FUNCTION CELLULAR COMPONENT
SDH1 tricarboxylic acid cycle succinate dehydrogenase mitochondrial inner membrane
NDI1 oxidative phosphorylation NADH dehydrogenase mitochondrial inner membrane
QCR7 electron transport ubiquinol--cytochrome-c reductase mitochondrial inner membrane
COX6 oxidative phosphorylation cytochrome-c oxidase mitochondrial inner membrane
RIP1 electron transport Rieske Fe-S protein mitochondrial inner membrane
COX15 oxidative phosphorylation cytochrome-c oxidase mitochondrial inner membrane
CYT1 electron transport cytochrome-c1 mitochondrial inner membrane
COR1 electron transport ubiquinol--cytochrome-c reductase mitochondrial inner membrane
SDH3 tricarboxylic acid cycle succinate dehydrogenase subunit mitochondrial inner membrane
SDH4 tricarboxylic acid cycle succinate dehydrogenase subunit mitochondrial inner membrane
SDH2 tricarboxylic acid cycle succinate dehydrogenase subunit mitochondrial inner membrane
MDH1 tricarboxylic acid cycle malate dehydrogenase mitochondrial matrix
QCR6 electron transport ubiquinol--cytochrome-c reductase mitochondrial inner membrane
CYT1 electron transport cytochrome-c1 mitochondrial inner membrane
Microarray data from Figure 2K of Eisen et al.
(1998). Cluster analysis and display of
genome-wide expression patterns, Proc. Natl.
Acad. Sci. 95 (25) 14863-14868.
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The Challenge of Communication
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Ontologies are essential to make sense of
biomedical data
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A Portion of the OBO Library
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Motivation to capture biological reality

• Inferences and decisions we make are based upon
  what we know of the biological reality.
• An ontology is a computable representation of
  this underlying biological reality.
• Enables a computer to reason over the data in
  (some of) the ways that we do.

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What is an ontology
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Ontology (as a branch of philosophy)

• The science of what exists in every area of
  reality
• The classification of entities what kinds of
  things exist
• The relations between these entities
• Defines a scientific field's vocabulary and the
  canonical formulations of its theories.
• Seeks to solve problems which arise in these
  domains.

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A biological ontology is

• A machine interpretable representation of some
  aspect of biological reality

• what kinds of things exist?

sense organ
eye disc
is_a
develops from

• what are the relationships between these things?

eye
part_of
ommatidium
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Entity a definition

• anything which exists, including things and
  processes, functions and qualities, beliefs and
  actions, software and images

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Representation a definition

• An image, idea, map, picture, name, description
  ... which refers to, or is intended to refer to,
  some entity or entities in reality
• this or is intended to refer to should always
  be assumed

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Ontologies represent types in reality
ontology
reality
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Scientific data represent instances in reality
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Two kinds of representational artifact

• Databases, inventories, images represent what is
  particular in reality instances
• Ontologies, terminologies, catalogs represent
  what is general in reality (exists in multiple
  instances) types (universals, kinds)

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Ontologies are not for representing concepts in
peoples heads
ontology
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The researcher has a cognitive representation of
what is general, based on his knowledge of the
science
Cognitive representation
ontology
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types
substance
organism
animal
mammal
cat
frog
siamese
instances
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An ontology is like a scientific text it is a
representation of types in reality
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Atomic representational unit a definition

• terms, icons, bar codes, alphanumeric identifiers
  ... which
• refer, or are intended to refer, to entities in
  reality, and
• are not built out of further sub-representations
• Representational units are the atoms in the
  domain of representations

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Modular representational unit a definition

• A representation which is built out of other
  representational units, which together form a
  structure that mirrors a corresponding structure
  in reality

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Periodic Table
The Periodic Table
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Ontology a definition

• A modular, representational artifact whose
  representational units are intended to represent
• types in reality
• the relations between these types which are true
  universally (i.e. for all instances)
• lung is_a anatomical structure
• lobe of lung part_of lung

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How to create an ontology

• Part 1
• The logic and science

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In computer science, there is an information
handling problem

• Different groups of data-gatherers develop their
  own idiosyncratic terms in which they represent
  information.
• To put this information together, methods must be
  found to resolve terminological and conceptual
  incompatibilities.
• Again, and again, and again

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The Reality

• Do not assume that data integration can be
  brought about by somehow mapping incompatible,
  low quality ontologies built for different
  purposes

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Two flavors of ontology

1. Application ontology
2. Reference ontology

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Application Ontology

• An application ontology is comparable to an
  engineering artifact such as a software tool. It
  is constructed for specific practical purposes.

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Reference Ontology

• A reference ontology is analogous to a scientific
  theory it seeks to optimize representational
  adequacy to its subject matter

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Assumptions

• There are best practices in ontology development
  which should be followed to create stable
  high-quality ontologies
• Shared high quality ontologies foster
  cross-disciplinary and cross-domain re-use of
  data, and create larger communities

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Why do we need rules/standards for good ontology?

• Ontologies must be intelligible both to humans
  (for annotation) and to machines (for reasoning
  and error-checking)
• Unintuitive rules lead to errors in
  classification
• Simple, intuitive rules facilitate training of
  curators and annotators
• Common rules allow alignment with other
  ontologies (and thus cross-domain exploitation of
  data)
• Logically coherent rules enhance harvesting of
  content through automatic reasoning systems

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Ontologies built according to common logically
coherent rules

• Will make entry easier and yield a safer growth
  path
• You can start small, annotating your data with
  initial fragments of a well-founded ontology,
  confident that the results will still be usable
  when the ontology grows larger and richer

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OBO Foundry
The OBO Foundry

• A subset of OBO ontologies whose developers
  agree in advance to accept a common set of
  principles designed to assure
• intelligibility to biologist curators,
  annotators, users
• formal robustness
• stability
• compatibility
• interoperability
• support for logic-based reasoning

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The OBO Foundry

1. The ontology is open and available to be used by
   all.
2. The developers of the ontology agree in advance
   to collaborate with developers of other OBO
   Foundry ontology where domains overlap.
3. The ontology is in, or can be instantiated in, a
   common formal language.
4. The ontology possesses a unique identifier space
   within OBO.
5. The ontology provider has procedures for
   identifying distinct successive versions.

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The OBO Foundry

1. The ontology has a clearly specified and clearly
   delineated content.
2. The ontology includes textual definitions for all
   terms.
3. The ontology is well-documented.
4. The ontology has a plurality of independent
   users.
5. The ontology uses relations which are
   unambiguously defined following the pattern of
   definitions laid down in the OBO Relation
   Ontology.

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Orthogonality
Orthogonality

• Ontology groups who choose to be part of the OBO
  Foundry thereby commit themselves to
  collaborating to resolve disagreements which
  arise where their respective domains overlap

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agreed on relations

• The success of ontology alignment demands that
  ontological relations (is_a, part_of, ...) have
  the same meanings in the different ontologies to
  be aligned.
• See Relations in Biomedical Ontologies, Genome
  Biology May 2005, Barry Smith , Werner Ceusters,
  Bert Klagges, Jacob Köhler, Anand Kumar, Jane
  Lomax, Chris Mungall, Fabian Neuhaus, Alan L
  Rector, and Cornelius Rosse

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Three fundamental dichotomies

• continuants vs. occurrents
• dependent vs. independent
• types vs. instances

ONTOLOGIES ARE REPRESENTATIVES OF TYPES IN REALITY
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For example in the GO

• Molecules, cell components , organisms are
  independent continuants which have functions
• Functions are dependent continuants which become
  realized through special sorts of processes we
  call functionings
• Processes are occurrents include functionings,
  side-effects, stochastic processes

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Continuants (aka endurants)

• have continuous existence in time
• preserve their identity through change
• exist in toto whenever they exist at all

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Occurrents (aka processes)

• have temporal parts
• unfold themselves in successive phases
• exist only in their phases

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Continuants vs. Occurrents

• Anatomy vs. Physiology
• Snapshot vs. Video
• Stocks vs. Flows
• Commodities vs. Services
• Products vs. Processes

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Dependent entities

• require independent continuants as their bearers
• There is no grin without a cat

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Dependent vs. independent continuants

• Independent continuants (organisms, cells,
  molecules, environments)
• Dependent continuants (qualities, shapes, roles,
  propensities, functions)
• E.g. the acidity of this gut

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All occurrents are dependent entities

• They are dependent on those independent
  continuants which are their participants (agents,
  patients, media ...)

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GOs three ontologies
biological process
molecular function
cellular component
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Pumping blood
To pump blood
heart
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UBO Upper Biomedical Ontology
Continuant 3D
Occurrent 4D
Independent Continuant
Dependent Continuant
Functioning
Side-Effect, Stochastic Process, ...
Quality
Function
Spatial Region
instances (in space and time)
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How to create an ontology

• Part 2
• The technical aspects

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Separate the Database from the Ontology

• For extensibility
• For generality
• For reasonability
• For interoperability

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Why ontologies are worth it

• Minimize database maintenance costs
• Communication between researchers
• As well as
• Better query facilities
• Ability to draw inferences
• Detect correlations
• Facilitate computational interpretation of text
• And more

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Before domain knowledge is embedded in the db
schema
Gene table
Exon table
RNA table
Protein table
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Embedding domain knowledge in the db schema is
expensive

• The logical description and the physical database
  description of the biology are co-mingled
• Therefore new biological knowledge will force
• Schema changes e.g. new tables
• Query changes that explicitly refer to tables
• Middleware changes to retrieve and format
• GUI changes to display

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After domain knowledge is embedded in the
ontology
feature table
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Ontology driven db schema is less expensive to
maintain

• The logical description and the physical database
  description of the biology are developed
  independently
• Therefore new biological knowledge will only
  require
• Ontology changes e.g. new terms
• GUI changes display
• No schema changes
• No query changes
• No middleware changes

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Reality ultimately this is what the ontology
must reflect
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Step 1 Build an ontology that reflects reality
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Observations and experimentation
Step 2 Data capture
Database UIDs serving as proxies for instances

• Patient IDs
• Sequence accessions
• Genetic strain IDs

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Step 1 Build an ontology that reflects reality
Step 2 Data capture
Step 3 Classify data using the ontology
Database UIDs serving as proxies for instances
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Ontology is a contract between researchers

• A common language that allows us to share
  knowledge
• Researchers have a stake in it
• Every individual will benefit by being able to
  accurately interpret someone elses data
• No more pre-scrubbing
• No more time spent translating

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Rules on types

• Dont confuse types with instances
• Dont confuse instances with leaf nodes
• Dont confuse types with ideas
• Dont confuse types with ways of getting to know
  types
• Dont confuse types with ways of talking about
  types
• Dont confuse types with data about types

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An astronomy ontology should not include 'Buzz
Aldrin'
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Rules on terms

• Terms should be in the singular
• Avoid abbreviations even when it is clear in
  context what they mean (breast for breast
  tumor)
• Think of each term A in an ontology is
  shorthand for a term of the form
• the type A

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Rules on Definitions

• The terms used in a definition should be simpler
  (more intelligible) than the term to be defined
  otherwise the definition provides no assistance
• Definitions should be intelligible to both
  machines and humans
• to human understanding
• Humans need clarity and modularity
• to machine processing
• Machines can cope with the full formal
  representation

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Confusing non-useful definitions

• Swimming
• Swimming is healthy and has 8 letters
• Poland
• The name of Poland

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When defining terms use Aristotelian definitions

• The definition of A takes the form
• an A def. is a B which ...
• where B is As parent in the hierarchy
• A human being def. an animal which is rational
• A helicase def. an enzyme which catalyzes the
  hydrolysis of ATP to unwind the DNA helix

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Use of Aristotelian definitions

• Makes defining terms easier
• Each definition encapsulates in modular form the
  entire parentage of the defined term
• The entire information content of the FMAs term
  hierarchy and definitions can be translated very
  cleanly into a computer representation
• Now accepted by GO

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Summary How to build your ontology

• Keep It Simple
• lowest possible barrier to entry
• Technology independence
• Clarity of definitions and scope
• With new data, we change our minds
• An ontology must adapt to reflect current
  understanding of reality
• Mechanisms to support change

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How to build an ontology

• Part 3
• The sociology and organizational aspects

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Elements for Success GO

• A Community with a common vision
• A pool of talented and motivated
  developers/scientists
• A mix of academic and commercial
• An organized, light weight approach to product
  development
• A leadership structure
• Communication
• A well-defined scope, (our business)

Adopted from Open Source Menu for Success
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Gene Ontology

• Community annotation production
• Extreme Programming techniques to distributed
  ontology generation
• Revision control
• Nightly conflict resolution
• Users are integral to the team
• Rapid iterations

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Why
Survey
Domain covered?
Public?
Community?
Active?
Salvage
Develop
Applied?
Improve
yes
no
Collaborate Learn
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Is your domain covered?Due diligence
background research

• Step 1 Learn what is out there
• The most comprehensive list is on the OBO site.
  http//obo.sourceforge.net
• Assess ontologies critically and realistically.

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It is privately held?Ontologies must be shared

• Communities form scientific theories
• that seek to explain all of the existing evidence
• and can be used for prediction
• These communities are all directed to the same
  biological reality, but have their own
  perspective
• The computable representation must be shared
• Ontology development is inherently collaborative
• Open ontologies become connected to instance data
  this feeds back on ontology development

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Is it active? Pragmatic assessment of an ontology

• Is there access to help, e.g.
• help-me_at_weird.ontology.net ?
• Does a warm body answer help mail within a
  reasonable timesay 2 working days ?

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Is it applied?Toy ontologies are not useful

• Every ontology improves when it is applied to
  actual instances of data
• It improves even more when these data are used to
  answer research questions
• There will be fewer problems in the ontology and
  more commitment to fixing remaining problems when
  important research data is involved that
  scientists depend upon
• Be very wary of ontologies that have never been
  applied

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Work with that community

• To improve (if you found one)
• To develop (if you did not)
• How?

Improve
Collaborate and Learn
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Community vs. Committee ?

• Many people have (understandably) reservations
  about collaborative development, because it can
  easily be confused with design-by-committee
  projects.
• Members of a committee represent themselves.
• Members of a community represent their community.

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Design for purpose - not in abstract

• Who will use it?
• If no one is interested, then go back to bed
• What will they use it for?
• Define the domain
• Who will maintain it?
• Be pragmatic and modest

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Start with a concrete proposal not a blank slate.

• But do not commit your ego to it.
• Distribute to a small group you respect
• With a shared commitment.
• With broad domain knowledge.
• Who will engage in vigorous debate without
  engaging their egos (or, at least not too much).
• Who will do concrete work.

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Step 1

• Alpha0 the first proposal - broad in breadth but
  shallow in depth. By one person with broad domain
  knowledge.
• Distribute to a small group (lt6).
• Get together for two days and engage in vigorous
  discussion. Be open and frank. Argue, but do not
  be dogmatic.
• Reiterate over a period of months. Do as much as
  possible face-to-face, rather than by
  phone/email. Meet for 2 days every 3 months or so.

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Step 2

• Distribute Alpha1 to your group.
• All now test this Alpha1 in real life
• By classifying representations of instances with
  these types
• Do not worry that (at this stage) you if do not
  have tools.

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Step 3

• Reconvene as a group for two days.
• Share experiences from implementation
• Can your Alpha1 be implemented in a useful way ?
• What are the conceptual problems ?
• What are the structural problems ?

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Step 4

• Establish a mechanism for change.
• Use CVS or Subversion.
• Limit the number of editors with write permission
  (ideally to one person).
• Unique stable identifiers
• History tracking of changes
• Release a Beta1.
• Seriously implement Beta1 in real life.
• Build the ontology in depth.

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Step 5

• After about 6 months reconvene and evaluate.
• Is the ontology suited to its purpose ?
• Is it, in practice, usable ?
• Are we happy about its broad structure and
  content ?

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Step 6

• Go public.
• Release ontology to community.
• Release the products of the annotations.
• Invite broad community input and establish a
  mechanism for this (e.g. SourceForge).

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Step 7

• Proselytize
• Publish in a high profile journal
• Engage new user groups
• Emphasize openness
• Write a grant

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Step 8

• Iterate
• Improvements come in two forms
• It is impossible to get it right the 1st (or 2nd,
  or 3rd, ) time.
• What we know about reality is continually growing

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Step 9

• Bon appetit

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Use the power of combination and collaboration

• as far as possible dont reinvent
• ontologies are like telephones they are valuable
  only to the degree that they are used and
  networked with other ontologies
• but choose working telephones
• most telephones were broken when the technology
  was first being developed

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The National Center for Biomedical Ontology

• Stanford Berkeley Mayo Victoria Buffalo
  UCSF Oregon Cambridge

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Bioinformatics and Computational Biology
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National Centers for Biomedical Computing2005

1. National Center for Integrative Biomedical
   Informatics (Michigan)
2. National Center for Multi-Scale Study of Cellular
   Networks (Columbia)
3. National Center for Biomedical Ontology

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• Stanford Tools for ontology alignment,
  indexing, and management (Cores 1, 47 Mark
  Musen)
• LawrenceBerkeley Labs Tools to use ontologies
  for data annotation (Cores 2, 57 Suzanna Lewis)
• Mayo Clinic Tools for access to large
  controlled terminologies (Core 1 Chris Chute)
• Victoria Tools for ontology and data
  visualization (Cores 1 and 2 Margaret-Anne
  Story)
• University at Buffalo Dissemination of best
  practices for ontology engineering (Core 6 Barry
  Smith)

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Driving Biological Projects

• Trial Bank UCSF, Ida Sim
• Flybase Cambridge, Michael Ashburner
• ZFIN Oregon, Monte Westerfield

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Case study Phenotypes, our current work on OBD
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Animal disease models
Animal models
Mutant Gene Mutant or missing
ProteinMutant Phenotype
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Animal disease models
Humans
Animal models
Mutant Gene Mutant or missing
ProteinMutant Phenotype (disease model)
Mutant Gene Mutant or missing
ProteinMutant Phenotype (disease)
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Animal disease models
Humans
Animal models
Mutant Gene Mutant or missing
ProteinMutant Phenotype (disease model)
Mutant Gene Mutant or missing
ProteinMutant Phenotype (disease)
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Animal disease models
Humans
Animal models
Mutant Gene Mutant or missing
ProteinMutant Phenotype (disease model)
Mutant Gene Mutant or missing
ProteinMutant Phenotype (disease)
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SHH-/
SHH-/-
shh-/
shh-/-
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Phenotype (clinical sign) entity
quality
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Phenotype (clinical sign) entity
quality P1 eye hypoteloric
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Phenotype (clinical sign) entity
quality P1 eye hypoteloric P2
midface hypoplastic
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Phenotype (clinical sign) entity
quality P1 eye hypoteloric P2
midface hypoplastic P3 kidney
hypertrophied
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Phenotype (clinical sign) entity
quality P1 eye hypoteloric P2
midface hypoplastic P3 kidney
hypertrophied
PATO hypoteloric hypoplastic
hypertrophied
ZFIN eye midface kidney

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Phenotype (clinical sign) entity
quality
Anatomical ontology Cell tissue ontology
Developmental ontology Gene ontology
biological process cellular component
PATO (phenotype and trait ontology)
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Phenotype (clinical sign) entity
quality P1 eye hypoteloric P2
midface hypoplastic P3 kidney
hypertrophied
Syndrome P1 P2 P3 (disease)
holoprosencephaly
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Human holo- prosencephaly
Zebrafish shh
Zebrafish oep
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OMIM gene ZFIN gene FlyBase gene FlyBase mut pub ZFIN mut pub mouse rat SNOMED OMIM disease
LAMB1 lamb1 LanB1 5 15 39 -
FECH fech Ferro- chelatase 2 5 2 29 Protoporphyria, Erythropoietic
GLI2 gli2a ci 388 41 22 -
SLC4A1 slc4a1 CG8177 7 7 19 Renal Tubular Acidosis, RTADR
MYO7A myo7a ck 84 5 9 3 16 Deafness DFNB2 DFNA11
ALAS2 alas2 Alas 1 7 14 Anemia, Sideroblastic, X-Linked
KCNH2 kcnh2 sei 27 3 12 -
MYH6 myh6 Mhc 166 3 1 12 Cardiomyopathy, Familial Hypertrophic CMH
TP53 tp53 p53 64 3 3 19 11 Breast Cancer
ATP2A1 atp2a1 Ca-P60A 32 6 1 11 Brody Myopathy
EYA1 eya1 eya 251 5 4 6 Branchiootorenal Dysplasia
SOX10 sox10 Sox100B 1 17 4 4 Waardenburg-Shah Syndrome
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National Center for Biomedical Ontology
Capture and index experimental results
Open Biomedical Ontologies (OBO)
Open Biomedical Data (OBD)
BioPortal
Revise biomedicalunderstanding
Relate experimental data to results from other
sources
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Phenotype as an observation
context
The class of thing observed
evidence
publication
environment
figure
assay
genetic
sequence ID
ontology
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Review of proposed EAV EQ model

• A phenotype is described using an Entity-Quality
  double
• Entities are drawn from various OBO
  ontologiescell, anatomies, GO,
• Qualities are drawn from one ontologyPATO

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Separation of concerns

• Not phenotypes
• Genotype
• Environment
• Assay, measurement systems
• Images

schema
Association Genotype Phenotype Environment
Assay Phenotype Entity Quality Entity
OBOClassID Quality PATOClassID
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2003 Pilot study

• Trial of EAV model on small collection of
  genotypes
• FlyBase
• ZFIN
• Genes were non-orthologous
• New curations - in progress
• orthologous genes with clinical relevance
• Use the same data model and exchange format?

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Example data records
Genotype Entity Value
npo gut dysplastic
gut small
r210 retina irregular
brain fused
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ZFIN schema extension stages
Genotype Stage Entity Quality
npo HatchingPec-fin gut dysplastic
HatchingPec-fin gut small
r210 HatchingLong-pec retina irregular
LarvalProtruding-mouth brain fused
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Stages
Association Genotype Phenotype Environment
Assay Phenotype Stage Entity Quality Entity
OBOClassID Stage OBOAnatomicalStageClassID Quali
ty PATOClassID
means zero or more
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Monadic and relational qualities

• Monadic
• the quality inheres in a single entity
• Relational
• the quality inheres in two or more entities
• sensitivity of an organism to a kind of drug
• sensitivity of an eye to a wavelength of light
• can turn relational qualities into cross-product
  monadic qualities
• e.g. sensitivityToRedLight
• better to use relational qualities
• avoids redundancy with existing ontologies

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Incorporating relational qualities
Association Genotype Phenotype Environment
Assay Phenotype Stage Entity Quality
Entity Entity OBOClassID Quality
PATOVersion2ClassID
Example data record
Phenotype organism sensitiveTo puromycin
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Measurable qualities

• Some qualities are inexact and implicitly
  relative to a wild-type or normal quality
• relatively short, relatively long, relatively
  reduced
• easier than explicitly representing
• this tail length shorter-than mouse wild-type
  tail length
• Some qualities are determinable
• use a measure function
• unit, value, time
• this tail has length L
• measure(L, cm) 2
• Keep measurements separate from (but linked to)
  quality ontology

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Incorporating measurements
Association Genotype Phenotype Environment
Assay Phenotype Stage Entity Quality Entity
Measurement Measurement Unit Value
(Time) Entity OBOClassID Quality
PATOVersion2ClassID
Example data record
Phenotype gut acidic Measurement pH 5
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The Methodology of Annotations

• Scientific curators use experimental observations
  reported in the biomedical literature to link
  gene products with GO terms in annotations.
• The gene annotations taken together yield a
  slowly growing computer-interpretable map of
  biological reality,
• The process of annotating literature also leads
  to improvements and extensions of the ontology
  itself, which institutes a virtuous cycle of
  improvement in the quality and reach of future
  annotations and of future versions of the
  ontology.

130
When we annotate the record of an experiment

• we use terms representing types to capture what
  we learn about the instances
• this experiment as a whole (a process)
• these instances experimented upon
• the instances are typical
• they are representatives of a type

131
Ontology

• A thing of beauty is a joy forever
• With acknowledgement and thanks to Mark Musen,
  Barry Smith, Sima Misra, Chris Mungall, Daniel
  Rubin, and David Hill

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Interpretation of the schema

• How should EAV data records be interpreted by a
  computer?
• What are the instances?
• Is the EAV schema just to improve database
  searching?
• Can it be used for meaningful cross-species
  comparisons?

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What is the Entity slot used for?
Genotype Entity Quality
npo gut dysplastic
gut small
r210 retina irregular
brain fused
tm84 d/v pattern formation abnormal
blood islands number increased
Bsb2 elongation of arista literal arrested
C-alpha1D adult behaviour uncoordinated
2003 trial data FB ZFIN
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What is the Entity slot used for?

• In practical terms
• An ID from one of the following ontologies
• GO CC, BP, and MF
• Species-specific anatomical ontology
• OBO Cell
• Or a cross-product
• e.g. acidification GO0045851
• which has_locationOBOREL midgut FBbt00005383
• example from FBal0062296 Acidification in the
  midgut of homozygous larvae is often less than in
  wild-type larvae
• But what does it mean in the context of an
  annotation?

135
Universals and particulars

• An ontology consist of universals (classes)
• Fruitfly, wing, flight
• Experimental data generally concerns particulars
  (instances) that instantiate universals
• this particular wing of this particular fruitfly
• this particular fruitfly participating in this
  particular flight from here to there
• In annotation we often use a class ID as a proxy
  for an (unnamed) instance (or collection of
  instances)
• It is important to always keep this distinction
  in mind

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What is the Quality slot used for?
Genotype Entity Quality Quality
npo gut structure dysplastic
gut relative size small
r210 retina pattern irregular
brain structure fused
tm84 d/v pattern formation qualitative abnormal
blood islands relative number number increased
Bsb2 elongation of arista literal process arrested
C-alpha1D adult behaviour behavioral activity uncoordinated
2003 trial data FB ZFIN
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Qualities

• Treat as Qualities
• a dependent entity
• a quality must have independent entity(s) as
  bearer
• the quality inheres_in the bearer
• Examples
• The particular shape of this ball
• The particular structure of this wing
• The particular length of this tail
• The particular rate of synaptic transmission
  between these two neurons

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Attribute universals vs Attribute particulars

• In an EAV annotation, a PATO class ID typically
  serves as a proxy for an unnamed attribute
  instance
• Universals (classes) must always be defined in
  terms of their instances

A Formal Theory of Substances, Qualities, and
Universals Fabian NEUHAUS Pierre GRENON Barry
SMITH
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How is the attribute slot used?
Genotype Entity Attribute Attribute
npo gut structure dysplastic
gut relative size small
r210 retina pattern irregular
brain structure fused
tm84 d/v pattern formation qualitative abnormal
blood islands relative number number increased
Bsb2 elongation of arista literal process arrested
C-alpha1D adult behaviour behavioral activity uncoordinated
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Current Model
Association Genotype Phenotype Environment
Assay Phenotype Stage Entity Attribute Entity
OBOClassID Stage OBOAnatomicalStageClassID Att
ribute PATOClassID
means zero or more
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Composite phenotype classes

• Mammalian phenotype has composite phenotype
  classes
• e.g. reduced B cell number
• Compose at annotation time or ontology curation
  time?
• False dichotomy
• Core 2 will help map between composite class
  based annotation and EAV annotation

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Interpreting annotations

• Annotations are data records
• typically use class IDs
• implicitly refer to instances
• How do we map an annotation to instances?
• Important for using annotations computationally

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Interpreting annotations (1)

• What does an EA (or EAV) annotation mean?
• Annotation
• GenotypeFBal00123 Ebrain Afused
• presumed implied meaning
• this organism
• has_part x, where
• x instance_of brain
• x has_quality fused
• or in natural language
• this organism has a fused brain
• Various built-in assumptions

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Interpreting annotations (II)

• What does this mean
• annotation
• GenotypeFBal00123 Ewing Aabsent
• using same mapping as annotation I
• fly98 has_part x, where
• x instance_of wing
• x has_quality absent
• or in natural language
• this fly has a wing which is not there!
• What we really intend
• NOT(this organism has_part x, where x instance_of
  wing)

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Interpreting annotations (II)

• What does this mean
• annotation
• GenotypeFBal00123 Ewing Aabsent
• using same mapping as annotation I
• this organism has_part x, where
• x instance_of wing
• x has_quality absent
• or in natural language
• this fly has a wing which is not there!
• What we really intend
• this organism has_quality wingless
• wingless the property of having
  count(has_part wing)0

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Are our computational representations intended to
capture reality?
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Does this matter?

• If we simply use the colloquial expression
  absent
• What are the consequences?
• Basic search will be fine
• e.g. find all wing phenotypes
• Logical reasoners will compute incorrect results
• Computers will not be able to reason correctly
• We must explicitly provide specific rules for
  certain attributes such as absent

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Interpreting annotations (III)

• What does this mean
• annotation
• Edigit Asupernumery
• using same interpretation as annotation I
• this organism has_part x, where
• x instance_of digit
• x has_quality supernumery
• or in natural language
• this organism has a particular finger which is
  supernumery!!
• What we really intend
• this person has_quality supernumery finger
• supernumery finger the property of having
  count(has_part digit) gt wild-type

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Interpreting annotations (IV)

• What does this mean
• annotation
• Gtmp001 Ebrown fat cell Aincreased
  quantity
• using same mapping as annotation I
• this organism has_part x, where
• x instance_of brown fat cell
• x has_quality increased quantity
• or in natural language
• this organism has a particular brown fat cell
  which is increased in quantity
• What we really intend
• this organism has_part population_of(brown fat
  cell) which has_quality increased size

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Other use cases

• Spermatocyte devoid of asters
• Homeotic transformations
• Increased distance between wing veins
• Some vs. all

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Alternate perspectives

• process vs. state
• regulatory processes
• acidification of midgut has_quality reduced rate
• midgut has_quality low acidity
• development vs. behavior
• wing development has_quality abnormal
• flight has_quality intermittent
• granularity (scale)
• chemical vs. molecular vs. cell vs. tissue vs.
  anatomical part