NCBO Wiki - User contributions [en]

Immunology Ontologies and Their Applications in Processing Clinical Data

2012-05-28T04:44:57Z

Mcourtot: /* Day 2: Tuesday, June 12, 2012 */ updated talk

The National Center for Biomedical Ontology [http://bioontology.org (NCBO)] in collaboration with the Protein Ontology [http://pir.georgetown.edu/pro/ (PRO)] and the Infectious Disease Ontology [http://infectiousdiseaseontology.org/page/Main_Page (IDO)] will host a three-day dissemination workshop in Buffalo, NY on June 11-13, 2012.
:Day 1 will provide a survey of current ontology-based research in immunology and infectious disease with a view to future coordination among ontology developers and users in this field.
:Day 2 will be focused on flow cytometry, including the question of the Cell and Protein Ontologies and of the role of surface protein expression in cell type classification.
:Day 3 will include a session devoted to the use of ontologies to assist clinicians working with infectious disease data, followed by a session on the Ontology for General Medical Science.

'''Venue:
:Days 1 and 2, [http://www.universityinn.com/ Ramada Inn, UB North Campus, Buffalo]
:Day 3: [http://www.hwi.buffalo.edu/about_hwi/visitor/maps_directions.html, Hauptmann-Woodward Institute, Buffalo]

'''Goals'''

Provisional goals of the meeting are:

To identify and coordinate activities on-going in immunology ontology and related fields, with special attention to the use of ontologies to support clinical data analysis in flow cytometry and related fields.

'''Registration'''

This meeting is free for registered participants. Space is limited and those interested in participating should contact [mailto:phismith@buffalo.edu Barry Smith] as soon as possible.

----
'''
== Day 1: Monday, June 11, 2012 ==
'''

08:30 Registration and Breakfast

'''An Overview of Ontologies to Support Research in Immunology and Infectious Disease'''

'''Morning: The Gene Ontology, Reactome, The Immunology Ontology, The Immune Epitope Ontology and the Allergy Ontology'''

09:15 Barry Smith (University at Buffalo) and Cathy Wu (University of Delaware)
::Bio-Ontologies for Immunology Research: An Introduction
:::Brief survey of the goals of the meeting.

09:30 Alexander Diehl (University at Buffalo)
::The Gene Ontology and Immune System Processes
:::The Gene Ontology contains a wealth of terms covering immune system processes for the annotation of proteins involved in the functioning of the immune system. I will provide a overview of these terms and their use in GO annotation.

10:00 Cliburn Chan (Duke University)
::Ontology for Cellular Immune Networks
:::Will describe initial work on an ontology of cellular immune networks that is designed to capture the qualitative cytokine expression patterns and cellular phenotypes associated with specific immune activation networks (e.g. Th1 network). We will outline use of the ontology for immune assay integration and statistical enrichment analysis.

10:30 Break

11:00 Anna Maria Masci (Duke University)
::The Immunology Ontology (with special focus on the liver)
:::An emerging scenario is uncovering immune response as a sophisticated biological process, which requires an intensive cross-talk between immunocytes, parenchymal and stromal cell types. These timely and anatomically restricted interactions regulate the outcome of immune response to damage induced by stress and pathogens. Due to its complexity and patho-physiological relevance, the liver represents an interesting prototype of context-dependent immune response. We will introduce the Liver Immunology Ontology (LIO), which has as primary goal the representation of the immune response induced in the context of the liver.

11:30 Peter d'Eustacho (New York University)
::Innate Immunity: Signaling via Toll-Like Receptors in Reactome
:::The innate immune responses mediated by Toll-like receptors (TLR) provide a first line of defense against microbial pathogens in many vertebrates. In Reactome we have integrated annotations of human TLR molecular functions with those of 6800 other human proteins involved in diverse biological processes to generate a resource suitable for data mining, pathway analysis, and other systems biology approaches. These annotations allow human TLR proteins, the complexes they form, and the functions they mediate to be classified and related to those of structurally similar TLR proteins from chicken, mouse, and other species.

12:00 '''Lunchtime talk'''
Atul Butte (Stanford)
::Discovery of a novel inflammatory receptor and related drug for type 2 diabetes from integration of publicly-available microarray data

13:30 Alexander C. Yu (University at Buffalo)
::The Allergy Ontology

14:00 Lindsay Cowell (University of Texas Southwestern Medical Center)
::An Introduction to the Infectious Disease Ontology
:::The IDO-Core; new approach to MIREOTing; new terms/definitions/relations; a template for creating an IDO Extension

14:30 Albert Goldfain (Blue Highway)
::Staph Aureus (Sa) IDO

15:00 Break

15:30 Christos (Kitsos) Louis (IMBB-FORTH, Crete)
::IDO Mal (Malaria Ontology)

16:00 Yu Lin (University of Michigan)
::IDO Bru (Brucellosis Ontology)
:::IDO Bru is an extension ontology of IDO. We will focus on those aspects of Brucellosis represented in IDOBru as outlined in [http://www.jbiomedsem.com/content/2/1/9]. We will also discuss IDOBru's policy on use of IDs, and its treatment of Brucella-host interaction.

16:30 Oliver He (University of Michigan)
::Contributions of the Vaccine Ontology (VO) to Immunology Research and Public Health
:::Vaccinology is applied immunology. VO is a community-based biomedical ontology in the domain of vaccine and vaccination. We will introduce the top level of VO, and sketch applications of VO in elucidating fundamental protective immune mechanisms and improving public health.

----
'''

== Day 2: Tuesday, June 12, 2012 ==
'''

'''Ontologies and Flow Cytometry Informatics'''

:'''Background''' Increasingly, flow cytometry is being employed in clinical laboratories for the diagnosis, prognosis and monitoring of disease. The advent of highly multidimensional flow cytometry and automated gating algorithms for the analysis of flow cytometry data, coupled with the rise of personalized medicine, are poised to expand greatly the need for a reliable, structured framework for the representation of the types of cells present in human blood and tissues. We are currently enhancing the representation of hematopoietic and other cell types in the Cell Ontology (CL) to allow for the logical definition of cell types based on cellular attributes, and in doing so we rely on relations to terms of the Protein Ontology (PRO) as a key component of these definitions. The goal of today's session is explore how the use of clinical flow cytometry data can serve as a driver of ontology development in both the PRO and the CL by assessing current standard clinical assays and recent approaches based on automated gating of multidimensional flow cytometry.

:Examples of questions to be addressed include:
::Which protein isoforms and post-translationally modified forms identified by flow cytometry typing reagents need to be represented in the PRO to enable cell types defined in their terms to be represented in the CL?
::How can use of the PRO and CL ontologies will promote standardization in interpretation and integration of clinical flow cytometry data?

:Background Reading
::[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013777/?tool=pubmed The Protein Ontology: a structured representation of protein forms and complexes]
::[http://www.biomedcentral.com/1471-2105/12/6 Logical Development of the Cell Ontology]
::Cytometry-Ontology Framework ([http://ontology.buffalo.edu/bio/pro/CytometryOntologyFramework.pdf Draft])

08:30 Breakfast

'''9:00-noon: Introduction to the Protein Ontology Flow Cytometry Driving Biological Project'''

9:00 Cathy Wu (University of Delaware): Introduction to the Protein Ontology

9:30 Alexander Diehl (Buffalo)
::Hematopoietic Cell Types in the Cell Ontology
:::The Cell Ontology includes over 350 terms that represent hematopoietic cell types. I will provide an overview of these terms and our strategy for representing key properties of cell types through logical definitions, with examples from Leukemia and Multiple Myeloma

10:00 Discussion of the PRO Driving Biomedical Project
Moderator: Lindsay Cowell (University of Texas Southwestern Medical Center)
:Presentation of key issues:
::Assessing representation requirements for Flow Cytometry in PRO, CL, IEDB, OBI, ImmPort, and Immune System Modeling.
::Development of a data store to collect extended cell type-protein relationships.
::Defining a tool wish-list for CL-linked flow cytometry analysis and CL-assisted marker selection for cell type analysis.

12:30 Lunch

'''Afternoon: Automated gating of Flow Cytometry results and linking to the Cell Ontology. Flow cytometry typing of normal and malignant cell types'''

13:30 Cliburn Chan (Duke)
::Automated flow cytometry analysis in HIV studies
:::Will describe recent work on automated cell subset identification and alignment across multiple HIV-related data sets with statistical mixture models. What do we need in order to be able to use ontologies for automated annotation and labeling of cell subsets?

14:00 Nikesh Kotecha (Cytobank)
::Incorporating annotations into the analysis workflow - examples using Cytobank and NCBO's BioPortal

14:30 Dave Parrish ([http://www.labanswer.com/ LabAnswer])
::Storing and Retrieving Flow Cytometry Data
:::The Flow Cytometry Laboratory at Roswell Park Cancer Institute has recently deployed an internally developed application managing the operational workflow of the laboratory. We will describe the use of the relational database in capturing assay results and ultimately associating with a final interpretation. Although early in the process the goal is to support the use of the Cell and Protein Ontologies in panel design and interpretation classification.

15:00 Break

15:30 Oliver He (University of Michigan)
::How Flow Cytometry can be used in Vaccine Research
:::To better understand fundamental protective immune mechanisms, flow cytometry has frequently been used to measure vaccine-induced innate immunity, and antigen-specific T-cell and B-cell responses. Biomedical ontologies (e.g., VO, OBI, and PRO) play important roles in data representation, integration, and automated reasoning in vaccine-related flow cytometry research.

16:00 Melanie Courtot (Ryan Brinkman's group, Vancouver)
::1. Overview of the representation of flow cytometry assays in [http://purl.obolibrary.org/obo/obi OBI]
::2. Connecting results from automated FCM analysis systems with the Cell Ontology

----
'''

== Day 3: Wednesday, June 13, 2012 ==
'''

Note Wednesday venue is: [http://www.hwi.buffalo.edu/about_hwi/visitor/maps_directions.html, Hauptmann-Woodward Institute, Buffalo]

8:30 Taxis will leave Ramada Hotel

9:00 Breakfast

'''9:00-noon: Immunology Ontologies (Continued)'''

9:30 Bjoern Peters (La Jolla Institute for Allergy and Immunology)
::Representation of immunology experiments using OBI
::Representing epitope mapping experiments for the Immune Epitope Database (IEDB)
:::The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meaning to describe all aspects of how biomedical investigations are conducted. OBI builds on the Gene Ontology (GO) and related efforts that provide a formal and interoperable representation of biomedical knowledge. OBI adds the ability to describe how this knowledge was derived. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. The presentation will describe the state of OBI and several applications that are using it. Specific focus will be on epitope mapping and characterization experiments captured in the Immune Epitope Database (IEDB) which heavily utilizes OBI. The presentation will also point out gaps in coverage of immunological terms that are currently in OBI but poorly defined and outside the scope of OBI, and which deserve a better home.

10:30 Break

11:00 Alexander Diehl (Buffalo)
:Towards an Auto-Immune Disease Ontology
::I will discuss the construction of an auto-immune disease ontology through use of the Ontology of General Medical Sciences as a general framework for ontology development.

'''noon-3pm SESSION OPEN TO THE PUBLIC''' (includes lunch)

'''The Role of Ontologies in Clinical Medicine'''

:Albert Goldfain (Blue Highway / Syracuse)
::Creating Personalized Infectious Disease Ontologies

:Alan Ruttenberg (Buffalo)
::The Protein Ontology and the Treatment of Protein Isoforms, Mutations, and Aggregates of Relevance to Alzheimer's Disease

:Christos (Kitsos) Louis (IMBB-FORTH, Crete)
::Ontologies and Vector-Borne Diseases

:Werner Ceusters (Buffalo)
::Assessment instruments and biomedical reality: examples in the pain domain

'''3pm-6pm: Working Session on the Ontology for General Medical Science (OGMS)'''

:Moderator: Albert Goldfain (Blue Highway / Syracuse)
:Topics to be treated will include:
::Current status of OGMS and the OGMS Reference
::Linking diseases to their underlying disorders using basis relations
::Defining 'relapse' and 'remission' processes.
::Updates on the Vital Sign Ontology
::Recipes for OGMS-conformant extension ontologies
:''Close: 6:00pm''
----
'''Relevant ontology efforts'''

:GO-IP Gene Ontology -- Immunological Process (Alexander Diehl)
:CL Cell ontology immune branches
:PRO Protein Ontology
:IO Immunology Ontology (Lindsay Cowell and Alexander Diehl)
:IEO Immune Epitope Ontology (Bjoern Peters)
:MHC Major Histocompatibility Complex Ontology (Bjoern Peters)
:OGMS Ontology for General Medical Science (Albert Goldfain)
:IDO Infectious Disease Ontology (Lindsay Cowell)
:Vaccine Ontology (Oliver He)
:AO Allergy Ontology (Alex C. Yu)
:ND Neurological Disease Ontology (Alexander Diehl)
----
'''

== Participants will include ==
'''

:Ryan Brinkman (University of British Columbia)
:Atul Butte (Stanford University)
:James S. Cavenaugh (University of Rochester Medical Center)
:Werner Ceusters (University at Buffalo)
:Cliburn Chan (Duke University)
:Quan Chen (NIH/NIAID)
:Melanie Courtot (BCCRC, Vancouver)
:Alexander Cox (University at Buffalo)
:Lindsay Cowell (University of Texas Southwestern Medical Center)
:Oliver Crespo (BD Biosciences, San Jose, CA)
:Paresh Dandona (Diabetes and Endocrinology Center of Western New York / University at Buffalo)
:Peter d'Eustachio (New York University)
:Alexander Diehl (University at Buffalo)
:Chester Fox (University at Buffalo)
:Lee Ann Garrett-Sinha (University at Buffalo)
:Albert Goldfain (University at Buffalo, Syracuse University and Blue Highway, Inc.)
:Oliver He (University of Michigan)
:Leonard Jacuzzo (University at Buffalo)
:Mark Jensen (University at Buffalo)
:Christos (Kitsos) Louis (IMBB-FORTH, Crete)
:Nikesh Kotecha (Cytobank)
:Yu Lin (University of Michigan)
:Wei Luo (University at Buffalo)
:Supriya Mahajan (University at Buffalo)
:Anna Maria Masci (Duke University)
:Darren Natale (Georgetown University)
:Dave Parrish (Digital Infuzion)
:Bjoern Peters (La Jolla Institute for Allergy and Immunology)
:Mark Ressler (University at Buffalo)
:Jessica L. Reynolds (University at Buffalo)
:Alan Ruttenberg (University at Buffalo)
:Stanley A. Schwartz (University at Buffalo)
:Prontip Saelee (University at Buffalo)
:Veronica Shamovsky (NYU School of Medicine)
:Barry Smith (University at Buffalo)
:Cathy Wu (University of Delaware, Georgetown University)
:Alex C. Yu (University at Buffalo)

RO:Main Page

2008-11-12T15:34:28Z

Mcourtot: added link to RO proposed file /* Proposed new type-level relations */

=RO - OBO Relation Ontology=

The main RO page is located on [http://obofoundry.org/ro The OBO Foundry Website]

You can browse the ontology and get e-mail list details there.

=Open issues=

An RO expert meeting took place in May, 2008. See [[OntologyRelations]] for notes and presentations.

Note that requests for new terms etc should go in the [http://sourceforge.net/tracker/?group_id=76834&atid=947684 RO tracker]

Mike And Chris' Relation Ontology Proposed (MACROP), a list targets relations is [[MACROP]]

==Three types of relations==

The OBO Relation Ontology (aka the OBO Relationship Types Ontology) distinguished three families of relations, according to whether they hold between instances, types, or combinations thereof, for example:

*1. '''instance_of''' holding between an instance and a type
*2. '''part_of''' holding between an instance and an instance
*3. ''part_of'' holding between a type and a type

We use bold face to mark out those relational expressions used in ontologies such as GO to represent the relations between the types these ontologies represent.

In the original Genome Biology [http://genomebiology.com/2005/6/5/R46 paper] we focused primarily on defining relations of type 3. in terms of those of types 1. and 2. This was to meet the need among biologists for clear guidance as to what the relational expressions used in ontologies such as GO precisely mean.

In our treatment of relations of types 1. and 2. we focused primarily on picking out certain instance level relations which we fixed on as primitive -- meaning that they are so basic to the relational architecture of reality that they cannot be defined in terms of anything more basic. The primitive relations selected were as follows:

*c '''instance_of''' C '''at''' t - a primitive relation between a continuant instance and a class which it instantiates at a specific time

*p '''instance_of''' P - a primitive relation between a process instance and a class which it instantiates holding independently of time

*c '''part_of''' c1 '''at''' t - a primitive relation between two continuant instances and a time at which the one is part of the other

*p '''part_of''' p1, r '''part_of''' r1 - a primitive relation of parthood, holding independently of time, either between process instances (one a subprocess of the other), or between spatial regions (one a subregion of the other)

*c '''located_in''' r '''at''' t - a primitive relation between a continuant instance, a spatial region which it occupies, and a time

*r '''adjacent_to''' r1 - a primitive relation of proximity between two continuants

*t '''earlier''' t1 - a primitive relation between two times

*c '''derives_from''' c1 - a primitive relation involving two distinct material continuants c and c1

*p '''has_participant''' c '''at''' t - a primitive relation between a process, a continuant, and a time

*p '''has_agent''' c at '''t''' - a primitive relation between a process, a continuant and a time at which the continuant is causally active in the process

In proposing new relations (both on the [http://www.bioontology.org/wiki/index.php/RO:Main_Page#Proposed_new_relations wiki] and in the http://sourceforge.net/tracker/?group_id=76834&atid=947684&func=browse Sourceforge Tracker], please specify to which of the three types your proposed relation belongs.

*If it is an instance-level relation, please answer the following questions:
**a. is it already on the list above?
**b. is it primitive in the above-mentioned sense?
*If the answer to both of these questions is no,
**c. can it be defined in terms of the relations on the above list?
*If yes, please supply a definition (an example is provided below)
*If no, please propose also those primitive instance-level relations which would need to be added to the RO in order to define it.

==How to Define an Instance-Level Relation==

First, check whether your proposed relation needs a definition -- perhaps it is primitive (see above).

All definitions specify necessary and sufficient conditions. Thus if we are defining what it is to be an A, then the definition might read, for example:

x is an A =def. x has features F1, F2, F3.

This definition would be correct if and only if everything which has features F1, F2, and F3 is an A, and everything which is an A has features F1, F2, and F3.

For instance-level relations, the definition might read as follows:

x stands in instance-level relation r to y =def. x has features F1, F2, y has features F3, F4, x stands in instance-level relations r1, r2 to y.

For a specific example consider '''preceded_by''', a relation between occurrents (drawn from the RO paper).

With the primitive relations '''has_participant''' and '''earlier''' at our disposal we first define the instance-level relation p '''occurring_at''' t as follows:

p '''occurring_at''' t =def. for some c, p '''has_participant''' c '''at''' t.

We can then define:

c '''exists_at''' t =def. for some p, p '''has_participant''' c '''at''' t

p '''preceded_by''' p1 =def. for all t, t1, if p '''occurring_at''' t and p1 '''occurring_at''' t1, then t1 '''earlier''' t

:t '''first_instant''' p =def.
::p '''occurring_at''' t, and
::for all t1, if t1 '''earlier''' t, then not p '''occurring_at''' t1

:t '''last_instant''' p =def.
::p '''occurring_at''' t and
::for all t1, if t '''earlier''' t1, then not p '''occurring_at''' t1

:p '''immediately_preceded_by''' p1 =def.
::for some t, t '''first_instant''' p and
::t '''last_instant''' p1.

In these terms we can also define the instance-level relation '''has_duration''' proposed by Liju:

:p '''has_duration''' y =def.
::p is an occurrent, and
::for some t1, t1 '''first_instant''' p, and
::for some t2, t2 '''last_instant''' p, and
::for all t, t1 '''earlier''' t and t '''earlier t2''' implies p '''occurring_at''' t [this to ensure that p is continuous; has no gaps],
;; y is the interval (t1,t2).

Here a new functional operator 'the interval ( , )' has been introduced, which generates the name of an interval from a pair of names for times.

==Proposed new type-level relations==

relations between generically dependent continuants and specifically dependent continuants:
* concretizes
* is_concretized_by

* about
* inheres_in
* depends_on
* output_of
* has_input
* has_function
* has_quality
* realization_of
* lacks

The lacks family of relations is discussed at: [http://ontology.buffalo.edu/medo/NegativeFindings.pdf]

Some of those are described in the [http://obo.cvs.sourceforge.net/*checkout*/obo/obo/ontology/OBO_REL/ro_proposed.obo RO proposed] file.

The treatment of the derives_from relation has been criticised from an ontological point of view: [http://www.ifomis.uni-saarland.de/Home/DerivationBookVersion1-2.pdf]. Transformation_of is always, by definition a 1-1 relation. The thesis in the original [http://genomebiology.com/2005/6/5/R46 RO paper] was (A) that the derives_from relation could be n-1 or 1-n (for n > 1) but also (B) that there are examples of 1-1 derives from relations (e.g. the relation between a living organism and a corpse). This thesis (B) has now been dropped. The relation between a corpse and the predecessor organism is one of transformation.

There is also the terminological problem that "derives_from" is used specifically for evolutionary relationships by some. We will report back on this after the september NCBO anatomy meeting. We may create a "develops_from" parent for transformation_of corresponding to how that relation is currently used in MOD AOs

See also

[http://obofoundry.org/ro/#pending Pending]

'''The relation of ''overlaps'''''

X ''overlaps'' Y =def. for every t and every x, if x '''instance_of''' X at t, then there is some instance y of Y at t such that (x '''overlaps''' y at t)

where

x '''overlaps''' y at t =def there is some z such that z is '''part_of''' x '''at t''' and z '''part_of''' y '''at t'''

Note that it can be the case that X ''overlaps'' Y as thus defined, even though Y does not ''overlap'' X.

Thus uterine tracts ''overlaps'' urinogenital sysem but not uriongenital system OVERLAPS uterine tract (because of male urinogenital systems)

Actually uterine tract is part_of urinogenital system, which raises the question of whether each of X's parts overlaps X.

==Proposed Gene Ontology 'Regulates' Relations==

[Typedef]
id: OBO_REL:regulates
:name: regulates
:def: "A relation between a process and a process or quality. A regulates B if the unfolding of A affects the frequency, rate or extent of B. A is called the regulating process, B the regulates process" []
:transitive_over: OBO_REL:part_of

[Typedef]
id: OBO_REL:positively_regulates
:name: positively_regulates
:def: "A regulation relation in which the unfolding of the regulating process *increases* the frequency, rate or extent of the regulated process"
:is_a: OBO_REL:regulates
:transitive_over: OBO_REL:part_of

[Typedef]
id: OBO_REL:negatively_regulates
:name: negatively_regulates
:def: "A regulation relation in which the unfolding of the regulating process *decreases* the frequency, rate or extent of the regulated process"
:is_a: OBO_REL:regulates
:transitive_over: OBO_REL:part_of

Example file:
:ftp://ftp.geneontology.org/pub/go/scratch/gene_ontology_with_regulates_relations_test.obo

Some follow-up comments at the sourceforge tracker page
:[https://sourceforge.net/tracker/index.php? func=detail&aid=1874192&group_id=76834&atid=947684 here]

==Hunter/Bada Proposal for new relations==

GRANULARITY/SPECIFICITY

We assert that the level of granularity/specifity of the proposed
relations is a central issue that, once resolved, will provide useful
guidelines as to what is needed to capture a piece of knowledge by a
relational link. The examples in this proposal use process terms from
the Gene Ontology, but we believe that this issue applies to other OBOs
as well.

We assert that the addition of relations should be primarily guided by
the effort to link OBO terms with other OBO terms, as is being done in
the OBO cross-product project. A composite set of links from a given
more complex OBO terms to more atomic OBO terms will provide the
(hopefully complete) definition of the former. A given link from the
term being defined, employing an RO relation, must unambiguously capture
some piece of knowledge, some part of the definition, of this term. It
is this unambiguous representation of some part of the complete
definition of the term that should determine the specificity of the
relation. This may require the use of a specific relation, but we assert
that it is more important to avoid losing knowledge in the represented
definition than to exclusively use general relations.

It is ideal to use general, reusable relations in such definitions
without losing information, and we believe that this is sometimes
possible. For example, for the many GO process terms that use “during”
to specify a process that is taking place within the span of another
process (''e.g.'', “actin filament reorganization during cell cycle”), it is
acceptable to use a standard temporal relation, as no information is
lost by doing so. However, especially in the definitions of processes,
we assert that the unambiguous capture of roles of participants will
require relatively specific relations.

There have been efforts to use general relations to denote roles, but
they have been difficult to define (''e.g.'', has_agent, has_patient,
has_central_participant) and/or insufficient to specify the role
(''e.g.'', has_output_participant). If suitably precise general relations
cannot be defined, relatively specific relations are needed. Thus, for
all of the growth terms (''e.g.'', “organ growth”, “filamentous growth”),
if a general relation to indicate what is growing cannot be suitably
defined, then a specific relation must be created to capture this,
either in the form of a lexically analogous relation (''e.g.'',
results_in_growth_of) or as one that incorporates the template
definitions of the term (''e.g.'', results_in_increase_in_size_or_mass_of,
since most of the growth terms are defined as the increase in size or
mass of an entity). These two approaches by themselves are
computationally synonymous but differ in terms of human comprehension.
The former, while not adding information for human users, can be
straightforwardly formed. The latter, while helpful for human users, can
get unwieldy in the case of complex definitions. For example, the
detection-of-stimulus terms are defined as the series of events in which
a stimulus is received by an entity and converted into a molecular
signal, and
results_in_reception_of_stimulus_and_conversion_into_molecular_signal_of
is clearly getting ridiculous.

It is also ideal for relations, especially relatively specific ones as
exemplified above, to be formally defined (''i.e.'', in a computationlly
procesable way) in terms of more atomic relations. However, it will be
very difficult to produce formal definitions in terms of more atomic
relations, especially for relatively specific relations. We assert that
the linking of OBO terms to generate cross-products should be a
priority, and this requires the specification of relations (as discussed
above) to link the terms. A requirement for any proposed relation to
have a formal decomposed definition in terms of more atomic relations
would be a significant bottleneck to this process. Just as there is no
requirement for an added OBO term to have a formal definition, there
should be no such requirement for an added OBO relation. We would like
to be clear that we believe it extremely beneficial to have such formal
definitions (and thus efforts should continually be put into creating
such definitions), but this should not be an obstacle to the introduction of
new relations.

LEXICAL FORM

We propose that each relation should canonically be in the form of a
verb phrase. We assert that this promotes usability in that it
emphasizes the fact that these are relationships between entities.

==TAIR Relations==

See http://sourceforge.net/tracker/index.php?func=detail&aid=1888149&group_id=76834&atid=947684

Relations between continuants and occurrents:

* has (function)
* involved in
* functions as
* required for
* functions in
* has protein modification of type
* contributes to
* is upregulated by
* is downregulated by

Relations between continuants:

* located in
* expressed in
* colocalizes with
* is subunit of
* constituent of
* has protein-protein physical interaction with
* has protein-DNA interaction with
* binds to cis-element of
* acts upstream of
* acts downstream of
* expressed during
* protein is modified by
* is regulated by
* represses

Relations between continuants and qualities (phenotypes in our case):

* suppresses gene
* enhances gene
* partially enhances gene
* partially suppresses gene

==Proposed homologous_to relation==

x1 '''directly_descends_from''' x2 iff there are y1, y2 such that:

- y1 is an organism

- x1 is an anatomical structure

- x1 '''part_of''' y1

- y2 is an organism

- x2 is an anatomical structure

- x2 '''part_of''' y2

- y2 is a parent of y1

- the genetic sequence that determined the morphology of x1 is partially a copy of the genetic sequence that determined the morphology of. *(see notes below)

'''descends_from''' is the instance level relation which is the transitive closure over '''directly_descends_from'''

From this we can define a type level relation:

A in B ''descends_from'' C in D :

For all A(a) -> exists b, d, c: B(b) & C(c) & D(d)

a '''part_of''' b

a '''descends_from''' c

c '''part_of''' d

(Note – B must be a subclade of the clade genealogically descended from D)

A1 in B1 ''homologous_to A2'' in B2

iff

exists A3, B3:

A1 in B1 ''descends_from A3'' in B3

&

A2 in B2 ''descends_from'' A3 in B3

(Note B1 and B2 must both be subclades of the clade descending (in the genealogical sense) from D)

[* This clause still needs some work]

[* On the Phenoscape project list, Jim Balhoff added the following critique of this:

Something that jumps out at me in the definition of directly_descends_from:

I would not say that genetic sequences "determine" any morphology. I would prefer something like "participates in the development of" the morphology of x1. Anyway, I don't see genetic sequences as an absolutely necessary component of homology (although they would very often be an important component).]

[* DS: comment - I agree that reference to genetic sequence is (probably) unnecessary. Anyway, it is clear that the current formulation doesn't work: The morphology of my leg is determined by a partial copy of the genetic sequence that determined morphology of my father's arm. One possible alternative, deliberately ignoring genetics: Of all the anatomical structures in y2, x2 is the most morphologically similar to x1.
]

Note: Do we need to include time (exists & existed)?

FN – just to be on the safe side we can include time – it's not obviously useful but it could block some objections and won't affect the logic.

=== relation to what is in RO proposed ===

Note that there are a number of synonyms for descended_from, including 'evolutionarily_derived_from' which is currently in ROproposed as follows:

id: OBO_REL:evolutionarily_derived_from

name: evolutionarily_derived_from

def: "Instance 3-ary relation: x edf y as T iff x specified_by gx and gx ancestral_copy_of gy and gy specifies y" []

synonym: "derived_from" RELATED []

synonym: "descended_from" RELATED []

synonym: "evolved_from" RELATED []

is_transitive: true

==OWL Conversion==

The standard GO obo->owl conversion is used. See [[OboInOwl:Main_Page]] for details

obo1.2 defines "builtin" tags for relations that are hardwired into the obo semantics - is_a and instance_of are tagged builtin. These are not exported in OWL, as these are also part of the OWL language

== Measurements ==

At the [http://neurocommons.org/page/First_IEO_workshop IEO meeting] people seemed to agree that we use a relation
called is_measurement_of to relate a measurement to some entity. (I
can't remember if these were the exact names we used).
is_measurement_of is subpropertyOf is_about

In the following we are discussing instance level relationships.

* measurement_datum:
** has_value:
** in_units:
** of_dimension:

m1 type measurement:

* m1 has_value 30^^xsd:float
* m1 in_units_of degree_celsius (UO:0000027)
* m1 of_dimension temperature_dimension (PATO:0000146? -that's what's in UO, but need to think about that)

(Unresolved: latter two are classes. I guess that means that
in_units_of and of_dimension are annotation properties, which is a
shame. Either that or degree_celsius and temperature_dimension are
instances of some sort. Barry?)

* room1 type site
* room1 has_quality t1
* t1 instance_of temperature (PATO:0000146)

* m1 is_measurement_of t1

It was left open exactly how to represent uncertainty in the
measurement, but this was thought to be perhaps something associated
with the instrument or with a collection of measurements, rather than
what was associated with the individual measurement.

Inference rule on is_about: forall x, y, z, if x is_about y and y inheres_in z then x is_about z

== Realization_of and Associated Relations ==

For OBI purposes there is a need for an instance-level relation between a plan (for instance a protocol) and the occurrent which realizes this plan.

In its terms we might define, for example,

x deviation_from y

=def. x is an occurrent and y is a plan and there is an agent z who is the agent_of x and is attempting in performing x to realize y and it is not the case that x realization_of y