Three ontologies to define phenotype measurement data.

Abstract

There is an increasing need to integrate phenotype measurement data across studies for both human studies and those involving model organisms. Current practices allow researchers to access only those data involved in a single experiment or multiple experiments utilizing the same protocol.
Three ontologies were created: Clinical Measurement Ontology, Measurement Method Ontology and Experimental Condition Ontology. These ontologies provided the framework for integration of rat phenotype data from multiple studies into a single resource as well as facilitated data integration from multiple human epidemiological studies into a centralized repository.
An ontology based framework for phenotype measurement data affords the ability to successfully integrate vital phenotype data into critical resources, regardless of underlying technological structures allowing the user to easily query and retrieve data from multiple studies.

Full text

Page 1

ORIGINAL RESEARCH ARTICLE
published: 28 May 2012
doi: 10.3389/fgene.2012.00087
Three ontologies to define phenotype measurement data
Mary Shimoyama1*, Rajni Nigam1, Leslie Sanders McIntosh2, Rakesh Nagarajan2,Treva Rice2, D. C. Rao2
and Melinda R. Dwinell1
1Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, WI, USA
2Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
Edited by:
John Hancock, Medical Research
Council, UK
Reviewed by:
Philippe Rocca-Serra, Oxford
e-Research Centre, UK
Peter N. Robinson, Charité, Germany
*Correspondence:
Mary Shimoyama, Department of
Surgery, Medical College of
Wisconsin, 8701 Watertown Plank
Road, Milwaukee, WI 53226, USA.
e-mail: shimoyama@mcw.edu
Background:Thereisanincreasingneedtointegratephenotypemeasurementdataacross
studies for both human studies and those involving model organisms. Current practices
allow researchers to access only those data involved in a single experiment or multiple
experiments utilizing the same protocol. Results: Three ontologies were created: Clini-
cal Measurement Ontology, Measurement Method Ontology and Experimental Condition
Ontology.These ontologies provided the framework for integration of rat phenotype data
from multiple studies into a single resource as well as facilitated data integration from mul-
tiple human epidemiological studies into a centralized repository. Conclusion:An ontology
based framework for phenotype measurement data affords the ability to successfully inte-
grate vital phenotype data into critical resources, regardless of underlying technological
structures allowing the user to easily query and retrieve data from multiple studies.
Keywords: ontology, phenotype
BACKGROUND
The quest to link characteristics of an individual or organism to
genetic structures dates to the mid-1800s and the work of Gregor
Mendel(Sorsby,1965).Inthepast20years,agreatdealof progress
hasbeenmadeinidentifying,naming,andstandardizingtheinfor-
mation about genetic structures. The International Nucleotide
Sequence Database Collaboration1was created to develop stan-
dard formats for genomic data to integrate data generated at
multiple laboratories using a variety of technologies resulting in
public databases housed at the National Center for Biotechnology
Information (NCBI; Sayers et al., 2010), the DNA Databank of
Japan (Kaminuma et al., 2010), and the European Bioinformatics
Institute(EBI;Goujonetal.,2010).Thisintegrationofdatahasled
to the development of numerous data mining, presentation, and
analysis tools and provides a platform for comparisons of genetic
and genomic structures across species. Unfortunately, a similar
development in data standards and integration has not occurred
for the characteristics of an individual or organism scientists wish
to link to these structures. The potential value of integrating phe-
notype data from multiple sources (e.g., different laboratories or
studies,varying techniques to measure similar phenotypes,multi-
ple populations,or strains of a particular organism) is enormous.
The power to identify novel genes associated with human disease
and the role a gene plays in disease is greatly increased with clearly
defined phenotype information and the inclusion of the envi-
ronmental and experimental context (Butte and Kohane, 2006).
However, most phenotype data is gathered or generated without
thought to integrating the results with those from other studies
even within the same laboratory or program, creating barriers
to integrating and comparing results reported in publications. In
both animal and human physiological and disease studies, there
1http://www.insdc.org/
hasbeenalongtraditionof designingnewprotocolsandadopting
evolving best practices available at the time the study is launched.
As a result, the same basic information gets collected differently
across protocols. This leads to a common belief that each study is
unique and cannot be compared to any other for anything more
than the most general elements. Moreover,the data sets and study
information are structured in such a way that, often, only those
who are intimately familiar with the study understand the full
depth of the data; this includes details such as the measurement
methodsusedandtheexperimentalconditionsimposed.Formost
researchers, ferreting out this information from different studies
requires extensive time and effort, as is generally experienced by
post hoc collaborations among multiple studies. Even when these
details are published, they are often described in widely different
ways without full inclusion of details making comparisons across
studies not only difficult but sometimes impossible.
Variations in experimental conditions, population, age, and
study design all contribute to the difficulty in comparing phe-
notype data from multiple sources. For example, the comparison
of blood pressure measured in different laboratories or programs
can be impacted by the way in which blood pressure is mea-
sured (e.g., direct measurement via catheter in artery, telemetry,
bloodpressurecuff),theexperimentalconditionsimposedaspart
of the study (e.g., low salt/high salt diet, exercise, oxygen lev-
els), surgical manipulations (e.g., removal of a kidney), gender,
and age. One approach to aggregating and integrating phenotype
data would be to develop standard phenotyping protocols to be
followed by all researchers. However, standardizing the methods
used for phenotyping protocols has significant drawbacks. Many
would see it as impractical since each researcher is testing fairly
unique hypotheses which cannot be easily investigated by using
a set protocol. Additionally, not all laboratories measure phe-
notypes using the same assays, nor do all investigators agree on
one perfect method to measure each phenotype. Any movement
www.frontiersin.org
May 2012 | Volume 3 | Article 87 | 1

Page 2

Shimoyama et al. Ontologies to define phenotype measurements
towardthistypeof standardizationwouldtakeyearsbeforeresults
were evident,keeping existing data resources inaccessible. A more
practical approach is to develop a method using ontologies and
standardized data formats to integrate phenotype measurement
data sets.
A number of groups have focused on standardizing biological
information through standardized vocabularies and ontologies.
Ontologiesarehierarchicallystructuredvocabulariesof termsand
relationshipsthatareclearlydefinedanddesignedtorepresentand
communicate information about a particular scientific domain
(Figure 2). The entities and concepts represented by the terms in
thelowernodesareassumedtoinheritthepropertiesandqualities
of those of nodes higher up the branch. The National Institutes of
Health, in recognition of the utility of ontologies and the need
for more ontologies to represent biological concepts, provided
funding for the creation of the National Center for Biomedical
Ontology (NCBO)2in 2006 (Rubin et al., 2006). There are cur-
rently 242 ontologies cataloged at NCBO including several which
focus on phenotypes.
MAMMALIAN PHENOTYPE ONTOLOGY
The Mammalian Phenotype (MP) Ontology was initially created
for annotating gene alleles at the Mouse Genome Informatics
(MGI) database (Smith et al., 2005). For the MP ontology,“phe-
notype refers to the observable morphological, physiological, and
behavioral characteristics of an individual in the context of the
environment”(SmithandEppig,2009).BecauseMPwasdesigned
to be used with mouse knockouts, mutations, and other types
of alleles, there is an underlying assumption of a comparison
to the trait exhibited by a mouse with the genetic background
from which the allele has been constructed or a comparison
to a normal or “wild type” trait. Thus the terms often contain
words such as “abnormal,” “increased,” or “decreased” with the
implication of “relative to” an assumed observation. The actual
measured values for observed traits are not connected to these
annotations. MP follows the open-source Open Biological and
Biomedical Ontology (OBO) file format and is organized on the
Directed Acyclic Graph (DAG) structure with the highest nodes
related to physiological systems such as cardiovascular, immune
system as well as behavioral, life span, and cellular phenotypes.
Each physiological system node is followed by a basic division
into physiological and morphological phenotype branches. MGI
currently has over 41,000 genotypes annotated with MP terms
for a total of more than 193,000 annotations. MP is consid-
ered a pre-coordinated term ontology since both the entity (i.e.,
anatomical site or physiological process) and the quality of it
(i.e., abnormal, increased, decreased) are included in the term.
MP is also being used for the EuroPhenome project to anno-
tate mutant mouse phenotype data generated using standard
phenotyping platforms (Morgan et al., 2010). The advantages
in terms of annotation are significant since curators only have
to search a single ontology and has terms that more closely
mimic those seen in literature and commonly used in laboratory
settings.
2http://bioontology.org/
PHENOTYPE AND TRAIT ONTOLOGY
Another approach to phenotype ontologies has been the Phen-
toype and Trait Ontology (PATO) project3(Gkoutos et al., 2004).
Unlike MP which is considered a pre-coordinated term phe-
notype ontology, PATO uses the EQ approach (entity+quality;
Gkoutosetal.,2004;SmithandEppig,2009).ThusPATOpresents
terms related to qualities and attributes that are then linked
to terms from other ontologies such as anatomy ontologies to
describe phenotypic characteristics. Thus, “big ears” would be
represented by the term “increased size” from PATO and the
word “ear” from an anatomy ontology (Mungall et al., 2010).
One of the advantages of this approach is the re-use of existing
ontologies such as the anatomy ontology. Representing morpho-
logical traits through the use of anatomy ontologies and the
qualities described in PATO is relatively straight forward. This
is one reason that resources housing data for some organisms
such as drosophila and zebrafish have found this approach use-
ful (Mungall et al., 2010); the majority of their reported traits
and phenotypes are morphological in nature. However,the repre-
sentation of physiological traits and specific clinical measurement
types is more problematic (Smith and Eppig, 2009). First, there
is not necessarily a single ontology that adequately represents
the physiological trait corresponding with the quality expressed
by PATO; and second, a single EQ term may not adequately
express the phenotype observed. For example while the mor-
phological trait of “big ears” is relatively easy to represent by
EQ, an MP term of “abnormal cochlear outer hair cell elec-
tromotility” provides a greater challenge. The disadvantages of
the PATO approach for annotation are also significant. Curators
would have to browse multiple ontologies to create a term on the
fly and this approach creates terms and phrases that sometimes
are stilted or not commonly used in the literature or laboratory
settings.
HUMAN PHENOTYPE ONTOLOGY
TheHumanPhenotypeOntology(HPO)wasdevelopedinpartto
address the shortcomings of information presented in the Online
Mendelian Inheritance in Man (OMIM) database4(Amberger
et al., 2009). OMIM has traditionally been the most commonly
used resource for information on genetic diseases. Unfortunately,
theinformationhousedthereisinfreetextformat,makingitdiffi-
cult to mine computationally because of the non-standard way in
whichtraitsandabnormalitiesaredescribed.Forinstance,OMIM
usesthesynonymousdescriptions“generalizedamyotrophy,”“gen-
eralized muscular atrophy,” and “muscular atrophy, generalized”
so even simple searches may not return the results a user desires.
While a human reader going through the free text of multiple
entries will recognize similar meanings, computers will not. The
initialversionofHPOwascreatedusingtheinformationatOMIM
in an effort to merge synonyms and create links and relation-
ships among the terms and concepts. This initial structure has
been expanded and refined through manual curation of infor-
mation from a variety of sources and consistent development of
definitions and relationships (Robinson and Mundlos,2010). As
3http://www.bioontology.org/wiki/index.php/PATO:Main_Page
4http://www.ncbi.nlm.nih.gov/omim
Frontiers in Genetics | Bioinformatics and Computational Biology
May 2012 | Volume 3 | Article 87 | 2

Page 3

Shimoyama et al.Ontologies to define phenotype measurements
with MP, the emphasis has been on phenotypes which diverge
from the normal or expected and disease states and terms are
pre-constructed as with the MP.
Ontologies such as MP, PATO, and HPO were originally
designed for use in simple annotations to a single data (e.g., gene
product or allele) or an individual and the term was expected to
appear alone in the annotation with the minimal accompanying
information of an evidence code indicating level of experimen-
tal evidence to support the annotation and the reference from
which the annotation was made. The existing phenotype ontolo-
gies were not developed to be attached to actual measurement
values but to indicate a state or characteristic observed relative to
that which has been determined to be“normal”or“wild type,”or
relative to that exhibited by an individual with a known genotype.
Information on experimental conditions and measurement assays
used are vital parts of the phenotype record and the use of mul-
tiple ontologies to represent these has been advocated as a way to
accomplish this (Shimoyama et al., 2005; Hancock et al., 2007).
Clearly, developing separate ontologies for the elements of phe-
notype measurement, method of measurement, and conditions
under which the measurement was made along with provisions
for additional information on actual values, duration of condi-
tions, and so on, will allow these aspects of the phenotype record
to be linked. Database structures which allow re-use of informa-
tion and multiple associations will facilitate data integration,data
mining,and data presentation.
In this paper, we present three ontologies created to standard-
ize phenotype measurement records for use in human studies and
those using laboratory animals: Clinical Measurement Ontology
(CMO), Measurement Method Ontology (MMO), Experimental
Condition Ontology.
MATERIALS AND METHODS
The standard elements of phenotype measurement records were
identified as: (1) what was measured, (2) how it was measured,
and (3) under what conditions it was measured. Ontologies were
developed to standardize each of these elements (Figure 1) and
include (1) CMO, (2) MMO, and (3) Experimental Conditions
Ontology (XCO).
TheontologiesareavailablethroughtheNCBOBioportal5,and
at the Rat Genome Database in ftp files6. The ontologies undergo
revisions and updates for both consistencies in format and to
extend the breadth and depth of coverage as new measurement
records are added. The ontologies were developed using the OBO
format and the OBO Edit tool (Day-Richter et al., 2007) available
at the NCBO, through its Foundry project (Smith et al., 2007).
While developed in OBO, developments in OBO to Web Ontol-
ogy Language (OWL) mapping tools should facilitate conversion
to this other highly used ontology format. The major ontology
5http://bioportal.bioontology.org/
6http://rgd.mcw.edu/pub/ontology
FIGURE 1 |Three ontologies were developed to standardize the three elements of a measurement record: what was measured, how it was measured
and under what conditions it was measured.
www.frontiersin.org
May 2012 | Volume 3 | Article 87 | 3

Page 4

Shimoyama et al. Ontologies to define phenotype measurements
development tools,OBO Edit for OBO and Protege for OWL now
offer widgets that facilitate conversion from one file format to the
other(seetextfootnote2).TheOBORelationOntology(RO)was
used to create consistency in relationship representations (Smith
et al., 2007). These ontologies follow the form of DAGs in which
there is a set of nodes with edges forming the linkage between
nodes (Robinson and Mundlos,2010). The nodes are the terms in
theontologywiththeedgesrepresentingtherelationshipsbetween
nodes and the overall visualization of such ontologies resembles
branches.InDAGs,theedgesorrelationshipsareoneway,moving
fromonenodetoanother,andtheydonotcycleback.Thegeneral
relationship pattern in many of these ontologies is a movement
from the more general (higher nodes) to the more specific (lower
nodes). The entities and concepts represented by the terms in the
lower nodes are assumed to inherit the properties and qualities of
those of nodes higher up the branch.
The development of these ontologies has included cross ref-
erences with other ontologies when an exact match of the entity
exists. For example, relationships were created with ChEBI in the
ExperimentalConditionOntologyandtotheElectrocardiography
Ontology (ECG) exist in the CMO. These relationships were cre-
atedmanuallyandarenotusedtocreatecrossproducts.Crossref-
erencing to other ontologies, such as the Cell Ontology, Evidence
Code Ontology, and other ontologies used for the reporting of
phenotypes,will continue with both manual and semi-automated
methods as the ontologies are extended.
CLINICAL MEASUREMENT ONTOLOGY
TheCMOprovidesthestandardizedvocabularynecessarytoindi-
cate the type of measurement made to assess a trait. For the
purposes of this project and these ontologies, trait and clinical
measurement are defined as follows:
Trait
A physiological or morphological state or property found in all
members of a species. Traits can be described or assessed quan-
titatively (numerically) or qualitatively based on the results of an
appropriate form of measurement. The assessment of the trait is
notequivalenttothetraititself.Traitsexistevenwhentheyarenot
assessed or measured. Often multiple forms of measurement are
used to assess a single property or state.
Measurement
The act or result of the act of assessing a morphological or phys-
iological state or property in a single individual or group of
individuals and assigning a quantitative or qualitative value. A
measurement does not exist until it is performed or taken. Often
a single measurement can be used to assess multiple properties or
states,sometimes in conjunction with other measurements.
For example, all humans have intelligence or mental capacity,
but not all human individuals have an IQ because it has not yet
been measured. Similarly, all humans have a body mass but they
donotallhaveabodyweightbecauseithasnotyetbeenmeasured.
EachtermintheCMOdescribesadistincttypeofmeasurement
usedtoassessoneormoretraits.Thetermsarearrangedinahier-
archical structure of classes so that lower classes are subclasses of
higher classes in the branch (Figure 2).
This represents an“is_a”type relationship so that a lower term
“is_a” subclass of a higher term. Thus, blood cell measurement
“is_a” blood measurement and complete blood cell count “is_a”
blood cell measurement. The measurements in the ontology are
primarilyorganizedonthehighestlevelaccordingtothebodysys-
tem in which the measurement is made. Trait areas were targeted
for ontology development based on the availability and extent of
datainlargescaleratphenotypingprojects,publishedratliterature
withphenotypemeasurementsandtargetedhumanepidemiolog-
ical studies. Ontology development began with the identification
of clinical measurements used to assess targeted traits,with terms
anddefinitionsbeingcreatedandrelationshipsamongtermsbeing
set (Figure 3).
Existing ontologies at NCBO were reviewed for associated
terms and definitions. Because the clinical measurements in the
targeted sources may be limited, additional literature including
medical and physiological textbooks, laboratory manuals, and
published research literature were reviewed to ensure complete-
ness in the ontology. For example, to assess kidney mass the
data source may only use right kidney weight. Further review
of a variety of sources reveals that other typical measurements
would include left kidney weight, weight of both kidneys, kid-
ney weight expressed as a percentage of body weight which are
also often used to assess the trait of kidney mass so these were
also added to the ontology. For every clinical measurement term
created, associated measurement method terms and experimen-
tal condition terms were created based on data in the originating
sources as well as the review of additional literature and exist-
ing ontologies. There are currently 523 terms in the CMO for
measurement types ranging from morphological to physiological
for blood, cardiovascular, respiratory, renal, and other systems as
wellasforgrowth,reproduction,consumption,tumors,andtissue
composition.
MEASUREMENT METHOD ONTOLOGY
A critical element in the description of a phenotype is the mea-
surement method used. Several types of methods are commonly
used to measure such things as blood pressure resulting in dif-
fering clinical measurement values so the inclusion of method as
part of the measurement reading is necessary for the integration
of data from multiple studies. The MMO is designed to provide
thisinformation.Asdescribedabove,thisontologywasdeveloped
in parallel with the CMO as trait areas are targeted. The MMO
is organized by the underlying principle or mechanism of the
method (Figure 4) with two major branches, “ex vivo method”
and “in vivo method.” Methods were identified from protocol
descriptionsanddatalabelsfromthetargeteddatasources.Aswith
the CMO, for completeness in the ontology, additional sources of
method information such as vendors’ catalogs, laboratory manu-
als,andpublishedliteraturewerereviewedforassociatedmethods.
Thus,ifoneoftheprotocolsforoneoftheoriginatingdatasources
indicatedthataballoontippedcatheterwasusedtomeasureblood
pressure, a quick review of a variety of publications revealed that
the basic category is vascular indwelling catheter with a variety of
types including fluid filled catheter,intravascular electromagnetic
flow sensor, and transducer tipped catheter. There are currently
195 terms in the MMO.
Frontiers in Genetics | Bioinformatics and Computational Biology
May 2012 | Volume 3 | Article 87 | 4

Page 5

Shimoyama et al.Ontologies to define phenotype measurements
FIGURE 2 |The Clinical Measurement Ontology is presented in a hierarchical structure with classes lower down a branch being subclasses of those
above with an “is_a” relationship.
FIGURE 3 | Each CMO term was created as phenotype domains addressed with appropriate definitions for each term.
www.frontiersin.org
May 2012 | Volume 3 | Article 87 | 5

Page 6

Shimoyama et al.Ontologies to define phenotype measurements
FIGURE 4 |The Measurement Method Ontology structure is based on two major branches, “ex vivo” and “in vivo” and the underlying mechanism or
technique used in the method.
EXPERIMENTAL CONDITION ONTOLOGY
While many phenotype measurements are made under baseline
conditions, changes to diet, atmosphere, activity level, and other
conditions are common aspects of phenotype experiments. Often
thisinformationisaddedaspartofthephenotypelabelintheindi-
vidual laboratory’s database or only included as part of a lengthy
text protocol. Creation of standardized terminology and format
for presenting this information with phenotype measurements
is crucial to the integration of phenotype data from multiple
datasets. An XCO was created to provide standardization and
structure for this important information (Figure 5).
In addition, to the “is_a” relationship, in certain areas a
“part_of” relationship is utilized so that parts of a whole can be
describedasin“airoxygencontent”is“part_of”“atmospherecom-
position.” The ontology was designed so that conditions related
to existing ontologies such as those involving chemicals or drugs
represented in ChEBI (Degtyarenko et al.,2009),follow the struc-
ture and terminology of these ontologies and provide appropriate
linkages through identifiers (Figure 5). Initial emphasis was on
the conditions used in the targeted data sets and expanded to
those conditions most commonly used in experiments involving
the targeted trait domains. Structural provisions in the database
structures of projects using the ontology provide ordinality infor-
mationtoindicatewhethermultipleconditionsweresimultaneous
or sequential. Use of this ontology in annotating phenotype data
allowsuserstoretrievemultiple,disparatephenotypeinformation
inwhichsimilarexperimentalconditionswereimposed.Thereare
currently 110 terms in the XCO.
DATA INTEGRATION
Thethreeontologieshavebeenusedtointegratemultipledatasets
for two major projects, one involving human data and the other
involving rat data. The Cardiovascular Ontologies andVocabular-
ies in Epidemiological Research project was designed to integrate
demographic and phenotype measurement data from three fam-
ily blood pressure studies, Hypertension Genetic Epidemiology
Network (HyperGEN; Williams et al., 2000); Genetic Epidemi-
ology Network of Salt Sensitivity (GenSalt; Gu et al., 2007);
Frontiers in Genetics | Bioinformatics and Computational Biology
May 2012 | Volume 3 | Article 87 | 6

Page 7

Shimoyama et al.Ontologies to define phenotype measurements
FIGURE 5 |The Experiment Condition Ontology is structured by type of condition with both “is_a” and “part_of” relationships with links to identifiers
found in other ontologies.
and HEalth, RIsk factors exercise Training And GEnetics (HER-
ITAGE; Bouchard et al., 1995). While all three studies focused
on cardiovascular disease and associated risk factors, they were
disparate in the types of interventions used, variety of measure-
ments taken and the methods used to make the measurements.
Measurements related to blood pressure, blood chemistry and
lipid levels, body weight and body fat were included as well as
interventions ranging from sodium controlled diets to exercise.
Invasive, non-invasive and imaging techniques were also used.
The HyperGEN study was designed to characterize the genes
influencing hypertension by recruiting hypertensive sibships (i.e.,
each participant with two or more hypertensive sibs) from across
multiple field centers and ethnic groups. The GenSalt study is
an intervention study of the genetic and environmental fac-
tors related to dietary sodium and potassium effects on blood
pressure in rural Chinese families. The HERITAGE study is an
intervention study designed to assess genetic and environmen-
tal factors underlying the effects of endurance exercise training
on several cardiorespiratory and cardiovascular disease risk fac-
tors. The CMO, MMO, and Experimental Condition Ontology
were used to map data elements from each of the studies to
a common format for integration into a single resource7. To
date, 16 phenotype classes with records for 8,778 subjects have
been integrated for the three studies and made available at the
7http://cover.wustl.edu/Cover/
website. Additionally, all variables across the studies are being
mapped and modeled with their associated ontology terms to
facilitate querying and access to raw data fields of interest; to
date11classeshavebeencreatedrepresentingover100phenotype
measurements.
TheratPhenoMinerproject8(Figure6)alsohasusedthethree
ontologiestodefinedataformatsandstandardsforintegratingrat
phenotype measurement data from a variety of sources includ-
ing two large scale phenotyping projects and published literature.
PhysGenProgramforGenomicApplications9(Kwiteketal.,2006),
one of the large scale phenotyping projects was designed to con-
duct high throughput phenotype screening for a targeted set of
inbredstrains,aswellasconsomicandmutantstrains.Thescreens
involved hundreds of different types of phenotype measurements
for heart, lung, renal, vascular, and blood function under base-
line conditions as well as varying diet, atmosphere, and activity
conditions. Data was organized, stored, and presented by proto-
col so even though some similar measurements such as weight or
blood pressure were measured in multiple protocols,the data was
notintegratedacrossprotocols.TheNationalBioResourceProject
for the Rat in Japan (Serikawa et al., 2009) was the second large
scale rat phenotyping project. Phenotype screens for body weight,
activity,behavior,blood pressure,blood chemistry,urine analysis,
8http://rgd.mcw.edu/phenotypes/
9http://pga.mcw.edu/
www.frontiersin.org
May 2012 | Volume 3 | Article 87 | 7

Page 8

Shimoyama et al.Ontologies to define phenotype measurements
FIGURE 6 |The PhenoMiner website.
andorganweightshavebeenconductedunderbaselineconditions
for inbred and mutant strains.
Because the rat is an ideal model organism for pharmacology,
biochemistry, and physiology research, the published literature
is a rich resource of data on phenotype measurements for par-
ticular strains. Papers reporting cardiovascular, respiratory, renal,
morphological,and blood chemistry measurement data as well as
those with measurements related to cancer were targeted for the
initial phase of the PhenoMiner resource. Over 13,000 measure-
ment records from these three sources have been mapped to the
three ontologies and integrated into PhenoMiner (Figure 7).
DISCUSSION AND CONCLUSION
The three ontologies created have proven to be excellent tools for
standardizing phenotype measurement data for projects involv-
ing a wide variety of data types and data sources. Targeting the
three basic elements of: (1) what was measured; (2) how it was
measured; and (3) under what conditions it was measured, facil-
itated standardization while allowing for flexibility in providing
associated information such as units of measurement or duration
of condition to be formatted in ways particular to the integrat-
ing resource. These ontologies allowed phenotype measurement
data from disparate studies to be integrated without compro-
mising study-specific aspects related to methodology. Multiple
datasets of human epidemiological data and rat phenotype data
were successfully integrated into resources designed to meet the
needs of diverse research communities even though the underly-
ingtechnologicalframeworkforthedatabasesandassociatedtools
differed. While integrating varied phenotype datasets was the pri-
mary motivation for the development of these ontologies, they
can be deployed in a variety of other projects as well. Because of
their availability at NCBO, they can be utilized with the NCBO
Annotator,aWeb service that annotates journal abstracts10which
facilitates curation efforts and queries for appropriate literature
for specific projects. Because of their focus on experimental data,
the use of these ontologies in text mining tools would also help
investigators identify and prioritize literature.
Creating structures to integrate phenotype measurement data
from multiple sources is an important task as investigators draw
on the strength of the genomic and sequence variation resources
to identify underlying genotype factors related to phenotypes
and diseases. In order to make these connections, researchers
need to easily access and analyze phenotype measurement data
related to individuals and various model strains, and informa-
tion on experimental conditions and methodologies that may
affect the measurement values. Employing multiple ontologies
to standardize data formats facilitates the integration of these
vital datasets and provides the structure on which innovative
data mining, analysis, and presentation tools can be built. These
types of resources can provide researchers with a more accurate
10http://bioportal.bioontology.org/annotator#
Frontiers in Genetics | Bioinformatics and Computational Biology
May 2012 | Volume 3 | Article 87 | 8

Page 9

Shimoyama et al.Ontologies to define phenotype measurements
FIGURE 7 | Example of phenotype measurement data from multiple studies mapped to the three ontologies for clinical measurement, measurement
method, and experimental condition.
picture of phenotype variations among populations and as well as
the impact of measurement methods may have on measurement
results. The influence of experimental and environmental condi-
tions on phenotypes and disease will also be easier to elucidate
when researchers have access to large numbers of measurements
from a wide variety of studies. This is an important step in help-
ing investigators link genotypes to phenotypes. Finally, the use
of multiple ontologies to standardize data elements into single
quantifiable records can be used in many paradigms to integrate
datasets. Convergence among phenotyping efforts can be fostered
using this methodology through the use of existing ontologies,
such as MP, PATO, and HPO, in conjunction with ontologies that
further refine the phenotype data. Engaging other communities
will provide the platform for data mining across species or across
phenotyping programs using a variety of phenotyping protocols.
AUTHORS’ CONTRIBUTIONS
Mary Shimoyama created the ontologies, organized the data
mapping, and integration at the Medical College of Wisconsin
and participated in data mapping for the COVER project. Rajni
Nigam assisted in ontology term creation and participated in data
mapping. Leslie Sanders McIntosh organized data mapping and
informatics component creation for the COVER project. Rakesh
Nagarajan participated in the informatics infrastructure design
for COVER project. Treva Rice coordinated data mapping and
integration for the COVER project. D. C. Rao participated in
design and coordinated data mapping and integration for the
COVER project. Melinda R. Dwinell participated in the design
of the PhenoMiner tool and data integration at the Medical
College of Wisconsin. All authors have read and approved the
manuscript.
ACKNOWLEDGMENTS
The authors would like to acknowledge the contributions of the
RGD curation team at the Medical College of Wisconsin and
the staff and informatics team at Washington University. This
project is funded in part by R01HL094271, R01HL094286, and
R01HL064541.
REFERENCES
Amberger, J., Bocchini, C. A., Scott, A.
F., and Hamosh, A. (2009). McKu-
sick’s online Mendelian inheritance
in man (OMIM). Nucleic Acids Res.
37, D793–D796.
Bouchard, C., Leon, A. S., Rao, D. C.,
Skinner, J. S., Wilmore, J. H., and
Gagnon, J. (1995). The HERITAGE
familystudy.Aims,design,andmea-
surement protocol. Med. Sci. Sports
Exerc. 27, 721–729.
Butte, A. J., and Kohane, I. S. (2006).
Creation and implications of a
phenome-genome
Biotechnol. 24, 55–62.
network.Nat.
Day-Richter, J., Harris, M. A., Haen-
del, M., and Lewis, S. (2007).
OBO-Edit – an ontology editor
for biologists. Bioinformatics 23,
2198–2200.
Degtyarenko, K.,
Matos, P., and Ennis, M. (2009).
ChEBI: an open bioinformatics
Hastings, J., De
and
Curr. Protoc. Bioinformatics Chapter
14, Unit 14.19.
Gkoutos, G. V., Green, E. C., Mallon,A.
M.,Hancock,J.M.,andDavidson,D.
(2004). Building mouse phenotype
ontologies. Pac. Symp. Biocomput.
178–189.
cheminformaticsresource.
www.frontiersin.org
May 2012 | Volume 3 | Article 87 | 9

Page 10

Shimoyama et al.Ontologies to define phenotype measurements
Goujon, M., Mcwilliam, H., Li, W.,
Valentin, F., Squizzato, S., Paern, J.,
and Lopez, R. (2010). A new bioin-
formatics analysis tools framework
at EMBL-EBI. Nucleic Acids Res.
38(Suppl.),W695–W699.
Gu, D., He, J., Hixson, J. E., Jaquish, C.
E.,Liu,D.,Rao,D.C.,Whelton,P.K.,
Yao, Z., He, J., Bazzano, L. A., Chen,
C.-S., Chen, J., Hamm, L., Munt-
ner, P., Reynolds, K., Reuben, J. R.,
Whelton, P. K.,Yang, W., Rao, D. C.,
Brown,M.,Gu,C.,Rice,T.,Schwan-
der, K., Wang, S., Gu, D., Cao, J.,
Chen,J.,Duan,X.,Huang,J.,Huang,
J., Li, J., Liu, D., Liu, D., Pan, E.,
Wei, Y., Wu, X., Lu, F., Jin, S., Meng,
Q., Wu, F., Zhao, Y., Ma, J., Li, W.,
Zhang, J., Hu, D., Ding, Y., Wen, H.,
Zhang, M., Zhang, W., Ji, X., Li, R.,
Zu,H.,Yao,C.,Li,Y.,Shen,C.,Zhou,
J.,Mu,J.,Chen,E.,Huang,Q.,Wang,
M., Yao, Z.-J., Chen, S., Gu, D., Li,
H., Wang, L., Zhang, P., Zhao, Q.,
Hixson, J. E., Shimmin, L. C., and
Jaquish,C.E. (2007).GenSalt:ratio-
nale, design, methods and baseline
characteristics of study participants.
J. Hum. Hypertens. 21, 639–646.
Hancock,J.M.,Adams,N.C.,Aidinis,V.,
Blake, A., Bogue, M., Brown, S. D.,
Chesler, E. J., Davidson, D., Duran,
C., Eppig, J. T., Gailus-Durner, V.,
Gates,H.,Gkoutos,G.V.,Greenaway,
S., Hrabe De Angelis, M., Kollias,
G., Leblanc, S., Lee, K., Lengger, C.,
Maier,H.,Mallon,A.M.,Masuya,H.,
Melvin, D. G., Muller, W., Parkin-
son, H., Proctor, G., Reuveni, E.,
Schofield, P., Shukla, A., Smith, C.,
Toyoda, T., Vasseur, L., Wakana, S.,
Walling, A., White, J., Wood, J., and
Zouberakis, M. (2007). Mouse Phe-
notype Database Integration Con-
sortium: integration [corrected] of
mouse phenome data resources.
Mamm. Genome 18, 157–163.
Kaminuma, E., Mashima, J., Kodama,
Y., Gojobori, T., Ogasawara, O.,
Okubo, K., Takagi, T., and Naka-
mura, Y. (2010). DDBJ launches a
new archive database with analytical
tools for next-generation sequence
data.NucleicAcidsRes.38,D33–D38.
Kwitek, A. E., Jacob, H. J., Baker, J.
E., Dwinell, M. R., Forster, H. V.,
Greene, A. S., Kunert, M. P., Lom-
bard,J.H.,Mattson,D.L.,Pritchard,
K. A. Jr., Roman, R. J., Tonellato,
P. J., and Cowley, A. W. Jr. (2006).
BN phenome: detailed characteriza-
tion of the cardiovascular,renal,and
pulmonarysystemsofthesequenced
rat. Physiol. Genomics 25, 303–313.
Morgan, H., Beck, T., Blake, A., Gates,
H.,Adams,N.,Debouzy,G.,Leblanc,
S., Lengger, C., Maier, H., Melvin,
D., Meziane, H., Richardson, D.,
Wells, S., White, J., Wood, J., De
Angelis, M. H., Brown, S. D., Han-
cock, J. M., and Mallon, A. M.
(2010). EuroPhenome: a repository
for high-throughput mouse pheno-
typing data. Nucleic Acids Res. 38,
D577–D585.
Mungall, C. J., Gkoutos, G. V., Smith,
C. L., Haendel, M. A., Lewis,
S. E., and Ashburner, M. (2010).
Integrating phenotype ontologies
acrossmultiplespecies.GenomeBiol.
11, R2.
Robinson,P.N.,andMundlos,S.(2010).
The human phenotype ontology.
Clin. Genet. 77, 525–534.
Rubin, D. L., Lewis, S. E., Mungall,
C. J., Misra, S., Westerfield, M.,
Ashburner, M., Sim, I., Chute, C.
G., Solbrig, H., Storey, M. A.,
Smith, B., Day-Richter, J., Noy,
N. F., and Musen, M. A. (2006).
National Center for Biomedical
Ontology: advancing biomedicine
through structured organization of
scientific knowledge. OMICS 10,
185–198.
Sayers, E. W., Barrett, T., Benson, D.
A., Bolton, E., Bryant, S. H., Canese,
K., Chetvernin, V., Church, D. M.,
Dicuccio,M.,Federhen,S.,Feolo,M.,
Geer, L. Y., Helmberg, W., Kapustin,
Y., Landsman, D., Lipman, D. J., Lu,
Z.,Madden,T.L.,Madej,T.,Maglott,
D. R., Marchler-Bauer, A., Miller,V.,
Mizrachi, I., Ostell, J., Panchenko,
A., Pruitt, K. D., Schuler, G. D.,
Sequeira, E., Sherry, S. T., Shumway,
M., Sirotkin, K., Slotta, D., Sou-
vorov, A., Starchenko, G., Tatusova,
T. A., Wagner, L., Wang, Y., John
Wilbur, W., Yaschenko, E., and Ye,
J. (2010). Database resources of the
National Center for Biotechnology
Information. Nucleic Acids Res. 38,
D5–D16.
Serikawa,T.,Mashimo,T.,Takizawa,A.,
Okajima,R.,Maedomari,N.,Kuma-
fuji,K.,Tagami,F.,Neoda,Y.,Otsuki,
M., Nakanishi, S., Yamasaki, K.,
Voigt, B., and Kuramoto, T. (2009).
National BioResource Project-Rat
and related activities. Exp. Anim. 58,
333–341.
Shimoyama, M., Petri, V., Pasko, D.,
Bromberg, S., Wu, W., Chen, J.,
Nenasheva, N., Kwitek, A., Twig-
ger, S., and Jacob, H. (2005).
Usingmultiple
integrate complex
data. Comp. Funct. Genomics 6,
373–378.
Smith, B., Ashburner, M., Rosse, C.,
Bard, J., Bug, W., Ceusters, W.,
Goldberg, L. J., Eilbeck, K., Ire-
land, A., Mungall, C. J., Leon-
tis, N., Rocca-Serra, P., Ruttenberg,
A., Sansone, S. A., Scheuermann,
R. H., Shah, N., Whetzel, P. L.,
and Lewis, S. (2007). The OBO
Foundry: coordinated evolution of
ontologies to support biomedical
data integration. Nat. Biotechnol. 25,
1251–1255.
Smith, C. L., and Eppig, J. T. (2009).
The mammalian phenotype ontol-
ogy:enablingrobustannotationand
comparative analysis.Wiley Interdis-
cip. Rev. Syst. Biol. Med. 1, 390–399.
ontologiesto
biological
Smith, C. L., Goldsmith, C. A., and
Eppig, J. T. (2005). The mam-
malian phenotype ontology as a
tool for annotating, analyzing and
comparingphenotypicinformation.
Genome Biol. 6, R7.
Sorsby, A. (1965). Gregor Mendel. Br.
Med. J. 1, 333–338.
Williams, R. R., Rao, D. C., Ellison,
R. C., Arnett, D. K., Heiss, G.,
Oberman, A., Eckfeldt, J. H., Lep-
pert, M. F., Province, M. A., Mock-
rin, S. C., and Hunt, S. C. (2000).
NHLBI family blood pressure pro-
gram: methodology and recruit-
ment in the HyperGEN network.
Hypertension genetic epidemiol-
ogy network. Ann. Epidemiol. 10,
389–400.
Conflict of Interest Statement: The
authors declare that the research was
conducted in the absence of any com-
mercial or financial relationships that
could be construed as a potential con-
flict of interest.
Received: 14 February 2012; paper pend-
ing published: 05 March 2012; accepted:
30 April 2012; published online: 28 May
2012.
Citation: Shimoyama M, Nigam R,
McIntosh LS, Nagarajan R, Rice T,
Rao DC and Dwinell MR (2012) Three
ontologies to define phenotype mea-
surement data. Front. Gene. 3:87. doi:
10.3389/fgene.2012.00087
This article was submitted to Frontiers in
Bioinformatics and Computational Biol-
ogy, a specialty of Frontiers in Genetics.
Copyright © 2012 Shimoyama, Nigam,
McIntosh, Nagarajan, Rice, Rao and
Dwinell. This is an open-access article
distributed under the terms of the Cre-
ative Commons Attribution Non Com-
mercial License, which permits non-
commercial use, distribution, and repro-
duction in other forums, provided the
original authors and source are credited.
Frontiers in Genetics | Bioinformatics and Computational Biology
May 2012 | Volume 3 | Article 87 | 10