Ontologies for the description of mouse phenotypesdownloads.hindawi.com/journals/ijg/2004/270124.pdfthat assays (protocols for measuring phenotypic characters) describe what has been

Comparative and Functional GenomicsComp Funct Genom 2004; 5: 545–551.Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cfg.430

Conference Paper

Ontologies for the description ofmouse phenotypes

G. V. Gkoutos1*, E. C. J. Green1, A.-M. Mallon1, A. Blake1, S. Greenaway1, J. M. Hancock1

and D. Davidson2

1MRC Mammalian Genetics Unit, Harwell, UK2MRC Human Genetics Unit, Edinburgh, UK

*Correspondence to:G. V. Gkoutos, MRC MammalianGenetics Unit, Harwell,Oxfordshire, OX11 0RD, UK.E-mail: [email protected]

Received: 12 October 2004Accepted: 18 October 2004

AbstractOntologies are becoming increasingly important for the efficient storage, retrievaland mining of biological data. The description of phenotypes using ontologies is aparticularly complex problem. We outline a schema that can be used to describephenotypes by combining orthologous axiomatic ontologies. We also describe tools forstoring, browsing and searching such complex ontologies. Central to this approach isthat assays (protocols for measuring phenotypic characters) describe what has beenmeasured as well as how this was done, allowing assays to link individual organisms toontologies describing phenotypes. We have evaluated this approach by automaticallyannotating data on 600 000 mutant mice phenotypes using the SHIRPA protocol. Webelieve this approach will enable the flexible, extensible and detailed description ofphenotypes from any organism. Copyright 2004 John Wiley & Sons, Ltd.

Keywords: phenotype ontology; phenotype description; PATO; bioinformatics

Introduction

Mouse mutant phenotypes

The completion of the human genome sequenceheralds a new era of understanding of the geneticbasis of human disease. Determining the functionof every one of the human genes and their rolein disease will be greatly assisted by the devel-opment and characterization of mouse models ofhuman disease. However, assessing the effect onthe organism of any change made in a gene willrequire systematic screens and tests that allow us todescribe the phenotypic consequences in a compre-hensive way. An increasing number of laboratoriesand companies worldwide are now carrying outdetailed analysis of mouse phenotypes that havebeen generated from the large-scale mutagenesis(Balling, 2001) of the mouse genome.

Large-scale projects, such as EUMORPHIA(http://www.eumorphia.org), in aiming at thestandardization and dissemination of primary andsecondary phenotyping protocols for all body

systems in the mouse, produce consistent method-ologies for systematic and standardized characteri-zation of mouse phenotype. This requires managinginformation about mutants in a paperless environ-ment, and building of databases that will allowthis data to be shared between laboratories andused to formulate hypotheses about gene function.The key to satisfying this need is the ability todescribe different phenotypes in a consistent andstructured way.

Bio-ontologies

Ontologies have their root in Greek philosophyand aim at the description of what exists. Anontology is a formal specification of entities andtheir relationships. In biology, since the advent ofthe Gene Ontology (GO) (Gene Ontology Con-sortium, 2000), ontologies have been used tospecify the semantic relationships among terms,and have become a standard used to supportknowledge representation in the field of genomics(GO Consortium, 2004). Bio-ontologies have been

Copyright 2004 John Wiley & Sons, Ltd.

546 G. V. Gkoutos et al.

employed for scientific data integration and explo-ration, clarifying scientific exchange by providinga shared vocabulary, and extending the power ofcomputational approaches and systems to performdata exploration, inference and mining (Blake,2004).

Phenotype ontologies

Phenotype information has traditionally been cap-tured in a free-text manner. Free text searching alsoforms the basis of information mining and retrieval,but it is extremely limited because of an inherentlack of accuracy and specificity. Complex free textdescriptions, such as are used for phenotypes, arealmost impossible to index and retrieve in a use-ful way and yet advanced searches are required tofully exploit and realize the potential of these data.With the success of the use of bio-ontologies inaddressing these problems in other areas of biol-ogy, it seems logical to use ontologies to describephenotypes.

However, phenotypic descriptions present amajor conceptual and practical problem that cannotbe addressed by the relatively simplistic approachthat has been used to describe the features ofwell-defined domains of most other bio-ontologies.Hence, in order to describe complex biologicalareas of knowledge, such as the description ofmutant phenotypes, a more sophisticated method-ology is required. The Mouse Genome Database(Blake, 2003) team have developed the MammalianPhenotype Ontology (MPO: http://obo.source-forge.net/cgi-bin/detail.cgi?musphen) to describemouse phenotypes in a GO-like manner. Thisdevelopment takes a pragmatic approach wherebyinstances are added as needed to annotate mousephenotypes. As the developers are part of the Phe-notype Consortium (see below), the database hasbeen created in such a way that it can easily beextended to form instances of the compositionalapproach used in the schema described here.

A compositional approach for describingmouse phenotypes

The Phenotype Consortium, part of the GO con-sortium, was formed with the objective of address-ing the issue of representing phenotype informa-tion. The consortium decided that a minimal set

of knowledge domains or core ontologies shouldbe part of forming phenotype ontologies. Thesedomains are:

• Anatomy• Ontogeny (developmental anatomy)• Behaviour• Pathology• Cell types• GO

Furthermore, and perhaps more importantly, theneed for a methodology for expressing phenotypeinformation that could be extended to any organ-ism, achieving interoperability between individualspecies communities, was highlighted.

Phenotype and trait ontology (PATO)

To that effect, Ashburner proposed the Phe-notype and Trait Ontology (PATO) (availableat the Open Biological Ontologies (OBO) site:http://obo.sourceforge.net/) with the objective ofcapturing information about phenotypes in anyorganism. The idea behind this proposal is thatPATO would form a common platform upon whichdifferent ontologies (such as behaviour, anatomy,etc.) would be mapped to provide a consistent rep-resentation of phenotypic data. PATO would pro-vide for concepts (deriving from different coreontologies) a set of relative attributes, their corre-sponding values and the assays that were employedto define these. The combination of the concepts,attributes, values and assays would form the basisfor the systematic description of phenotype.

Figure 1 presents a schema for phenotype rep-resentation based on this concept (Gkoutos et al.,2004). According to this schema, the whole organ-ism has certain attributes, as presented in Table 1,and exists under certain handling conditions.

The organism also has a set of core compo-nents: its anatomy, development, physiology andbehaviour. Each of these core components is rep-resented by a separate ontology and each has aset of attributes, again represented by an ontology.For example, the organism may have an anatom-ical component left eye, which is a term fromthe anatomy ontology. The left eye, in turn, mayhave attributes of colour, size, etc., taken from theattributes ontology. This combination of core con-cept and attribute constitutes a phenotypic charac-ter — something that can be measured. Phenotypic

Copyright 2004 John Wiley & Sons, Ltd. Comp Funct Genom 2004; 5: 545–551.

Ontologies for the description of mouse phenotypes 547

Figure 1. Proposed schema by Ashburner, Davidson and Gkoutos (modified from Gkoutos et al., 2004). Organismattributes: id, identifier for individual (n); T, species; G, genotype (I, strain; S, genotypic sex; A, alleles at named loci); E,handling conditions; D, age/stage of development

Table 1. Organism attributes

id Identifier for individual (n)

T Species (e.g. NCBI taxonomy browser;http://www.ncbi.nlm.nih.gov/Taxonomy/)

G GenotypeI Strain (e.g. MGI; http://www.informatics.jax.org/)S Genotypic sexA Alleles at named loci (e.g. MGI; http://www.informatics.

jax.org/)E Handling conditions (i.e. EUMORPHIA)D Age/stage of development (Theiler, 1989; and other

staging criteria, e.g. EMAP;http://genex.hgu.mrc.ac.uk/Databases/Anatomy/MAstaging.html)

characters, in turn, link to an ontology of assayswhich return a variety of values, again representedby an ontology.

How it will workWhen this schema is used to describe actual phe-notypes, instances of single phenotypic charactersare linked together to provide a full phenotypicdescription of an individual organism. Each char-acter can be represented by a line in a table, wherethe table represents the full phenotype (Figure 2).In other words, phenotypic character instances andassociated phenotypic instances are only linked inthe knowledgebase (an ontology together with aset of individual instances of the kinds of entitiesit specifies; Stevens, 2004).

Storing and accessing phenotypeontologies

DatabaseA database was designed to reflect the functional-ity of the schema proposed for modelling pheno-type ontologies. The relational schema is based on



Figure 2. Schematic of phenotype description as the sum of the results of assaying different characters. PC,phenotypic character

the functionality of Gene Ontology database butallows the storage of multiple phenotype ontolo-gies of different species and domains. Further-more, the database is designed to hold instancesof phenotypic descriptions, generating knowledge-bases for individual domains and allowing cross-referencing and indexing between them. The rela-tional schema was implemented in a MySQL(http://www.mysql.com/) database and the SQLcode for building and populating the database canbe obtained from the authors.

We have also written parsers in Perl that takeontologies in OBOL format (http://obo.source-forge.net/), parse them into SQL statements, andload them into the databases. We are currently inthe process of generating parsers that would auto-matically load ontologies expressed in DAML+OIL/OWL into the database.

Due to the nature of our schema and the constantcommunity input required for the success of thisproposal, a mechanism was required that couldeasily access, update and reflect any change to ourschema proposal. For this purpose, we have createda set of tools, collectively known as Chameleon.

Chameleon

Database designs are rarely stable and are insteadlikely to change quite frequently, depending onnew features required from the system. Usingour schema approach, this would require quitesignificant re-coding of any applications each timea change is made. In order to minimize codemaintenance and initial development times, a multi-layer model was developed (Figure 3).

The lowest ‘database’ layer allows the datacontained in the database to be accessed as Javaobjects and also maintain these objects in server

memory, allowing very fast data manipulation.A ‘middle’ layer provides higher functions, suchas searching, and this layer provides an API toapplication programs.

Chameleon is a program that automaticallygenerates source code that implements the lowdatabase layer in a matter of seconds. Eachdatabase table becomes a Java object class withmethods to allow fetching, editing, creating anddeleting data via a Java object. The classes writ-ten and the methods they contain are dependent onthe structure of the database table. Database designchanges now result in a small number of middle-ware code changes, rather than changing applica-tion code.

Chameleon can be set to produce code that canbe read only or read/write. Any type of SQLdatabase can be supported, so long as there is a suit-able JDBC (http://java.sun.com/j2se/1.5.0/docs/api/) driver that supports SQL meta-data. Whenthe resultant Java classes are first used, all the datafrom the database is read into memory and indexesare built. The JVM memory size (http://java.sun.com/j2se/1.5.0/docs/tooldocs/windows/java.html) may need to be adjusted to enable this. Thisreading of data will lead to a small delay the firsttime the classes are accessed. When used underan application, such as JRUN or Tomcat, the datawill be loaded once for all users of the databaseclasses. Any changes to the data objects will thus beinstantly reflected in other applications data objects(in the read/write model). An API method call canalso be used to force the classes to reload them-selves. Applications can also register a call-backinterface to enable them to be informed if theunderlying data has changed.

Chameleon creates the object-relational map-ping as ‘personalized’ Java source code, which



is then compiled as part of the application’scode base. Unlike a system such as Hibernate(http://www.hibernate.org/), which acts as anactive layer between the application and thedatabase, it is a ‘run once and forget’ system.Using Chameleon, the mapping layer can be dis-tributed simply as code or within a jar/war filewithout requiring local installation of additionalsoftware.

Although Chameleon itself does not producecode to manipulate hierarchical data, it providesa simple and efficient way of implementing it.Chameleon is written in Java 1.5.0 beta compliantcode and will not compile under other versionsof Java, although it will run under Java 1.4JREs.

We have employed Chameleon to allow usto access our test-bed ontology and produced avisualization tool, termed CRAVE.

Visualizing and searching phenotypeontologies

Concept Relation Assay Value Explorer(CRAVE)

CRAVE was developed due to the need to beable to navigate and visualize complex ontolo-gies, such as those developed based on the pro-posed schema, that the current bio-ontologiesbrowsers available on the Gene Ontology Con-sortium Website (http://www.geneontology.org/)could not represent. It is a visualization tool thataccesses these ontologies using the Chameleonpackage described above. It is based on a variety of

open-source Java classes and developer tools, plusour own custom software engineering. We choseJava and JavaScript to achieve platform indepen-dence.

Figure 4 shows the browser interface. CRAVE isopen source and can be accessed at: http://www.mgu.har.mrc.ac.uk/servlet/browser.frameset

Searching the ontologies

CRAVE performs Boolean and advanced searchesin a simple, single interface. So, besides theBoolean ‘AND’, ‘OR’ and ‘NOT’ operations, itallows organism and domain-specific searches.Hence the user, or an external application, canquery the ontology based on organism, specificdomains, or even on individual groups of ontolo-gies. It employs ‘one-line commands’ to facilitatereverse engineering of the API by external appli-cations.

CRAVE queries the ontologies in a case-insensi-tive manner, converting any punctuation mark (i.e.underscore or slash character, etc.) used in the termname to white space and allow users to searchfor them either way, e.g. body position could besearched for as body position or as body position,or even BODY POSITION.

Finally, although ontologies should hold syn-onyms for term names and different spellings (e.g.British vs. American English), we have adopted,in a separate standalone database, lists of termsspelled differently according to British and Ameri-can spelling and are populating common synonymsto allow CRAVE to search for them.

Figure 3. Schema showing Chameleon



Figure 4. The CRAVE interface

Applying phenotype ontologies

Populating the assay ontology and automatingphenotype annotation

Assays are central to the schema in Figure 1.EUMORPHIA is producing agreed, validatedstandarized operating procedures (SOPs) for mousephenotyping. These are in many ways equiv-alent to those assays. We have converted theSOPs produced by EUMORPHIA into XML for-mat, placed them in a database and they arecurrently being validated by the EUMORPHIAcommunity. These SOPs will be accessed on-line by a browser produced by us available at:http://www.eumorphia.org/ECFLP and will beadded to the Assay ontology, allowing us to auto-matically annotate phenotypes based on associa-tions between assays and the phenotypic charactersthey measure. As a trial, we have employed ourtest-bed ontology, the mouse behaviour ontology,and have used this approach to annotate around600 000 mice that were tested for different aspects

of behaviour using a collection of behaviouralassays, collectively known as the SHIRPA proto-col (Rogers et al., 2001). The test-bed ontologywas based on the Mammalian phenotype ontology(Blake, 2003), which was purposely designed insuch a way that it could be easily mapped into ourschema proposal. The assays that were employedto characterize the behaviour of these mice wereadded to the assay ontology as part of the PATOontology and the annotations were automaticallygenerated via the Chameleon packages, describedabove. Having successfully annotated the recordedphenotypes we plan to apply some basic statisticalanalysis to answer questions such as whether wecan automatically detect any dependencies betweenphenotype observations.

Conclusions

The schema we have proposed allows extensibil-ity and interoperability. Although ontology should



not cover all possible information about a domain,the main idea behind this concept is to allow phe-notype ontologies to cope with novel and unpre-dictable phenotypes and account for new assays,serving scientific autonomy and information valid-ity and integrity. We have created methodolo-gies and databases that allow uploading and stor-ing of ontologies modelled based on this schema,and dynamic update of different parts of thecore ontologies, including PATO, without the lossof applied facets. We have also implemented abrowser which allows searching and viewing of theknowledge captured though the PATO relations.

The approach described here has been discussedat a theoretical level at a number of internationalworkshops, including the Phenotype Consortiummeeting held in Bar Harbor in September 2003, aswell as with representatives of the Mouse GenomeDatabase and Mouse Phenome Project database(http://www.jax.org/phenome), the major interna-tional repositories for phenotype data in the mouse.As an example, we plan to use it to annotate dataemerging from EUMORPHIA, thereby demonstrat-ing the utility of the approach and allowing it to beimplemented in other relevant databases.

Acknowledgements

This project is funded by the European Commission undercontract number QLG2-CT-2002-00930. We thank MichaelAshburner and Suzie Lewis for their comments and PatNolan and Valter Tucci for discussions on mouse behaviour.

References

Balling R. 2001. ENU mutagenesis: analyzing gene function inmice. Annu Rev Genomics Hum Genet 2: 463–492.

Blake J. 2004. Bio-ontologies — fast and furious. NatureBiotechnol 22: 773–774.

Blake JA, Richardson JE, Bult CJ, Kadin JA, Eppig JT and themembers of the Mouse Genome Database Group. 2003. MGD:the Mouse Genome Database. Nucleic Acids Res 31: 193–195.

EMAP staging criteria: http://genex.hgu.mrc.ac.uk/Databases/Anatomy/MAstaging.html

EUMORPHIA: http://www.eumorphia.orgGene Ontology Consortium Website: http://www.geneontology.

org/Gkoutos GV, Green ECJ, Mallon A, Hancock JM, Davidson D.

2004. Building mouse phenotype ontologies. Pac SympBiocomput 9: 179–189.

GO Consortium. 2004. The Gene Ontology (GO) database andinformatics resource. Nucleic Acids Res 32: 258–261.

Mammalian Phenotype Ontology (MPO): http://obo.sourceforge.net/cgi-bin/detail.cgi?musphen

Mouse Genome Informatics (MGI): http://www.informatics.jax.org/

MySQL: http://www.mysql.com/NCBI taxonomy: http://www.ncbi.nlm.nih.gov/Taxonomy/Open Global Ontologies, OBO: http://obo.sourceforge.net/Mouse Phenome Project: http://www.jax.org/phenomeRogers DC, Peters J, Martin JE, et al. 2001. Neurosci Lett

306(1–2): 89–92.Stevens R. 2004. Knowledge base. In Dictionary of Bioinformatics

and Computational Biology, Hancock JM, Zvelebil MJ (eds).Wiley: New York.

Sun Microsystems. Java 1.5.0 API: http://java.sun.com/j2se/1.5.0/docs/api/

Sun Microsystems. Java VM Runtime: http://java.sun.com/j2se/1.5.0/docs/tooldocs/windows/java.html

The Gene Ontology Consortium. 2000. Gene Ontology: tool forthe unification of biology. Nature Genet 25: 25–29.

Theiler K. 1989. The House Mouse: Atlas of EmbryonicDevelopment. Springer: New York.


Submit your manuscripts athttp://www.hindawi.com

Hindawi Publishing Corporationhttp://www.hindawi.com Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation http://www.hindawi.com

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation http://www.hindawi.com Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttp://www.hindawi.com Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttp://www.hindawi.com

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research


International Journal of

Microbiology

Documents

Ontologies for the description of mouse phenotypesdownloads.hindawi.com/journals/ijg/2004/270124.pdfthat assays (protocols for measuring phenotypic characters) describe what has been