Ontologies are widely used constructs for encoding and analyzing biomedical data, but the absence of simple and consistent tools has made exploratory and systematic analysis of such data unnecessarily difficult. Here we present three packages which aim to simplify such procedures. The ontologyIndex package enables arbitrary ontologies to be read into R, supports representation of ontological objects by native R types, and provides a parsimonius set of performant functions for querying ontologies. ontologySimilarity and ontologyPlot extend ontologyIndex with functionality for straightforward visualization and semantic similarity calculations, including statistical routines. 

$\textbf{AVAILABILITY AND IMPLEMENTATION}$: ontologyIndex, ontologyPlot and ontologySimilarity are all available on the Comprehensive R Archive Network website under https://cran.r-project.org/web/packagesThis work was supported by National Institute for Health Research award RG65966 (D.G. and E.T.) and the Medical Research Council programme grant MC_UP_ 0801/1 (D.G. and S.R.)

Greene, D

Richardson, S

Turro, E

Bioinformatics

PubMed

Apollo (Cambridge)

Databases and ontologiesontologyX: a suite of R packages for workingwith ontological dataDaniel Greene1,2,*, Sylvia Richardson1 and Ernest Turro1,21MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR,UK and 2Department of Haematology, University of Cambridge School of Clinical Medicine, Cambridge Institute forMedical Research, Wellcome Trust/MRC Building, Cambridge CB2 0XY, UK*To whom correspondence should be addressedAssociate Editor: John HancockReceived on September 14, 2016; revised on November 23, 2016; editorial decision on November 24, 2016; accepted on November 28, 2016AbstractSummary: Ontologies are widely used constructs for encoding and analyzing biomedical data, but theabsence of simple and consistent tools has made exploratory and systematic analysis of such data un-necessarily difficult. Here we present three packages which aim to simplify such procedures. TheontologyIndex package enables arbitrary ontologies to be read into R, supports representation ofontological objects by native R types, and provides a parsimonius set of performant functions for query-ing ontologies. ontologySimilarity and ontologyPlot extend ontologyIndex with functionalityfor straightforward visualization and semantic similarity calculations, including statistical routines.Availability and Implementation: ontologyIndex, ontologyPlot and ontologySimilarity areall available on the Comprehensive R Archive Network website under https://cran.r-project.org/web/packages/.Contact: Daniel Greene dg333@cam.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.1 IntroductionOntological annotation is now used to describe many different bio-logical phenomena, including gene function (Gene OntologyConsortium et al., 2015) and human phenotype abnormality(Ko¨hler et al., 2014), with many ontologies, and ontological datasetspublicly available. Accounting for dependency between termsinduced by the structure of their ontologies is vital for downstreamstatistical analysis and visualization. Therefore, software methodsare required which integrate ontologies and ontological data withmainstream statistical programming environments, so that the datacan be analyzed effectively. The ontoCAT (Adamusiak et al., 2011)package enables simple querying and traversal of ontologies, butmany of its key functions are slow and it requires a Java runtimeinstallation.There are software packages enabling manipulation and plottingof graphs, for example graph (Gentleman et al., 2016) andRgraphviz (Hansen et al., 2016) respectively, which can be usedto view sections of ontologies. However, their functions are lowlevel, which makes procedures such as plotting of ontological termsets and fine-grained control of graphical parameters quite involved.There are R packages which provide procedures for computing se-mantic similarities between terms and sets of terms for specificontologies (Fro¨hlich et al., 2007; Yu et al., 2010, 2015) but they donot support semantic similarity computation for arbitrary ontolo-gies. Furthermore, the currently available methods are too slow toapply to large datasets.Here we present a suite of R packages, dubbed ‘ontologyX’, consistingof ontologyIndex, ontologyPlot and ontologySimilarity,which together address the issues described and form a consistent inter-operable set of tools that is readily extensible with additional ontologicalfunctionality.2 MethodsontologyIndex is an R package which was developed in order toprovide a terse, low-level and easy to use set of functions for exploitingthe structure of ontologies. Ontologies can be read into R from files inOpen Biomedical Ontologies (OBO) format, with most commonlyVC The Author 2017. Published by Oxford University Press. 1This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permitsunrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Bioinformatics, 2017, 1–3doi: 10.1093/bioinformatics/btw763Applications Noteused ontologies available in this format on the OBO Foundry’s website(Smith et al., 2007) . Ontologies which are only available in a WebOntology Language (OWL) format may be used by first convertingthem into OBO format, for example using the ROBOT command linetool (Overton et al., 2015) . A custom internal representation ofontologies—the ontology_index class—is used which stores prop-erties of terms including term ancestors, enables fast ontological oper-ations, and can be queried using base R functions. It uses native Rtypes to represent ontological terms and sets of ontological terms,enabling simple integration with R’s features, high-level functions andother packages. It includes functions for performing set operationsrespecting the structure of the ontology, for example: exclude_descendants, which given term sets A and B, excludes terms inB and their descendants from set A; prune_descendants, whichpreserves terms in B which are ancestors of terms in A afterapplying exclude_descendants), and minimal_set, whichmaps a set of ontological terms onto a non-redundant set.ontologyIndex is lightweight, fast (see Table 1) and readilyextended by other packages. For example, the R package gsEasy(Greene, 2016) facilitates gene-set enrichment analysis (Subramanianet al., 2005) using the get_ancestors function to propagateparent-child relations through the GO. ontologyPlot extendsontologyIndex with functions which considerably ease the task ofplotting sets of ontological terms and the ‘is-a’ relations betweenthem, as the user need only pass an ontology_index and a vectorof term IDs to the plotting function. It includes several functions fortransforming sets of terms to distill the important features for particu-lar visualizations. For example, given a set of ontologically annotatedobjects, the function remove_uninformative_terms removesterms whose children are annotated to the same objects, leading tosimpler diagrams. Figure 1 demonstrates how ontologyPlot can beused to visualize GO annotation for QPCTL and CRNN, and theeffect of using remove_uninformative_terms to simplify the fig-ure. ontologyPlot utilizes the Rgraphviz package’s interface tothe graphviz (Gansner and North, 2000) graphical layout engine. Itfurther allows graphs to be exported in standard DOT format anddoes not constrain the graphical parameters, so users can take full ad-vantage of options in any rendering software.Semantic similarity quantifies similarity between ontologicalterms and sets of ontological terms. ontologySimilarity ex-tends ontologyIndex to enable similarities between ontologicalobjects to be computed given an ontology_index and sets ofterm IDs. It facilitates the calculation of similarity at three levels: be-tween ontological terms (ID strings), between ontologically anno-tated objects (ID string vectors), and within groups of ontologicallyannotated objects (lists of ID string vectors). It implements Resnik’s(Resnik et al., 1999) and Lin’s (Lin, 1998) expressions for the simi-larity of terms. Unlike other packages for calculating semantic simi-larities, ontologySimilarity does not depend on static, pre-built SQLite databases or Bioconductor annotation packages andworks with arbitrary term annotations. Furthermore, it offers infer-ential procedures such as get_sim_p, which assesses the strengthof similarity between groups of objects (Westbury et al., 2015) .Flexible functions facilitate use in complex methods, for example asin the R package SimReg (Greene et al., 2016), which implements asemantic similarity based regression algorithm. All similarity rou-tines are written in Cþþ and called from R (Eddelbuettel et al.,2011), and the user can balance performance and memory usage fordownstream analysis by selecting whether to store similarities be-tween terms or term sets, or store an index for fast similarity look-ups. We compared the performance of ontologySimilarityagainst other packages offering functions for calculating pairwiseterm and gene similarities, the results of which are shown in Table2. The results indicate that ontologySimilarity executes sub-stantially faster, and suggests tangible advantages for use with largedatasets.3 ConclusionThe key advantage of ontologyIndex is that it can read in arbitraryontologies, integrates naturally with R, and provides a solid base forCellular_componentCell PartIntracellularPartCytoplasmic PartOrganelle PartIntracellularOrganelle PartGolgi ApparatusPartMembraneOrganelleMembraneBounding MembraneOf OrganelleGolgi MembraneOrganelleMembrane boundedOrganelleIntracellularOrganelleIntracellularMembrane boundedOrganelleGolgi ApparatusMembrane PartIntrinsicComponent OfMembraneIntegralComponent OfMembraneCytoplasm VesicleExtracellularRegion PartExtracellularOrganelleExtracellularVesicleExtracellularExosomeCellular_componentIntracellularPartMembraneGolgi MembraneMembrane boundedOrganelleGolgi ApparatusIntegralComponent OfMembraneCytoplasm ExtracellularExosomeFig. 1. Plot of terms descending from the cellular_component term in the GO, extracted using the exclude_descendants function from ontologyIndex, forgenes QPCTL and CRNN using ontologyPlot. The left panel shows the full set of ancestral terms used in the annotation of the genes, while the right panelshows only those remaining after remove_uninformative_terms has been called. Terms annotated to both genes, either implicitly or explicitly, are shown inlight blue, while those annotated only QPCTL and CRNN are shown in green and purple respectively. The size of the nodes has been set to be proportional to theinformation content (i.e. negative log frequency) of the terms with respect to gene annotation downloaded from the GO websiteTable 1. Mean execution time for retrieving descendants and an-cestors for individual terms in the Human Phenotype OntologyDescendants (ms) Ancestors (ms)ontoCAT 11.99 12.75ontologyIndex 0.38 0.142 D.Greene et al.extension. ontologyPlot enables uniquely simple and aestheticallypleasing visualization of ontological terms and ontological annotationwith a wide variety of graphical options. ontologySimilarity fa-cilitates fast and flexible semantic similarity functionality for onto-logical objects including assessment of statistical significance and issuitable for application to high-throughput datasets.Software: The following versions of software packages were usedto generate the results presented in this manuscript: ontologyIndex2.2, ontologyPlot 1.4, ontologySimilarity 2.1, GOSim 1.11,GOSemSim 1.99.4 and ontoCAT 1.26.0.FundingThis work was supported by National Institute for Health Research awardRG65966 (D.G. and E.T.) and the Medical Research Council programmegrant MC_UP_ 0801/1 (D.G. and S.R.).Conflict of Interest: none declared.ReferencesAdamusiak,T. et al. (2011) OntoCAT–simple ontology search and integrationin Java, R and REST/JavaScript. BMC Bioinf., 12, 1.Eddelbuettel,D. et al. (2011) Rcpp: Seamless R and Cþþ integration. J. Stat.Softw., 40, 1–18.Fro¨hlich,H. et al. (2007) GOSim–an R-package for computation of informa-tion theoretic GO similarities between terms and gene products. BMCBioinf., 8, 1.Gansner,E.R. and North,S.C. (2000) An open graph visualization system andits applications to software engineering. Softw. Pract. Exp., 30, 1203–1233.Gene Ontology Consortium. et al. (2015) Gene ontology consortium: goingforward. Nucleic Acids Res., 43, D1049–D1056.Gentleman,R. et al. (2016) graph: A package to handle graph data structures.R package version 1.51.0.Greene,D. (2016) gsEasy: Gene Set Enrichment Analysis in R. R package ver-sion 1.2.Greene,D. et al. (2016) Phenotype similarity regression for identifying the gen-etic determinants of rare diseases. Am. J. Hum. Genet., 98, 490–499.Hansen,K.D. et al. (2016) Rgraphviz: Provides plotting capabilities for Rgraph objects. R package version 2.17.0.Ko¨hler. et al. (2014) The Human Phenotype Ontology project: linking molecu-lar biology and disease through phenotype data. Nucleic Acids Res., 42,D966–D974.Lin,D. (1998) An information-theoretic definition of similarity. In: ICML vol.98, pp. 296–304 Citeseer.Overton,J.A. et al. (2015) Robot: a command-line tool for ontology develop-ment. In: Lisbon, Portuga: 5th International Conference on BiomedicalOntology.Resnik,P. et al. (1999) Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural lan-guage. J. Artif. Intell. Res. (JAIR), 11, 95–130.Smith,B. et al. (2007) The OBO Foundry: coordinated evolution of ontologiesto support biomedical data integration. Nat. Biotechnol., 25, 1251–1255.Subramanian,A. et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc.Natl. Acad. Sci., 102, 15545–15550.Westbury,S.K. et al. (2015) Human phenotype ontology annotation and clus-ter analysis to unravel genetic defects in 707 cases with unexplained bleed-ing and platelet disorders. GenomeMed., 7, 1.Yu,G. et al. (2010) GOSemSim: an R package for measuring semantic similar-ity among GO terms and gene products. Bioinformatics, 26, 976–978.Yu,G. et al. (2015) DOSE: an R/Bioconductor package for disease ontology se-mantic and enrichment analysis. Bioinformatics, 31, 608–609.Table 2. Execution times for computing pairwise similarity matricesfor 1000 randomly selected GO terms and 100 randomly selectedgene GO annotation sets using Lin’s expression for term similarityTerm sim (s) Gene sim (s)GOSim 1075.43 298.34GOSemSim 1.71 116.72ontologySimilarity 0.31 0.06ontologySimilarity (indexed) 0.04ontologyX 3

ontologyX: a suite of R packages for working with ontological data

Abstract
               
                  Summary
                  Ontologies are widely used constructs for encoding and analyzing biomedical data, but the absence of simple and consistent tools has made exploratory and systematic analysis of such data unnecessarily difficult. Here we present three packages which aim to simplify such procedures. The ontologyIndex package enables arbitrary ontologies to be read into R, supports representation of ontological objects by native R types, and provides a parsimonius set of performant functions for querying ontologies. ontologySimilarity and ontologyPlot extend ontologyIndex with functionality for straightforward visualization and semantic similarity calculations, including statistical routines.
               
               
                  Availability and Implementation
                  ontologyIndex, ontologyPlot and ontologySimilarity are all available on the Comprehensive R Archive Network website under https://cran.r-project.org/web/packages/.
               
               
                  Supplementary information
                  Supplementary data are available at Bioinformatics online.
               </jats:sec

ontologyX: a suite of R packages for working with ontological data

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