an improved method for scoring protein-protein interactions using semantic similarity within the...
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METHODOLOGY ARTICLE Open Access
An improved method for scoring protein-proteininteractions using semantic similarity withinthe gene ontologyShobhit Jain1,2, Gary D Bader1,2*
Background: Semantic similarity measures are useful to assess the physiological relevance of protein-proteininteractions (PPIs). They quantify similarity between proteins based on their function using annotation systems likethe Gene Ontology (GO). Proteins that interact in the cell are likely to be in similar locations or involved in similarbiological processes compared to proteins that do not interact. Thus the more semantically similar the genefunction annotations are among the interacting proteins, more likely the interaction is physiologically relevant.However, most semantic similarity measures used for PPI confidence assessment do not consider the unequaldepth of term hierarchies in different classes of cellular location, molecular function, and biological processontologies of GO and thus may over-or under-estimate similarity.
Results: We describe an improved algorithm, Topological Clustering Semantic Similarity (TCSS), to computesemantic similarity between GO terms annotated to proteins in interaction datasets. Our algorithm, considersunequal depth of biological knowledge representation in different branches of the GO graph. The central idea is todivide the GO graph into sub-graphs and score PPIs higher if participating proteins belong to the same sub-graphas compared to if they belong to different sub-graphs.
Conclusions: The TCSS algorithm performs better than other semantic similarity measurement techniques that weevaluated in terms of their performance on distinguishing true from false protein interactions, and correlation withgene expression and protein families. We show an average improvement of 4.6 times the F1 score over Resnik, thenext best method, on our Saccharomyces cerevisiae PPI dataset and 2 times on our Homo sapiens PPI dataset usingcellular component, biological process and molecular function GO annotations.
BackgroundGene Ontology (GO)  is a useful and popular taxon-omy of controlled biological terms that can be used toassess the functional relationship between different geneproducts. GO organizes knowledge about gene functionin a directed acyclic graph (DAG) of terms and their rela-tionships. It is organized in three orthogonal ontologiescapturing knowledge about cellular location, biologicalprocess and molecular function . Experts annotate GOterms to genes in different organisms based on diverseevidence sources. GO has become the most used ontol-ogy and annotation system for assessing the confidence
and biological relevance of high-throughput experimentsbased on the notion that if two or more genes are relatedby an experiment, they should also be related by knowngene function. For instance, GO is often used as a bench-mark for protein-protein interaction (PPI) experimentalmapping and prediction [2-5], protein function predic-tion [6-8], and pathway analysis . In this paper, we arespecifically interested in the use of GO in a metric forprotein-protein interactions (PPIs).The relationship between gene products annotated to
GO is quantified either simply from the annotatedterms (for instance, by finding a set of common GOterms annotated to gene products) or more globally byusing semantic similarity measures that consider theentire GO DAG. The GO DAG is a complex network ofover 31,000 terms and 46,900 relations (GO release
* Correspondence: email@example.comDepartment of Computer Science, University of Toronto, 10 Kings CollegeRoad, Toronto, Ontario M5 S 3G4, CanadaFull list of author information is available at the end of the article
Jain and Bader BMC Bioinformatics 2010, 11:562http://www.biomedcentral.com/1471-2105/11/562
2010 Jain and Bader; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.
March, 2010). The cellular component ontology of GOdescribes gene product locations at the levels of sub-cellular structure and macromolecular complexesthrough over 2,650 terms and 5,000 relations. The mole-cular function ontology of GO is described using over8,650 terms and 10,150 relations. The complexity of bio-logical process ontology is even greater with over 18,500terms and 38,700 relations. The large number of termsand relations describing the cellular knowledge coveredby GO makes it difficult to naively quantify relationshipsbetween gene products. For example, Saccharomycescerevisiae proteins Rpl10p (annotated to GO cellularcomponent term large ribosomal sub-unit) and Sqt1p(annotated to GO cellular component term ribosome)physically interact with each other but do not share aGO term. Often, a sub-set of GO terms or a reducedversion of GO, like GO slim , is used for relatinggenes. This makes GO terms and annotations easier towork with and compare, but valuable information is lostin the simplification.Semantic similarity is a technique used to measure the
likeness of concepts belonging to an ontology. Mostearly semantic similarity measures [10-12] were devel-oped for linguistic studies in natural language proces-sing. Recently, semantic similarity measurementmethods have been applied to and further developedand tailored for biological uses [13-16]. A semantic simi-larity function returns a numerical value describing thecloseness between two (or sometimes more) concepts orterms of a given ontology . In the context of PPIdatasets, semantic similarity can be used as an indicatorfor the plausibility of an interaction because proteinsthat interact in the cell (in vivo) are expected to partici-pate in similar cellular locations and processes. Forexample, a high semantic similarity value between GOcellular component terms annotated to a set of proteinsindicates that they are in close proximity and thus havea higher probability of interaction compared to proteinsrandomly selected from the proteome [2,4,18]. Thus,semantic similarity measures are useful for scoring theconfidence of a predicted PPI using the full informationstored in the ontology.Semantic similarity measures can be broadly classified
into two groups: edge based and node based. Edgebased methods [19-22] determine semantic similaritybased on the shared paths between two terms in a givenontology, whereas node based methods [10-12] rely oncomparing the properties of the input terms (nodes),their ancestors, or descendants. One commonly usedproperty is the specificity, or the information content(entropy), of the common ancestors between a pair ofterms, which captures the notion of closeness in theDAG - the more specific the common ancestors of theterms, the closer the terms. The information content of
a term c can be defined as the negative log likelihood ofthe term (eq. 1),
IC c p c( ) = ( )ln (1)where p(c) is the probability of occurrence of the term
c in a specific corpus (e.g. GO annotations) . Whilecalculating p(c) in GO, the descendants of term c arealso considered. For example, the probability of occur-rence of the term cytosol in the cellular componenthierarchy of GO for S. cerevisiae defined by the numberof genes assigned to it is 0.104 and its information con-tent is 0.98. Comparative studies to determine the bestsemantic similarity measurement method have shownthat performance on a variety of tests varies greatlydepending upon the type of biological datasets used[23-26]. For example, for function prediction Resniks and Graph Information Content (simGIC) work best and for cellular location prediction, the sup-port vector machine (SVM) based method from Leiet al.  is preferable. In 2006, Guo et al.  com-pared a number of semantic similarity methods (Resnik (AVG), Lin , Jiang , and graph similarity-based methods ) on a test to distinguish true fromfalse human PPIs. They used proteins within a complex,or neighboring each other in Kyoto Encyclopedia ofGenes and Genomes (KEGG) regulatory pathways, as apositive PPI dataset, and randomly chosen protein pairsas a negative interaction dataset. From receiver operat-ing characteristic (ROC) curve performance analysisthey concluded that Resnik (AVG) is better than othermeasures at distinguishing positive from negative PPIs.In 2008, Xu et al.  compared the Resnik  (MAX,AVG), Tao , Schlicker [13,28], and Wang semantic similarity measurement methods on the sametest with a S. cerevisiae PPI dataset from the Databaseof Interacting Proteins (DIP). They used SchlickersrfunSimAll method which considers all GO ontologiestogether. From ROC analysis they found that the Resnik(MAX) method is best for GO ontologies taken sepa-rately or together. Thus, recent independent studies[24,26] show Resniks method for calculating semanticsimilarity is best for measuring the likelihood of truePPIs.Resniks method defines semantic similarity between
two ontology terms s and t for a given set C of commonancestors of s and t as,
r s t p cc C
( , ) [ ln( ( ))]=
where p(c) is the frequency of proteins annotated toterm c and its descendants in the ontology. However, inmost cases, proteins are assigned to more than one termin the same GO ontology. Suppose, proteins A and B
Jain and Bader BMC Bioinformatics 2010, 11:562http://www.biomedcentral.com/1471-2105/11/562
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are annotated to sets of GO terms S and T, respectively.The semantic similarity between A and B is defined asthe maximum information content (Resnik (MAX)) ofthe set S T (3).
sim A B r s ts t S T
i ji j
( , ) ( , ), , ,