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A Web Tool for Protein Semantic Similarity ProteInOn: A Web Tool for Protein Semantic Similarity Daniel Faria Catia Pesquita Francisco M. Couto Andr´eO. Falc˜ao DI–FCUL TR–07–6 March 2007 Departamento de Inform´atica Faculdade de Ciˆenciasda Universidade de Lisboa Campo Grande, 1749–016 Lisboa Portugal Technical reports are available at http://www.di.fc.ul.pt/tech-reports. The files are stored in PDF, with the report number as filename. Alternatively, reports are available by post from the above address. ProteInOn: A Web Tool for Protein Semantic Similarity Daniel Faria Catia Pesquita [email protected] [email protected] Francisco M. Couto André O. Falcão [email protected] [email protected] Departamento de Informática, Faculdade de Ciências da Universidade de Lisboa Campo Grande, 1749-016 Lisboa, Portugal ABSTRACT proaches [22]. Bioinformatics arose from the need to manage and extract Protein comparison is also essential to integrate the knowl- knowledge from the vast amount of sequence data generated edge from the sequence level to the system level. Sequence by automated Molecular Biology techniques. One impor- similarity, pattern discovery and structural comparisons are tant step for this was the development of the Gene Ontol- among the most common approaches for this purpose, but ogy (GO), which provided a unified and structured vocabu- it is also possible to compare proteins based on their an- lary to describe proteins, and also a background to compare notations within a BioOntology [14]. This type of compar- them, contributing to the integration of knowledge from the ison, called semantic similarity, is based on the structure sequence level to the system level. Several approaches have of the BioOntology and the relations between its terms, fo- been proposed to measure protein similarity in this context, cusing either on a structural comparison or more frequently but there are few tools for this purpose available to the sci- on the semantic similarity between the terms themselves. entific community. The main advantage over the traditional sequence compari- We present a new web tool, ProteInOn, focused on calcu- son approach is that it captures similarity between proteins lating GO-based protein semantic similarity. It features a with common functional aspects even if they have low se- stepwise query selection menu, which together with the pos- quence similarity. sibility of selecting results as input for new queries, makes it Given that Ontologies can be represented as graphs, there flexible and customizable. It also incorporates data on pro- are two main approaches to quantify the semantic similarity tein interactions, allowing for comparative studies between between their terms: edge-based and node-based. Edge- protein similarity and interactions. based (or conceptual-distance) methods calculate distance The tool implements a preliminary weighting factor which between terms as the minimum number of edges between increases the specificity of existing semantic similarity mea- them [16], whereas node-based (or information content) meth- sures, and a score for measuring the representativeness of a ods focus on the amount of information conveyed by the GO term within a set of proteins. terms themselves, which can be captured through their us- ProteInOn is available at: age frequency within the corpus [18]. The latter are prefer- http://xldb.fc.ul.pt/biotools/proteinon/ able in Ontologies where term specificity is not well corre- lated with term depth, which is the case of most BioOntolo- Keywords gies. Despite this, the graph structure can still provide rele- Protein Similarity, Semantic Similarity, Gene Ontology, Bioin- vant information, which has lead to the development of hy- formatics brid measures that integrate information content with con- ceptual distance [13, 10]. 1. INTRODUCTION Being one of the main BioOntologies for annotating pro- teins, the Gene Ontology (GO) has been frequently used In the last decade Molecular Biology has generated a vast as a background for protein semantic similarity, mainly by amount of data in the form of gene and protein sequences. combining the similarities of the proteins’ GO terms. Sev- However, the annotation of these sequences is lagging far eral methods have been used for this combination, including behind, since there are still no experimental techniques to the average, the maximum and the best match average [15, characterize genes/proteins to match the high-throughput of 5, 21, 20]. GO based semantic similarity has been measured the sequencing techniques [8]. Bioinformatics has been used with both node-based and hybrid approaches, and correlated to bridge this gap, mainly through the use of sequence simi- with sequence similarity, gene expression and protein family larity to extrapolate annotations from characterized proteins [15, 21, 5]. Among other applications, it has been used to to similar uncharacterized ones. However, this approach has predict and validate protein functional aspects, interactions limitations, having been estimated that up to 30% of the an- and cellular location, as well as to validate other comparison notations made by sequence similarity may be inaccurate [6]. methods [7, 24, 12, 1]. The knowledge contained in the scientific literature is also an Here we present ProteInOn, an online tool for exploring and important source for protein annotation, but being written comparing proteins within the context of Gene Ontology. It in natural language, it is difficult to explore computation- implements several semantic similarity measures for calcu- ally [17, 4]. This has lead to the development of structured lating protein and term similarity, and combines information and controlled vocabularies (BioOntologies) to describe pro- on protein-protein interactions and GO term assignment for teins with specific terms which facilitate computational ap- 3 protein characterization. It was designed to be simple to use nodes representing terms and edges representing the rela- and flexible, in order to be useful for researchers with varied tionships (is-a or part-of) between them. A term can have backgrounds and different biological questions. several parents as well as several children (see Figure 2). This paper is organized as follows: section 2 presents the As of its release of November 2006, GO comprises 22, 521 data sources used and the ProteInOn database; section 3 distinct terms. explains the similarity measures used and their implemen- tation; section 4 describes the ProteInOn interface; and sec- 2.3 GOA tion 5 states the conclusions. The Gene Ontology Annotation database (GOA) is the cen- tral source for gene/protein-GO term annotations [3]. It 2. DATABASE is committed to the comprehensive annotation of a set of The ProteInOn database is an integrated database of pro- disease-related gene products in human, mouse and rat species, teins and GO terms, which is structured in a relational but it also stores annotations for genes/proteins from most model (see Figure 1), and implemented in MySQL. The current biological databases (e.g. UniProt, NCBI). main entities are protein and GO term, and the main re- Annotations in GOA have a code to indicate the type of ev- lationships between them are protein-GO term annotations, idence that supports it, which includes several experimental protein-protein interactions and GO term-GO term ances- techniques, computational methods, literature sources and try. The data corresponding to these entities and relation- annotations inferred electronically. The reliability of the an- ships is imported from four public databases: notation varies with the evidence code, for instance, many researchers disregard electronically inferred annotations in their studies. • UniProt is the source for protein data, including ac- Proteins can have several annotations (even within the same cession numbers, names and sequences. GO type) which represent different aspects, but many have also redundant annotations, being annotated to both a term • GO is the source for GO term data and also GO term- and one or more of its ancestors, albeit with different evi- GO term ancestry relationship data. dence codes (see Figure 2). Note that ancestral terms inherit the annotations of their descendents. • GOA is the source for protein-GO term annotations. In ProteInOn, we use the GOA-UniProt release, which con- tains only data corresponding to proteins present in the • IntAct is the source for protein-protein interactions. UniProt database. The database is updated with an automated procedure that 2.4 IntAct can be used after each new release of GO. The data is re- IntAct is an open source database and software suite for trieved either in XML format (UniProt and Intact) or in modelling, storing and analyzing molecular interaction data tab-delimited text format (GO and GOA) and processed [11]. It is focused on the curation of protein-protein interac- accordingly. tions, containing over 700, 000 binary interactions as of its The following subsections describe the data sources in more November 2006 release. The data it provides is manually detail. annotated by experts and derives from published literature to ensure a high level of detail. 2.1 UniProt The inclusion of protein-protein interactions in ProteInOn The Universal Protein Knowledgebase (UniProt) is a unified provides an interesting background for comparing protein re- database of protein sequence and functional data, created lationships with protein semantic similarity, since proteins by joining the information in SwissProt, TrEMBL and PIR which interact
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