Semantic Similarity Measures as Tools for Exploring the Gene Ontology

Lord, Phillip; Stevens, Robert; Brass, Andy; Goble, Carole

doi:10.1142/9789812776303_0056

Cited by 133 publications

(200 citation statements)

References 7 publications

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“…The candidate genes are then given scores based on a comparison to the expression profiles found in the match set, using Spearman's rho rank-order correlation. The remainder of the scores are given according to the number of shared Interpro domains, with the match set, and the number of GO annotations that are semantically similar, at a significant level, to the annotations found in the match set (Lord et al, 2003). PosMed (http://omicspace.riken.jp/PosMed/ search).…”

Section: Software Toolsmentioning

confidence: 99%

Prioritization of Positional Candidate Genes Using Multiple Web-Based Software Tools

Thornblad¹,

Elliott

Jowett

et al. 2007

Twin Res Hum Genet

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The prioritization of genes within a candidate genomic region is an important step in the identification of causal gene variants affecting complex traits. Surprisingly, there have been very few reports of bioinformatics tools to perform such prioritization. The purpose of this article is to investigate the performance of 3 positional candidate gene software tools available, PosMed, GeneSniffer and SUSPECTS. The comparison was made for 40, 20 and 10 Mb regions in the human genome centred around known susceptibility genes for the common diseases breast cancer, Crohn's disease, age-related macular degeneration and schizophrenia. The known susceptibility gene was not always ranked highly, or not ranked at all, by 1 or more of the software tools. There was a large variation between the 3 tools regarding which genes were prioritized, and their rank order. PosMed and GeneSniffer were most similar in their prioritization gene list, whereas SUSPECTS identified the same candidate genes only for the narrowest (10 Mb) regions. Combining 2 or all of the candidate gene finding tools was superior in terms of ranking positional candidates. It is possible to reduce the number of candidate genes from a starting set in a region of interest by combining a variety of candidate gene finding tools. Conversely, we recommend caution in relying solely on single positional candidate gene prioritization tools. Our results confirm the obvious, that is, that starting with a narrower positional region gives a higher likelihood that the true susceptibility gene is selected, and that it is ranked highly. A narrow confidence interval for the mapping of complex trait genes by linkage can be achieved by maximizing marker informativeness and by having large samples. Our results suggest that the best approach to classify a minimum set of candidate genes is to take those genes that are prioritized by multiple prioritization tools.

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Section: Software Toolsmentioning

confidence: 99%

Prioritization of Positional Candidate Genes Using Multiple Web-Based Software Tools

Thornblad¹,

Elliott

Jowett

et al. 2007

Twin Res Hum Genet

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show abstract

“…A different use of GO is that applied in the SemSim Bioconductor package, which allows the estimation of information content-based similarity scores of GO terms and gene products [74][75][76]. GO-based semantic similarity scores can be used to perform annotation-based clustering as described by Wolting [77].…”

Section: Pathway Analysismentioning

confidence: 99%

Microarray data analysis and mining approaches

Cordero

Botta²,

Calogero³

2008

Briefings in Functional Genomics and Proteomics

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Microarray based transcription profiling is now a consolidated methodology and has widespread use in areas such as pharmacogenomics, diagnostics and drug target identification. Large-scale microarray studies are also becoming crucial to a new way of conceiving experimental biology. A main issue in microarray transcription profiling is data analysis and mining. When microarrays became a methodology of general use, considerable effort was made to produce algorithms and methods for the identification of differentially expressed genes. More recently, the focus has switched to algorithms and database development for microarray data mining. Furthermore, the evolution of microarray technology is allowing researchers to grasp the regulative nature of transcription, integrating basic expression analysis with mRNA characteristics, i.e. exon-based arrays, and with DNA characteristics, i.e. comparative genomic hybridization, single nucleotide polymorphism, tiling and promoter structure. In this article, we will review approaches used to detect differentially expressed genes and to link differential expression to specific biological functions.

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“…But more importantly, they also serve as obstacles too ontology integration, since they amount to the conscious adoption of a policy according to which 'is a' means different things in different contexts. [11] We here leave open the question whether division into levels and single inheritance involving genuine is a relations can be achieved throughout the realm of classifications treated of by GO. We note only that, as Guarino and Welty [19] have shown, methods exist for systematically removing cases of multiple inheritance from class hierarchies by distinguishing is a relations from ontological relations of other sorts.…”

Section: Problems With Is Amentioning

confidence: 99%

“…Another problematic example, which also illustrates once more GO's multiple ways of handling the relation of localization, is GO's postulation of: [10] bud tip is a site of polarized growth (sensu Saccharomyces) from which we can infer that: [11] every instance of bud tip has an instance of Saccaromyces polarized growth located therein.…”

Section: Problems With 'Sensu'mentioning

confidence: 99%

“…For example, there is no linkage between the term taste [GO:0007607], which is a biological process term, and the term taste receptor activity [GO:0008527], which is a molecular function term. This has an adverse impact on data integration when GO's data structure is used to retrieve or link related information from different data sources [11].…”

Section: Introductionmentioning

confidence: 99%

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On the Application of Formal Principles to Life Science Data: a Case Study in the Gene Ontology

Smith

Köhler

Kumar³

2004

Lecture Notes in Computer Science

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Abstract. Formal principles governing best practices in classification and definition have for too long been neglected in the construction of biomedical ontologies, in ways which have important negative consequences for data integration and ontology alignment. We argue that the use of such principles in ontology construction can serve as a valuable tool in error-detection and also in supporting reliable manual curation. We argue also that such principles are a prerequisite for the successful application of advanced data integration techniques such as ontology-based multi-database querying, automated ontology alignment and ontology-based text-mining. These theses are illustrated by means of a case study of the Gene Ontology, a project of increasing importance within the field of biomedical data integration.

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Semantic Similarity Measures as Tools for Exploring the Gene Ontology

Cited by 133 publications

References 7 publications

Prioritization of Positional Candidate Genes Using Multiple Web-Based Software Tools

Prioritization of Positional Candidate Genes Using Multiple Web-Based Software Tools

Microarray data analysis and mining approaches

On the Application of Formal Principles to Life Science Data: a Case Study in the Gene Ontology

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