Abstract
BackgroundMeasures of gene functional similarity are essential tools for gene clustering, gene function prediction, evaluation of protein-protein interaction, disease gene prioritization and other applications. In recent years, many gene functional similarity methods have been proposed based on the semantic similarity of GO terms. However, these leading approaches may make errorprone judgments especially when they measure the specificity of GO terms as well as the IC of a term set. Therefore, how to estimate the gene functional similarity reliably is still a challenging problem.ResultsWe propose WIS, an effective method to measure the gene functional similarity. First of all, WIS computes the IC of a term by employing its depth, the number of its ancestors as well as the topology of its descendants in the GO graph. Secondly, WIS calculates the IC of a term set by means of considering the weighted inherited semantics of terms. Finally, WIS estimates the gene functional similarity based on the IC overlap ratio of term sets. WIS is superior to some other representative measures on the experiments of functional classification of genes in a biological pathway, collaborative evaluation of GO-based semantic similarity measures, protein-protein interaction prediction and correlation with gene expression. Further analysis suggests that WIS takes fully into account the specificity of terms and the weighted inherited semantics of terms between GO terms.ConclusionsThe proposed WIS method is an effective and reliable way to compare gene function. The web service of WIS is freely available at http://nclab.hit.edu.cn/WIS/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0359-z) contains supplementary material, which is available to authorized users.
Highlights
Measures of gene functional similarity are essential tools for gene clustering, gene function prediction, evaluation of protein-protein interaction, disease gene prioritization and other applications
Gene Ontology (GO) is a standardized, precisely defined and controlled vocabulary of terms. It comprises three orthogonal ontologies: cellular component (CC), molecular function (MF) and biological process (BP) [1]. These ontologies are structured as three directed acyclic graphs (DAGs) in which, the nodes correspond to the terms describing a certain biological semantic category and the edges represent the linkages between terms describing defined relationships [2]
We proposed a novel method, namely Weighted Inherited Semantics (WIS), to measure gene functional similarity based on GO
Summary
Measures of gene functional similarity are essential tools for gene clustering, gene function prediction, evaluation of protein-protein interaction, disease gene prioritization and other applications. Many gene functional similarity methods based on GO [2, 5, 7,8,9,10,11,12,13,14,15,16,17,18,19] have been proposed by researchers These measures have been widely used in all kinds of important applications such as proteinprotein interaction prediction [20,21,22,23], network prediction [24,25,26], cellular localization prediction [27], disease gene prioritization [8, 28, 29], pathway modeling [30] and improving analysis of microarray data quality [31]. Measuring the functional similarity is more informative for understanding the biological roles and functions of
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