Ehsani Rezvan, Drabløs Finn
Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, P.O. Box 8905, NO-7491, Trondheim, Norway.
Department of Mathematics, University of Zabol, Zabol, Iran.
BMC Bioinformatics. 2016 Jul 29;17(1):296. doi: 10.1186/s12859-016-1160-0.
The Gene Ontology (GO) is a dynamic, controlled vocabulary that describes the cellular function of genes and proteins according to tree major categories: biological process, molecular function and cellular component. It has become widely used in many bioinformatics applications for annotating genes and measuring their semantic similarity, rather than their sequence similarity. Generally speaking, semantic similarity measures involve the GO tree topology, information content of GO terms, or a combination of both.
Here we present a new semantic similarity measure called TopoICSim (Topological Information Content Similarity) which uses information on the specific paths between GO terms based on the topology of the GO tree, and the distribution of information content along these paths. The TopoICSim algorithm was evaluated on two human benchmark datasets based on KEGG pathways and Pfam domains grouped as clans, using GO terms from either the biological process or molecular function. The performance of the TopoICSim measure compared favorably to five existing methods. Furthermore, the TopoICSim similarity was also tested on gene/protein sets defined by correlated gene expression, using three human datasets, and showed improved performance compared to two previously published similarity measures. Finally we used an online benchmarking resource which evaluates any similarity measure against a set of 11 similarity measures in three tests, using gene/protein sets based on sequence similarity, Pfam domains, and enzyme classifications. The results for TopoICSim showed improved performance relative to most of the measures included in the benchmarking, and in particular a very robust performance throughout the different tests.
The TopoICSim similarity measure provides a competitive method with robust performance for quantification of semantic similarity between genes and proteins based on GO annotations. An R script for TopoICSim is available at http://bigr.medisin.ntnu.no/tools/TopoICSim.R .
基因本体论（GO）是一个动态的、受控的词汇表，它根据三个主要类别描述基因和蛋白质的细胞功能：生物过程、分子功能和细胞成分。它已广泛应用于许多生物信息学应用中，用于注释基因并测量它们的语义相似性，而非序列相似性。一般来说，语义相似性度量涉及GO树拓扑结构、GO术语的信息内容或两者的组合。
在此，我们提出一种新的语义相似性度量方法，称为TopoICSim（拓扑信息内容相似性），它基于GO树的拓扑结构使用GO术语之间特定路径的信息，以及沿这些路径的信息内容分布。基于KEGG通路和归为家族的Pfam结构域，使用来自生物过程或分子功能的GO术语，在两个人类基准数据集上评估了TopoICSim算法。与五种现有方法相比，TopoICSim度量的性能更优。此外，还使用三个人类数据集在由相关基因表达定义的基因/蛋白质集上测试了TopoICSim相似性，与之前发表的两种相似性度量相比，性能有所提高。最后，我们使用了一个在线基准测试资源，该资源在三项测试中针对一组11种相似性度量评估任何相似性度量，使用基于序列相似性、Pfam结构域和酶分类的基因/蛋白质集。TopoICSim的结果显示，相对于基准测试中包含的大多数度量，性能有所提高，特别是在不同测试中表现出非常稳健的性能。
TopoICSim相似性度量提供了一种具有竞争力的方法，用于基于GO注释定量基因和蛋白质之间的语义相似性，性能稳健。可在http://bigr.medisin.ntnu.no/tools/TopoICSim.R获取TopoICSim的R脚本。
