by Zhixia Teng, Maozu Guo, Xiaoyan Liu, Qiguo Dai, Chunyu Wang, Ping Xuan
Abstract:
MOTIVATION: Compared with sequence and structure similarity, functional similarity is more informative for understanding the biological roles and functions of genes. Many important applications in computational molecular biology require functional similarity, such as gene clustering, protein function prediction, protein interaction evaluation and disease gene prioritization. Gene Ontology (GO) is now widely used as the basis for measuring gene functional similarity. Some existing methods combined semantic similarity scores of single term pairs to estimate gene functional similarity, whereas others compared terms in groups to measure it. However, these methods may make error-prone judgments about gene functional similarity. It remains a challenge that measuring gene functional similarity reliably. RESULT: We propose a novel method called SORA to measure gene functional similarity in GO context. First of all, SORA computes the information content (IC) of a term making use of semantic specificity and coverage. Second, SORA measures the IC of a term set by means of combining inherited and extended IC of the terms based on the structure of GO. Finally, SORA estimates gene functional similarity using the IC overlap ratio of term sets. SORA is evaluated against five state-of-the-art methods in the file on the public platform for collaborative evaluation of GO-based semantic similarity measure. The carefully comparisons show SORA is superior to other methods in general. Further analysis suggests that it primarily benefits from the structure of GO, which implies expressive information about gene function. SORA offers an effective and reliable way to compare gene function. AVAILABILITY: The web service of SORA is freely available at http://nclab.hit.edu.cn/SORA/
Reference:
Measuring gene functional similarity based on groupwise comparison of GO terms (Zhixia Teng, Maozu Guo, Xiaoyan Liu, Qiguo Dai, Chunyu Wang, Ping Xuan), In Bioinformatics (Oxford, England), volume 29, 2013.
Bibtex Entry:
@article{Teng2013,
abstract = {MOTIVATION: Compared with sequence and structure similarity, functional similarity is more informative for understanding the biological roles and functions of genes. Many important applications in computational molecular biology require functional similarity, such as gene clustering, protein function prediction, protein interaction evaluation and disease gene prioritization. Gene Ontology (GO) is now widely used as the basis for measuring gene functional similarity. Some existing methods combined semantic similarity scores of single term pairs to estimate gene functional similarity, whereas others compared terms in groups to measure it. However, these methods may make error-prone judgments about gene functional similarity. It remains a challenge that measuring gene functional similarity reliably. RESULT: We propose a novel method called SORA to measure gene functional similarity in GO context. First of all, SORA computes the information content (IC) of a term making use of semantic specificity and coverage. Second, SORA measures the IC of a term set by means of combining inherited and extended IC of the terms based on the structure of GO. Finally, SORA estimates gene functional similarity using the IC overlap ratio of term sets. SORA is evaluated against five state-of-the-art methods in the file on the public platform for collaborative evaluation of GO-based semantic similarity measure. The carefully comparisons show SORA is superior to other methods in general. Further analysis suggests that it primarily benefits from the structure of GO, which implies expressive information about gene function. SORA offers an effective and reliable way to compare gene function. AVAILABILITY: The web service of SORA is freely available at http://nclab.hit.edu.cn/SORA/},
author = {Teng, Zhixia and Guo, Maozu and Liu, Xiaoyan and Dai, Qiguo and Wang, Chunyu and Xuan, Ping},
doi = {10.1093/bioinformatics/btt160},
issn = {1367-4811},
journal = {Bioinformatics (Oxford, England)},
keywords = {Algorithms,Computational Biology,Computational Biology: methods,Controlled,Genes,Molecular Sequence Annotation,Proteins,Proteins: genetics,Proteins: physiology,SML-LIB-BIBLIO,Semantics,Vocabulary},
mendeley-tags = {SML-LIB-BIBLIO},
month = jun,
number = {11},
pages = {1424--32},
pmid = {23572412},
title = {{Measuring gene functional similarity based on groupwise comparison of GO terms}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23572412},
volume = {29},
year = {2013}
}