GO-based functional dissimilarity of gene sets
- PMID: 21884611
- PMCID: PMC3248071
- DOI: 10.1186/1471-2105-12-360
GO-based functional dissimilarity of gene sets
Abstract
Background: The Gene Ontology (GO) provides a controlled vocabulary for describing the functions of genes and can be used to evaluate the functional coherence of gene sets. Many functional coherence measures consider each pair of gene functions in a set and produce an output based on all pairwise distances. A single gene can encode multiple proteins that may differ in function. For each functionality, other proteins that exhibit the same activity may also participate. Therefore, an identification of the most common function for all of the genes involved in a biological process is important in evaluating the functional similarity of groups of genes and a quantification of functional coherence can helps to clarify the role of a group of genes working together.
Results: To implement this approach to functional assessment, we present GFD (GO-based Functional Dissimilarity), a novel dissimilarity measure for evaluating groups of genes based on the most relevant functions of the whole set. The measure assigns a numerical value to the gene set for each of the three GO sub-ontologies.
Conclusions: Results show that GFD performs robustly when applied to gene set of known functionality (extracted from KEGG). It performs particularly well on randomly generated gene sets. An ROC analysis reveals that the performance of GFD in evaluating the functional dissimilarity of gene sets is very satisfactory. A comparative analysis against other functional measures, such as GS2 and those presented by Resnik and Wang, also demonstrates the robustness of GFD.
Figures


Similar articles
-
GFD-Net: A novel semantic similarity methodology for the analysis of gene networks.J Biomed Inform. 2017 Apr;68:71-82. doi: 10.1016/j.jbi.2017.02.013. Epub 2017 Mar 6. J Biomed Inform. 2017. PMID: 28274758
-
IntelliGO: a new vector-based semantic similarity measure including annotation origin.BMC Bioinformatics. 2010 Dec 1;11:588. doi: 10.1186/1471-2105-11-588. BMC Bioinformatics. 2010. PMID: 21122125 Free PMC article.
-
GS2: an efficiently computable measure of GO-based similarity of gene sets.Bioinformatics. 2009 May 1;25(9):1178-84. doi: 10.1093/bioinformatics/btp128. Epub 2009 Mar 16. Bioinformatics. 2009. PMID: 19289444 Free PMC article.
-
Semantic particularity measure for functional characterization of gene sets using gene ontology.PLoS One. 2014 Jan 28;9(1):e86525. doi: 10.1371/journal.pone.0086525. eCollection 2014. PLoS One. 2014. PMID: 24489737 Free PMC article.
-
Novel symmetry-based gene-gene dissimilarity measures utilizing Gene Ontology: Application in gene clustering.Gene. 2018 Dec 30;679:341-351. doi: 10.1016/j.gene.2018.08.062. Epub 2018 Sep 2. Gene. 2018. PMID: 30184472
Cited by
-
Optimal Threshold Determination for Interpreting Semantic Similarity and Particularity: Application to the Comparison of Gene Sets and Metabolic Pathways Using GO and ChEBI.PLoS One. 2015 Jul 31;10(7):e0133579. doi: 10.1371/journal.pone.0133579. eCollection 2015. PLoS One. 2015. PMID: 26230274 Free PMC article.
-
Development and use of the Cytoscape app GFD-Net for measuring semantic dissimilarity of gene networks.F1000Res. 2014 Jul 1;3:142. doi: 10.12688/f1000research.4573.1. eCollection 2014. F1000Res. 2014. PMID: 25400907 Free PMC article.
-
Improving clustering with metabolic pathway data.BMC Bioinformatics. 2014 Apr 10;15:101. doi: 10.1186/1471-2105-15-101. BMC Bioinformatics. 2014. PMID: 24717120 Free PMC article.
-
GraphTeams: a method for discovering spatial gene clusters in Hi-C sequencing data.BMC Genomics. 2018 May 8;19(Suppl 5):308. doi: 10.1186/s12864-018-4622-0. BMC Genomics. 2018. PMID: 29745835 Free PMC article.
-
Gene network biological validity based on gene-gene interaction relevance.ScientificWorldJournal. 2014;2014:540679. doi: 10.1155/2014/540679. Epub 2014 Sep 8. ScientificWorldJournal. 2014. PMID: 25295303 Free PMC article.
References
-
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics. 2000;25:25–29. doi: 10.1038/75556. - DOI - PMC - PubMed
-
- Pontius J, Wagner L, Schuler G. UniGene: a unified view of the transcriptome. The NCBI Handbook. Bethesda (MD): National Center for Biotechnology Information. 2003.
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Molecular Biology Databases