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Gene Ontology Semantic Similarity Analysis Using GOSemSim

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Stem Cell Transcriptional Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2117))

Abstract

The GOSemSim package, an R-based tool within the Bioconductor project, offers several methods based on information content and graph structure for measuring semantic similarity among GO terms, gene products and gene clusters. In this chapter, I illustrate the use of GOSemSim on a list of regulators in preimplantation embryos. A step-by-step analysis was provided as well as instructions on interpretation and visualization of the results. GOSemSim is open-source and is available from https://www.bioconductor.org/packages/GOSemSim.

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References

  1. Han Y, Yu G, Sarioglu H et al (2013) Proteomic investigation of the interactome of FMNL1 in hematopoietic cells unveils a role in calcium-dependent membrane plasticity. J Proteome 78:72–82. https://doi.org/10.1016/j.jprot.2012.11.015

    Article  CAS  Google Scholar 

  2. Yu G, He Q-Y (2011) Functional similarity analysis of human virus-encoded miRNAs. J Clin Bioinforma 1:15. https://doi.org/10.1186/2043-9113-1-15

    Article  CAS  Google Scholar 

  3. Pirkl M, Diekmann M, van der Wees M et al (2017) Inferring modulators of genetic interactions with epistatic nested effects models. PLoS Comput Biol 13:e1005496. https://doi.org/10.1371/journal.pcbi.1005496

    Article  Google Scholar 

  4. Lei C, Ruan J (2013) A novel link prediction algorithm for reconstructing protein–protein interaction networks by topological similarity. Bioinformatics 29:355–364. https://doi.org/10.1093/bioinformatics/bts688

    Article  CAS  Google Scholar 

  5. Bhattacharya A, Cui Y (2015) miR2GO: comparative functional analysis for microRNAs. Bioinformatics 31:2403–2405. https://doi.org/10.1093/bioinformatics/btv140

    Article  CAS  Google Scholar 

  6. Zhou H, Yang Y, Shen H-B (2017) Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features. Bioinformatics 33:843–853. https://doi.org/10.1093/bioinformatics/btw723

    CAS  PubMed  Google Scholar 

  7. Yu G, Li F, Qin Y et al (2010) GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics 26:976–978. https://doi.org/10.1093/bioinformatics/btq064

    Article  CAS  Google Scholar 

  8. Resnik P (1999) Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res 11:95–130. https://doi.org/10.1613/jair.514

    Article  Google Scholar 

  9. Jiang JJ, Conrath DW (1997) Semantic similarity based on corpus statistics and lexical taxonomy. ArXivcmp-Lg9709008

    Google Scholar 

  10. Lin D (1998) An information-theoretic definition of similarity. In: Proceedings of the fifteenth international conference on machine learning. Morgan Kaufmann Publishers Inc., San Francisco, pp 296–304

    Google Scholar 

  11. Schlicker A, Domingues FS, Rahnenführer J, Lengauer T (2006) A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinformatics 7:302. https://doi.org/10.1186/1471-2105-7-302

    Article  Google Scholar 

  12. Wang JZ, Du Z, Payattakool R et al (2007) A new method to measure the semantic similarity of GO terms. Bioinforma Oxf Engl 23:1274–1281. https://doi.org/10.1093/bioinformatics/btm087

    Article  CAS  Google Scholar 

  13. Eddelbuettel D, Francois R (2011) Rcpp: Seamless R and C++ Integration. J Stat Softw 40:1–18. https://doi.org/10.18637/jss.v040.i08

    Google Scholar 

  14. Yu G, Wang L-G, Han Y, He Q-Y (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS J Integr Biol 16:284–287. https://doi.org/10.1089/omi.2011.0118

    Article  CAS  Google Scholar 

  15. Yu G, Wang L-G, Yan G-R, He Q-Y (2015) DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31:608–609. https://doi.org/10.1093/bioinformatics/btu684

    Article  CAS  Google Scholar 

  16. Paradis E, Schliep K (2019) ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35:526–528. https://doi.org/10.1093/bioinformatics/bty633

    Article  CAS  Google Scholar 

  17. Yu G, Smith DK, Zhu H et al (2017) Ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol 8:28–36. https://doi.org/10.1111/2041-210X.12628

    Article  Google Scholar 

  18. Wu J, Huang B, Chen H et al (2016) The landscape of accessible chromatin in mammalian preimplantation embryos. Nature 534:652–657. https://doi.org/10.1038/nature18606

    Article  CAS  Google Scholar 

  19. Yu G, Lam TT-Y, Zhu H, Guan Y (2018) Two methods for mapping and visualizing associated data on phylogeny using Ggtree. Mol Biol Evol 35:3041–3043. https://doi.org/10.1093/molbev/msy194

    Article  CAS  Google Scholar 

  20. Yu G (2018) Using meshes for MeSH term enrichment and semantic analyses. Bioinformatics 34:3766–3767. https://doi.org/10.1093/bioinformatics/bty410

    Article  CAS  Google Scholar 

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Acknowledgments

I thank Drs. Yin Ge and Zhongtian Xu for providing useful feedback and helpful comments on the manuscript. This work was supported by Startup funds from Southern Medical University (G618289088).

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Authors and Affiliations

  1. Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China

    Guangchuang Yu

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  1. Guangchuang Yu
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Correspondence to Guangchuang Yu .

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Editors and Affiliations

  1. Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI, USA

    Benjamin L. Kidder

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Yu, G. (2020). Gene Ontology Semantic Similarity Analysis Using GOSemSim. In: Kidder, B. (eds) Stem Cell Transcriptional Networks. Methods in Molecular Biology, vol 2117. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0301-7_11

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  • DOI: https://doi.org/10.1007/978-1-0716-0301-7_11

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0300-0

  • Online ISBN: 978-1-0716-0301-7

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