GOSemSim: GO semantic similarity measurement

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The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. GOSemSim implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively.

GOSemSim is released within the Bioconductor project and the source code is hosted on GitHub.


Guangchuang Yu, School of Public Health, The University of Hong Kong.


Please cite the following article when using GOSemSim:

doi Altmetric citation

Yu G, Li F, Qin Y, Bo X*, Wu Y and Wang S*. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics. 2010, 26(7):976-978.


Install GOSemSim is easy, follow the guide in the Bioconductor page:

## try http:// if https:// URLs are not supported
## biocLite("BiocUpgrade") ## you may need this



  • Information content based methods proposed by Resnik, Schlicker, Jiang and Lin
  • Graph structure based method proposed by Wang

Combine methods for aggregating multiple GO terms

  • max
  • avg
  • rcmax
  • BMA


  • goSim and mgoSim for measureing semantic similarity among GO terms
  • geneSim and mgeneSim for measureing semantic similarity among genes
  • clusterSim and mclusterSim for measureing semantic similarity among gene clusters

Supported organisms

  • 20 species that have OrgDb available in Bioconductor
  • Many other species with e GO annotation query online via AnnotationHub)

Find out details and examples on Documentation.

Projects that depend on GOSemSim

CRAN packages

  • BiSEp: Toolkit to Identify Candidate Synthetic Lethality
  • LANDD: Liquid Association for Network Dynamics Detection
  • ppiPre: Predict Protein-Protein Interactions Based on Functional and Topological Similarities

Bioconductor packages

  • clusterProfiler: statistical analysis and visualization of functional profiles for genes and gene clusters
  • DOSE: Disease Ontology Semantic and Enrichment analysis
  • meshes: MeSH Enrichment and Semantic analyses
  • Rcpi: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery
  • tRanslatome: Comparison between multiple levels of gene expression