Applied Bioinformatics

, Volume 4, Issue 4, pp 281–283 | Cite as


An R Tool for Analysing Gene Ontology™ Term Enrichment
  • Qikai Xu
  • Gad ShaulskyEmail author
Application Note


Understanding the composition of gene lists that result from high-throughput experiments requires elaborate processing of gene annotation lists. In this article we present GOAT (Gene Ontology Analysis Tool), a tool based on the statistical software ‘R’ for analysing Gene Ontology™ (GO) term enrichment in gene lists. Given a gene list, GOAT calculates the enrichment and statistical significance of every GO term and generates graphical presentations of significantly enriched terms. GOAT works for any organism with a genome-scale GO annotation and allows easy updates of ontologies and annotations.


Gene Ontology Gene List Annotation Data Term Enrichment Annotation File 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by grant no. P01 HD39691 from the National Institute of Health, National Institute of Child Health and Human Development (NIH-NICHD).

The authors have no conflicts of interest that are directly relevant to the content of this article.


  1. 1.
    Berriz GF, King OD, Bryant B, et al. Characterizing gene sets with FuncAssociate. Bioinformatics 2003; 19(18): 2502–4PubMedCrossRefGoogle Scholar
  2. 2.
    Feng W, Wang G, Zeeberg BR, et al. Development of gene ontology tool for biological interpretation of genomic and proteomic data. AMIA Annu Symp Proc 2003, 839Google Scholar
  3. 3.
    Al-Shahrour F, Diaz-Uriarte R, Dopazo J. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 2004; 20(4): 578–80PubMedCrossRefGoogle Scholar
  4. 4.
    Beissbarth T, Speed TP. GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 2004; 20(9): 1464–5PubMedCrossRefGoogle Scholar
  5. 5.
    Zhang B, Schmoyer D, Kirov S, et al. GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies. BMC Bioinformatics 2004; 5(1): 16PubMedCrossRefGoogle Scholar
  6. 6.
    Gentleman RC, Carey VJ, Bates DM, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004; 5(10): R80PubMedCrossRefGoogle Scholar
  7. 7.
    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 1995; 57: 289–300Google Scholar
  8. 8.
    Katoh M, Shaw C, Xu Q, et al. An orderly retreat: dedifferentiation is a regulated process. Proc Natl Acad Sci U S A 2004; 101(18): 7005–10PubMedCrossRefGoogle Scholar
  9. 9.
    Xu Q, Ibarra M, Mahadeo D, et al. Transcriptional transitions during Dictyostelium spore germination. Eukaryot Cell 2004; 3(5): 1101–10PubMedCrossRefGoogle Scholar

Copyright information

© Adis Data Information BV 2005

Authors and Affiliations

  1. 1.Graduate Program in Structural and Computational Biology and Molecular BiophysicsBaylor College of MedicineHoustonUSA
  2. 2.Department of Molecular and Human GeneticsBaylor College of MedicineHoustonUSA

Personalised recommendations