Abstract
Many freely available tools exist for analysing functional enrichment among short filtered or long unfiltered gene lists. These analyses are typically performed against either Gene Ontologies (GO) or KEGG pathways (Kyoto Encyclopedia of Genes and Genomes) database. The functionality to carry out these various analyses is currently scattered in different tools, many of which are also often very limited regarding result visualization. GeneFuncster is a tool that can analyse the functional enrichment in both the short filtered gene lists and full unfiltered gene lists towards both GO and KEGG and provide a comprehensive result visualisation for both databases. GeneFuncster is a simple to use publicly available web tool accessible at http://bioinfo.utu.fi/GeneFuncster .
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gentleman, R., et al.: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 10(5), R80 (2004)
Subramanian, A., et al.: GSEA-P: a desktop application for Gene Set Enrichment Analysis. Bioinformatics 23, 3251–3253 (2007)
R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008)
Huang, D.W., et al.: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 1(37), 1–13 (2009)
Koh, K.P., et al.: Tet1 and Tet2 regulate 5-hydroxymethylcytosine production and cell lineage specification in mouse embryonic stem cells. Cell Stem Cell 8(2), 200–213 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Laiho, A., Király, A., Gyenesei, A. (2012). GeneFuncster: A Web Tool for Gene Functional Enrichment Analysis and Visualisation. In: Gilbert, D., Heiner, M. (eds) Computational Methods in Systems Biology. CMSB 2012. Lecture Notes in Computer Science(), vol 7605. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33636-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-33636-2_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33635-5
Online ISBN: 978-3-642-33636-2
eBook Packages: Computer ScienceComputer Science (R0)