PAGE 11 OF 11 provides for high quality, this process together with the volume of research activity in diﬀerent areas means that the coverage of less heavily studied species is still limited and this has a direct eﬀect on the power of our method. Incorporation of additional gene-citation links, perhaps from species-speciﬁc databases, would increase the amount of textual information in the corpus and improve the power of the proposed methods. Our methods are currently based on a simple processing and analysis of the text corpus. There are several areas where this could be made more sophisticated and complex in the future, such as the removal of stopwords, the use of thesauri to allow for the identiﬁcation of multi-word biological con- cepts and synonyms mapped to the same gene. These steps should reduce the noise caused by natural language variation and improve the information content of the over-represented tokens. To conclude, we have described the problems and challenges associated with existing ORA methods when adapting them for mining text-based information, and three novel approaches have been proposed to address some of these problems. Analysis performed on several independent datasets show that the proposed methods produce biologically meaningful results that are in good agreement with the manually determined annotations (Supplementary Data 3). These examples also demon- strate that a coherent picture that exists within complex group of genes can be discerned by incorporating textual information embedded in literature as a knowledge source into the analysis of gene expression data. We believe that the proposed text-based ORA approaches can be used to complement and extend existing ontology-based func- tional analysis tools for guiding the biological interpreta- tion of complex microarray data. SUPPLEMENTARY DATA Supplementary Data are available at NAR Online. ACKNOWLEDGEMENTS We thank Peter Giles for help with establishment of the web server for these methods. We are also grateful to Peter Holmans, Alex Richards, Peter Giles and Suraj Menon for reading this manuscript and making constructive suggestions. FUNDING Cancer Research UK (grant number C8731/A5579). Funding for open access charge: Cancer Research UK (grant number C8731/A5579). Conﬂict of interest statement. None declared. REFERENCES 1. Hosack,D.A., Dennis,G. Jr, Sherman,B.T., Lane,H.C. and Lempicki,R.A. (2003) Identifying biological themes within lists of genes with EASE. Genome Biol., 4, R70. Nucleic Acids Research, 2009, Vol. 37, No. 11 e79 2. Al Shahrour,F., Diaz-Uriarte,R. and Dopazo,J. (2004) FatiGO: a web tool for ﬁnding signiﬁcant associations of Gene Ontology terms with groups of genes. Bioinformatics, 20, 578–580. 3. Dahlquist,K.D., Salomonis,N., Vranizan,K., Lawlor,S.C. and Conklin,B.R. 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