Published online 8 May 2009 Nucleic Acids Research, 2009, Vol. 37, No. 11 e79 doi:10.1093/nar/gkp310 Text-based over-representation analysis of microarray gene lists with annotation bias Hui Sun Leong and David Kipling* Department of Pathology, School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, UK Received December 1, 2008; Revised April 14, 2009; Accepted April 16, 2009 ABSTRACT A major challenge in microarray data analysis is the functional interpretation of gene lists. A common approach to address this is over-representation analysis (ORA), which uses the hypergeometric test (or its variants) to evaluate whether a particular functionally defined group of genes is represented more than expected by chance within a gene list. Existing applications of ORA have been largely lim- ited to pre-defined terminologies such as GO and KEGG. We report our explorations of whether ORA can be applied to a wider mining of free-text. We found that a hitherto underappreciated feature of experimentally derived gene lists is that the consti- tuents have substantially more annotation asso- ciated with them, as they have been researched upon for a longer period of time. This bias, a result of patterns of research activity within the biomedical community, is a major problem for classical hyper- geometric test-based ORA approaches, which cannot account for such bias. We have therefore developed three approaches to overcome this bias, and demonstrate their usability in a wide range of published datasets covering different species. A comparison with existing tools that use GO terms suggests that mining PubMed abstracts can reveal additional biological insight that may not be possible by mining pre-defined ontologies alone. INTRODUCTION The output of a microarray experiment is typically one or more lists of genes that show an ‘interesting’ change in expression in the context of that experiment. This is often not the end point of the analysis, but the starting point of a complex process of deriving biological interpre- tation. Many researchers interpret their results by manu- ally reviewing the function of each gene based on literature or database searches, or by prior familiarity with the gene and a plausible link to the biology under study. This ad hoc annotation process is both time-consuming and prone to user bias. The need to formalise this inter- pretation process has led to the development of a range of tools, of which a family of statistical methods collectively known as over-representation analysis (ORA) is becoming increasingly popular among researchers undertaking microarray analysis. The fundamental question asked by ORA is: what biological terms or functional categories are represented in the gene list more often than expected by chance. The most common approach to test this statisti- cally is by using the hypergeometric test (or its variants such as Fisher’s exact test) to calculate the probability of seeing at least a particular number of genes containing the biological term of interest in the gene list. This mode of analysis has been implemented (with minor variations) in several publicly available software tools, including DAVID/EASEonline (1), FatiGO (2), GenMAPP (3), GoMiner (4) and OntoTools (5). Currently, the applications of ORA are largely limited to the mining of pre-deﬁned ontologies (e.g. GO, MeSH) or pathway annotation (e.g. KEGG, BioCarta). These resources are, to a large extent, generated from manual literature reading by experts, with the aim of providing a structured, condensed and reduced description of the biological knowledge about genes in the scientiﬁc litera- ture. However, due to its labour-intensive nature, such pre-deﬁned functional annotations are inevitably limited in scope and ﬂexibility, and cannot fully reﬂect the detail of all areas of biology that might be of interest. A much greater wealth of biological knowledge about genes is present only in the primary, text-based biomedical litera- ture, which is readily accessed in the form of abstracts, and increasingly as full-text articles from selected biomed- ical journals. We were therefore interested to determine whether the successful applications of ORA can be extended beyond the mining of controlled vocabularies to a wider mining of free-text, initially in the form of PubMed abstracts. Our initial exploration into this approach was based on a simple tokenisation of PubMed abstracts, followed by the identiﬁcation of over-represented tokens using the classical hypergeometric test. When this approach was tested on 52 literature-derived gene lists, we discovered a *To whom correspondence should be addressed. Tel: +44 (29) 206 87037; Email: kiplingd@cardiff.ac.uk 2009 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.