PAGE 5 OF 11 Nucleic Acids Research, 2009, Vol. 37, No. 11 e79 Table 1. Signiﬁcantly over-represented abstract terms in the ISG gene list identiﬁed using the classical hypergeometric test Rank Term Rank Term Rank Term 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 INTERFERON IFN ANTIVIRAL IFN-BETA IFN-ALPHA INDUCIBLE INTERFERON-ALPHA INFECTION VIRAL IMMUNE TREAT INNATE IFN-GAMMA VIRUS IMMUNITY DSRNA INDUCTION OLIGOADENYLATE LYMPHOBLASTOID ISRE HOST ISG MHC TREATMENT HLA-A STOMATITIS BETA RESPONSE HLA-CLASS EVASION CYTOKINE ANTIGEN 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 53 52 54 55 56 57 58 59 60 61 62 63 64 INFECT INDUCE HLA-B HISTOCOMPATIBILITY LINE HEPATITIS MELANOMA ENCEPHALOMYOCARDITIS REPLICATION AFTER MONOCLONAL EPSTEIN-BARR UPREGULATE SYNTHESIS BETA2-MICROGLOBULIN EBV GAMMA-INTERFERON HLA INTERFERON-GAMMA OAS HLA-G TYPE MXA ALPHA DEFINE IMMUNODEFICIENCY PROMYELOCYTIC INTACT LEUKEMIA INDEPENDENT EACH TAPASIN 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 LYSIS AUTOIMMUNE INDIGENOUS PROTEASOME LMP2 LMP7 PKR INDUCIBILITY CORRESPONDING MOLECULE DEFENSE DIFFERENTIAL ACTION TAP STIMULATE CONFER LOAD REACTIVITY OR-C MEDIATE RECOMBINANT CTL MICROGLOBULIN STRAND RECOGNIZE ALSO DERIVE P69 VSV DOUBLE Over-represented abstract terms are deﬁned as those tokens with P 0.05 after Bonferroni correction. The most signiﬁcant hits are ranked at the top of the table. See Supplementary Data 1, Table S1 for term frequencies and P-values associated with the individual terms. Biologically plausible terms such as ‘interferon’, ‘IFN’, ‘antiviral’, ‘IFN-alpha’, ‘IFN-beta’, ‘inducible’ and ‘immune’, were amongst the most signiﬁcant hits being called over-represented. These terms are related in princi- ple to the role of interferon in modulating host immune responses against viruses and infection. However, these biologically relevant terms were interspersed with rela- tively uninformative terms for which it was less plausible that they were speciﬁcally associated with the biology of interferon-regulated gene expression. These include common words (such as ‘after’, ‘each’, ‘also’) and non- speciﬁc biological words (such as ‘synthesis’, ‘molecule’, ‘beta’). A similar mix of speciﬁc and non-speciﬁc tokens was generally seen for other gene lists that we have ana- lysed (data not shown). Some areas of biology have, historically, been subject to greater levels of research activity and this is reﬂected in the biological literature. We reasoned that if a particular experiment were focused on a particularly well-studied area of biology this might therefore lead to a greater number of PMIDs associated with the resultant gene list than might otherwise occur. This in turn would introduce a bias that would aﬀect the application of the classical hypergeometric test. The interferon response is an example of a well- researched area and the 77 genes in the ISG gene list are annotated by 1514 PMIDs. However, if we were to create a 77-gene list by random sampling from the same set of background genes on the chip we would expect to see, on average, only 660 PMIDs associated with such a random gene list. Thus, in this example the ‘real world’ ISG gene list has 2.3 times more PMIDs associated with it than would be expected by chance. The consequence of this on the classical hypergeometric distribution-based ORA approach is that, for some tokens, there is a general shift towards appearing over-represented, simply because the background frequency is artefactually under- estimated. Therefore, even a relatively modest increase in token frequency of a common word would produce a sig- niﬁcant P-value. To explore this further we collated 52 gene lists from the published literature that were based on use of the human HG-U133A array (see Supplementary Data 2 for details of these literature gene lists), and then compared the amount of PMIDs in them with that in an equivalent set of random gene lists. We found that gene lists derived by experimental means (i.e. the result of mining a real biological dataset) tend to have a greater number of asso- ciated PMIDs than equivalently sized random gene lists (Figure 1a). A similar trend was also seen for other spe- cies, such as mouse and rat, for which suﬃcient data were available (data not shown). These ﬁndings suggest that