Table 3. Signiﬁcantly over-represented GO terms in the ISG gene list identiﬁed by DAVID GO term Chip List Response to biotic stimulus Immune response Defense response Response to stimulus Organismal physiological process Response to virus Response to pest, pathogen or parasite Response to other organism Response to stress MHC protein complex MHC class I protein complex Antigen presentation, endogenous antigen Antigen processing, endogenous antigen via MHC class I MHC class I receptor activity Antigen processing Antigen presentation Immunological synapse 853 737 816 1765 1660 70 503 514 956 18 18 27 28 36 36 42 31 49 44 45 52 46 14 25 25 27 6 6 7 7 7 7 7 6 PAGE 9 OF 11 Nucleic Acids Research, 2009, Vol. 37, No. 11 e79 Bonferroni P-value 6.40E–33 8.30E–29 2.90E–28 1.10E–21 2.00E–16 2.20E–12 2.40E–11 3.90E–11 6.00E–07 6.30E–05 6.30E–05 6.70E–05 8.50E–05 2.00E–04 4.20E–04 1.10E–03 1.20E–03 The ontological tool DAVID 2.0 was used to identify over-represented GO terms in the ISG gene list. The analysis was performed using all levels of GO terms and HG-U133A chip as background (database version as of 19 December 2007). Over-represented GO terms were deﬁned as having Bonferroni P-value 0.05 based on Fisher’s exact test (threshold settings: Count = 2, EASE = 0.1). abstracts is available, although their power will depend on the extent and quality of annotation. To test this, we ana- lysed 354 gene lists collected from published literature spanning 10 major Aﬀymetrix chip types and eight model organisms including human, mouse, rat, Arabidopsis, Drosophila, C. elegans, Xenopus and Zebraﬁsh (see Supplementary Data 2 for details of these gene lists). We found that the number of tokens identiﬁed as over-represented by the two methods varies substan- tially between species (Supplementary Data Figures S3 and S4). This appears to be related to the amount of anno- tation available to each species in the text corpus used. Those species with a higher amount of overall annotation per gene tend to produce, on average, more signiﬁcant tokens per gene list tested (Figure 3). Therefore at this moment gene lists based on well-researched species such as human and mouse produce more detailed insight than those from less well-studied organisms. Nevertheless, useful information can still be obtained from species such as Arabidopsis, as shown by an analysis of data pre- sented in Nishimura et al. (8). They studied the eﬀect of the pmr4 mutation on pathogen response in Arabidopsis and concluded that the basis for the resistance in pmr4 mutant plant to pathogens was due to an enhanced acti- vation of the salicylic-acid (SA) signal transduction path- way. We re-analysed the list of diﬀerentially expressed genes reported by the authors using a ‘trimmed’ version of the text corpus built from only those papers published before 2003, so as to mine no more than the knowledge that was available to the authors of the original paper. Two tokens, ‘salicylic’ and ‘SA’, were identiﬁed as over- represented in the gene list, hence recapitulating the Figure 3. A comparison of the performance of Outlier (a) and ExtendedHG (b) across diﬀerent species. The average number of tokens called signiﬁcant by the two approaches, Outlier and ExtendedHG, is plotted against the annotation density (i.e. number of PMID per gene) for experimentally derived gene lists that were per- formed on 10 Aﬀymetrix platforms representing eight diﬀerent species, including HG-U133A (hsa), HG-U133 Plus 2.0 (hum), Mouse 430 2.0 (mou), Rat 230 2.0 (rat), Arabidopsis ATH1 (ath); DrosGenome1 (dm), Drosophila 2.0 (dros), Xenopus laevis (xl), C. elegans (ce) and Zebraﬁsh (dr). conclusions by the authors. Both tokens were called sig- niﬁcant by Outlier; whilst only ‘salicylic’ was called signif- icant by ExtendedHG. This shows that despite the lower level of annotation, plausible results can still be obtained