BMC Genomics 2008, 9(Suppl 2):S20 http://www.biomedcentral.com/1471-2164/9/S2/S20 Reviewed Computational Evidence (RCA) by the SGD. Another notable annotation is that of YIL060W. This ORF is putatively assigned to Transposable Elements (38) func- tional category and is the only ORF given the same anno- tation across all three datasets. Interestingly, the translation of this ORF seems to have a significant similar- ity (p-value = 2.2e -21) to retrotransposons TyA Gag and TyB Pol genes. This would seem to corroborate our puta- tive annotation. The two unclassified ORFs labelled as ribosomal proteins (12.01.01) also have additional labels supported over two datasets. In the case of YLR196W both predicted functions appear to be supported given that this ORF is already thought to be involved in rRNA processing. Of BALBOA's other putative annotations, the ORF assigned to amino acid metabolism (01.01), YJR154W, has been localized to the cytosol by green fluorescent protein (GFP) fusion localization experiments. Furthermore, the translation of YJR154W has some small similarity (p-value = 2.8e-5) to cytosolic L-asparginase. Two ORFs were puta- tively assigned to respiration (02.13) functional category. Of these the YDL072C mutant shows decreased levels of secreted invertase. Invertase is an enzyme involved in converting sucrose into glucose and fructose, a required step if sucrose to be used as an energy source. Of the ORFs assigned to the transport routes (20.09) category the best supported seems to be YIL039W. The fact that deletion of this ORF confers sensitivity to the drug GSAO might sug- gest a possible involvement in the export of this com- pound. Lastly, the two ORFs putatively annotated as Biogenesis of Cellular Components: mitochondrion (42.16) are actually both localized to the mitochondrion, YIL157C by co-purification and YML030W by GFP- fusion. The YIL157C mutant also shows some defect in cytochrome oxidase, a mitochondrial enzyme involved in generating the proton gradient needed for ATP synthe- sis. Intuitively, annotations that correspond across two inde- pendent datasets should have more support than those from a single dataset only. One method of quantifying this cross dataset support is the union of probability rule for two corresponding independent events, given by P1 <Math4font>½ P2 = P1 + (1 - P1)(P2). As a result, even the least supported annotations, i.e. those of Cellular Transport (20) with cross validation precision 0.50 and 0.66, become more significant when consistently supported over two datasets, increasing to 0.83. With better sup- ported classes, such as Transcription (11), such cross refer- encing achieves precision values close to 1. The α and β values we use in BALBOA may be somewhat stringent, especially if we include the dataset cross refer- encing step. However, looking at the final list, we see that most functional annotations seem to be supported by external evidence. One may consider this final cross refer- encing step a more valid selection procedure than the β selection threshold, as it is derived from the data. Interest- ingly, we find that upon reducing β to 0.5, 56 ORF anno- tations then agree across two or more datasets. This includes 12 that agree across all three. However, that being said, our chosen α and β values are perhaps more conducive to minimizing false positives and also proving the efficacy of the BALBOA classification approach via external corroboration. In this section we applied BAL- BOA to annotate individual unclassified ORFs. In the next section we look at a related semi-supervised strategy of predicting novel functional modules within the unclassi- fied ORFs. Discovery of novel functional modules In the previous section functional labelling of individual unclassified ORFs was performed. The best supported annotations, that agreed across two or more datasets, were presented along with supporting experimental and pro- tein sequence evidence. In this section we aim to analyse the similarly expressed groups within the set of unclassi- fied ORFs in an attempt model unclassified functional modules or at least unclassified parts of known functional modules. As in the previous section we first employ an initial data- set partitioning in which the expression data is split into its annotated genes and unannotated ORFs. Again this allows the class specific gene expression patterns (labelled biclusters) to be used as classifiers. The full process is illus- trated in Figure 3. In this process each annotated bicluster generally labels several similarly expressed ORFs from the unannotated set. Unlike BALBOA we now employ dataset cross-referencing at this stage to discover which ORF groups are maintained across multiple independent data views. Groups of unclassifed ORFs, consistently associ- ated together across independent expression datasets may represent novel functional modules. The nature of these predicted functional modules may then be gauged by ref- erence to the functional enrichment of the annotated biclusters. The largest unclassified ORF groupings retrieved in this manner are presented in Table 3. By far the largest group of unclassified ORFs associated together in the Eisen, Gasch and Hughes expression data- sets was a set of 19 ORFs: YBR271W, YCR016W, YDL063C, YDL167C, YDR361C, YGR187C, YIL064W, YIL096C, YIL110W, YIL127C, YJR003C, YLR051C, YLR196W, YLR287C, YOL022C, YOR021C, YOR154W, YOR252W, YPL183C. As we can see from Figure 4 this set is highly correlated across all three datasets. The nature of this possible functional module may be inferred by exam- ining the functional enrichment of the annotated biclus- ters used to predict this set in each dataset. In all three Page 10 of 15 (page number not for citation purposes)