points. Time points still in the linear range were considered for calculation of the activation strength. bGal activity ¼ OD405ðtÞ OD405i OD600 · tðminÞ : Finally, the mean bGal activity of a randomly chosen set of genes not found to possess auto-activation properties was sub- tracted. This assay was done in triplicates and mean and SEM were calculated for each activator (Supplementary Table 1). Data sources and analysis General yeast protein properties, e.g. molecular mass and pI, protein annotations and gene ontology (GO) classiﬁcation were downloaded from SGD (32) as of June 2005. Protein localization data were from Huh et al. (33), which was assumed to be less biased compared with curated data (e.g. GO com- ponent data). The abundance of proteins was compared using the genome-wide measurements by Ghaemmaghami et al. (34). Protein interaction data were downloaded from the MIPS database (35). Overrepresentation of certain GO terms was assessed using the program FuncAssociate (36). For in detail analysis of known TR function and deﬁnition of the set of known TRs the GO slim term ‘transcription regulator activity (F-GO 30528)’ was used. Table 1 summarizes the protein sets that were used for analysis. Proteins annotated with the F-GO term ‘Transcriptional activator activity’ (TermID 16563) from the YPD database (Biobase, Germany) served as an additional reference set (37). Physicochemical properties General physicochemical properties, e.g. molecular mass and isoelectric point, were taken directly from the datasets of the SGD (32). Calculation of amino acid groups and segments/clusters with speciﬁc physicochemical properties, e.g. charge, were done employing the SAPS program (38). Frequencies of amino acids were directly calculated from SGD sequence data. The minimal and maximal pI of a protein was calculated as the lowest and highest pI of a 20 amino acid window, respectively. Amino acid clusters in a protein were deﬁned as the maximal count of a speciﬁc amino acid in a 20 amino acid window of the protein. GRAVY (grand average of Hydropathicity) values were calculated as the sum of hydropathy values for all of the amino acids, divided by the number of residues in the sequence (39). Aromaticity is the Table 1. Protein sets Protein set All yeast Non acts TR All acts Nucleus Acts nucleus LTHw, LTHm, LTHs TR+1, nucleus+1, acts nucleus+1 Description All yeast proteins All yeast proteins not in the activator list Known transcriptional activators/regulators (GO slim term ‘transcription activator activity’) All Y2H activators Nuclear proteins [Huh et al. (33)] Y2H activators localized to the nucleus Weak (LTHw), medium (LTHm), and strong (LTHs) activators in LTH assay Protein sets as indicated above but including directly interacting proteins in the analysis of binary protein interactions. Nucleic Acids Research, 2006, Vol. 34, No. 3 957 relative frequency of aromatic amino acids (39) and the codon adaptation index (CAI) is an empirical measure of synonym- ous codon usage bias, which is positively correlated with the expression level of genes (40) (see also ‘http://www. yeastgenome.org/help/protein_page.html’). Protein domains The occurrence of protein domains in the set of Y2H activators was assessed using the SMART domain database (8). The number of proteins with each domain in the Y2H activator set and the whole genome was counted and the enrichment in the former set was calculated. Signiﬁcance was tested using Fisher’s exact test and Holm’s procedure for multiple testing correction (41). Protein interactions A protein–protein interaction map of transcription activators was generated using Cytoscape (42). In the Cytoscape map only physical binary protein interactions from the MIPS data- base were considered. Only activators and their direct inter- action partners, bridging at least two activators, were selected. The percentage of activators that interacted with other transcription factors (i.e. proteins with the GO slim term ‘tran- scription [P-GO 6350])’ was calculated using the set of physical binary protein interactions from the MIPS database. Similarly, we counted with how many transcription com- plexes our activators interacted. A ‘transcription complex’ was deﬁned as a protein complex with at least 50% of the proteins annotated as being involved in transcription. The protein interactions with these complexes were assessed by using the MIPS data of protein complexes ﬁltered to contain only high-throughput data (for reduction of bias of well- characterized proteins). Ranking of interaction partners was done by counting with how many proteins of a speciﬁc protein set a given yeast protein interacted (using high-throughput protein com- plex data from the MIPS database). Statistical analysis Data processing was done with PERL (www.perl.org) and statistical calculations with R (43). Correction for multiple testing was done with Holm’s procedure using the multtest package of R (41,44). RESULTS Y2H activators and their activation strength About 450 Y2H activators were identiﬁed previously in two genome-wide Y2H screens (Supplementary Table 1) (26,27). However, these proteins were not listed in the original pub- lication nor did these studies provide any quantitative data for the activation strength. Therefore, we measured the activation strength of the activators using two different reporter genes whose expression depends on their level of transcription: His3 and b-galactosidase (bGal assay) (Figure 1, see also Materials and Methods and Supplementary Table 1). According to the activation strength in the LTH assay the activators were grouped into weak (3–25 mM 3-AT), medium (50–200 mM 3-AT) and strong (>200 mM 3-AT) activators. These groups