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Figure S1 A Temperatures Proliferation cluster (G12) The proliferation cluster is a stable one. A dendrogram depicting results of cluster analysis of all varying genes on the array using the Super paramagnetic Clustering (SPC) algorithm. The proliferation cluster is shown with red arrow, it constitute a well-defined, stable cluster that is intact across a range of “temperatures” Figure S1 B The proliferation cluster is a well defined entity that is clearly separable from the rest of the genes Right – Expression matrix of 200 varying genes that are sorted according to expression using the SPIN algorithm (Ref) such that genes in adjacent rows have similar expression profiles; Left – a distance matrix between the genes using the same order as in the right expression matrix on the right. The proliferation cluster genes are marked by the red box and are shown to constitute once cluster that does not break down naturally into sub-clusters Figure S1 C Expression Distance matrix The proliferation cluster has an elongated shape Left – Expression matrix of the proliferation cluster genes sorted according to SPIN (Ref); Right – a distance matrix between the genes using the same order as in the right expression matrix on the right. The proliferation cluster is shown to have an “elongated shape”, i.e. each gene is close to its neighbor on the sorted cluster, yet the genes at the two ends are relatively remote. (see Bioinformatics. 2005 21(10):2301-8 for typical distance matrices of elongated clouds.) Figure S1 D Principal Component Scatter Plot 0.8 Second Principal Component 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -0.8 -0.6 -0.4 -0.2 0 0.2 First Principal Component 0.4 0.6 0.8 The proliferation cluster has an elongated shape and it does not break-down naturally into sub-clusters Principal component analysis of expression profiles of the genes in the proliferation cluster. This analysis too demonstrates that the proliferation cluster is an elongated (rather than spherical for example) entity that does not break down naturally into sub-clusters Figure S2 4 3 Gene Expression 2 1 0 Disrtribution of cell cycle periodicity Proportion -1 -2 -3 G2/M G1/S CHR 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 All genes Proliferation genes 1 2 3 4 -4 5 10 15 The proliferation cluster genes are cell cycle periodic 20 25 Samples 30 35 5 6 CCP score 40 7 8 9 10 45 Expression profiles of 53 proliferation cluster genes with cell cycle periodicity (CCP) index > 3 during three cell cycles of HeLa cells (Whitfield et al. 2002) (another 45 proliferation cluster genes for which expression data exists in the cell cycle experiment had CCP<3 (not shown). In black are colored G2/M-phase peaking genes, in blue are G1-S peaking genes, in green are genes that contain the CHR motif in their promoters (see below). The inset shows the distribution of the CCP index (Tavazoie et al. 1999) calculated using Fast Fourier Transform (fft) function of Matlab, for the genes in the proliferation cluster (black) and for the rest of the genome (red), shaded is the area below the black curve that correspond to CCP>3. Only 6.7% of the genes in the genome have such CCP value or higher. Figure S3 A 45 40 Real orthologs Random pairs 35 30 25 20 15 10 5 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Orthologous human and mouse proliferation cluster genes preserve the composition of the regulatory motifs in their promoters. Distribution of motif compositional similarity of orthologous human – mouse promoters from the proliferation cluster, and random human – mouse gene pairs. Given the five motifs in the present analysis (E2F, NFY, CHR, CDE and ELK1), each promoter was characterized by a 5long binary signature indicating the presence or absence of the motifs in it. The similarity between a pair of promoters was defined as the number of motifs that are in the two promoters divided by the number of motifs in the promoter with the maximal number of motifs. The (blue) distribution for real orthologous pairs is shifted relative to the high similarity values compared to the (red) random pairs distribution. V$E2F.03: distance distrebution 60 B C 40 ELK1: distance distribution 30 NFY.01: distance distribution 100 E D 20 50 0 1000 1000 0 -1000 -500 0 0 -1000 500 500 0 500 human location 1000 500 0 0 500 1000 human location 500 0 0 -500 1000 1000 mouse location mouse location 1000 0 mouse location 0 -1000 mouse location 2 10 20 0 CHR: distance distribution 4 1000 500 500 1000 human location F Orthologous human and mouse proliferation cluster genes preserve the location of the regulatory motifs relative to the TSS. 0 500 1000 human location CDE.01: distance distribution 80 60 40 20 0 -1000 0 1000 1000 mouse location For the motifs E2F, ELK1, NFY, CHR, and CDE (B through F respectively) we show in the upper panel a distribution of distances (in bps) between the distance of the motif from the TSS of human and its mouse ortholog. The lower panel shows for each motif a scatter plot in which each gene that contains the motif in both species is a dot whose x-axis depicts the distance of the motif from the human TSS, while the y-axis depicts the 500 1000 0 0 0 500 0 0 500 1000 human location 0.4 Figure S4 points point 7 0.3 0.2 p16 + p21 sum gate p16+p21 sum gate 0.1 0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 200 300 400 500 mean chip expression 600 700 800 proliferation cluster genes mean chip expression The negative correlation between p21 + p16 and the proliferation cluster is obeyed by all time points Scatter plot depicting the negative correlation between the summated profile of (normalized) p16 and p21 levels and the averaged expression profile of the proliferation cluster. Each time point is shown as a dot (two duplicates are shown); time point 7, which had among the proliferation cluster genes a lower expression profiles compared to its neighboring time points, is show in red. Clearly time point 7 (and all the other points) obey the same trend of negative correlation Figure S5 ELK1 -Log(p-value) -Log(p-value) NFY.01 -score -score An annotation-based procedure to determine a threshold score in assigning genes to a PSSM Alternative assignments of genes to a PSSM, each corresponding to a different threshold score of the basic assignment algorithm (Elkon et al., 2003), are examined. At each assignment, that corresponds to a (typically different) set of genes, we calculate a hypergeometric p-value on the hypothesis that the genes assigned to the motif are enriched in the cluster. The threshold score that is chosen is that one that minimizes such p-value. A Bonferroni test is then applied to ensure that p-value is corrected for multiple hypotheses tested (number of motifs x number of thresholds tested per motif). Show here for example are the case of ELK1 and NYF. 0.15 Figure S6 Normalized expression 0.1 0.05 0 -0.05 -0.1 -0.15 -0.2 1 2 3 4 5 6 7 8 9 10 11 12 Samples Only genes that contain the CHR motif in its preferred strand and location display the proliferation signature The expression profiles (same sample order as in Fig. 1B) of all CHR-containing genes in the genome, partitioned into 20 categories that correspond to the distance of CHR from the TSS Samples and the strand in which the motif occurs. The 20 categories correspond to either coding or noncoding strand and to one of 100 bp-long non-overlapping sequence windows that span the entire 1000 bp-long promoters. Genes belonging to each category were grouped together and their mean expression levels are represented as blue lines. The genes that contain the CHR motif on the coding strand in the first 100-bp window are shown in red.