The hypomethylated loci represented the remaining subset that was ampliﬁed in both channels (HpaII and MspI signal intensities above the levels of background signals). Overlap of each methylated or hypomethylated locus with CpG islands and CG clusters was quantiﬁed using a set of custom PERL scripts, and the results were analyzed by SQL query following their entry into a MySQL database. RESULTS We pursued the hypothesis that there is a subpopulation of sequences in the genome deﬁned solely by their clustering of CG dinucleotides. This clustering is a result of the genome-wide decay of CG dinucleotide content, with preservation of CG density at certain regions. By measuring the distance spanned by a ﬁxed number of CG dinucleotides for every such group genome-wide, we observed that there are two populations of loci with distinctive CG clustering densities (Figure 1a and b). Using the ﬁrst local minimum in the distribution of spanned sequence fragment lengths as the boundary of the short, CG-dense population, we identiﬁed the maximum fragment length for each cluster corresponding to a ﬁxed number of CGs. In analyzing these cutoﬀs, we deﬁned a linear relationship between CG dinucleotide number and the associated maximum fragment length (Figure 1c). The clear diﬀerentiation of CG-dense fragments from the rest of the genome provides a means of mathematically deﬁning CG-dense regions and can therefore be used as a robust foundation for computational genomic annotation. Given a ﬁxed number of CGs, the CG-dense fragments below the maximum fragment length could be identiﬁed and mapped back onto the genome. But, as Figures 2a and b show, each of the ﬁxed number of CGs generates diﬀerent annotations. Using fewer CGs and correspond- ingly smaller fragments, many small CG clusters are identiﬁed, whereas by using a greater number of CGs and correspondingly larger fragments, fewer clusters are identiﬁed, but each extends into large ﬂanking regions of lower CG density. We were able to optimize the criteria when we recognized that at any individual CG-dense locus, a given number of CGs generates multiple overlapping fragments. More CG-dense clusters require a greater number of fragments to span all of the CGs they contain. Accordingly, the more overlapping fragments that repre- sent a given locus, the more likely it is to be signiﬁcantly CG-dense. For each number of CGs, we calculated the number of overlapping fragments per cluster. We obtained a representation of information content for each CG number by summing this total across all loci in the genome and dividing by maximum fragment length. We then determined the optimal number of CGs per fragment using the maximum value obtained (Figure 2c). For the human genome, this optimum corresponds to 27 or more CG dinucleotides in a sequence of no more than 531 bp in length. This new means of identifying CG clusters is neither constrained by (G+C) content nor by the associated observed/expected CG dinucleotide ratio. In Figure 3, we show that the thresholds imposed by Nucleic Acids Research, 2007, Vol. 35, No. 20 6801 Figure 1. The analytical technique used to deﬁne CG clusters. First, a ﬁxed number of CG dinucleotides is chosen, as illustrated in the example using six CGs (a). The ﬁrst CG is identiﬁed in a chromosome, then the sixth, allowing the number of nucleotides between them to be recorded. The second and seventh CGs are then identiﬁed and the distance recorded and so on until the data for the entire genome is collected. When this analysis is performed for the entire human genome using 30 CGs at a time, the resulting lengths can be represented as a frequency histogram as shown in panel (b). Two populations are apparent—the peak on the left with short fragment lengths for this number of CGs, and the remainder of the genome where CGs do not cluster. The maximum fragment length for the clustered CGs is shown as a vertical red line. When the analysis is repeated for 5, 10, 15, . . . 100 CGs genome-wide, diﬀerent maximum fragment lengths for each number of CGs are derived, with a near-linear relationship between these variables as shown in panel (c). The arrowhead refers to the observation made for 30 CGs illustrated in panel (b). even the least stringent original base compositional criteria (2) cause many CG-dense loci in the genome to be missed. However, even though we are annotating the entire sequenced genome, including repetitive DNA, we identify only a small fraction of the 350 000 CpG islands predicted by these old criteria (2) (Table 1). We compared the functional signiﬁcance of CpG islands and CG clusters in two ways—testing their relative