6802 Nucleic Acids Research, 2007, Vol. 35, No. 20 Figure 2. Creating a CG cluster annotation for the human genome. For a given number of CGs, signiﬁcantly CG-dense fragments are deﬁned as being shorter than the maximum fragment length. When these fragments are mapped back to the genome, some loci have multiple overlapping fragments, indicating that they are more likely to be CG-dense. These conglomerations deﬁne a genomic annotation track for each number of CGs used. Fewer CGs per fragment produces an annotation that is highly sensitive to local changes in CG density, deﬁning a large number of small CG clusters, as shown in panel (a). On the other hand, a high number of CGs per fragment deﬁnes fewer CG clusters, but can extend far into ﬂanking CG-poor regions (b). To ﬁnd the intermediate optimum, we calculated the average number of fragments per CG cluster genome-wide. When recalculated relative to maximum fragment length, this measure of information content per CG cluster generated a peak at 27 CGs per fragment (c). This value is associated with a maximum fragment length value from the regression line in Figure 1c of 531 bp. Figure 3. The base compositional characteristics of CG clusters (black) are shown in terms of observed to expected CG dinucleotide densities (O/E CG ratio) on the x-axis with (G+C) content on the y-axis. The dashed lines illustrate the relatively non-stringent thresholds of the original CpG island deﬁnition (3). Any points to the left of the vertical threshold or below the horizontal threshold show how many CG-dense loci would fail to be identiﬁed using base compositional criteria alone. The arrowhead illustrates extremely (A+T)-rich, CG-dense alpha satellite DNA sequences. frequency co-localizing with promoters and with hypo- methylated loci. A major use of the CpG island annota- tion has been to predict the location of transcription start sites in the genome. Approximately 40% (18) to 50% (6) of human promoters have been found to co-localize with CpG islands, while promoters of housekeeping genes have been described to have a near-universal association with CpG islands (18). We cross-correlated our CG clusters and the CpG island locations annotated at the UCSC Genome Browser with transcription start sites of refSeq genes from the same database, ﬁnding CpG islands to overlap 57% of refSeq transcription start sites, 79% of a published list of housekeeping genes (19) and 38% of a published list of tissue-speciﬁc genes (20). In contrast, the proportion of refSeq transcription start sites asso- ciated with CG clusters is substantially higher (68%, an additional 11% or 2701 refSeq transcription start sites), with 45% of the tissue-speciﬁc genes and 91% of housekeeping genes co-localizing with these CG clusters (Figure 4a; Table 1). As the UCSC CpG island annotation is of non-repetitive sequence and our CG cluster annota- tion was generated without this ﬁlter, we were concerned that the comparison was unfairly penalizing the UCSC CpG island annotation, so we tested the relative propor- tions of refSeq promoter overlaps for two other annota- tions, the 350 201 CpG islands that occur in the genome as a whole, and the 31 225 CG clusters that are not deﬁned due to substantial overlap with transposable elements. In Figure 4b, we show that the performance of the CG cluster annotation is stronger for both unﬁltered sequence (positive predictive values of 0.381 compared with 0.049) and non-transposon sequence (0.535 compared with 0.508) in identifying refSeq promoters. Similar patterns are found for the mouse genome (Table 1). Given that