human genome (Table 1), many of which are in repetitive sequences. Recognizing this problem, other groups have modiﬁed the original base compositional criteria (8) or the analytical approach used (9) in order to increase the stringency for CpG islands identiﬁcation, greatly reducing the number of repetitive sequences annotated while preserving most CpG islands located at promoters. However, the more common approach, used by genome browsers such as that at UCSC (http://genome.ucsc.edu/) (10), is simply to remove all repetitive sequence prior to annotating CpG islands. CpG island annotations are meant to identify constitu- tively unmethylated sites in the genome. However, the traditional CpG island criteria mostly identify repetitive sequences, as we show in Table 1, and these repeats are generally highly methylated (11). Furthermore, when we (12) and others (13) performed high-throughput cytosine methylation studies, even the annotated unique sequence CpG islands were subject to methylation at non-imprinted autosomal loci from normal tissues. It follows that the original base compositional criteria by themselves are not suﬃcient to predict methylation status. Rather than modify existing base compositional criteria further, we decided to focus on the single characteristic of CG dinucleotides in which we had conﬁdence: that they cluster at certain loci. We sought to identify whether such loci form a distinctive population within the genome as a whole. This approach allowed us to develop a new means of deﬁning what we call CG clusters, and for the ﬁrst time allows a species-speciﬁc deﬁnition that reveals the pattern of preservation of CGs to be genome speciﬁc and more conserved at orthologous loci than previously recognized. As CpG islands have been used as fundamental predictors of functionally important sites such as promoters (6), and we show that the CG cluster annotation has a substantially better positive predictive value for annotated Nucleic Acids Research, 2007, Vol. 35, No. 20 6799 transcription start sites than do CpG islands, it is likely that prior bioinformatic studies based on using CpG islands will be greatly improved by the use of CG clusters instead. We also show that the potential utility of CG clusters extends beyond sequence analysis alone, with demonstration of epigenetic predictive capacity, identify- ing substantially more hypomethylated sites than CpG islands in human CD34+ and embryonic stem cells. Because CpG islands are used as a basis for microarray studies of methylation changes, particularly in cancer (14), the use of CG clusters is likely to improve the sensitivity of such studies. MATERIALS AND METHODS CG cluster generation The CG cluster annotation was generated using a set of custom PERL, R (http://www.r-project.org/) and shell scripts (available at http://greallylab.aecom.yu.edu/ cgClusters/). Initially, the locations of every CG dinucleo- tide in the human genome were extracted from raw genomic DNA sequences (human May 2004 assembly hg17, http://genome.ucsc.edu/). Using these positions, every overlapping sequence fragment Sn ¼ fSn1,Sn2, . . .g containing a ﬁxed number of CGs (n = 5,10, . . . 100) and having variable length was identiﬁed. For each number n of CGs, the frequency of each fragment length was recorded and the distribution of fragment lengths was examined using the R statistical package for the presence of a short, CG-dense population Cn ¼ fSin lengthðSinÞ4 ng distinct from the longer frag- ments Cn0 ¼ fSin lengthðSinÞ4 ng. The threshold for each CG number n (maximum fragment length) was deﬁned to be the location of the local minimum in the frag- ment length histogram, estimated by identifying zero Table 1. The total numbers of CpG islands and CG clusters in human and mouse, using unmasked sequence that contains repetitive elements, the UCSC CpG island track (generated from sequence in which transposons have been masked) and CG clusters excluding those with 427 CGs derived from unique sequence (24 for mouse, the minimum number needed to deﬁne a CG cluster in each species). CpG islands (original deﬁnitiona) CpG islands (UCSC annotationb) CG clusters CG clusters (non-transposon)d Humanb Mousec Human Mouse Human Mouse Human Mouse All 350 201 165 379 27 801 15 974 44 165 42 971 31 225 21 587 Sequence feature conserved at orthologous locus 42 445 (12.1%) 47 139 (28.5%) 14 452 (52.0%) 14 057 (88.0%) 19 410 (44.0%) 18 970 (44.2%) 19 071 (61.1%) 17 614 (81.6%) Overlap with refSeq transcription start site 17 209 (4.9%) 11 822 (7.1%) 14 121 (50.8%) 9114 (57.1%) 16 822 (38.1%) 11 859 (27.6%) 16 690 (53.5%) 11 677 (54.1%) The exclusion of transposon-derived CG clusters creates an annotation that is comparable to the UCSC CpG island annotations. We show the numbers and percentages of total for each sequence feature in terms of conservation of that sequence feature at the orthologous locus in the other species. We also quantify the numbers and proportions overlapping refSeq gene transcription start sites in each genome. Comparison of annotation performance in unmasked sequence shows CpG islands to suﬀer from excessive non-speciﬁcity, while the performance for non-repetitive sequences shows comparable proportional but quantitatively greater identiﬁcation of conserved CG-dense regions or refSeq promoters using the CG cluster annotation in both human and mouse. aUsing cpgi130 program (4) (http:// http://cpgislands.usc.edu/) using parameters (G+C) 50.50, O/E CpG 50.60, window size 5200 bp. bUsing hg17 assembly at the UCSC genome browser (http://genome.ucsc.edu/) (11). cUsing mm7 assembly at the UCSC genome browser. dRemoving CG clusters for which 427 CGs are contributed by unique sequence (24 CGs for mouse).