CpG islands or CpG clusters: how to identify functional GC-rich regions in a genome?

Abstract

Background: CpG islands (CGIs), clusters of CpG dinucleotides in GC-rich regions, are often located in the 5' end of genes and considered gene markers. Hackenberg et al. (2006) recently developed a new algorithm, CpGcluster, which uses a completely different mathematical approach from previous traditional algorithms. Their evaluation suggests that CpGcluster provides a much more efficient approach to detecting functional clusters or islands of CpGs.

Results: We systematically compared CpGcluster with the traditional algorithm by Takai and Jones (2002). Our comparisons of (1) the number of islands versus the number of genes in a genome, (2) the distribution of islands in different genomic regions, (3) island length, (4) the distance between two neighboring islands, and (5) methylation status suggest that Takai and Jones' algorithm is overall more appropriate for identifying promoter-associated islands of CpGs in vertebrate genomes.

Conclusion: The generation of genome sequence and DNA methylation data is expected to accelerate greatly. The information in this study is important for its extensive utility in gene feature analysis and epigenomics including gene prediction and methylation chip design in different genomes.

Figure 1

Length distribution of CGIs or CGCs in the human genome. (A) CGIs versus CGCs. (B) For CGCs, promoter regions versus intergenic regions.

Figure 2

Multiple short CGCs embedded in one CGI in the promoter region. Dark box: CGCs identified by CpGcluster. Grey box: CGI identified by Takai and Jones' algorithm. The length of each CGC is labeled below the dark box and the distance between two neighboring CGCs is above the line. The transcription start site (TSS) is marked by an arrow. (A) CAP1. (B) ADAM33.

Figure 3

Distribution of distance between two neighbouring CGCs in the promoter region of a gene.