CpG islands are typically located in the 5′ end of genes and considered as gene markers because they play important roles in gene regulation via epigenetic change. In this study, we compared the features of CpG islands identified by several major algorithms by setting the parameter cutoff values in order to obtain a similar number of CpG islands in a genome. This approach allows us to systematically compare the methylation and gene expression patterns in the identified CpG islands. We found that Takai and Jones’ algorithm tends to identify longer CpG islands but with weaker CpG island features (e.g., lower GC content and lower ratio of the observed over expected CpGs) and higher methylation level. Conversely, the CpG clusters identified by Hackenberg et al.’s algorithm using stringent criteria are shorter and have stronger features and lower methylation level. In addition, we used the genome-wide base-resolution methylation profile in two cell lines to show that genes with a lower methylation level at the promoter-associated CpG islands tend to express in more tissues and have stronger expression. Our results validated that the DNA methylation of promoter-associated CpG islands suppresses gene expression at the genome level. 1. Introduction CpG islands (CGIs), which are clusters of CpG dinucleotides in GC-rich regions, are often located in the 5′ end of genes and are considered as gene markers in vertebrate genomes [1–3]. These CpG islands, especially promoter-associated CpG islands, play important roles in gene silencing, genomic imprinting, X-chromosome inactivation, and tumorigenesis [4]. Due to the functional importance of CpG islands in transcriptional regulation and epigenetic modifications [5], multiple algorithms have been developed to identify CpG islands in a genome or a specific sequence. Overall, these algorithms can be classified into two groups: traditional algorithms and new algorithms. Traditional algorithms are based on three features and parameters (length, GC content, and ratio of the observed over the expected CpGs (CpG O/E)) [2, 4, 6, 7], while new algorithms are based on statistical property [8, 9]. Substantial debate exists as to which algorithm performs better and in which context, such as in organisms, tissues, or developmental stages [4, 8, 10–12]. Comparing different features of CpG islands, especially length of the predicted islands [11], our previous study suggested that Takai and Jones’ algorithm is more appropriate overall for identifying promoter-associated islands of CpGs in vertebrate genomes [10]. However, the major
References
[1]
A. P. Bird, “CpG islands as gene markers in the vertebrate nucleus,” Trends in Genetics, vol. 3, no. 12, pp. 342–347, 1987.
[2]
M. Gardiner-Garden and M. Frommer, “CpG islands in vertebrate genomes,” Journal of Molecular Biology, vol. 196, no. 2, pp. 261–282, 1987.
[3]
F. Larsen, G. Gundersen, R. Lopez, and H. Prydz, “CpG islands as gene markers in the human genome,” Genomics, vol. 13, no. 4, pp. 1095–1107, 1992.
[4]
D. Takai and P. A. Jones, “Comprehensive analysis of CpG islands in human chromosomes 21 and 22,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 6, pp. 3740–3745, 2002.
[5]
F. Antequera, “Structure, function and evolution of CpG island promoters,” Cellular and Molecular Life Sciences, vol. 60, no. 8, pp. 1647–1658, 2003.
[6]
L. Ponger and D. Mouchiroud, “CpGProD: identifying CpG islands associated with transcription start sites in large genomic mammalian sequences,” Bioinformatics, vol. 18, no. 4, pp. 631–633, 2002.
[7]
Y. Wang and F. C. C. Leung, “An evaluation of new criteria for CpG islands in the human genome as gene markers,” Bioinformatics, vol. 20, no. 7, pp. 1170–1177, 2004.
[8]
M. Hackenberg, C. Previti, P. L. Luque-Escamilla, P. Carpena, J. Martínez-Aroza, and J. L. Oliver, “CpGcluster: a distance-based algorithm for CpG-island detection,” BMC Bioinformatics, vol. 7, article 446, 2006.
[9]
J. L. Glass, R. F. Thompson, B. Khulan et al., “CG dinucleotide clustering is a species-specific property of the genome,” Nucleic Acids Research, vol. 35, no. 20, pp. 6798–6807, 2007.
[10]
L. Han and Z. Zhao, “CpG islands or CpG clusters: how to identify functional GC-rich regions in a genome?” BMC Bioinformatics, vol. 10, article 65, 2009.
[11]
M. Hackenberg, G. Barturen, P. Carpena, P. L. Luque-Escamilla, C. Previti, and J. L. Oliver, “Prediction of CpG-island function: CpG clustering vs. sliding-window methods,” BMC Genomics, vol. 11, no. 1, article 327, 2010.
[12]
Z. Zhao and L. Han, “CpG islands: algorithms and applications in methylation studies,” Biochemical and Biophysical Research Communications, vol. 382, no. 4, pp. 643–645, 2009.
[13]
L. Han, B. Su, W. H. Li, and Z. Zhao, “CpG island density and its correlations with genomic features in mammalian genomes,” Genome Biology, vol. 9, no. 5, article R79, 2008.
[14]
L. Han and Z. Zhao, “Comparative analysis of CpG Islands in four fish genomes,” Comparative and Functional Genomics, vol. 2008, Article ID 565631, 6 pages, 2008.
[15]
A. Bird, “DNA methylation patterns and epigenetic memory,” Genes and Development, vol. 16, no. 1, pp. 6–21, 2002.
[16]
B. W. Futscher, M. M. Oshiro, R. J. Wozniak et al., “Role for DNA methylation in the control of cell type-specific maspin expression,” Nature Genetics, vol. 31, no. 2, pp. 175–179, 2002.
[17]
F. Song, J. F. Smith, M. T. Kimura et al., “Association of tissue-specific differentially methylated regions (TDMs) with differential gene expression,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 9, pp. 3336–3341, 2005.
[18]
M. Weber, I. Hellmann, M. B. Stadler et al., “Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome,” Nature Genetics, vol. 39, no. 4, pp. 457–466, 2007.
[19]
R. Illingworth, A. Kerr, D. Desousa et al., “A novel CpG island set identifies tissue-specific methylation at developmental gene loci,” PLoS Biology, vol. 6, no. 1, article e22, 2008.
[20]
M. Esteller, “The necessity of a human epigenome project,” Carcinogenesis, vol. 27, no. 6, pp. 1121–1125, 2006.
[21]
A. F. Fernandez, C. Rosales, P. Lopez-Nieva et al., “The dynamic DNA methylomes of double-stranded DNA viruses associated with human cancer,” Genome Research, vol. 19, no. 3, pp. 438–451, 2009.
[22]
S. Kangaspeska, B. Stride, R. Métivier et al., “Transient cyclical methylation of promoter DNA,” Nature, vol. 452, no. 7183, pp. 112–115, 2008.
[23]
R. Métivier, R. Gallais, C. Tiffoche et al., “Cyclical DNA methylation of a transcriptionally active promoter,” Nature, vol. 452, no. 7183, pp. 45–50, 2008.
[24]
S. Beck and V. K. Rakyan, “The methylome: approaches for global DNA methylation profiling,” Trends in Genetics, vol. 24, no. 5, pp. 231–237, 2008.
[25]
R. Lister, M. Pelizzola, R. H. Dowen et al., “Human DNA methylomes at base resolution show widespread epigenomic differences,” Nature, vol. 462, no. 7271, pp. 315–322, 2009.
[26]
Z. Su, L. Han, and Z. Zhao, “Conservation and divergence of DNA methylation in eukaryotes: new insights from single base-resolution DNA methylomes,” Epigenetics, vol. 6, no. 2, pp. 134–140, 2011.
[27]
C. Bock, E. Kiskinis, G. Verstappen et al., “Reference maps of human es and ips cell variation enable high-throughput characterization of pluripotent cell lines,” Cell, vol. 144, no. 3, pp. 439–452, 2011.
[28]
A. I. Su, T. Wiltshire, S. Batalov et al., “A gene atlas of the mouse and human protein-encoding transcriptomes,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 16, pp. 6062–6067, 2004.
[29]
J. Yang, A. I. Su, and W. H. Li, “Gene expression evolves faster in narrowly than in broadly expressed mammalian genes,” Molecular Biology and Evolution, vol. 22, no. 10, pp. 2113–2118, 2005.
[30]
C. Jiang, L. Han, B. Su, W. H. Li, and Z. Zhao, “Features and trend of loss of promoter-associated CpG islands in the human and mouse genomes,” Molecular Biology and Evolution, vol. 24, no. 9, pp. 1991–2000, 2007.
[31]
L. Han and Z. Zhao, “Contrast features of CpG islands in the promoter and other regions in the dog genome,” Genomics, vol. 94, no. 2, pp. 117–124, 2009.
[32]
E. S. Lander, L. M. Linton, B. Birren et al., “Initial sequencing and analysis of the human genome,” Nature, vol. 409, no. 6822, pp. 860–921, 2001.
[33]
C. Bock, J. Walter, M. Paulsen, and T. Lengauer, “Inter-individual variation of DNA methylation and its implications for large-scale epigenome mapping,” Nucleic Acids Research, vol. 36, no. 10, article e55, 2008.
[34]
M. Weber, J. J. Davies, D. Wittig et al., “Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells,” Nature Genetics, vol. 37, no. 8, pp. 853–862, 2005.
[35]
S. Fan and X. Zhang, “CpG island methylation pattern in different human tissues and its correlation with gene expression,” Biochemical and Biophysical Research Communications, vol. 383, no. 4, pp. 421–425, 2009.
