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CpG island density and its correlations with genomic features in mammalian genomes
Leng Han, Bing Su, Wen-Hsiung Li, Zhongming Zhao
Genome Biology , 2008, DOI: 10.1186/gb-2008-9-5-r79
Abstract: In this study, we performed a systematic analysis of CpG islands in ten mammalian genomes. We found that both the number of CpG islands and their density vary greatly among genomes, though many of these genomes encode similar numbers of genes. We observed significant correlations between CpG island density and genomic features such as number of chromosomes, chromosome size, and recombination rate. We also observed a trend of higher CpG island density in telomeric regions. Furthermore, we evaluated the performance of three computational algorithms for CpG island identifications. Finally, we compared our observations in mammals to other non-mammal vertebrates.Our study revealed that CpG islands vary greatly among mammalian genomes. Some factors such as recombination rate and chromosome size might have influenced the evolution of CpG islands in the course of mammalian evolution. Our results suggest a scenario in which an increase in chromosome number increases the rate of recombination, which in turn elevates GC content to help prevent loss of CpG islands and maintain their density. These findings should be useful for studying mammalian genomes, the role of CpG islands in gene function, and molecular evolution.CpG islands (CGIs) are clusters of CpG dinucleotides in GC-rich regions and represent an important feature of mammalian genomes [1]. Mammalian genomic DNA generally shows a great deficit of CpG dinucleotides, for example, the ratio of the observed over the expected CpGs (ObsCpG/ExpCpG) is approximately 0.20-0.25 in the human and mouse genomes [2-4]. This deficit is largely attributed to the hypermutability of methylated CpGs to TpGs (or CpAs in the complementary strand) [5,6]. In comparison, CpGs in CGIs are often unmethylated and their frequencies are close to random expectation (for example, ObsCpG/ExpCpG = ~0.8 in the promoter-associated CGIs [7]). CGIs are often associated with the 5' end of genes and considered as gene markers [8,9]. However, a comparison of
CpG Island Mapping by Epigenome Prediction  [PDF]
Christoph Bock ,J?rn Walter,Martina Paulsen,Thomas Lengauer
PLOS Computational Biology , 2007, DOI: 10.1371/journal.pcbi.0030110
Abstract: CpG islands were originally identified by epigenetic and functional properties, namely, absence of DNA methylation and frequent promoter association. However, this concept was quickly replaced by simple DNA sequence criteria, which allowed for genome-wide annotation of CpG islands in the absence of large-scale epigenetic datasets. Although widely used, the current CpG island criteria incur significant disadvantages: (1) reliance on arbitrary threshold parameters that bear little biological justification, (2) failure to account for widespread heterogeneity among CpG islands, and (3) apparent lack of specificity when applied to the human genome. This study is driven by the idea that a quantitative score of “CpG island strength” that incorporates epigenetic and functional aspects can help resolve these issues. We construct an epigenome prediction pipeline that links the DNA sequence of CpG islands to their epigenetic states, including DNA methylation, histone modifications, and chromatin accessibility. By training support vector machines on epigenetic data for CpG islands on human Chromosomes 21 and 22, we identify informative DNA attributes that correlate with open versus compact chromatin structures. These DNA attributes are used to predict the epigenetic states of all CpG islands genome-wide. Combining predictions for multiple epigenetic features, we estimate the inherent CpG island strength for each CpG island in the human genome, i.e., its inherent tendency to exhibit an open and transcriptionally competent chromatin structure. We extensively validate our results on independent datasets, showing that the CpG island strength predictions are applicable and informative across different tissues and cell types, and we derive improved maps of predicted “bona fide” CpG islands. The mapping of CpG islands by epigenome prediction is conceptually superior to identifying CpG islands by widely used sequence criteria since it links CpG island detection to their characteristic epigenetic and functional states. And it is superior to purely experimental epigenome mapping for CpG island detection since it abstracts from specific properties that are limited to a single cell type or tissue. In addition, using computational epigenetics methods we could identify high correlation between the epigenome and characteristics of the DNA sequence, a finding which emphasizes the need for a better understanding of the mechanistic links between genome and epigenome.
Codon Usage Domains over Bacterial Chromosomes  [PDF]
Marc Bailly-Bechet,Antoine Danchin,Mudassar Iqbal,Matteo Marsili,Massimo Vergassola
PLOS Computational Biology , 2006, DOI: 10.1371/journal.pcbi.0020037
Abstract: The geography of codon bias distributions over prokaryotic genomes and its impact upon chromosomal organization are analyzed. To this aim, we introduce a clustering method based on information theory, specifically designed to cluster genes according to their codon usage and apply it to the coding sequences of Escherichia coli and Bacillus subtilis. One of the clusters identified in each of the organisms is found to be related to expression levels, as expected, but other groups feature an over-representation of genes belonging to different functional groups, namely horizontally transferred genes, motility, and intermediary metabolism. Furthermore, we show that genes with a similar bias tend to be close to each other on the chromosome and organized in coherent domains, more extended than operons, demonstrating a role of translation in structuring bacterial chromosomes. It is argued that a sizeable contribution to this effect comes from the dynamical compartimentalization induced by the recycling of tRNAs, leading to gene expression rates dependent on their genomic and expression context.
Particle Swarm Optimization with Reinforcement Learning for the Prediction of CpG Islands in the Human Genome  [PDF]
Li-Yeh Chuang, Hsiu-Chen Huang, Ming-Cheng Lin, Cheng-Hong Yang
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0021036
Abstract: Background Regions with abundant GC nucleotides, a high CpG number, and a length greater than 200 bp in a genome are often referred to as CpG islands. These islands are usually located in the 5′ end of genes. Recently, several algorithms for the prediction of CpG islands have been proposed. Methodology/Principal Findings We propose here a new method called CPSORL to predict CpG islands, which consists of a complement particle swarm optimization algorithm combined with reinforcement learning to predict CpG islands more reliably. Several CpG island prediction tools equipped with the sliding window technique have been developed previously. However, the quality of the results seems to rely too much on the choices that are made for the window sizes, and thus these methods leave room for improvement. Conclusions/Significance Experimental results indicate that CPSORL provides results of a higher sensitivity and a higher correlation coefficient in all selected experimental contigs than the other methods it was compared to (CpGIS, CpGcluster, CpGProd and CpGPlot). A higher number of CpG islands were identified in chromosomes 21 and 22 of the human genome than with the other methods from the literature. CPSORL also achieved the highest coverage rate (3.4%). CPSORL is an application for identifying promoter and TSS regions associated with CpG islands in entire human genomic. When compared to CpGcluster, the islands predicted by CPSORL covered a larger region in the TSS (12.2%) and promoter (26.1%) region. If Alu sequences are considered, the islands predicted by CPSORL (Alu) covered a larger TSS (40.5%) and promoter (67.8%) region than CpGIS. Furthermore, CPSORL was used to verify that the average methylation density was 5.33% for CpG islands in the entire human genome.
A novel method to quantify local CpG methylation density by regional methylation elongation assay on microarray
Dingdong Zhang, Yan Wang, Yunfei Bai, Qinyu Ge, Yingjuan Qiao, Junfeng Luo, Chao Jia, Zuhong Lu
BMC Genomics , 2008, DOI: 10.1186/1471-2164-9-59
Abstract: We have developed a novel approach for quantitative analysis of CpG methylation density on the basis of microarray-based hybridization and incorporation of Cy5-dCTP into the Cy3 labeled target DNA by using Taq DNA Polymerase on microarray. The quantification is achieved by measuring Cy5/Cy3 signal ratio which is proportional to methylation density. This methylation-sensitive technique, termed RMEAM (regional methylation elongation assay on microarray), provides several advantages over existing methods used for methylation analysis. It can determine an exact methylation density of the given region, and has potential of high throughput. We demonstrate a use of this method in determining the methylation density of the promoter region of the tumor-related gene MLH1, TERT and MGMT in colorectal carcinoma patients.This technique allows for quantitative analysis of regional methylation density, which is the representative of all allelic methylation patterns in the sample. The results show that this technique has the characteristics of simplicity, rapidness, specificity and high-throughput.In the human genome, GC-rich DNA sequences are found frequently within the promoter and first exon of ~50% of all genes [1]. These sequences, also known as CpG islands, can be targets of DNA methylation. An epigenetic phenomenon is known to be associated with genomic imprinting and X-chromosome inactivation, and essential for normal mammalian development [2]. Both global hypomethylation and regional hypermethylation have been described in human tumor cell lines and a wide spectrum of cancers [3]. Global hypomethylation has been associated with instability of chromosomal or microsatellite, while regional hypermethylation of CpG islands within promoter region of tumor suppressor genes is associated with transcriptional inactivation and represents an important mechanism of gene silencing in the pathogenesis of neoplasia [4,5]. There is emerging evidence that each tumor may harbor multiple ge
CpG content affects gene silencing in mice: evidence from novel transgenes
Christine Chevalier-Mariette, Isabelle Henry, Lucile Montfort, Suzanne Capgras, Sylvie Forlani, John Muschler, Jean-Fran?ois Nicolas
Genome Biology , 2003, DOI: 10.1186/gb-2003-4-9-r53
Abstract: We describe a novel method for analyzing epigenetic controls that allows direct testing of CpGs involvement by using LacZ reporter genes with a CpG content varying from high to zero that are combined with a CpG island-containing promoter of a widely expressed gene - the α-subunit of the translation elongation factor 1. Our data revealed that a LacZ transgene with null CpG content abolished the strong transgene repression observed in the somatic tissues of transgenic lines with higher CpG content. Investigation of transgene expression and methylation patterns suggests that during de novo methylation of the genome the CpG island-containing promoter escapes methylation only when combined with the CpG-null transgene. In the other transgenic lines, methylation of the promoter may have led to transcriptional silencing.We demonstrate that the density of CpG sequences in the transcribed regions of transgenes can have a causal role in repression of transcription. These results show that the mechanism by which CpG islands escape de novo methylation is sensitive to CpG density of adjacent sequences. These findings are of importance for the design of transgenes for controlled expression.Methylation of cytosine residues of the CpG dinucleotides of DNA constitute the basis of an epigenetic control of gene expression in vertebrate animals [1,2]. Genomic methylation patterns are of critical importance in various biological processes such as silencing of parasitic elements, development, tumorigenesis and genomic imprinting [3-5]. In the genome, CpGs are not uniformly distributed: the average level is only 1% and there are about 30,000 short regions rich in CpGs [6]. Frequently, the promoters of widely expressed genes are included in a CpG island whereas promoters of strictly expressed genes generally are not [7]. In mammals, DNA methylation patterns are established by two distinctive DNA cytosine methyltransferases, Dnmt3a and Dnmt3b, during development after implantation [8]. DNA m
Nucleosomes Correlate with In Vivo Progression Pattern of De Novo Methylation of p16 CpG Islands in Human Gastric Carcinogenesis  [PDF]
Zhe-Ming Lu, Jing Zhou, Xiuhong Wang, Zhenpo Guan, Hua Bai, Zhao-Jun Liu, Na Su, Kaifeng Pan, Jiafu Ji, Dajun Deng
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0035928
Abstract: Background The exact relationship between nucleosome positioning and methylation of CpG islands in human pathogenesis is unknown. Methodology/Principal Findings In the present study, we characterized the nucleosome position within the p16 CpG island and established a seeding methylation-specific PCR (sMSP) assay based on bisulfite modification to enrich the p16 alleles containing methylated-CpG at the methylation “seeding" sites within its intron-1 in gastric carcinogenesis. The sMSP-positive rate in primary gastric carcinoma (GC) samples (36/40) was significantly higher than that observed in gastritis (19/45) or normal samples (7/13) (P<0.01). Extensive clone sequencing of these sMSP products showed that the density of methylated-CpGs in p16 CpG islands increased gradually along with the severity of pathological changes in gastric tissues. In gastritis lesions the methylation was frequently observed in the region corresponding to the exon-1 coding-nucleosome and the 5′UTR-nucleosome; the methylation was further extended to the region corresponding to the promoter-nucleosome in GC samples. Only few methylated-CpG sites were randomly detected within p16 CpG islands in normal tissues. The significantly inversed relationship between the p16 exon-1 methylation and its transcription was observed in GC samples. An exact p16 promoter-specific 83 bp-MSP assay confirms the result of sMSP (33/55 vs. 1/6, P<0.01). In addition, p16 methylation in chronic gastritis lesions significantly correlated with H. pylori infection; however, such correlation was not observed in GC specimens. Conclusions/Significance It was determined that de novo methylation was initiated in the coding region of p16 exon-1 in gastritis, then progressed to its 5′UTR, and ultimately to the proximal promoter in GCs. Nucleosomes may function as the basic extension/progression unit of de novo methylation of p16 CpG islands in vivo.
Predicted methylation landscape of all CpG islands on the human genome
ShiCai Fan,JianXiao Zou,HongBing Xu,XueGong Zhang
Chinese Science Bulletin , 2010, DOI: 10.1007/s11434-009-3731-1
Abstract: CpG island methylation plays important role in various biological processes. To investigate methylation landscape of all CpG islands on the human genome, we develop a model for predicting the CpG island methylation status. This model outperforms other existing methods. We apply the model on the whole human genome and predict the landscape of DNA methylation of all CpG islands. Based on the methylation profile, we find that about 31% of CpG islands are methylation-prone and CpG islands located in promoter regions are seldom methylated. There is no significant difference in the CpG island methylation level between R and G bands among the chromosomes. The occupancy of RNA polymerase II is significantly higher in methylation-resistant promoter CpG islands, indicating that genes with such promoter CpG islands tend to be more active.
Comparative Analysis of CpG Islands in Four Fish Genomes  [PDF]
Leng Han,Zhongming Zhao
Comparative and Functional Genomics , 2008, DOI: 10.1155/2008/565631
Abstract: There has been much interest in CpG islands (CGIs), clusters of CpG dinucleotides in GC-rich regions, because they are considered gene markers and involved in gene regulation. To date, there has been no genome-wide analysis of CGIs in the fish genome. We first evaluated the performance of three popular CGI identification algorithms in four fish genomes (tetraodon, stickleback, medaka, and zebrafish). Our results suggest that Takai and Jones' (2002) algorithm is most suitable for comparative analysis of CGIs in the fish genome. Then, we performed a systematic analysis of CGIs in the four fish genomes using Takai and Jones' algorithm, compared to other vertebrate genomes. We found that both the number of CGIs and the CGI density vary greatly among these genomes. Remarkably, each fish genome presents a distinct distribution of CGI density with some genomic factors (e.g., chromosome size and chromosome GC content). These findings are helpful for understanding evolution of fish genomes and the features of fish CGIs.
Comparative Analysis of CpG Islands among HBV Genotypes  [PDF]
Yongmei Zhang, Chenxiao Li, Yijun Zhang, Haoxiang Zhu, Yaoyue Kang, Hongyan Liu, Jinyu Wang, Yanli Qin, Richeng Mao, Yi Xie, Yuxian Huang, Jiming Zhang
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0056711
Abstract: DNA methylation is being increasingly recognized to play a role in regulation of hepatitis B virus (HBV) gene expression. The aim of this study was to compare the CpG island distribution among different HBV genotypes. We analyzed 176 full-length HBV genomic sequences obtained from the GenBank database, belonging to genotypes A through J, to identify the CpG islands in the HBV genomes. Our results showed that while 79 out of 176 sequences contained three conventional CpG islands (I–III) as previously described, 83 HBV sequences harbored only two of the three known islands. Novel CpG islands were identified in the remaining 14 HBV isolates and named as CpG island IV, V, and VI. Among the eight known HBV genotypes and two putative genotypes, while HBV genomes containing three CpG islands were predominant in genotypes A, B, D, E, and I; genotypes C, F, G, and H tended to contain only two CpG islands (II and III). In conclusion, the CpG islands, which are potential targets for DNA methylation mediated by the host functions, differ among HBV genotypes, and these genotype-specific differences in CpG island distribution could provide new insights into the understanding of epigenetic regulation of HBV gene expression and hepatitis B disease outcome.
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