GaussianCpG: a Gaussian model for detection of CpG island in human genome sequences

Background

As crucial markers in identifying biological elements and processes in mammalian genomes, CpG islands (CGI) play important roles in DNA methylation, gene regulation, epigenetic inheritance, gene mutation, chromosome inactivation and nuclesome retention. The generally accepted criteria of CGI rely on: (a) %G+C content is ≥ 50%, (b) the ratio of the observed CpG content and the expected CpG content is ≥ 0.6, and (c) the general length of CGI is greater than 200 nucleotides. Most existing computational methods for the prediction of CpG island are programmed on these rules. However, many experimentally verified CpG islands deviate from these artificial criteria. Experiments indicate that in many cases %G+C is < 50%, CpG _obs /CpG _exp varies, and the length of CGI ranges from eight nucleotides to a few thousand of nucleotides. It implies that CGI detection is not just a straightly statistical task and some unrevealed rules probably are hidden.

Results

A novel Gaussian model, GaussianCpG, is developed for detection of CpG islands on human genome. We analyze the energy distribution over genomic primary structure for each CpG site and adopt the parameters from statistics of Human genome. The evaluation results show that the new model can predict CpG islands efficiently by balancing both sensitivity and specificity over known human CGI data sets. Compared with other models, GaussianCpG can achieve better performance in CGI detection.

Conclusions

Our Gaussian model aims to simplify the complex interaction between nucleotides. The model is computed not by the linear statistical method but by the Gaussian energy distribution and accumulation. The parameters of Gaussian function are not arbitrarily designated but deliberately chosen by optimizing the biological statistics. By using the pseudopotential analysis on CpG islands, the novel model is validated on both the real and artificial data sets.

     

A Hybrid Approach for CpG Island Detection in the Human Genome

Background

CpG islands have been demonstrated to influence local chromatin structures and simplify the regulation of gene activity. However, the accurate and rapid determination of CpG islands for whole DNA sequences remains experimentally and computationally challenging.

Methodology/Principal Findings

A novel procedure is proposed to detect CpG islands by combining clustering technology with the sliding-window method (PSO-based). Clustering technology is used to detect the locations of all possible CpG islands and process the data, thus effectively obviating the need for the extensive and unnecessary processing of DNA fragments, and thus improving the efficiency of sliding-window based particle swarm optimization (PSO) search. This proposed approach, named ClusterPSO, provides versatile and highly-sensitive detection of CpG islands in the human genome. In addition, the detection efficiency of ClusterPSO is compared with eight CpG island detection methods in the human genome. Comparison of the detection efficiency for the CpG islands in human genome, including sensitivity, specificity, accuracy, performance coefficient (PC), and correlation coefficient (CC), ClusterPSO revealed superior detection ability among all of the test methods. Moreover, the combination of clustering technology and PSO method can successfully overcome their respective drawbacks while maintaining their advantages. Thus, clustering technology could be hybridized with the optimization algorithm method to optimize CpG island detection.

Conclusion/Significance

The prediction accuracy of ClusterPSO was quite high, indicating the combination of CpGcluster and PSO has several advantages over CpGcluster and PSO alone. In addition, ClusterPSO significantly reduced implementation time.     

Particle swarm optimization with reinforcement learning for the prediction of CpG islands in the human genome

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 human CpG island randomly inserted into a plant genome is protected from methylation

In vertebrate genomes the dinucleotide CpG is heavily methylated, except in CpG islands, which are normally unmethylated. It is not clear why the CpG islands are such poor substrates for DNA methyltransferase. Plant genomes display methylation, but otherwise the genomes of plants and animals represent two very divergent evolutionary lines. To gain a further understanding of the resistance of CpG islands to methylation, we introduced a human CpG island from the proteasome-like subunit I gene into the genome of the plant Arabidopsis thaliana. Our results show that prevention of methylation is an intrinsic property of CpG islands, recognized even if a human CpG island is transferred to a plant genome. Two different parts of the human CpG island – the promoter region/ first exon and exon2–4 – both displayed resistance against methylation, but the promoter/ exon1 construct seemed to be most resistant. In contrast, certain sites in a plant CpG-rich region used as a control transgene were always methylated. The frequency of silencing of the adjacent nptII (Km^R) gene in the human CpG constructs was lower than observed for the plant CpG-rich region. These results have implications for understanding DNA methylation, and for construction of vectors that will reduce transgene silencing.     

Mutations and CpG islands among hepatitis B virus genotypes in Europe

Background

Hepatitis B virus (HBV) genotypes have a distinct geographical distribution and influence disease progression and treatment outcomes. The purpose of this study was to investigate the distribution of HBV genotypes in Europe, the impact of mutation of different genotypes on HBV gene abnormalities, the features of CpG islands in each genotype and their potential role in epigenetic regulation.

Results

Of 383 HBV isolates from European patients, HBV genotypes A-G were identified, with the most frequent being genotype D (51.96%) in 12 countries, followed by A (39.16%) in 7 countries, and then E (3.66%), G (2.87%), B (1.57%), F (0.52%) and C (0.26%). A higher rate of mutant isolates were identified in those with genotype D (46.7%) followed by G (45.5%), and mutations were associated with structural and functional abnormalities of HBV genes. Conventional CpG island I was observed in genotypes A, B, C, D and E. Conventional islands II and III were detected in all A-G genotypes. A novel CpG island IV was found in genotypes A, D and E, and island V was only observed in genotype F. The A-G genotypes lacked the novel CpG island VI. “Split” CpG island I in genotypes D and E and “split” island II in genotypes A, D, E, F and G were observed. Two mutant isolates from genotype D and one from E were found to lack both CpG islands I and III.

Conclusions

HBV genotypes A-G were identified in European patients. Structural and functional abnormalities of HBV genes were caused by mutations leading to the association of genotypes D and G with increased severity of liver disease. The distribution, length and genetic traits of CpG islands were different between genotypes and their biological and clinical significances warrant further study, which will help us better understand the potential role of CpG islands in epigenetic regulation of the HBV genome.     

SIGI: score-based identification of genomic islands

Background  

Genomic islands can be observed in many microbial genomes. These stretches of DNA have a conspicuous composition with regard to sequence or encoded functions. Genomic islands are assumed to be frequently acquired via horizontal gene transfer. For the analysis of genome structure and the study of horizontal gene transfer, it is necessary to reliably identify and characterize these islands.     

The Chromosomal Distribution of CpG Islands in the Mouse: Evidence for Genome Scrambling in the Rodent Lineage

We have prepared a library of mouse whole CpG islands using a methyl-CpG binding domain column. The distribution of CpG islands in the mouse genome was determined by FISH, using the library as a probe. Unlike in other vertebrate genomes that have been examined (human and chicken), extreme clustering of CpG islands was not seen in the mouse genome. No individual murine chromosome stood out as being either very gene-rich or very gene-poor. Despite the more even distribution of CpG islands in the mouse at a gross chromosomal level, at finer resolution concentrations of CpG islands are seen to correspond to the R-band early replicating regions of the genome.     

Analyzes of genome-wide association follow-up study for calving traits in dairy cattle

Background

Genomic islands play an important role in medical, methylation and biological studies. To explore the region, we propose a CpG islands prediction analysis platform for genome sequence exploration (CpGPAP).

Results

CpGPAP is a web-based application that provides a user-friendly interface for predicting CpG islands in genome sequences or in user input sequences. The prediction algorithms supported in CpGPAP include complementary particle swarm optimization (CPSO), a complementary genetic algorithm (CGA) and other methods (CpGPlot, CpGProD and CpGIS) found in the literature. The CpGPAP platform is easy to use and has three main features (1) selection of the prediction algorithm; (2) graphic visualization of results; and (3) application of related tools and dataset downloads. These features allow the user to easily view CpG island results and download the relevant island data. CpGPAP is freely available at http://bio.kuas.edu.tw/CpGPAP/.

Conclusions

The platform's supported algorithms (CPSO and CGA) provide a higher sensitivity and a higher correlation coefficient when compared to CpGPlot, CpGProD, CpGIS, and CpGcluster over an entire chromosome.     

Alu-PCR Approach to Isolating NotI-Linking Clones from the 3p14-p21 Region Frequently Deleted in Renal Cell Carcinoma

In the mammalian genome CpG islands are associated with functional genes and cloning of these islands could be an alternative approach for cloning functional genes. Recently we have developed a new approach for cloning CpG islands and constructing NotI linking libraries. We have initiated the construction of a NotI restriction map for chromosome 3, especially focusing on the rearrangements in the 3p14-p21 region, which are associated with different malignancies. CpG islands from this region are useful for isolation of candidate tumor suppressor genes that map to this region and for isolating NotI-linking clones from 3p14-p21 for mapping purposes. Here we suggest a modification of Alu-PCR as an approach to isolating Not I sites (e.g., CpG islands) from defined regions of the chromosome. Instead of using whole chromosomal DNA for Alu-PCR, we have used representative NotI-linking libraries from hybrid cell lines containing either whole or deleted human chromosome 3 (MCH903.1 and MCH924.4, respectively). This decreases the complexity of the Alu-PCR products 10-100 times compared to the whole human genome. Using this modification, we can isolate NotI-linking clones, which are natural markers on the chromosome, rather than random genomic fragments. Among eight clones selected by this method, seven were from the region deleted in MCH924.4. The results clearly demonstrate the feasibility of Alu-PCR for isolating CpG islands from defined regions of the genome.     

GenomeViz: visualizing microbial genomes

Background

An increasing number of microbial genomes are being sequenced and deposited in public databases. In addition, several closely related strains are also being sequenced in order to understand the genetic basis of diversity and mechanisms that lead to the acquisition of new genetic traits. These exercises have necessitated the requirement for visualizing microbial genomes and performing genome comparisons on a finer scale. We have developed GenomeViz to enable rapid visualization and subsequent comparisons of several microbial genomes in an interactive environment.

Results

Here we describe a program that allows visualization of both qualitative and quantitative information from complete and partially sequenced microbial genomes. Using GenomeViz, data deriving from studies on genomic islands, gene/protein classifications, GC content, GC skew, whole genome alignments, microarrays and proteomics may be plotted. Several genomes can be visualized interactively at the same time from a comparative genomic perspective and publication quality circular genome plots can be created.

Conclusions

GenomeViz should allow researchers to perform visualization and comparative analysis of up to eight different microbial genomes simultaneously.