Identification Of CpG Islands Research Articles

GC-Profile 2.0: an extended web server for the prediction and visualization of CpG islands.

Due to the spontaneous deamination of 5'-methylcytosine into thymine, the number of CpG dinucleotides is less than expected in vertebrate genomes. Exceptionally, there are a large number of CpG dinucleotides clustered at certain genomic loci, known as CpG islands (CGIs), where CpG dinucleotides are free from methylation. Identification of CGIs is of great significance in the field of genomics and epigenetics because they can serve as important gene markers or regulatory elements. Here, GC-Profile 2.0 has been presented as a newly extended application for CGIs detection and visualization. Based on a benchmark test of assembled sequences, GC-Profile 2.0 has shown better overall performance compared with other four popular methods. In addition, cumulative CpG profile, a visualization tool of CpG content variation, is also proposed to intuitively display the change trend of CpG content. GC-Profile 2.0 is freely available at http://tubic.org/GC-Profile2. Supplementary data are available at Bioinformatics online.

MODIFIED P-SPECTRUM-BASED APPROACH TO ENHANCE SENSITIVITY FOR THE DETECTION OF CpG ISLANDS IN DNA SEQUENCES IN HUMAN SPECIES

CpG Island (CGI) is considered to be one of the important segments of deoxyribonucleic acid (DNA) sequences. Out of the various epigenetic events which are associated with CGIs, some such events are like: CGIs are useful in the prediction of promoter region and subsequently for gene prediction, CGIs’ contribution in finding the epigenetic reasons of cancer is of great importance, CGIs can be used to identify chromosome inactivation. Therefore, the exact and maximum number of CGIs hidden in DNA sequences need to explored. A lot of computational, transform-based approaches have been developed and reported in literature for the identification of CGIs in DNA sequences since last many years. The problem associated with transform-based approaches is that the domain of functioning of algorithm requires to be changed which can probably lead to biasing and result in loss of important information in terms of CGIs. Hence, to provide a solution to this issue, a modified P-spectrum-based approach has been proposed here which does not suffer from domain transformation issue. Also, the performance of proposed algorithm has been tested on a large data set of 100 DNA sequences of human species and the performance has been compared with other recently reported methods of CGIs identification in DNA sequences. The results obtained prove that the proposed algorithm is better than the existing methods in terms of identification of more number of CGIs in DNA sequences. Therefore, the proposed algorithm has been considered as an efficient approach to enhance the sensitivity of CGIs identification.

CpG islands identification in DNA sequences using modified P-spectrum based algorithm

The identification of CpG Islands play a major role in the analysis of DNA sequences because of association of CpG Islands with many epigenetic events. Some of these events are promoter activity and consequently gene prediction, chromosome inactivation, and for early detection of cancer etc. And hence the exact identification of CpG Islands in the stretch of DNA sequences has always remained a challenging task. Numerous computational algorithms have been developed for the identification of CpG Islands in DNA sequences. Recently various transform based methods have been reported for the CpG Islands detection in literature. In these transform based methods, there is a requirement of transforming the signal from time domain to frequency domain and correspondingly there is probability of transform biasing. Hence to overcome this issue, a modified P-spectrum based algorithm has been proposed in this paper. Also the performance of the proposed method has been compared with recently reported methods of CpG Islands detection using standard evaluation metrics. The performance of proposed method has been proved to be much better than the other methods and hence the proposed approach is an efficient method in detecting CpG Islands.

Whole genome comparative analysis of CpG islands in camelid and other mammalian genomes

Camels bear unique genotypes and phenotypes for adaptation in their environment, and as such could be very useful in the weather extremes accelerated by global climate change. Published sequences of Camelidae genomes provide an opportunity to elucidate the genomic architecture of these animals. CpG islands (CGIs) sequence patterns in complex genomes play important roles in gene regulation via epigenetic change. Comparative large-scale genome analysis of CGIs in Camelidae was carried out using five different CGI detection algorithms, evaluating numbers of CGIs, CGI density and CGI length distribution in Camelidae. All algorithms identified the alpaca genome as having the largest number of CGIs, CGI density and average length of CGIs, though, the CGI length distribution and their number could be implicitly attributed to the characteristics of an algorithm for CGI identification rather than that of the species. In addition, high algorithm-to-algorithm variability of the observed CGI properties was shown. It was well expected, since there is no clear definition of a CGI and different algorithms could identify different genome regions as CGIs. Comparison with other mammalian genomes (human, mouse, dog, horse and cow) shows that CGIs features in cow were most similar to camelid genomes. Further analysis of camelid genomes may shed more light on molecular origins and mechanisms of adaptation in these extreme heat-adapted animals.

HRGFish: A database of hypoxia responsive genes in fishes

Several studies have highlighted the changes in the gene expression due to the hypoxia response in fishes, but the systematic organization of the information and the analytical platform for such genes are lacking. In the present study, an attempt was made to develop a database of hypoxia responsive genes in fishes (HRGFish), integrated with analytical tools, using LAMPP technology. Genes reported in hypoxia response for fishes were compiled through literature survey and the database presently covers 818 gene sequences and 35 gene types from 38 fishes. The upstream fragments (3,000 bp), covered in this database, enables to compute CG dinucleotides frequencies, motif finding of the hypoxia response element, identification of CpG island and mapping with the reference promoter of zebrafish. The database also includes functional annotation of genes and provides tools for analyzing sequences and designing primers for selected gene fragments. This may be the first database on the hypoxia response genes in fishes that provides a workbench to the scientific community involved in studying the evolution and ecological adaptation of the fish species in relation to hypoxia.

Locating Regions in a Sequence under Density Constraints

Several biological problems require the identification of regions in a sequence where some feature occurs within a target density range: examples including the location of GC-rich regions, identification of CpG islands, and sequence matching. Mathematically, this corresponds to searching a string of 0's and 1's for a substring whose relative proportion of 1's lies between given lower and upper bounds. We consider the algorithmic problem of locating the longest such substring, as well as other related problems (such as finding the shortest substring or a maximal set of disjoint substrings). For locating the longest such substring, we develop an algorithm that runs in $\mathcal{O}(n)$ time, improving upon the previous best-known $\mathcal{O}(n \log n)$ result. For the related problems we develop $\mathcal{O}(n \log \log n)$ algorithms, again improving upon the best-known $\mathcal{O}(n \log n)$ results. Practical testing verifies that our new algorithms enjoy significantly smaller time and memory footprints, and can process sequences that are orders of magnitude longer as a result.

Identification of CpG islands in DNA sequences using statistically optimal null filters

CpG dinucleotide clusters also referred to as CpG islands (CGIs) are usually located in the promoter regions of genes in a deoxyribonucleic acid (DNA) sequence. CGIs play a crucial role in gene expression and cell differentiation, as such, they are normally used as gene markers. The earlier CGI identification methods used the rich CpG dinucleotide content in CGIs, as a characteristic measure to identify the locations of CGIs. The fact, that the probability of nucleotide G following nucleotide C in a CGI is greater as compared to a non-CGI, is employed by some of the recent methods. These methods use the difference in transition probabilities between subsequent nucleotides to distinguish between a CGI from a non-CGI. These transition probabilities vary with the data being analyzed and several of them have been reported in the literature sometimes leading to contradictory results. In this article, we propose a new and efficient scheme for identification of CGIs using statistically optimal null filters. We formulate a new CGI identification characteristic to reliably and efficiently identify CGIs in a given DNA sequence which is devoid of any ambiguities. Our proposed scheme combines maximum signal-to-noise ratio and least squares optimization criteria to estimate the CGI identification characteristic in the DNA sequence. The proposed scheme is tested on a number of DNA sequences taken from human chromosomes 21 and 22, and proved to be highly reliable as well as efficient in identifying the CGIs.

CpG islands: Algorithms and applications in methylation studies

Methylation occurs frequently at 5’-cytosine of the CpG dinucleotides in vertebrate genomes; however, this epigenetic feature is rarely observed in CpG islands (CGIs) or CpG clusters in the promoter regions of genes. Aberrant methylation of the promoter-associated CGIs might influence gene expression and cause carcinogenesis. Because of the functional importance, multiple algorithms have been available for identifying CGIs in a genome or a sequence. They can be categorized into the traditional algorithms (e.g., Gardiner-Garden and Frommer (1987), Takai and Jones (2002), and CpGPRoD (2002)) or statistical property based algorithms (CpGcluster (2006) and CG cluster (2007)). We reviewed the features of these algorithms and evaluated their performance on identifying functional CGIs using genome-wide methylation data. Moreover, identification of CGIs is an initial step in many recent studies for predicting methylation status as well as in the design of methylation detection platforms. We reviewed the benchmarks and features used in these studies.

Comparative Analysis of CpG Islands in Four Fish Genomes

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.

A novel technique for the identification of CpG islands exhibiting altered methylation patterns (ICEAMP).

Aberrant CpG methylation changes occurring during tumour progression include the loss (hypomethylation) and gain (hypermethylation) of methyl groups. Techniques currently available for examining such changes either require selection of a region, then examination of methylation changes, or utilise methylation-sensitive restriction enzymes to identify an alteration. We describe here a novel method that identifies genomic regions as a consequence of altered methylation during tumourigenesis. A methyl-CpG binding domain column isolates methylated GC-rich sequences from both tumours and surrounding normal tissue. Subsequent subtractive hybridisation removes sequences common to both, leaving only methylated sequences unique to the tumour. Libraries of sequences generated using DNA derived from a breast tumour (histological grade; poorly differentiated) as 'tester' and from matched normal tissue as 'driver' were examined; 26% of clones had the sequence criteria of a CpG island (CGI). Analysis using the bisulfite technique revealed that a number of these sequences were methylated in tumour DNA relative to the normal control. We have therefore demonstrated the ability of this technique, the identification of CGI exhibiting altered methylation patterns (ICEAMP), to isolate tumour-specific methylated GC-rich sequences. This will allow a comprehensive identification of methylation changes during tumourigenesis and will lead to a better understanding of the processes involved.

Identification of CpG islands hypermethylated in tumor and transformed cells by the methylation-sensitive arbitrarily primed polymerase chain reaction

Carcinogenesis is associated with hypermethylation of certain DNA regions, which can suppress transcription of the corresponding genes. With a version of methylation-sensitive arbitrarily primed polymerase chain reaction (MS-AP-PCR), three fragments were identified that are hypermethylated in human cervical carcinomas and transformed hamster cells expressing exogenous h-N-ras. The fragments were cloned, sequenced, and shown to possess properties characteristic of CpG islands. Of these, one was identified as a fragment of the β3A-adaptin gene and two showed no homology to known sequences. MS PCR with specific primers showed that the CpG island of the β3A-adaptin gene was hypermethylated in four (15%) of 27 cervical carcinomas examined. Southern blotting with the cloned hamster DNA fragment confirmed hypermethylation of the corre-sponding CpG island in three hamster cell lines expressing N-ras. The method proved useful for identification of CpG islands, including those hypermethylated in tumor and transformed cells.

Identification of CpG islands hypermethylated in human lung cancer by the arbitrarily primed-PCR method.

DNA hypermethylation is believed to be involved in human carcinogenesis, since it suppresses the transcription of defined genes and is associated with chromosomal instability. In this study, we identified CpG islands that are hypermethylated in human lung cancer by a modified arbitrarily primed-polymerase chain reaction method using genomic DNAs digested with a methylation-sensitive restriction enzyme, HpaII, as templates. When we analyzed genomic DNAs from normal lung tissues and non-small cell lung carcinoma cell lines using three arbitrary primers, three DNA fragments were amplified from lung cancer DNAs but not from normal lung DNAs. Restriction mapping and Southern blot analysis revealed that all of these bands were amplified from CpG islands that were hypermethylated in the lung cancer cell lines. These islands were mapped to chromosomes 4q34, 10q26 and 17p13.1-p13.2, respectively, and these chromosomal regions were also hypermethylated in a subset of primary lung tumors in vivo. Thus, diverse chromosomal regions are hypermethylated in lung cancer cells. The results also indicate that this method is simple and effective for screening of CpG islands that are hypermethylated in cancer cells.

4.P.14 Identification of CpG island in extracellular superoxide-dismutase gene with marked hypomethylation in the arterial wall

4.P.14 Identification of CpG island in extracellular superoxide-dismutase gene with marked hypomethylation in the arterial wall

Mapping AAC1, AAC2 and AACP, the genes for arylamine N-acetyltransferases, carcinogen metabolising enzymes on human chromosome 8p22, a region frequently deleted in tumours.

Arylamine N-acetyltransferases (NATs) are encoded at two loci on 8p22, a region subject to deletions in bladder tumours. The two functional genes (AAC1 and AAC2 alias NAT1 and NAT2) without introns in the coding region, encode enzymes which metabolise carcinogens, including bladder carcinogens. They are both multi-allelic and certain alleles have been implicated as susceptibility factors in bladder cancer. There is a third N-acetyltransferase gene, a pseudogene, AACP alias NATP, which we show is also located on chromosome 8 at the p22 region. We have mapped a series of YAC clones (ICI and CEPH) containing the NAT genes and the markers D8S21, an RFLP marker, and D8S261, a microsatellite marker. We show that D8S21 is a portion of the coding region of AAC2. The order of genes in this region, covering some 2 Mb, is TEL-D8S261-AAC1-AACP-AAC2 (D8S21)-CEN. The restriction map also illustrates that there are likely to be other expressed genes in the region through the identification of CpG islands.

Walking, Cloning, and Mapping with YACs in 3q27: Localization of Five ESTs Including Three Members of the Cystatin Gene Family and Identification of CpG Islands

Using yeast artificial chromosomes, we have generated a high-resolution physical map for 2.7 Mb of human chromosomal region 3q27. The YAC clones group into three contigs, one of which has also been linked to the CEPH YAC contig map of human chromosome 3. Fluorescencein situhybridization has been used to order the contigs on the chromosome and to estimate the distance between them. Expressed sequence tags for five genes, including three members of the cystatin gene family and a gene thought to be involved in B-cell non-Hodgkin lymphoma, have been placed within the YAC contigs, and 12 putative CpG islands have been identified. These YACs provide a useful resource to complete the physical mapping of 3q27 and to begin identification and characterization of further genes that are located there.

A 1.8-Mb YAC contig in Xp11.23: identification of CpG islands and physical mapping of CA repeats in a region of high gene density.

The genes ARAF1, SYN1, TIMP, and PFC are clustered within 70 kb of one another, and, as reported in the accompanying paper (J. Knight et al., 1994, Genomics 21: 180-187), at least four more genes map within 400 kb: a cluster of Krüppel-type zinc finger genes (including ZNF21, ZNF41, and ZNF81) and ELK-1, a member of the ets oncogene superfamily. This gene-rich region is of particular interest because of the large number of disease genes mapping to Xp11.23: at least three eye diseases (retinitis pigmentosa type 2, congenital stationary night blindness CSNB1, and Aland Island eye disease), Wiskott-Aldrich syndrome, X-linked nephrolithiasis, and a translocation breakpoint associated with synovial sarcoma. We have constructed a 1.8-Mb YAC contig in this region, confirming the link between TIMP and OATL1 reported by Knight et al. (1994) and extending the map in the distal direction. To investigate the likelihood that more genes are located within this region, we have carried out detailed mapping of rare-cutter restriction sites in these YACs and identified seven CpG islands. At least six of these islands are located over 50 kb from any known gene locations, suggesting that the region contains at least this many as yet unidentified genes. We have also mapped the physical locations of six highly polymorphic CA repeats within the contig, thus integrating the physical, genetic, and transcriptional maps of the region and facilitating the mapping and identification of disease genes.(ABSTRACT TRUNCATED AT 250 WORDS)

Identification of CpG island at the 5' end of murine leukemia inhibitory factor gene

We identified a CpG island at the 5' end of murine leukemia inhibitory factor gene (LIF). The CpG island is 0.6 kb long and covers most of the first exon and first intron. The region is non-methylated, its G+C content is 65% and relative frequency of CpG dinucleotide is 0.7. The block of 150 nucleotides, which is 72% conserved between murine, human, ovine and porcine genes, is a part of the CpG island. Two DNA fragments from this CpG island interact with nuclear proteins from NIH 3T3 cells. One fragment partially covers the block of conserved nucleotides. Human, ovine and porcine LIF genes also contain G+C- and CpG-rich sequences in the corresponding region.

Identification of CpG islands around the DXS178 locus in the region of the X-linked agammaglobulinaemia gene locus in Xq22.

The X-linked agammaglobulinaemia (XLA) gene locus has previously been mapped to Xq22. Genetic linkage analysis has shown tight linkage between the disease and the DXS178 locus and that DXS3 and DXS94 are the closet proximal and distal flanking markers, respectively, separated by a genetic distance of 10-12 cM. We attempted to construct a physical map of Xq22 using pulsed field gel electrophoresis (PFGE) and rare-cutting restriction enzymes in order to obtain a finite physical value for the distance between DXS3 and DXS94. However, these attempts were hampered by the large number of rare-cutting restriction enzyme sites around the DXS178 locus, indicative of the presence of CpG rich regions of DNA. We were able to construct a physical map of the sites close to DXS178 that suggests the presence of at least three, and perhaps as many as five, CpG islands. These are arranged on either side of DXS178, extending over about 550kb of genomic DNA. Each of these regions must be considered as being associated with a potential "candidate" gene sequence for the XLA gene and we have initiated a chromosome walk from DXS178 to the nearest of these islands.