Identify CpG Islands Research Articles

Analysis of population heterogeneity in CHO cells by genome-wide DNA methylation analysis and by multi-modal single-cell sequencing

CHO cells are major hosts for the industrial production of therapeutic proteins and their production stability is of considerable economic significance. It is widely known that CHO cells can rapidly acquire genetic alterations, which affects their genetic homogeneity over time. However, the role of non-genetic mechanisms, including epigenetic mechanisms such as DNA methylation, remains poorly understood. We have now used whole-genome bisulfite sequencing to establish single-base methylation maps of eight independent CHO cell lines. Our results identify CpG islands and low-methylated regions as conserved elements with dynamic DNA methylation. Interestingly, methylation patterns were found to cluster clearly along the three main branches of CHO evolution, with no directional changes over short culture periods. Furthermore, multi-ome single-cell sequencing of 9833 nuclei from three independent cultures revealed dynamic subpopulation structures characterized by robust expression differences in pathways related to protein production. Our findings thus provide novel insights into the epigenetic landscape and heterogeneity of CHO cells and support the development of epigenetic biomarkers that trace the emergence of subpopulations in CHO cultures.

Mathematics behind the identifying CpG islands

The major objective of the paper is to review the theory for an hidden Markov model, a very general type of probabilistic model for sequences of symbols. In order for the hidden Markov model to be applicable to real-world applications, three key problems about the model must be addressed, and to do this, first we go over how to choose the best state sequence to explain an observation sequence, then we go over how to calculate the probability of an observation sequence, and finally we go over how to compute the maximization of the probability of the observation sequence. From these three angles, we review the mathematical concept behind the identification of CpG islands. The entire process and study of the outcomes have been tackled by examining both hypothetical and real DNA sequences side by side. We use well-known biological sequence analysis servers to carry out the experiment. Analytical and algorithmic approaches are compared while taking the hypothetical DNA sequence example into consideration.

Differences in CpG island distribution between exogenous and endogenous jaagsiekte sheep retrovirus strains.

The jaagsiekte sheep retrovirus (JSRV), belonging to the betaretrovirus genus of the retroviridae family, includes both exogenous and endogenous jaagsiekte sheep retroviruses (exJSRV and enJSRV, respectively). At the proviral genome level, exJSRV and enJSRV strains have a high degree of similarity with their main variation regions being the LTR, gag, and env genes. In this study, for the first time, we investigated and compared the distribution of CpG islands between these enJSRV and exJSRV strains. Specifically, we analyzed a total of 42 full-length JSRV genomic sequences obtained from the GenBank® database to identify CpG islands in the exJSRV and enJSRV genomes using the MethPrimer software. Our results showed that the CpG islands in the two JSRV strains were mainly distributed in the LTR, gag, and env genes. In exJSRVs, 66.66% (6/9), 33.33% (3/9), and 100% (9/9) of the sequences presented at least one CpG island in LTR, gag, env genes, respectively, and for enJSRVs, 84.84% (28/33), 57.57% (19/33), and 96.96% (32/33) of the sequences presented at least one CpG island in the LTR, gag, and env genes. These findings suggested that the distribution, length, and genetic traits of CpG islands were different for the exJSRV and enJSRV strains. In future, it would be necessary to demonstrate the biological significance of CpG islands within these genes in exJSRV and enJSRV genomes. This will enhance understanding regarding the potential role of CpG islands in epigenetic regulation.

Oncometabolite profiling identifying estrogen-responsive genes in breast cancer.

e13611 Background: Metabolomic profiling in breast cancer (BC) is an emerging avenue to identify clinically actionable biomarkers as well as targeted therapeutics. Altered metabolic pathways has been identified in BC. Studies have discovered an association between oncometabolite levels, DNA methylation and other epigenetic changes, with prognosis in BC. Changes in cancer cells metabolism may thus indicate disease aggressiveness and its response to therapy. Methods: An in-silico based oncometabolite profiling was performed in ER +ve and TNBC (ER-/PR-/Her2-) data sets using NCBO-GEO tools. . Inclusion criteria included studies on Human subjects, having at least 2 ER+ PR+ and TNBC samples. After manually filtering the top 250 most significant differentially expressed genes in all studies, those with clear metabolic functions as per the GO category were pooled together, which included both the overexpressed and downregulated genes. 15 unique genes were cherry-picked and taken for further analysis out of which six were downregulated, and nine were upregulated. The identified genes harbouring ERE in their upstream 1000bp region were further analysed to identify CpG islands. In addition, we also performed a Bio-Reader based Text Mining Tool to streamline Pubmed abstracts with the positive strings “oncometabolite,” “breast cancer,” “stem cells” “survival”. We also queried against negative strings “sequencing,” “mutations,” “infections,” “drug-resistance” to develop a list of highly focused abstracts. Results: Novel estrogen-regulated oncometabolite that were differentially expressed in ER+ve vs TNBC BC were identified. Further, we reinforced the fact that 4 out of 7 targets had upstream CpG islands within 1000bp n Both upregulated AMY2A, B3GNT5, PNPLA3 and 2HG whereas and downregulated genes in PLCD4, FBP1 and CA12 along with one target identified from the literature were selectively expressed in ER+ve BC. Conclusions: Our study has identified estrogen-responsive metabolic genes involved in BC. We are currently validating our results in a wet lab setup from breast cancer patient samples. Validated targets may be tested for therapeutic intervention in BC.

Epigenetic and post-transcriptional regulation of somatostatin receptor subtype 5 (SST5 ) in pituitary and pancreatic neuroendocrine tumors.

Somatostatin receptor subtype 5 (SST5) is an emerging biomarker and actionable target in pituitary (PitNETs) and pancreatic (PanNETs) neuroendocrine tumors. Transcriptional and epigenetic regulation of SSTR5 gene expression and mRNA biogenesis is poorly understood. Recently, an overlapping natural antisense transcript, SSTR5‐AS1, potentially regulating SSTR5 expression, was identified. We aimed to elucidate whether epigenetic processes contribute to the regulation of SSTR5 expression in PitNETs (somatotropinomas) and PanNETs. We analyzed the SSTR5/SSTR5‐AS1 human locus in silico to identify CpG islands. SSTR5 and SSTR5‐AS1 expression was assessed by quantitative real‐time PCR (qPCR) in 27 somatotropinomas, 11 normal pituitaries (NPs), and 15 PanNETs/paired adjacent (control) samples. We evaluated methylation grade in four CpG islands in the SSTR5/SSTR5‐AS1 genes. Results revealed that SSTR5 and SSTR5‐AS1 were directly correlated in NP, somatotropinoma, and PanNET samples. Interestingly, selected CpG islands were differentially methylated in somatotropinomas compared with NPs. In PanNETs cell lines, SSTR5‐AS1 silencing downregulated SSTR5 expression, altered aggressiveness features, and influenced pasireotide response. These results provide evidence that SSTR5 expression in PitNETs and PanNETs can be epigenetically regulated by the SSTR5‐AS1 antisense transcript and, indirectly, by DNA methylation, which may thereby impact tumor behavior and treatment response.

Comparative analysis of CpG islands in equine infectious anemia virus strains.

Increasing evidence suggests that DNA methylation has key roles in the replication of retroviruses, including lentiviruses, and pathogenesis of diseases. However, the precise characteristics of CpG islands are not known for many retroviruses. In this study, we compared the distribution of CpG islands among strains of equine infectious anemia virus (EIAV), a lentivirus in the family Retroviridae and a model for HIV research. We identified CpG islands in 32 full-length EIAV genomic sequences obtained from the GenBank database using MethPrimer. Only one CpG island, from 100 to 120bp, was identified in the genomes of EIAV strains DV10, DLV3-A, and DLV5-10 from China, V26 and V70 from Japan, and IRE H3, IRE F2, IRE F3, and IRE F4 from Ireland. Importantly, the CpG island was located within the Rev gene, which is required for the expression of viral cis-acting elements and the production of new virions. These results suggest that the distribution, length, and genetic properties of CpG islands differ among EIAV strains. Future research should focus on the biological significance of this CpG island within rev to improve our understanding of the precise roles of CpG islands in epigenetic regulation in the species.

COHCAP: an integrative genomic pipeline for single-nucleotide resolution DNA methylation analysis

COHCAP (City of Hope CpG Island Analysis Pipeline) is an algorithm to analyze single-nucleotide resolution DNA methylation data produced by either an Illumina methylation array or targeted bisulfite sequencing. The goal of the COHCAP algorithm is to identify CpG islands that show a consistent pattern of methylation among CpG sites. COHCAP is currently the only DNA methylation package that provides integration with gene expression data to identify a subset of CpG islands that are most likely to regulate downstream gene expression, and it can generate lists of differentially methylated CpG islands with ∼50% concordance with gene expression from both cell line data and heterogeneous patient data. For example, this article describes known breast cancer biomarkers (such as estrogen receptor) with a negative correlation between DNA methylation and gene expression. COHCAP also provides visualization for quality control metrics, regions of differential methylation and correlation between methylation and gene expression. This software is freely available at https://sourceforge.net/projects/cohcap/.

DNA Hypermethylation Encroachment at CpG Island Borders in Cancer Is Predisposed by H3K4 Monomethylation Patterns

Promoter CpG islands are typically unmethylated in normal cells, but in cancer a proportion are subject to hypermethylation. Using methylome sequencing we identified CpG islands that display partial methylation encroachment across the 5' or 3' CpG island borders. CpG island methylation encroachment is widespread in prostate and breast cancer and commonly associates with gene suppression. We show that the pattern of H3K4me1 at CpG island borders in normal cells predicts the different modes of cancer CpG island hypermethylation. Notably, genetic manipulation of Kmt2d results in concordant alterations in H3K4me1 levels and CpG island border DNA methylation encroachment. Our findings suggest a role for H3K4me1 in the demarcation of CpG island methylation borders in normal cells, which become eroded in cancer.

Enhanced sensitivity of CpG island search and primer design based on predicted CpG island position

DNA methylation has important biological roles, such as gene expression regulation, as well as practical applications in forensics, such as in body fluid identification and age estimation. DNA methylation often occurs in the CpG site, and methylation within the CpG islands affects various cellular functions and is related to tissue-specific identification. Several programs have been developed to identify CpG islands; however, the size, location, and number of predicted CpG islands are not identical due to different search algorithms. In addition, they only provide structural information for predicted CpG islands without experimental information, such as primer design. We developed an analysis pipeline package, CpGPNP, to integrate CpG island prediction and primer design. CpGPNP predicts CpG islands more accurately and sensitively than other programs, and designs primers easily based on the predicted CpG island locations. The primer design function included standard, bisulfite, and methylation-specific PCR to identify the methylation of particular CpG sites. In this study, we performed CpG island prediction on all chromosomes and compared CpG island search performance of CpGPNP with other CpG island prediction programs. In addition, we compared the position of primers designed for a specific region within the predicted CpG island using other bisulfite PCR primer programs. The primers designed by CpGPNP were used to experimentally verify the amplification of the target region of markers for body fluid identification and age estimation. CpGPNP is freely available at http://forensicdna.kr/cpgpnp/.

A CpGCluster-Teaching-Learning-Based Optimization for Prediction of CpG Islands in the Human Genome.

Many CpG island detection methods have been proposed based on sliding window and clustering technology, but the accuracy of these methods is proportional to the time required. Therefore, an accurate and rapid method for identifying CpG islands remains an important challenge in the complete human genome. We propose a hybrid method CpGTLBO to detect the CpG islands in the human genome. The method uses the clustering approach and the teaching-learning-based optimization (TLBO) algorithm. The clustering approach is used to detect CpG island candidates, and it can effectively reduce the huge volume of unnecessary DNA fragments. TLBO was used to accurately predict CpG islands among promising CpG island candidates. A comparison based on six contig data sets and a whole human genome analysis showed that the identifying stability of CpGTLBO outperformed eight existing methods in terms of sensitivity (SN), specificity (SP), accuracy (ACC), performance coefficient (PC), and correlation coefficient (CC) and processing time. Results indicated that ClusterTLBO can effectively overcome the drawbacks and maintain the advantages in both the CpGcluster and TLBO.

Abstract 1049: Prognostication of patients with clear cell renal cell carcinomas based on quantification of DNA methylation levels of CpG island methylator phenotype marker genes using anion exchange high performance liquid chromatography

Abstract Background: We have identified CpG island methylator phenotype (CIMP)-positive clear cell renal cell carcinomas (ccRCCs) characterized by accumulation of DNA hypermethylation of CpG islands, tumor aggressiveness and poorer patient outcome. Furthermore, the criteria for prognostication of patients with ccRCCs have been established based on DNA methylation levels of ccRCC-specific CIMP marker genes. Recently, we have developed an anion exchange High Performance Liquid Chromatography (HPLC) column for detection of methylated DNA. To make the prognostication of patients with ccRCCs clinically applicable, the newly developed HPLC column has been applied to DNA methylation analysis of CIMP marker genes. Methods: Genomic DNA extracted from tumorous tissue samples of nephrectomy specimens of 5 patients with CIMP-positive ccRCCs and 5 patients with CIMP-negative ccRCCs was amplified by Polymerase Chain Reaction (PCR) after sodium bisulfite conversion with the EpiTect Bisulfite Kit (QIAGEN). Three-hundred and eighty-four bp-PCR products encompassing the promoter region of the FAM150A gene, a CIMP marker gene, and including 39 CpG sites were subjected to the HPLC column. DNA methylation levels were quantitatively evaluated using 0% and 100% methylated DNA controls (QIAGEN). Results: The analysis time for all samples was within 10 minutes. The retention time difference between 0% and 100% methylated DNA controls was 0.34 min. CIMP-negative ccRCCs were detected as a distinct single peak which had similar retention time with the 0% methylated DNA control, whereas CIMP-positive ccRCCs showed a bimodal peak pattern. Retention time of the later peak of CIMP-positive ccRCCs was similar with the 0% methylated DNA control. On the other hand, an early peak was eluted at various positions depending on its DNA methylation level. Based on the differences of chromatogram patterns, CIMP-positive ccRCCs were completely discriminated from CIMP-negative ccRCCs. DNA methylation levels of the FAM150A gene of each tissue sample calculated from retention time were consistent with those previously evaluated using MassARRAY system (Sequenom). Conclusion: Using newly developed anion exchange HPLC column, tissue specimens of CIMP-positive ccRCCs were readily, quickly and clearly distinguished from those of CIMP-negative ccRCCs, indicating that this HPLC method may be suitable for clinical application of prognostication of patients with ccRCCs based on CIMP diagnosis. Citation Format: Takuya Yotani, Yuriko Yamada, Eri Arai, Ying Tian, Masahiro Gotoh, Motokiyo Komiyama, Hiroyuki Fujimoto, Michiie Sakamoto, Yae Kanai. Prognostication of patients with clear cell renal cell carcinomas based on quantification of DNA methylation levels of CpG island methylator phenotype marker genes using anion exchange high performance liquid chromatography. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1049. doi:10.1158/1538-7445.AM2015-1049

Hypermethylation of Adenomatosis Polyposis Coli-2 and its Tumor Suppressor Role in Retinoblastoma

Purpose: Retinoblastoma (RB) is a progressive eye cancer of infancy and childhood. Hypermethylation, epigenetic silencing of genes is one of the key events in tumorigenesis. The purpose of this study is to investigate hypermethylation of adenomatosis polyposis coli homologue, APC-2 and possible interaction of APC-2 with Wnt signaling β-catenin protein in Retinoblastoma.Methodology: Primary RB tumor samples and cell line were used for the study. DNA isolation, bisulfite conversion, methylation specific PCR and DNA sequencing analysis of PCR products were performed to identify CpG islands and methylation in primary RB tumor samples (n = 30). Chemical demethylation and retrieval of APC-2 expression was studied using 5-Azacytidine (5′-AZC). Flow cytometry, immunofluorescence, western blot were performed for APC-2 expression analysis in demethylated Y79 cells. Co-localization study was conducted to understand the interaction between APC-2 and β-catenin.Results: APC-2 gene was methylated and down regulated in primary RB tumors. We observed that 70% of RB tumors (21/30) showed positivity with APC-2 methylation. The RB Y79 cells after treatment with demethylating agent 5′-AZC retrieved APC-2 expression, which was confirmed by immunofluorescence and Western blot. Flow cytometry showed APC-2 expression of 29.22% in 5′-AZC treated cells. Co-localization study showed interaction of APC-2 and RB upregulated β-catenin in Y79 cells.Conclusion: We report that APC-2 gene is hypermethylated in both RB tumor samples and Y79 cells. Reduced APC-2 lead to increased Wnt signaling pathway protein, β-catenin suggesting tumor suppressive role of APC-2 gene.

Methylation of a Novel CpG Island of Intron I Is Associated With Steroidogenic Factor I Expression in Endometriotic Stromal Cells

Steroidogenic factor 1 (SF-1), a transcriptional factor essential for estrogen biosynthesis, is undetectable in endometrial stromal cells and aberrantly expressed in endometriotic stromal cells. We tried to gain further insight into the mechanism for differential SF-1 expression in endometrial and endometriotic stromal cells. We had previously identified a novel CpG island in SF-1, which is located in the downstream intron 1 region. Here, we evaluated the methylation status of this CpG island. We obtained the eutopic endometrium from disease-free participants (n = 8) and the walls of cystic endometriosis lesions of the ovaries from another group of participants (n = 8). None of the patients had received any preoperative hormonal therapy. Stromal cells were isolated from these 2 types of tissues and subjected to DNA bisulfite treatment and sequence analysis. The SF-1 messenger RNA (mRNA) levels in endometriotic stromal cells were significantly higher than those in endometrial stromal cells. Bisulfite sequencing showed strikingly increased methylation of a 1-kbp region around the previously identified CpG island in endometriotic cells compared with endometrial cells (P < .001). A strong correlation between SF-1 mRNA levels and percentage methylation of the intron 1 region of the SF-1 gene was observed in endometriotic cells (Spearman correlation coefficient, .96; P < .001). Methylation of the intron 1 region of the SF-1 gene is associated with its expression in endometriotic cells. This CpG island therefore plays an important role in regulating SF-1 expression.

Establishment of a CpG island microarray for analyses of genome-wide DNA methylation in Chinese hamster ovary cells

Optimizing productivity and growth rates of recombinant Chinese hamster ovary (CHO) cells requires insight into the regulation of cellular processes. In this regard, the elucidation of the epigenetic process of DNA methylation, known to influence transcription by a differential occurrence in CpG islands in promoter regions, is increasingly gaining importance. However, DNA methylation has not yet been investigated on a genomic scale in CHO cells and suitable tools have not existed until now. Based on the genomic and transcriptomic CHO data currently available, we developed a customized oligonucleotide microarray covering 19598 CpG islands (89 % of total bioinformatically identified CpG islands) in the CHO genome. We applied our CHO-specific CpG island microarray to investigate the effect of butyrate treatment on differential DNA methylation in CHO cultures in a time-dependent approach. Supplementation of butyrate is known to enhance cell specific productivities in CHO cells and leads to alterations of epigenetic silencing events. Gene ontology clusters regarding, e.g., chromatin modification or DNA repair, were significantly overrepresented 24 h after butyrate addition. Functional classifications furthermore indicated that several major signaling systems such as the Wnt/β-catenin pathway were affected by butyrate treatment. Our novel CHO-specific CpG island microarray will provide valuable information in future studies of cellular processes associated with productivity and product characteristics.

A distinct group of CpG islands shows differential DNA methylation between replicas of the same cell line in vitro

BackgroundCpG dinucleotide-rich genomic DNA regions, known as CpG islands (CGIs), can be methylated at their cytosine residues as an epigenetic mark that is stably inherited during cell mitosis. Differentially methylated regions (DMRs) are genomic regions showing different degrees of DNA methylation in multiple samples. In this study, we focused our attention on CGIs showing different DNA methylation between two culture replicas of the same cell line.ResultsWe used methylation data of 35 cell lines from the Encyclopedia of DNA Elements (ENCODE) consortium to identify CpG islands that were differentially methylated between replicas of the same cell line and denoted them Inter Replicas Differentially Methylated CpG islands (IRDM-CGIs). We identified a group of IRDM-CGIs that was consistently shared by different cell lines, and denoted it common IRDM-CGIs. X chromosome CGIs were overrepresented among common IRDM-CGIs. Autosomal IRDM-CGIs were preferentially located in gene bodies and intergenic regions had a lower G + C content, a smaller mean length, and a reduced CpG percentage. Functional analysis of the genes associated with autosomal IRDM-CGIs showed that many of them are involved in DNA binding and development.ConclusionsOur results show that several specific functional and structural features characterize common IRDM-CGIs. They may represent a specific subset of CGIs that are more prone to being differentially methylated for their intrinsic characteristics.

COHCAP: an integrative genomic pipeline for single-nucleotide resolution DNA methylation analysis

COHCAP (City of Hope CpG Island Analysis Pipeline) is an algorithm to analyze single-nucleotide resolution DNA methylation data produced by either an Illumina methylation array or targeted bisulfite sequencing. The goal of the COHCAP algorithm is to identify CpG islands that show a consistent pattern of methylation among CpG sites. COHCAP is currently the only DNA methylation package that provides integration with gene expression data to identify a subset of CpG islands that are most likely to regulate downstream gene expression, and it can generate lists of differentially methylated CpG islands with ∼50% concordance with gene expression from both cell line data and heterogeneous patient data. For example, this article describes known breast cancer biomarkers (such as estrogen receptor) with a negative correlation between DNA methylation and gene expression. COHCAP also provides visualization for quality control metrics, regions of differential methylation and correlation between methylation and gene expression. This software is freely available at https://sourceforge.net/projects/cohcap/.

Features of Methylation and Gene Expression in the Promoter-Associated CpG Islands Using Human Methylome Data

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.

Identification and Validation of DNA Methylation Markers to Predict Lymph Node Metastasis of Esophageal Squamous Cell Carcinomas

The presence of lymph node metastasis in esophageal squamous cell carcinoma (ESCC) patients is a critical factor for decision of treatment strategy. However, there have been no molecular markers to assess lymph node metastasis. In this study, we aimed to identify CpG islands (CGIs) whose DNA methylation statuses are associated with the presence of lymph node metastasis. A total of 96 ESCCs were divided into a screening set (n=48) and a validation set (n=48). Genome-wide methylation analysis was performed by methylated DNA immunoprecipitation-CGI microarray analysis. Methylation levels were analyzed by quantitative methylation-specific PCR (qMSP). Genome-wide methylation analysis identified 25 CGIs differentially methylated between 8 ESCCs with lymph node metastasis and 4 without. In the screening set, 7 CGIs had significantly different methylation levels (P<0.05) between the ESCCs with and without lymph node metastasis, and cut-off methylation levels for these CGIs were determined. The validation set was analyzed with the prefixed cut-offs, and methylation statuses of 2 CGIs in the vicinities of PAX6 and ENST00000363328 were validated to be associated with the presence of lymph node metastasis. Using these 2 markers, the presence was predicted with a sensitivity of 93% and specificity of 57%. In addition, the methylation statuses of the 2 CGIs were significantly associated with disease-free survival (P=0.006). Methylation statuses of these 2 CGIs were significantly associated with the presence of lymph node metastasis of ESCCs. These CGIs are promising markers to predict the presence of lymph node metastases.

CpG Islands Detector: a Window-based CpG Island Search Tool

Abstract CpG is the pair of nucleotides C and G, appearing suc-cessively, in this order, along one DNA strand. It is known that due to biochemical considerations CpG is relatively rare in most DNA sequences. However, in par-ticular subsequences, which are a few hundred to a few thousand nucleotides long, the couple CpG is more frequent. These subsequences, called CpG islands, are known to appear in biologically more significant parts of the genome. The ability to identify CpG islands along a chromosome will therefore help us spot its more sig-nificant regions of interest, such as the promoters or 'start' regions of many genes. In this respect, I devel-oped the CpG islands search tool, CpG Islands Detector, which was implemented in JAVA to be run on any platform. The window-based graphical user inter-face of CpG Islands Detector may facilitate the end user to employ this tool to pinpoint CpG islands in a ge-nomic DNA sequence. In addition, this tool can be used to highlight potential genes in genomic sequences since CpG islands are very often found in the 5' regions of vertebrate genes.Availability: The tool is available free of charge for non-commercial use only. Contact the corresponding authorKeywords: CpG island, promoter, CpG islands detector, JAVA, graphical user interface

Identifying CpG Islands by Different Computational Techniques

CpG islands (CGIs) are generally regarded as important epigenetic regulatory elements due to their association with promoter regions. However, identification of functional CGIs is hampered by repetitive elements and species-specific particularities. Here, we compared the performance of different CGI detection programs on genomic sequences of human and mouse genes. Although mouse CGIs are shorter and G+C poorer than their human counterparts, the different tools tested in our study reliably identify CGIs in promoter regions in both species. Our study confirms that substantially fewer murine than human CGIs coincide with repetitive elements and indicates that such CGIs are subject to accelerated cytosine deamination. In addition, CpG depletion appears to anticorrelate with the epigenetic features of functional regulatory CGIs. Taking into account different deamination rates in unmethylated CGIs versus those in methylated CGIs might support the detection of functional CGIs in other species for which there is little epigenetic information available.