Identification of DNA CpG Islands Using Inter-dinucleotide Distances

2015 ◽  
pp. 162-172 ◽  
Author(s):  
Vera Afreixo ◽  
Carlos A. C. Bastos ◽  
João M. O. S. Rodrigues ◽  
Raquel M. Silva
Keyword(s):  
Cpg Islands

An Unbiased Predictive Model to Detect DNA Methylation Propensity of CpG Islands in the Human Genome

2020 ◽  
Vol 15 ◽  
Author(s):  
Dicle Yalcin ◽  
Hasan H. Otu
Keyword(s):  
Model Building ◽  
De Novo ◽  
Cpg Islands ◽  
Treatment Strategies ◽  
Area Under The Curve ◽  
Global Methylation ◽  
Sequence Features ◽  
A Genome ◽  
Combined Features ◽  
Epigenetic Repression

Background: Epigenetic repression mechanisms play an important role in gene regulation, specifically in cancer development. In many cases, a CpG island’s (CGI) susceptibility or resistance to methylation are shown to be contributed by local DNA sequence features. Objective: To develop unbiased machine learning models–individually and combined for different biological features–that predict the methylation propensity of a CGI. Methods: We developed our model consisting of CGI sequence features on a dataset of 75 sequences (28 prone, 47 resistant) representing a genome-wide methylation structure. We tested our model on two independent datasets that are chromosome (132 sequences) and disease (70 sequences) specific. Results: We provided improvements in prediction accuracy over previous models. Our results indicate that combined features better predict the methylation propensity of a CGI (area under the curve (AUC) ~0.81). Our global methylation classifier performs well on independent datasets reaching an AUC of ~0.82 for the complete model and an AUC of ~0.88 for the model using select sequences that better represent their classes in the training set. We report certain de novo motifs and transcription factor binding site (TFBS) motifs that are consistently better in separating prone and resistant CGIs. Conclusion: Predictive models for the methylation propensity of CGIs lead to a better understanding of disease mechanisms and can be used to classify genes based on their tendency to contain methylation prone CGIs, which may lead to preventative treatment strategies. MATLAB and Python™ scripts used for model building, prediction, and downstream analyses are available at https://github.com/dicleyalcin/methylProp_predictor.

CpGIScan: An Ultrafast Tool for CpG Islands Identification from Genome Sequence

2017 ◽  
Vol 12 (2) ◽  
pp. 181-184 ◽  
Author(s):  
Zhenxin Fan ◽  
Bisong Yue ◽  
Xiuyue Zhang ◽  
Lianming Du ◽  
Zuoyi Jian
Keyword(s):  
Genome Sequence ◽  
Cpg Islands

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

scholarly journals Epigenetic Effects of Benzene in Hematologic Neoplasms: The Altered Gene Expression

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2392
Author(s):  
Giovanna Spatari ◽  
Alessandro Allegra ◽  
Mariella Carrieri ◽  
Giovanni Pioggia ◽  
Sebastiano Gangemi
Keyword(s):  
Tumor Suppressor ◽  
Cpg Islands ◽  
Hematologic Malignancies ◽  
Genetic Alterations ◽  
Dna Hypomethylation ◽  
Negative Effects ◽  
Promoter Regions ◽  
Hematological Neoplasms ◽  
Altered Gene ◽  
Hematological Tumors

Benzene carcinogenic ability has been reported, and chronic exposure to benzene can be one of the risk elements for solid cancers and hematological neoplasms. Benzene is acknowledged as a myelotoxin, and it is able to augment the risk for the onset of acute myeloid leukemia, myelodysplastic syndromes, aplastic anemia, and lymphomas. Possible mechanisms of benzene initiation of hematological tumors have been identified, as a genotoxic effect, an action on oxidative stress and inflammation and the provocation of immunosuppression. However, it is becoming evident that genetic alterations and the other causes are insufficient to fully justify several phenomena that influence the onset of hematologic malignancies. Acquired epigenetic alterations may participate with benzene leukemogenesis, as benzene may affect nuclear receptors, and provoke post-translational alterations at the protein level, thereby touching the function of regulatory proteins, comprising oncoproteins and tumor suppressor proteins. DNA hypomethylation correlates with stimulation of oncogenes, while the hypermethylation of CpG islands in promoter regions of specific tumor suppressor genes inhibits their transcription and stimulates the onset of tumors. The discovery of the systems of epigenetic induction of benzene-caused hematological tumors has allowed the possibility to operate with pharmacological interventions able of stopping or overturning the negative effects of benzene.

scholarly journals Absence of S100A4 in the mouse lens induces an aberrant retina-specific differentiation program and cataract

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rupalatha Maddala ◽  
Junyuan Gao ◽  
Richard T. Mathias ◽  
Tylor R. Lewis ◽  
Vadim Y. Arshavsky ◽  
...  
Keyword(s):  
Calcium Binding ◽  
Late Onset ◽  
Cpg Islands ◽  
Transcriptome Profiling ◽  
Specific Gene ◽  
Pathway Network ◽  
S100a4 Expression ◽  
Histone H3k27 ◽  
Pathway Analyses ◽  
Upregulated Genes

AbstractS100A4, a member of the S100 family of multifunctional calcium-binding proteins, participates in several physiological and pathological processes. In this study, we demonstrate that S100A4 expression is robustly induced in differentiating fiber cells of the ocular lens and that S100A4(−/−) knockout mice develop late-onset cortical cataracts. Transcriptome profiling of lenses from S100A4(−/−) mice revealed a robust increase in the expression of multiple photoreceptor- and Müller glia-specific genes, as well as the olfactory sensory neuron-specific gene, S100A5. This aberrant transcriptional profile is characterized by corresponding increases in the levels of proteins encoded by the aberrantly upregulated genes. Ingenuity pathway network and curated pathway analyses of differentially expressed genes in S100A4(−/−) lenses identified Crx and Nrl transcription factors as the most significant upstream regulators, and revealed that many of the upregulated genes possess promoters containing a high-density of CpG islands bearing trimethylation marks at histone H3K27 and/or H3K4, respectively. In support of this finding, we further documented that S100A4(−/−) knockout lenses have altered levels of trimethylated H3K27 and H3K4. Taken together, our findings suggest that S100A4 suppresses the expression of retinal genes during lens differentiation plausibly via a mechanism involving changes in histone methylation.

scholarly journals Two competing mechanisms of DNMT3A recruitment regulate the dynamics of de novo DNA methylation at PRC1-targeted CpG islands

2021 ◽  
Author(s):  
Daniel N. Weinberg ◽  
Phillip Rosenbaum ◽  
Xiao Chen ◽  
Douglas Barrows ◽  
Cynthia Horth ◽  
...  
Keyword(s):  
Dna Methylation ◽  
De Novo ◽  
Cpg Islands

scholarly journals DNA Methylation of Human Choline Kinase Alpha Promoter-Associated CpG Islands in MCF-7 Cells

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 853
Author(s):  
Siti Aisyah Faten Mohamed Sa’dom ◽  
Sweta Raikundalia ◽  
Shaharum Shamsuddin ◽  
Wei Cun See Too ◽  
Ling Ling Few
Keyword(s):  
Dna Methylation ◽  
Transcription Factor ◽  
Binding Site ◽  
Promoter Activity ◽  
Cytosine Methylation ◽  
Cpg Island ◽  
Cpg Islands ◽  
Site Directed Mutagenesis ◽  
Choline Kinase ◽  
Mcf 7

Choline kinase (CK) is the enzyme catalyzing the first reaction in CDP-choline pathway for the biosynthesis of phosphatidylcholine. Higher expression of the α isozyme of CK has been implicated in carcinogenesis, and inhibition or downregulation of CKα (CHKA) is a promising anticancer approach. This study aimed to investigate the regulation of CKα expression by DNA methylation of the CpG islands found on the promoter of this gene in MCF-7 cells. Four CpG islands have been predicted in the 2000 bp promoter region of ckα (chka) gene. Six CpG island deletion mutants were constructed using PCR site-directed mutagenesis method and cloned into pGL4.10 vectors for promoter activity assays. Deletion of CpG4C region located between –225 and –56 significantly increased the promoter activity by 4-fold, indicating the presence of important repressive transcription factor binding site. The promoter activity of methylated full-length promoter was significantly lower than the methylated CpG4C deletion mutant by 16-fold. The results show that DNA methylation of CpG4C promotes the binding of the transcription factor that suppresses the promoter activity. Electrophoretic mobility shift assay analysis showed that cytosine methylation at MZF1 binding site in CpG4C increased the binding of putative MZF1 in nuclear extract. In conclusion, the results suggest that DNA methylation decreased the promoter activity by promoting the binding of putative MZF1 transcription factor at CpG4C region of the ckα gene promoter.

scholarly journals Histone Modifying Enzymes in Gynaecological Cancers

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 816
Author(s):  
Priya Ramarao-Milne ◽  
Olga Kondrashova ◽  
Sinead Barry ◽  
John D. Hooper ◽  
Jason S. Lee ◽  
...  
Keyword(s):  
Genome Instability ◽  
Cpg Islands ◽  
Gene Promoter ◽  
Driver Mutations ◽  
Promoter Regions ◽  
Post Translational Modifications ◽  
Chromatin Dynamics ◽  
Cancer Types ◽  
Histone Modifying Enzymes

Genetic and epigenetic factors contribute to the development of cancer. Epigenetic dysregulation is common in gynaecological cancers and includes altered methylation at CpG islands in gene promoter regions, global demethylation that leads to genome instability and histone modifications. Histones are a major determinant of chromosomal conformation and stability, and unlike DNA methylation, which is generally associated with gene silencing, are amenable to post-translational modifications that induce facultative chromatin regions, or condensed transcriptionally silent regions that decondense resulting in global alteration of gene expression. In comparison, other components, crucial to the manipulation of chromatin dynamics, such as histone modifying enzymes, are not as well-studied. Inhibitors targeting DNA modifying enzymes, particularly histone modifying enzymes represent a potential cancer treatment. Due to the ability of epigenetic therapies to target multiple pathways simultaneously, tumours with complex mutational landscapes affected by multiple driver mutations may be most amenable to this type of inhibitor. Interrogation of the actionable landscape of different gynaecological cancer types has revealed that some patients have biomarkers which indicate potential sensitivity to epigenetic inhibitors. In this review we describe the role of epigenetics in gynaecological cancers and highlight how it may exploited for treatment.

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