3-Methylcytosine in Cancer: An Underappreciated Methyl Lesion?

Pataillot-Meakin, Thomas; Pillay, Nischalan; Beck, Stephan

doi:10.2217/epi.15.121

Cited by 14 publications

(16 citation statements)

References 18 publications

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“…In DNA modification, methylation is a fundamental epigenetic tag that plays a major role in biological processes such as genomic imprinting, preservation of chromosomal stability, X-chromosome inactivation, cell cycle progression, and regulation of gene expression [1,2]. Cytosine methylation has been widely studied in both eukaryotic and prokaryotic genomes, where it creates bases such as 5-methylcytosine (5mC), 3-methylcytosine, and N4-methylcytosine [3,4]. 5mC is produced by adding a methyl group via DNA methyltransferase (DNMT) to the C5 position of cytosine, whereas 3methylcytosine accrues due to the action of environmental alkylation agents [3,5].…”

Section: Introductionmentioning

confidence: 99%

“…Cytosine methylation has been widely studied in both eukaryotic and prokaryotic genomes, where it creates bases such as 5-methylcytosine (5mC), 3-methylcytosine, and N4-methylcytosine [3,4]. 5mC is produced by adding a methyl group via DNA methyltransferase (DNMT) to the C5 position of cytosine, whereas 3methylcytosine accrues due to the action of environmental alkylation agents [3,5]. 5mC is known to play a significant role in various biological functions [6,7] and is associated with diabetes, cancer, and neurological diseases [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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i4mC-Deep: An Intelligent Predictor of N4-Methylcytosine Sites Using a Deep Learning Approach with Chemical Properties

Alam

Tayara

Chong

2021

Genes

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DNA is subject to epigenetic modification by the molecule N4-methylcytosine (4mC). N4-methylcytosine plays a crucial role in DNA repair and replication, protects host DNA from degradation, and regulates DNA expression. However, though current experimental techniques can identify 4mC sites, such techniques are expensive and laborious. Therefore, computational tools that can predict 4mC sites would be very useful for understanding the biological mechanism of this vital type of DNA modification. Conventional machine-learning-based methods rely on hand-crafted features, but the new method saves time and computational cost by making use of learned features instead. In this study, we propose i4mC-Deep, an intelligent predictor based on a convolutional neural network (CNN) that predicts 4mC modification sites in DNA samples. The CNN is capable of automatically extracting important features from input samples during training. Nucleotide chemical properties and nucleotide density, which together represent a DNA sequence, act as CNN input data. The outcome of the proposed method outperforms several state-of-the-art predictors. When i4mC-Deep was used to analyze G. subterruneus DNA, the accuracy of the results was improved by 3.9% and MCC increased by 10.5% compared to a conventional predictor.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

i4mC-Deep: An Intelligent Predictor of N4-Methylcytosine Sites Using a Deep Learning Approach with Chemical Properties

Alam

Tayara

Chong

2021

Genes

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“…The most common post-replicative DNA modification is cytosine methylation, which occurs in the genomes of both prokaryotes and eukaryotes. Cytosine methylation can be mediated enzymatically by DNA methyltransferases, resulting in two epigenetic nucleobases, 5-methylcytosine (5mC) and N4-methylcytosine (4mC), or chemically by endogenous and environmental alkylation agents, resulting in 3-methylcytosine 1, 2. The most well-studied and frequently occurring cytosine methylation, 5mC plays key roles in normal development, genomic imprinting, preservation of chromosome stability, aging, suppression of repetitive element transcription and transposition, and X chromosome inactivation 3, 4, 5, 6.…”

Section: Introductionmentioning

confidence: 99%

Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation

Manavalan

Basith

Shin

et al. 2019

Molecular Therapy - Nucleic Acids

193

104

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DNA N4-methylcytosine (4mC) is an important genetic modification and plays crucial roles in differentiation between self and non-self DNA and in controlling DNA replication, cell cycle, and gene-expression levels. Accurate 4mC site identification is fundamental to improve the understanding of 4mC biological functions and mechanisms. Hence, it is necessary to develop in silico approaches for efficient and high-throughput 4mC site identification. Although some bioinformatic tools have been developed in this regard, their prediction accuracy and generalizability require improvement to optimize their usability in practical applications. For this purpose, we here proposed Meta-4mCpred, a meta-predictor for 4mC site prediction. In Meta-4mCpred, we employed a feature representation learning scheme and generated 56 probabilistic features based on four different machine-learning algorithms and seven feature encodings covering diverse sequence information, including compositional, physicochemical, and position-specific information. Subsequently, the probabilistic features were used as an input to support vector machine and developed a final meta-predictor. To the best of our knowledge, this is the first meta-predictor for 4mC site prediction. Cross-validation results show that Meta-4mCpred achieved an overall average accuracy of 84.2% from six different species, which is ∼2%–4% higher than those attainable using the state-of-the-art predictors. Furthermore, Meta-4mCpred achieved an overall average accuracy of 86% on independent datasets evaluation, which is over 4% higher than those yielded by the state-of-the-art predictors. The user-friendly webserver employed to implement the proposed Meta-4mCpred is freely accessible at http://thegleelab.org/Meta-4mCpred .

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“… 7 The most common types of enzymatic cytosine methylation are 5-methylcytosine and N 4 -methylcytosine (4mC). 8 , 9 , 10 Unlike the former, which has been extensively studied, 8 , 11 , 12 4mC has not been thoroughly investigated. 4mC formation, predominantly found in prokaryotes, is catalyzed by N 4 -cytosine-specific DNA methyl transferases, which methylate the amino group at the fourth position of cytosine.…”

Section: Introductionmentioning

confidence: 99%

Empirical Comparison and Analysis of Web-Based DNA N4-Methylcytosine Site Prediction Tools

Manavalan

Hasan

Basith

et al. 2020

Molecular Therapy - Nucleic Acids

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DNA N 4 -methylcytosine (4mC) is a crucial epigenetic modification involved in various biological processes. Accurate genome-wide identification of these sites is critical for improving our understanding of their biological functions and mechanisms. As experimental methods for 4mC identification are tedious, expensive, and labor-intensive, several machine learning-based approaches have been developed for genome-wide detection of such sites in multiple species. However, the predictions projected by these tools are difficult to quantify and compare. To date, no systematic performance comparison of 4mC tools has been reported. The aim of this study was to compare and critically evaluate 12 publicly available 4mC site prediction tools according to species specificity, based on a huge independent validation dataset. The tools 4mCCNN ( Escherichia coli ), DNA4mC-LIP ( Arabidopsis thaliana ), iDNA-MS ( Fragaria vesca ), DNA4mC-LIP and 4mCCNN ( Drosophila melanogaster ), and four tools for Caenorhabditis elegans achieved excellent overall performance compared with their counterparts. However, none of the existing methods was suitable for Geoalkalibacter subterraneus , Geobacter pickeringii , and Mus musculus , thereby limiting their practical applicability. Model transferability to five species and non-transferability to three species are also discussed. The presented evaluation will assist researchers in selecting appropriate prediction tools that best suit their purpose and provide useful guidelines for the development of improved 4mC predictors in the future.

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3-Methylcytosine in Cancer: An Underappreciated Methyl Lesion?

Cited by 14 publications

References 18 publications

i4mC-Deep: An Intelligent Predictor of N4-Methylcytosine Sites Using a Deep Learning Approach with Chemical Properties

i4mC-Deep: An Intelligent Predictor of N4-Methylcytosine Sites Using a Deep Learning Approach with Chemical Properties

Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation

Empirical Comparison and Analysis of Web-Based DNA N4-Methylcytosine Site Prediction Tools

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