Search

DNA methylation constitutes one of the pillars of epigenetics, relying on covalent bonds for addition and/or removal of chemically distinct marks within the major groove of the double helix. DNA methyltransferases, enzymes which introduce methyl marks, initially evolved in prokaryotes as components of restriction‐modification systems protecting host genomes from bacteriophages and other invading foreign DNA. In early eukaryotic evolution, DNA methyltransferases were horizontally transferred from bacteria into eukaryotes several times and independently co‐opted into epigenetic regulatory systems, primarily via establishing connections with the chromatin environment. While C5‐methylcytosine is the cornerstone of plant and animal epigenetics and has been investigated in much detail, the epigenetic role of other methylated bases is less clear. The recent addition of N4‐methylcytosine of bacterial origin as a metazoan DNA modification highlights the prerequisites for foreign gene co‐option into the host regulatory networks, and challenges the existing paradigms concerning the origin and evolution of eukaryotic regulatory systems. [ABSTRACT FROM AUTHOR]

N4-methylcytosine (4mC) is one of the most common DNA methylation modifications found in both prokaryotic and eukaryotic genomes. Since the 4mC has various essential biological roles, determining its location helps reveal unexplored physiological and pathological pathways. In this study, we propose an effective computational method called i4mC-GRU using a gated recurrent unit and duplet sequence-embedded features to predict potential 4mC sites in mouse (Mus musculus) genomes. To fairly assess the performance of the model, we compared our method with several state-of-the-art methods using two different benchmark datasets. Our results showed that i4mC-GRU achieved area under the receiver operating characteristic curve values of 0.97 and 0.89 and area under the precision-recall curve values of 0.98 and 0.90 on the first and second benchmark datasets, respectively. Briefly, our method outperformed existing methods in predicting 4mC sites in mouse genomes. Also, we deployed i4mC-GRU as an online web server, supporting users in genomics studies.

Abstract N4-methylcytosine (4mC) is an important epigenetic mechanism, which regulates many cellular processes such as cell differentiation and gene expression. The knowledge about the 4mC sites is a key foundation to exploring its roles. Due to the limitation of techniques, precise detection of 4mC is still a challenging task. In this paper, we presented a multi-scale convolution neural network (CNN) and adaptive embedding-based computational method for predicting 4mC sites in mouse genome, which was referred to as MultiScale-CNN-4mCPred. The MultiScale-CNN-4mCPred used adaptive embedding to encode nucleotides, and then utilized multi-scale CNNs as well as long short-term memory to extract more in-depth local properties and contextual semantics in the sequences. The MultiScale-CNN-4mCPred is an end-to-end learning method, which requires no sophisticated feature design. The MultiScale-CNN-4mCPred reached an accuracy of 81.66% in the 10-fold cross-validation, and an accuracy of 84.69% in the independent test, outperforming state-of-the-art methods. We implemented the proposed method into a user-friendly web application which is freely available at: http://www.biolscience.cn/MultiScale-CNN-4mCPred/ .

Despite numerous studies on transcriptional level regulation by single genes in drug producing Actinomyces, the global regulation based on epigenetic modification is not well explored. N4-methylcytosine (m4C), an abundant epigenetic marker in Actinomycetes’ genome, but its regulatory mechanism remains unclear. In this study, we identify a m4C methyltransferase (SroLm3) in Streptomyces roseosporus L30 and multi-omics studies were performed and revealed SroLm3 as a global regulator of secondary metabolism. Notably, three BGCs in ΔsroLm3 strain exhibited decreased expression compared to wild type. In-frame deletion of sroLm3 in S.roseosporus L30 further revealed its role in enhancing daptomycin production. In summary, we characterized a m4C methyltransferase, revealed the function of m4C in secondary metabolism regulation and biosynthesis of red pigment, and mapped a series of novel regulators for daptomycin biosynthesis dominated by m4C methylation. Our research further indicated that m4C DNA methylation may contribute to a metabolic switch from primary to secondary metabolism in Actinomyces.

N4-methylcytosine (4mC) is an important epigenetic mechanism, which regulates many cellular processes such as cell differentiation and gene expression. The knowledge about the 4mC sites is a key foundation to exploring its roles. Due to the limitation of techniques, precise detection of 4mC is still a challenging task. In this paper, we presented a multi-scale convolution neural network (CNN) and adaptive embedding-based computational method for predicting 4mC sites in mouse genome, which was referred to as MultiScale-CNN-4mCPred. The MultiScale-CNN-4mCPred used adaptive embedding to encode nucleotides, and then utilized multi-scale CNNs as well as long short-term memory to extract more in-depth local properties and contextual semantics in the sequences. The MultiScale-CNN-4mCPred is an end-to-end learning method, which requires no sophisticated feature design. The MultiScale-CNN-4mCPred reached an accuracy of 81.66% in the 10-fold cross-validation, and an accuracy of 84.69% in the independent test, outperforming state-of-the-art methods. We implemented the proposed method into a user-friendly web application which is freely available at: http://www.biolscience.cn/MultiScale-CNN-4mCPred/. [ABSTRACT FROM AUTHOR]

• An deep learning-based method was developed to identify N4-methylcytosine site in Escherichia coli. • A natural language processing scheme called Word2Vec were proposed to formulate modification samples. • The distribution of nucleotides around positive and negative sequences were statistical analysis. N4-methylcytosine (4mC) is a type of DNA modification which could regulate several biological progressions such as transcription regulation, replication and gene expressions. Precisely recognizing 4mC sites in genomic sequences can provide specific knowledge about their genetic roles. This study aimed to develop a deep learning-based model to predict 4mC sites in the Escherichia coli. In the model, DNA sequences were encoded by word embedding technique 'word2vec'. The obtained features were inputted into 1-D convolutional neural network (CNN) to discriminate 4mC sites from non-4mC sites in Escherichia coli genome. The examination on independent dataset showed that our model could yield the overall accuracy of 0.861, which was about 4.3% higher than the existing model. To provide convenience to scholars, we provided the data and source code of the model which can be freely download from https://github.com/linDing-groups/Deep-4mCW2V. [ABSTRACT FROM AUTHOR]

DNA modification plays a pivotal role in regulating gene expression in cell development. As prevalent markers on DNA, 5‐methylcytosine (5mC), N6‐methyladenine (6mA), and N4‐methylcytosine (4mC) can be recognized by specific methyltransferases, facilitating cellular defense and the versatile regulation of gene expression in eukaryotes and prokaryotes. Recent advances in DNA sequencing technology have permitted the positions of different modifications to be resolved at the genome‐wide scale, which has led to the discovery of several novel insights into the complexity and functions of multiple methylations. In this review, we summarize differences in the various mapping approaches and discuss their pros and cons with respect to their relative read depths, speeds, and costs. We also discuss the development of future sequencing technologies and strategies for improving the detection resolution of current sequencing technologies. Lastly, we speculate on the potentially instrumental role that these sequencing technologies might play in future research. [ABSTRACT FROM AUTHOR]

Much is known about the generation, removal, and roles of 5-methylcytosine (5mC) in eukaryote DNA, and there is a growing body of evidence regarding N6-methyladenine, but very little is known about N4-methylcytosine (4mC) in the DNA of eukaryotes. The gene for the first metazoan DNA methyltransferase generating 4mC (N4CMT) was reported and characterized recently by others, in tiny freshwater invertebrates called bdelloid rotifers. Bdelloid rotifers are ancient, apparently asexual animals, and lack canonical 5mC DNA methyltransferases. Here, we characterize the kinetic properties and structural features of the catalytic domain of the N4CMT protein from the bdelloid rotifer Adineta vaga. We find that N4CMT generates high-level methylation at preferred sites, (a/c)CG(t/c/a), and low-level methylation at disfavored sites, exemplified by ACGG. Like the mammalian de novo 5mC DNA methyltransferase 3A/3B (DNMT3A/3B), N4CMT methylates CpG dinucleotides on both DNA strands, generating hemimethylated intermediates and eventually fully methylated CpG sites, particularly in the context of favored symmetric sites. In addition, like DNMT3A/3B, N4CMT methylates non-CpG sites, mainly CpA/TpG, though at a lower rate. Both N4CMT and DNMT3A/3B even prefer similar CpG-flanking sequences. Structurally, the catalytic domain of N4CMT closely resembles the Caulobacter crescentus cell cycle-regulated DNA methyltransferase. The symmetric methylation of CpG, and similarity to a cell cycle-regulated DNA methyltransferase, together suggest that N4CMT might also carry out DNA synthesis-dependent methylation following DNA replication., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interests with the contents of this article., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

N4-methylcytosine (4mC) is an important DNA chemical modification pattern which is a new methylation modification discovered in recent years and plays critical roles in gene expression regulation, defense against invading genetic elements, genomic imprinting, and so on. Identifying 4mC site from DNA sequence segment contributes to discovering more novel modification patterns. In this paper, we present a model called 4mCBERT that encodes DNA sequence segments by sequence characteristics including one-hot, electron-ion interaction pseudopotential, nucleotide chemical property, word2vec and chemical information containing physicochemical properties (PCP), chemical bidirectional encoder representations from transformers (chemical BERT) and employs ensemble learning framework to develop a prediction model. PCP and chemical BERT features are firstly constructed and applied to predict 4mC sites and show positive contributions to identifying 4mC. For the Matthew's Correlation Coefficient, 4mCBERT significantly outperformed other state-of-the-art models on six independent benchmark datasets including A. thaliana , C. elegans , D. melanogaster , E. coli , G. Pickering, and G. subterraneous by 4.32 % to 24.39 %, 2.52 % to 31.65 %, 2 % to 16.49 %, 6.63 % to 35.15, 8.59 % to 61.85 %, and 8.45 % to 34.45 %. Moreover, 4mCBERT is designed to allow users to predict 4mC sites and retrain 4mC prediction models. In brief, 4mCBERT shows higher performance on six benchmark datasets by incorporating sequence- and chemical-driven information and is available at http://cczubio.top/4mCBERT and https://github.com/abcair/4mCBERT. [ABSTRACT FROM AUTHOR]

N 4 -methylcytosine (4mC) is one of the most common DNA methylation modifications found in both prokaryotic and eukaryotic genomes. Since the 4mC has various essential biological roles, determining its location helps reveal unexplored physiological and pathological pathways. In this study, we propose an effective computational method called i4mC-GRU using a gated recurrent unit and duplet sequence-embedded features to predict potential 4mC sites in mouse ( Mus musculus ) genomes. To fairly assess the performance of the model, we compared our method with several state-of-the-art methods using two different benchmark datasets. Our results showed that i4mC-GRU achieved area under the receiver operating characteristic curve values of 0.97 and 0.89 and area under the precision-recall curve values of 0.98 and 0.90 on the first and second benchmark datasets, respectively. Briefly, our method outperformed existing methods in predicting 4mC sites in mouse genomes. Also, we deployed i4mC-GRU as an online web server, supporting users in genomics studies., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript., (© 2023 The Authors.)

N4-methylcytosine (4 mC) is an important and common methylation which widely exists in prokaryotes. It plays a crucial role in correcting DNA replication errors and protecting host DNA against degradation by restrictive enzymes. Hence, the accurate identification for 4 mC sites is greatly significant for understanding biological functions and treating gene diseases. In this paper, a novel model is designed for identifying 4 mC sites. Firstly, we extract features from original sequences by multi-source feature representation methods, which are mono-nucleotide binary and k -mer frequency, dinucleotide binary and position-specific frequency, ring-function-hydrogen-chemical properties, dinucleotide-based DNA properties and trinucleotide-based DNA properties. Subsequently, gradient boosting decision tree is applied to select the optimal feature set and remove redundant information. Finally, support vector machine is employed to predict 4 mC or non-4mC sites. The accuracies of six datasets reach 0.851, 0.859, 0.801, 0.87, 0.859 and 0.901, respectively, which are superior to previous prediction methods. Therefore, the results show that our predictor is a feasible and effective tool for identifying 4 mC sites. Furthermore, an online web server is established at http://dnan4c.zhanglab.site. [Display omitted] • A predictor is designed to identify DNA N4-methylcytosine sites based on multi-source features and GBDT. • Extract features from original sequences by multi-source feature representation methods. • GBDT is applied to select the optimal feature set and remove redundant information. • An online web server is established at http://dnan4c.zhanglab.site. [ABSTRACT FROM AUTHOR]

N4-methylcytosine (4mC) has a significant effect on altering protein interactions, DNA conformation, gene expression and genomic imprinting. Accurate recognition of the 4mC sites is helpful for in-depth study of biomedical research. Although there are experimental methods for detecting 4mC sites, these techniques are time-consuming and laborious, and cannot be applied to large-scale genome scanning. Therefore, supplementation with an efficient computational method is absolutely necessary. In this study, we propose a prediction tool, 4mCPred-FSVM, to solve the above problems. We use position-specific trinucleotide propensity (PSTNP) to construct feature vectors. Subsequently, the feature vector was used as the input of the fuzzy support vector machine (FSVM) and the final predictor was developed. We measure the performance of the model on six datasets. In comparison to the state-of-the-art predictor, our predictor has achieved much higher accuracies in predicting 4mC sites. • A fuzzy support vector machine is proposed. • The original feature space and kernelized self representation-membership functions are proposed. • The proposed model is robust in predicting 4mC. [ABSTRACT FROM AUTHOR]

Despite numerous studies on transcriptional level regulation by single genes in drug producing Actinomyces , the global regulation based on epigenetic modification is not well explored. N4-methylcytosine (m4C), an abundant epigenetic marker in Actinomycetes ' genome, but its regulatory mechanism remains unclear. In this study, we identify a m4C methyltransferase (SroLm3) in Streptomyces roseosporus L30 and multi-omics studies were performed and revealed SroLm3 as a global regulator of secondary metabolism. Notably, three BGCs in Δ sroLm3 strain exhibited decreased expression compared to wild type. In-frame deletion of sroLm3 in S.roseosporus L30 further revealed its role in enhancing daptomycin production. In summary, we characterized a m4C methyltransferase, revealed the function of m4C in secondary metabolism regulation and biosynthesis of red pigment, and mapped a series of novel regulators for daptomycin biosynthesis dominated by m4C methylation. Our research further indicated that m4C DNA methylation may contribute to a metabolic switch from primary to secondary metabolism in Actinomyces., Competing Interests: All authors declare no competing interests., (© 2022 The Authors.)

N4-methylcytosine (4 mC) is an important epigenetic modification that occurs enzymatically by the action of DNA methyltransferases. 4 mC sites exist in prokaryotes and eukaryotes while playing a vital role in regulating gene expression, DNA replication, and cell cycle. The efficient and accurate prediction of 4 mC sites has a significant role in the insight of 4 mC biological properties and functions. Therefore, a sequence-based predictor is proposed, namely 4 mC-RF, for identifying 4 mC sites through the integration of statistical moments along with position, and composition-dependent features. Relative and absolute position-based features are computed to extract optimal features. A popular machine learning classifier Random Forest was used for training the model. Validation results were obtained through rigorous processes of self-consistency, 10-fold cross-validation, Independent set testing, and Jackknife yielding 95.1%, 95.2%, 97.0%, and 94.7% accuracies, respectively. Our proposed model depicts the highest prediction accuracies as compared to existing models. Subsequently, the developed 4 mC-RF model was constructed into a web server. A significant and more accurate predictor of 4 mC Methylcytosine sites helps experimental scientists to gather faster, efficient, and cost-effective results. [Display omitted] • Human DNA undergoes methylation over time which can lead to various diseases or disorders. • There are several types of methylations, 4 mC is one of the types that modifies cytosine bases. • The proposed predictor helps to identify Cysteine sites that are susceptible to 4 mC modification. • Assiduous results are obtained from a random forest-based model as compared with other classifiers. • The proposed model outperforms existing models such as 4mCPred, Meta-4mcPred and 4mCPred-SVM. [ABSTRACT FROM AUTHOR]

DNA N4-methylcytosine(4mC) plays an important role in numerous biological functions and is a mechanism of particular epigenetic importance. Therefore, accurate identification of the 4mC sites in DNA sequences is necessary to understand the functional mechanism. Although some effective calculation tools have been proposed to identifying DNA 4mC sites, it is still challenging to improve identification accuracy and generalization ability. Therefore, there is a great need to build a computational tool to accurately identify the position of DNA 4mC sites. Hence, this study proposed a novel predictor XGB4mcPred, a predictor for the identification of 4mC sites trained using an extreme gradient boosting algorithm (XGBoost) and DNA sequence information. Firstly, we used the One-Hot encoding on adjacent and spaced nucleotides, dinucleotides, and trinucleotides of the original 4mC site sequences as feature vectors. Then, the importance values of the feature vectors pre-trained by the XGBoost algorithm were used as a threshold to filter redundant features, resulting in a significant improvement in the identification accuracy of the constructed XGB4mcPred predictor to identify 4mC sites. The analysis shows that there is a clear preference for nucleotide sequences between 4mC sites and non-4mC site sequences in six datasets from multiple species, and the optimized features can better distinguish 4mC sites from non-4mC sites. The experimental results of cross-validation and independent tests from six different species show that our proposed predictor XGB4mcPred significantly outperformed other state-of-the-art predictors and was improved to varying degrees compared with other state-of-the-art predictors. Additionally, the user-friendly webserver we used to developed the XGB4mcPred predictor was made freely accessible. [ABSTRACT FROM AUTHOR]