6mA-Finder: a novel online tool for predicting DNA N6-methyladenine sites in genomes.
Ontology highlight
ABSTRACT: Motivation
DNA N6-methyladenine (6 mA) has recently been found as an essential epigenetic modification, playing its roles in a variety of cellular processes. The abnormal status of DNA 6 mA modification has been reported in cancer and other disease. The annotation of 6 mA marks in genome is the first crucial step to explore the underlying molecular mechanisms including its regulatory roles.Results
We present a novel online DNA 6 mA site tool, 6 mA-Finder, by incorporating seven sequence-derived information and three physicochemical-based features through recursive feature elimination strategy. Our multiple cross-validations indicate the promising accuracy and robustness of our model. 6 mA-Finder outperforms its peer tools in general and species-specific 6 mA site prediction, suggesting it can provide a useful resource for further experimental investigation of DNA 6 mA modification.Availability and implementation
https://bioinfo.uth.edu/6mA_Finder.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Xu H
PROVIDER: S-EPMC7214014 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Triple negative breast cancer (TNBC) is a subtype of breast cancer with strong aggressiveness and poor clinical treatment effect, accounting for about 10-20% of breast cancer cases. N(6)-methyldeoxyadenosine (6mA) is the most conservative DNA modification in prokaryotes and eukaryotes. It is widely found in bacteria and has such functions as DNA mismatch repair, chromosome separation and virulence regulation. We determined that 6mA was modified in TNBC cell line MDA-MB-231 and the TNBC tissue. Meanwhile, compared with normal tissues, the expression level of 6mA and its methylase N6AMT1 was significantly decreased in TNBC tissue. MDA-MB-231cells were cultured with 8μM Olaparib for 2 months to construct drug-resistant cell line 231-RO. It was found that the level of 6mA also increased significantly, and the expression of N6AMT1 or ALKBH1 could effectively influence the drug resistance. Subsequently, we found that LINP1 was highly expressed in 231-RO, which was involved in DNA repair, and the expression of LINP1 could be positively regulated by 6mA modification. LINP1 expression level is directly related to TNBC drug resistance. The above results indicate that 6mA may be a new biological marker of TNBC. Meanwhile, 6mA modification may be involved in the regulation of Olaparib resistance.
| S-EPMC7887291 | biostudies-literature
Project description:DNA N6-methyladenine (6mA) is an important epigenetic modification, which is involved in many biology regulation processes. An accurate and reliable method for 6mA identification can help us gain a better insight into the regulatory mechanism of the modification. Although many experimental techniques have been proposed to identify 6mA sites genome-wide, these techniques are time consuming and laborious. Recently, several machine learning methods have been developed to identify 6mA sites genome-wide. However, there is room for the improvement on their performance for predicting 6mA sites in rice genome. In this paper, we developed a simple and lightweight deep learning model to identify DNA 6mA sites in rice genome. Our model needs no prior knowledge of 6mA or manually crafted sequence feature. We built our model based on two rice 6mA benchmark datasets. Our method got an average prediction accuracy of ?93% and ?92% on the two datasets we used. We compared our method with existing 6mA prediction tools. The comparison results show that our model outperforms the state-of-the-art methods.
| S-EPMC6797597 | biostudies-literature
Project description:DNA N6-methyladenine (6mA) is a dominant DNA modification form and involved in many biological functions. The accurate genome-wide identification of 6mA sites may increase understanding of its biological functions. Experimental methods for 6mA detection in eukaryotes genome are laborious and expensive. Therefore, it is necessary to develop computational methods to identify 6mA sites on a genomic scale, especially for plant genomes. Based on this consideration, the study aims to develop a machine learning-based method of predicting 6mA sites in the rice genome. We initially used mono-nucleotide binary encoding to formulate positive and negative samples. Subsequently, the machine learning algorithm named Random Forest was utilized to perform the classification for identifying 6mA sites. Our proposed method could produce an area under the receiver operating characteristic curve of 0.964 with an overall accuracy of 0.917, as indicated by the fivefold cross-validation test. Furthermore, an independent dataset was established to assess the generalization ability of our method. Finally, an area under the receiver operating characteristic curve of 0.981 was obtained, suggesting that the proposed method had good performance of predicting 6mA sites in the rice genome. For the convenience of retrieving 6mA sites, on the basis of the computational method, we built a freely accessible web server named iDNA6mA-Rice at http://lin-group.cn/server/iDNA6mA-Rice.
| S-EPMC6746913 | biostudies-literature
Project description:BACKGROUND:An increasing number of nucleic acid modifications have been profiled with the development of sequencing technologies. DNA N6-methyladenine (6mA), which is a prevalent epigenetic modification, plays important roles in a series of biological processes. So far, identification of DNA 6mA relies primarily on time-consuming and expensive experimental approaches. However, in silico methods can be implemented to conduct preliminary screening to save experimental resources and time, especially given the rapid accumulation of sequencing data. RESULTS:In this study, we constructed a 6mA predictor, p6mA, from a series of sequence-based features, including physicochemical properties, position-specific triple-nucleotide propensity (PSTNP), and electron-ion interaction pseudopotential (EIIP). We performed maximum relevance maximum distance (MRMD) analysis to select key features and used the Extreme Gradient Boosting (XGBoost) algorithm to build our predictor. Results demonstrated that p6mA outperformed other existing predictors using different datasets. CONCLUSIONS:p6mA can predict the methylation status of DNA adenines, using only sequence files. It may be used as a tool to help the study of 6mA distribution pattern. Users can download it from https://github.com/Konglab404/p6mA.
| S-EPMC7038560 | biostudies-literature
Project description:DNA methylation has a role in the pathogenesis of essential hypertension. DNA N6-methyladenine (6mA) modification as a novel adenine methylation exists in human tissues, but whether it plays a role in hypertension development remains unclear. Here, we reported that the global 6mA DNA level in leukocytes was significantly reduced in patients with hypertension and was reversed with successful treatment. Age, systolic blood pressure, and serum total cholesterol and high-density lipoprotein levels were associated with decreased leukocyte 6mA DNA level. Elevated ALKBH1 (AlkB homolog 1), a demethylase of 6mA, level mediated this dynamic change in 6mA level in leukocytes and vascular smooth muscle cells in hypertension mouse and rat models. Knockdown of ALKBH1 suppressed angiotensin II-induced vascular smooth muscle phenotype transformation, proliferation and migration. ALKBH1-6mA directly and negatively regulated hypoxia inducible factor 1 ? (HIF1?), which responded to angiotensin II-induced vascular remodeling. Collectively, our results demonstrate a potential epigenetic role for ALKBH1-6mA regulation in hypertension development, diagnosis and treatment.
| S-EPMC7185115 | biostudies-literature
Project description:N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA's biological functions. However, the existing experimental techniques for detecting 6mA sites are cost-ineffective, which implies the great need of developing new computational methods for this problem. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species: Arabidopsis thaliana, Fragaria vesca and Rosa chinensis with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression.
| S-EPMC7924747 | biostudies-literature
Project description:DNA N6-methyladenine (6mA) plays an important role in regulating the gene expression of eukaryotes. Accurate identification of 6mA sites may assist in understanding genomic 6mA distributions and biological functions. Various experimental methods have been applied to detect 6mA sites in a genome-wide scope, but they are too time-consuming and expensive. Developing computational methods to rapidly identify 6mA sites is needed. In this paper, a new machine learning-based method, i6mA-DNCP, was proposed for identifying 6mA sites in the rice genome. Dinucleotide composition and dinucleotide-based DNA properties were first employed to represent DNA sequences. After a specially designed DNA property selection process, a bagging classifier was used to build the prediction model. The jackknife test on a benchmark dataset demonstrated that i6mA-DNCP could obtain 84.43% sensitivity, 88.86% specificity, 86.65% accuracy, a 0.734 Matthew's correlation coefficient (MCC), and a 0.926 area under the receiver operating characteristic curve (AUC). Moreover, three independent datasets were established to assess the generalization ability of our method. Extensive experiments validated the effectiveness of i6mA-DNCP.
| S-EPMC6826501 | biostudies-literature
Project description:DNA N6-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou's 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on Mus musculus, Rice, and "Combined-species" genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely.
| S-EPMC7463462 | biostudies-literature
Project description:Adenine N6 methylation in DNA (6mA) is widespread among bacteria and phage and is detected in mammalian genomes, where its function is largely unexplored. Here we show that 6mA deposition and removal are catalyzed by the Mettl4 methyltransferase and Alkbh4 dioxygenase, respectively, and that 6mA accumulation in genic elements corresponds with transcriptional silencing. Inactivation of murine Mettl4 depletes 6mA and causes sublethality and craniofacial dysmorphism in incross progeny. We identify distinct 6mA sensor domains of prokaryotic origin within the MPND deubiquitinase and ASXL1, a component of the Polycomb repressive deubiquitinase (PR-DUB) complex, both of which act to remove monoubiquitin from histone H2A (H2A-K119Ub), a repressive mark. Deposition of 6mA by Mettl4 triggers the proteolytic destruction of both sensor proteins, preserving genome-wide H2A-K119Ub levels. Expression of the bacterial 6mA methyltransferase Dam, in contrast, fails to destroy either sensor. These findings uncover a native, adversarial 6mA network architecture that preserves Polycomb silencing.
| S-EPMC6591016 | biostudies-literature
Project description:DNA N6-methyladenine (6mA) modifications expand the information capacity of DNA and have long been known to exist in bacterial genomes. Xanthomonas oryzae pv. Oryzicola (Xoc) is the causative agent of bacterial leaf streak, an emerging and destructive disease in rice worldwide. However, the genome-wide distribution patterns and potential functions of 6mA in Xoc are largely unknown. In this study, we analyzed the levels and global distribution patterns of 6mA modification in genomic DNA of seven Xoc strains (BLS256, BLS279, CFBP2286, CFBP7331, CFBP7341, L8 and RS105). The 6mA modification was found to be widely distributed across the seven Xoc genomes, accounting for percent of 3.80, 3.10, 3.70, 4.20, 3.40, 2.10, and 3.10 of the total adenines in BLS256, BLS279, CFBP2286, CFBP7331, CFBP7341, L8, and RS105, respectively. Notably, more than 82% of 6mA sites were located within gene bodies in all seven strains. Two specific motifs for 6?mA modification, ARGT and AVCG, were prevalent in all seven strains. Comparison of putative DNA methylation motifs from the seven strains reveals that Xoc have a specific DNA methylation system. Furthermore, the 6?mA modification of rpfC dramatically decreased during Xoc infection indicates the important role for Xoc adaption to environment.
| S-EPMC6215013 | biostudies-literature