1
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Jiang T, Zhou Q, Yu KK, Chen SY, Li K. Identification and quantification of N6-methyladenosine by chemical derivatization coupled with 19F NMR spectroscopy. Org Biomol Chem 2024; 22:2566-2573. [PMID: 38465392 DOI: 10.1039/d4ob00169a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
N 6-Methyladenosine (6mA) is a well-known prokaryotic DNA modification that has been shown to play epigenetic roles in eukaryotic DNA. Accurate detection and quantification of 6mA are prerequisites for molecular understanding of the impact of 6mA modification on DNA. However, the existing methods have several problems, such as high false-positive rate, time-consuming and complex operating procedures. Chemical sensors for the selective detection of 6mA modification are rarely reported in the literature. Fluorinated phenylboronic acid combined with 19F NMR analysis is an effective method for determining DNA or RNA modification. In this study, we presented a simple and fast chemical method for labelling the 6th imino group of 6mA using a boric-acid-derived probe. Besides, the trifluoromethyl group of trifluoromethyl phenylboronic acid (2a) could detect 6mA modification through 19F NMR. Combined with this sensor system, 6mA modification could be detected well and quickly in 6 types of deoxynucleoside mixtures and DNA samples. Taken together, the method developed in the current study has potential for specific detection of 6mA in biological samples.
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Affiliation(s)
- Ting Jiang
- Key Laboratory of Green Chemistry and Technology (Ministry of Education), College of Chemistry, Sichuan University, Chengdu 610064, P. R. China.
| | - Qian Zhou
- Department of Chemistry, Xihua University, Chengdu 610039, P. R. China
| | - Kang-Kang Yu
- Key Laboratory of Green Chemistry and Technology (Ministry of Education), College of Chemistry, Sichuan University, Chengdu 610064, P. R. China.
| | - Shan-Yong Chen
- Key Laboratory of Green Chemistry and Technology (Ministry of Education), College of Chemistry, Sichuan University, Chengdu 610064, P. R. China.
| | - Kun Li
- Key Laboratory of Green Chemistry and Technology (Ministry of Education), College of Chemistry, Sichuan University, Chengdu 610064, P. R. China.
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2
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Liu N, Zhang Z, Wu Y, Wang Y, Liang Y. CRBSP:Prediction of CircRNA-RBP Binding Sites Based on Multimodal Intermediate Fusion. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2898-2906. [PMID: 37130249 DOI: 10.1109/tcbb.2023.3272400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Circular RNA (CircRNA) is widely expressed and has physiological and pathological significance, regulating post-transcriptional processes via its protein-binding activity. However, whereas much work has been done on linear RNA and RNA binding protein (RBP), little is known about the binding sites of CircRNA. The current report is on the development of a medium-term multimodal data fusion strategy, CRBSP, to predict CircRNA-RBP binding sites. CRBSP represents the CircRNA trinucleotide semantic, location, composition and frequency information as the corresponding coding methods of Word to vector (Word2vec), Position-specific trinucleotide propensity (PSTNP), Pseudo trinucleotide composition (PseTNC) and Trinucleotide nucleotide composition (TNC), respectively. CNN (Convolution Neural Networks) was used to extract global information and BiLSTM (bidirectional Long- and Short-Term Memory network) encoder and LSTM (Long- and Short-Term Memory network) decoder for local sequence information. Enhancement of the contributions of key features by the self-attention mechanism was followed by mid-term fusion of the four enhanced features. Logistic Regression (LR) classifier showed that CRBSP gives a mean AUC value of 0.9362 through 5-fold Cross Validation of all 37 datasets, a performance which is superior to five current state-of-the-art models. Similar evaluation of linear RNA-RBP binding sites gave an AUC value of 0.7615 which is also higher than other prediction methods, demonstrating the robustness of CRBSP.
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3
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Han K, Wang J, Wang Y, Zhang L, Yu M, Xie F, Zheng D, Xu Y, Ding Y, Wan J. A review of methods for predicting DNA N6-methyladenine sites. Brief Bioinform 2023; 24:6887111. [PMID: 36502371 DOI: 10.1093/bib/bbac514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 10/07/2022] [Accepted: 10/27/2022] [Indexed: 12/14/2022] Open
Abstract
Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA site prediction. With the continuous development of technology, traditional techniques with the high costs and low efficiencies are gradually being replaced by computer methods. Computer methods that are widely used can be divided into two categories: traditional machine learning and deep learning methods. We first list some existing experimental methods for predicting the 6mA site, then analyze the general process from sequence input to results in computer methods and review existing model architectures. Finally, the results were summarized and compared to facilitate subsequent researchers in choosing the most suitable method for their work.
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Affiliation(s)
- Ke Han
- School of Computer and Information Engineering, Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin University of Commerce, Harbin, 150028, China.,College of Pharmacy, Harbin University of Commerce, Harbin, 150076, China
| | - Jianchun Wang
- School of Computer and Information Engineering, Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin University of Commerce, Harbin, 150028, China
| | - Yu Wang
- School of Computer and Information Engineering, Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin University of Commerce, Harbin, 150028, China
| | - Lei Zhang
- School of Computer and Information Engineering, Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin University of Commerce, Harbin, 150028, China
| | - Mengyao Yu
- School of Computer and Information Engineering, Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin University of Commerce, Harbin, 150028, China
| | - Fang Xie
- School of Computer and Information Engineering, Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin University of Commerce, Harbin, 150028, China
| | - Dequan Zheng
- School of Computer and Information Engineering, Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin University of Commerce, Harbin, 150028, China
| | - Yaoqun Xu
- School of Computer and Information Engineering, Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin University of Commerce, Harbin, 150028, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, China
| | - Jie Wan
- Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin, 150001, China
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4
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Nabeel Asim M, Ali Ibrahim M, Fazeel A, Dengel A, Ahmed S. DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method. Brief Bioinform 2023; 24:6931721. [PMID: 36528802 DOI: 10.1093/bib/bbac546] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 11/06/2022] [Accepted: 11/12/2022] [Indexed: 12/23/2022] Open
Abstract
Accurate prediction of deoxyribonucleic acid (DNA) modifications is essential to explore and discern the process of cell differentiation, gene expression and epigenetic regulation. Several computational approaches have been proposed for particular type-specific DNA modification prediction. Two recent generalized computational predictors are capable of detecting three different types of DNA modifications; however, type-specific and generalized modifications predictors produce limited performance across multiple species mainly due to the use of ineffective sequence encoding methods. The paper in hand presents a generalized computational approach "DNA-MP" that is competent to more precisely predict three different DNA modifications across multiple species. Proposed DNA-MP approach makes use of a powerful encoding method "position specific nucleotides occurrence based 117 on modification and non-modification class densities normalized difference" (POCD-ND) to generate the statistical representations of DNA sequences and a deep forest classifier for modifications prediction. POCD-ND encoder generates statistical representations by extracting position specific distributional information of nucleotides in the DNA sequences. We perform a comprehensive intrinsic and extrinsic evaluation of the proposed encoder and compare its performance with 32 most widely used encoding methods on $17$ benchmark DNA modifications prediction datasets of $12$ different species using $10$ different machine learning classifiers. Overall, with all classifiers, the proposed POCD-ND encoder outperforms existing $32$ different encoders. Furthermore, combinedly over 5-fold cross validation benchmark datasets and independent test sets, proposed DNA-MP predictor outperforms state-of-the-art type-specific and generalized modifications predictors by an average accuracy of 7% across 4mc datasets, 1.35% across 5hmc datasets and 10% for 6ma datasets. To facilitate the scientific community, the DNA-MP web application is available at https://sds_genetic_analysis.opendfki.de/DNA_Modifications/.
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Affiliation(s)
- Muhammad Nabeel Asim
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern 67663, Germany.,German Research Center for Artificial Intelligence GmbH, Kaiserslautern 67663, Germany
| | - Muhammad Ali Ibrahim
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern 67663, Germany.,German Research Center for Artificial Intelligence GmbH, Kaiserslautern 67663, Germany
| | - Ahtisham Fazeel
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern 67663, Germany.,German Research Center for Artificial Intelligence GmbH, Kaiserslautern 67663, Germany
| | - Andreas Dengel
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern 67663, Germany.,German Research Center for Artificial Intelligence GmbH, Kaiserslautern 67663, Germany
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern 67663, Germany
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5
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Sheng Y, Zhou M, You C, Dai X. Dynamics and biological relevance of epigenetic N6-methyladenine DNA modification in eukaryotic cells. CHINESE CHEM LETT 2022. [DOI: 10.1016/j.cclet.2021.08.109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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6
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Tang X, Zheng P, Li X, Wu H, Wei DQ, Liu Y, Huang G. Deep6mAPred: A CNN and Bi-LSTM-based deep learning method for predicting DNA N6-methyladenosine sites across plant species. Methods 2022; 204:142-150. [PMID: 35477057 DOI: 10.1016/j.ymeth.2022.04.011] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/16/2022] [Accepted: 04/20/2022] [Indexed: 12/11/2022] Open
Abstract
DNA N6-methyladenine (6mA) is a key DNA modification, which plays versatile roles in the cellular processes, including regulation of gene expression, DNA repair, and DNA replication. DNA 6mA is closely associated with many diseases in the mammals and with growth as well as development of plants. Precisely detecting DNA 6mA sites is of great importance to exploration of 6mA functions. Although many computational methods have been presented for DNA 6mA prediction, there is still a wide gap in the practical application. We presented a convolution neural network (CNN) and bi-directional long-short term memory (Bi-LSTM)-based deep learning method (Deep6mAPred) for predicting DNA 6mA sites across plant species. The Deep6mAPred stacked the CNNs and the Bi-LSTMs in a paralleling manner instead of a series-connection manner. The Deep6mAPred also employed the attention mechanism for improving the representations of sequences. The Deep6mAPred reached an accuracy of 0.9556 over the independent rice dataset, far outperforming the state-of-the-art methods. The tests across plant species showed that the Deep6mAPred is of a remarkable advantage over the state of the art methods. We developed a user-friendly web application for DNA 6mA prediction, which is freely available at http://106.13.196.152:7001/ for all the scientific researchers. The Deep6mAPred would enrich tools to predict DNA 6mA sites and speed up the exploration of DNA modification.
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Affiliation(s)
- Xingyu Tang
- School of Electrical Engineering, Shaoyang University, Shaoyang, Hunan 422000, China
| | - Peijie Zheng
- School of Electrical Engineering, Shaoyang University, Shaoyang, Hunan 422000, China
| | - Xueyong Li
- School of Electrical Engineering, Shaoyang University, Shaoyang, Hunan 422000, China
| | - Hongyan Wu
- The Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Dong-Qing Wei
- The Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Yuewu Liu
- College of Information and Intelligence, Hunan Agricultural University, Changsha, Hunan 410081, China
| | - Guohua Huang
- School of Electrical Engineering, Shaoyang University, Shaoyang, Hunan 422000, China.
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7
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Liu M, Sun ZL, Zeng Z, Lam KM. MGF6mARice: prediction of DNA N6-methyladenine sites in rice by exploiting molecular graph feature and residual block. Brief Bioinform 2022; 23:6553606. [PMID: 35325050 DOI: 10.1093/bib/bbac082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 02/13/2022] [Accepted: 02/16/2022] [Indexed: 11/12/2022] Open
Abstract
DNA N6-methyladenine (6mA) is produced by the N6 position of the adenine being methylated, which occurs at the molecular level, and is involved in numerous vital biological processes in the rice genome. Given the shortcomings of biological experiments, researchers have developed many computational methods to predict 6mA sites and achieved good performance. However, the existing methods do not consider the occurrence mechanism of 6mA to extract features from the molecular structure. In this paper, a novel deep learning method is proposed by devising DNA molecular graph feature and residual block structure for 6mA sites prediction in rice, named MGF6mARice. Firstly, the DNA sequence is changed into a simplified molecular input line entry system (SMILES) format, which reflects chemical molecular structure. Secondly, for the molecular structure data, we construct the DNA molecular graph feature based on the principle of graph convolutional network. Then, the residual block is designed to extract higher level, distinguishable features from molecular graph features. Finally, the prediction module is used to obtain the result of whether it is a 6mA site. By means of 10-fold cross-validation, MGF6mARice outperforms the state-of-the-art approaches. Multiple experiments have shown that the molecular graph feature and residual block can promote the performance of MGF6mARice in 6mA prediction. To the best of our knowledge, it is the first time to derive a feature of DNA sequence by considering the chemical molecular structure. We hope that MGF6mARice will be helpful for researchers to analyze 6mA sites in rice.
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Affiliation(s)
- Mengya Liu
- School of Computer Science and Technology, Anhui University, Hefei, 230601, China
| | - Zhan-Li Sun
- School of Artificial Intelligence, Anhui University, Hefei, 230601, China
| | - Zhigang Zeng
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Kin-Man Lam
- Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, China
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8
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Terrazas-Salgado L, García-Gasca A, Betancourt-Lozano M, Llera-Herrera R, Alvarado-Cruz I, Yáñez-Rivera B. Epigenetic Transgenerational Modifications Induced by Xenobiotic Exposure in Zebrafish. Front Cell Dev Biol 2022; 10:832982. [PMID: 35281093 PMCID: PMC8914061 DOI: 10.3389/fcell.2022.832982] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 01/21/2022] [Indexed: 11/23/2022] Open
Abstract
Zebrafish (Danio rerio) is a well-established vertebrate model in ecotoxicology research that responds to a wide range of xenobiotics such as pesticides, drugs, and endocrine-disrupting compounds. The epigenome can interact with the environment and transform internal and/or external signals into phenotypic responses through changes in gene transcription. Environmental exposures can also generate epigenetic variations in offspring even by indirect exposure. In this review, we address the advantages of using zebrafish as an experimental animal model to study transgenerational epigenetic processes upon exposure to xenobiotics. We focused mostly on DNA methylation, although studies on post-translational modifications of histones, and non-coding RNAs related to xenobiotic exposure in zebrafish are also discussed. A revision of the methods used to study epigenetic changes in zebrafish revealed the relevance and reproducibility for epigenetics-related research. PubMed and Google Scholar databases were consulted for original research articles published from 2013 to date, by using six keywords: zebrafish, epigenetics, exposure, parental, transgenerational, and F2. From 499 articles identified, 92 were considered, of which 14 were selected as included F2 and epigenetic mechanisms. Current knowledge regarding the effect of xenobiotics on DNA methylation, histone modifications, and changes in non-coding RNAs expressed in F2 is summarized, along with key experimental design considerations to characterize transgenerational effects.
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Affiliation(s)
| | | | | | - Raúl Llera-Herrera
- Instituto de Ciencias del Mar y Limnología—Unidad Académica Mazatlán, Universidad Nacional Autónoma de México, Mazatlán, Mexico
| | - Isabel Alvarado-Cruz
- Department of Cellular and Molecular Medicine, University of Arizona Cancer Center, Tucson, AZ, United States
| | - Beatriz Yáñez-Rivera
- Centro de Investigación en Alimentación y Desarrollo, A. C., Mazatlán, Mexico
- Consejo Nacional de Ciencia y Tecnología, México, Mexico
- *Correspondence: Beatriz Yáñez-Rivera,
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9
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Cai J, Xiao G, Su R. GC6mA-Pred: A deep learning approach to identify DNA N6-methyladenine sites in the rice genome. Methods 2022; 204:14-21. [PMID: 35149214 DOI: 10.1016/j.ymeth.2022.02.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 01/31/2022] [Accepted: 02/05/2022] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION DNA N6-methyladenine (6mA) is a pivotal DNA modification for various biological processes. More accurate prediction of 6mA methylation sites plays an irreplaceable part in grasping the internal rationale of related biological activities. However, the existing prediction methods only extract information from a single dimension, which has some limitations. Therefore, it is very necessary to obtain the information of 6mA sites from different dimensions, so as to establish a reliable prediction method. RESULTS In this study, a neural network based bioinformatics model named GC6mA-Pred is proposed to predict N6-methyladenine modifications in DNA sequences. GC6mA-Pred extracts significant information from both sequence level and graph level. In the sequence level, GC6mA-Pred uses a three-layer convolution neural network (CNN) model to represent the sequence. In the graph level, GC6mA-Pred employs graph neural network (GNN) method to integrate various information contained in the chemical molecular formula corresponding to DNA sequence. In our newly built dataset, GC6mA-Pred shows better performance than other existing models. The results of comparative experiments have illustrated that GC6mA-Pred is capable of producing a marked effect in accurately identifying DNA 6mA modifications.
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Affiliation(s)
- Jianhua Cai
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China; College of Mathematics and Computer Science, Fuzhou University, Fuzhou, PR China
| | - Guobao Xiao
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China.
| | - Ran Su
- College of Intelligence and Computing, Tianjin University, Tianjin, China.
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10
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He S, Kong L, Chen J. iDNA6mA-Rice-DL: A local web server for identifying DNA N6-methyladenine sites in rice genome by deep learning method. J Bioinform Comput Biol 2021; 19:2150019. [PMID: 34291710 DOI: 10.1142/s0219720021500190] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Accurate detection of N6-methyladenine (6mA) sites by biochemical experiments will help to reveal their biological functions, still, these wet experiments are laborious and expensive. Therefore, it is necessary to introduce a powerful computational model to identify the 6mA sites on a genomic scale, especially for plant genomes. In view of this, we proposed a model called iDNA6mA-Rice-DL for the effective identification of 6mA sites in rice genome, which is an intelligent computing model based on deep learning method. Traditional machine learning methods assume the preparation of the features for analysis. However, our proposed model automatically encodes and extracts key DNA features through an embedded layer and several groups of dense layers. We use an independent dataset to evaluate the generalization ability of our model. An area under the receiver operating characteristic curve (auROC) of 0.98 with an accuracy of 95.96% was obtained. The experiment results demonstrate that our model had good performance in predicting 6mA sites in the rice genome. A user-friendly local web server has been established. The Docker image of the local web server can be freely downloaded at https://hub.docker.com/r/his1server/idna6ma-rice-dl.
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Affiliation(s)
- Shiqian He
- School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology, Qinhuangdao 066000, P. R. China
| | - Liang Kong
- School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology, Qinhuangdao 066000, P. R. China
| | - Jing Chen
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066000, P. R. China
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11
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Chachar S, Liu J, Zhang P, Riaz A, Guan C, Liu S. Harnessing Current Knowledge of DNA N6-Methyladenosine From Model Plants for Non-model Crops. Front Genet 2021; 12:668317. [PMID: 33995495 PMCID: PMC8118384 DOI: 10.3389/fgene.2021.668317] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 04/06/2021] [Indexed: 12/12/2022] Open
Abstract
Epigenetic modifications alter the gene activity and function by causing change in the chromosomal architecture through DNA methylation/demethylation, or histone modifications without causing any change in DNA sequence. In plants, DNA cytosine methylation (5mC) is vital for various pathways such as, gene regulation, transposon suppression, DNA repair, replication, transcription, and recombination. Thanks to recent advances in high throughput sequencing (HTS) technologies for epigenomic “Big Data” generation, accumulated studies have revealed the occurrence of another novel DNA methylation mark, N6-methyladenosine (6mA), which is highly present on gene bodies mainly activates gene expression in model plants such as eudicot Arabidopsis (Arabidopsis thaliana) and monocot rice (Oryza sativa). However, in non-model crops, the occurrence and importance of 6mA remains largely less known, with only limited reports in few species, such as Rosaceae (wild strawberry), and soybean (Glycine max). Given the aforementioned vital roles of 6mA in plants, hereinafter, we summarize the latest advances of DNA 6mA modification, and investigate the historical, known and vital functions of 6mA in plants. We also consider advanced artificial-intelligence biotechnologies that improve extraction and prediction of 6mA concepts. In this Review, we discuss the potential challenges that may hinder exploitation of 6mA, and give future goals of 6mA from model plants to non-model crops.
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Affiliation(s)
- Sadaruddin Chachar
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Horticulture, Northwest A&F University, Yangling, China.,Department of Biotechnology, Faculty of Crop Production, Sindh Agriculture University, Tandojam, Pakistan
| | - Jingrong Liu
- College of Mathematics and Statistics, Northwest Normal University, Lanzhou, China
| | - Pingxian Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Adeel Riaz
- Deaprtment of Biochemistry, Faculty of Life Sciences, University of Okara, Okara, Pakistan
| | - Changfei Guan
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Horticulture, Northwest A&F University, Yangling, China
| | - Shuyuan Liu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Horticulture, Northwest A&F University, Yangling, China
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12
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Hasan MM, Shoombuatong W, Kurata H, Manavalan B. Critical evaluation of web-based DNA N6-methyladenine site prediction tools. Brief Funct Genomics 2021; 20:258-272. [PMID: 33491072 DOI: 10.1093/bfgp/elaa028] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 12/11/2020] [Accepted: 12/15/2020] [Indexed: 12/13/2022] Open
Abstract
Methylation of DNA N6-methyladenosine (6mA) is a type of epigenetic modification that plays pivotal roles in various biological processes. The accurate genome-wide identification of 6mA is a challenging task that leads to understanding the biological functions. For the last 5 years, a number of bioinformatics approaches and tools for 6mA site prediction have been established, and some of them are easily accessible as web application. Nevertheless, the accurate genome-wide identification of 6mA is still one of the challenging works that lead to understanding the biological functions. Especially in practical applications, these tools have implemented diverse encoding schemes, machine learning algorithms and feature selection methods, whereas few systematic performance comparisons of 6mA site predictors have been reported. In this review, 11 publicly available 6mA predictors evaluated with seven different species-specific datasets (Arabidopsis thaliana, Tolypocladium, Diospyros lotus, Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans and Escherichia coli). Of those, few species are close homologs, and the remaining datasets are distant sequences. Our independent, validation tests demonstrated that Meta-i6mA and MM-6mAPred models for A. thaliana, Tolypocladium, S. cerevisiae and D. melanogaster achieved excellent overall performance when compared with their counterparts. However, none of the existing methods were suitable for E. coli, C. elegans and D. lotus. A feasibility of the existing predictors is also discussed for the seven species. Our evaluation provides useful guidelines for the development of 6mA site predictors and helps biologists selecting suitable prediction tools.
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Affiliation(s)
| | - Watshara Shoombuatong
- Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics in the Kyushu Institute of Technology, Japan
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13
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Ao C, Yu L, Zou Q. Prediction of bio-sequence modifications and the associations with diseases. Brief Funct Genomics 2020; 20:1-18. [PMID: 33313647 DOI: 10.1093/bfgp/elaa023] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 11/09/2020] [Accepted: 11/10/2020] [Indexed: 12/22/2022] Open
Abstract
Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM_data/ some sample data on protein, RNA and DNA modification can be downloaded from that website.
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