Research Progress in Predicting DNA Methylation Modifications and the Relation with Human Diseases

Jasiulionis, M.G. Abnormal epigenetic regulation of immune system during aging. Front. Immunol., 2018, 9, 197.
[http://dx.doi.org/10.3389/fimmu.2018.00197]

Chen, K.; Zhao, B.S.; He, C. Nucleic acid modifications in regulation of gene expression. Cell Chem. Biol., 2016, 23(1), 74-85.
[http://dx.doi.org/10.1016/j.chembiol.2015.11.007] [PMID: 26933737]

Roberts, R.J.; Vincze, T.; Posfai, J.; Macelis, D. REBASE-a database for DNA restriction and modification: enzymes, genes and genomes. Nucleic Acids Res., 2015, 43(Database issue), D298-D299.
[http://dx.doi.org/10.1093/nar/gku1046] [PMID: 25378308]

Blow, M.J.; Clark, T.A.; Daum, C.G.; Deutschbauer, A.M.; Fomenkov, A.; Fries, R.; Froula, J.; Kang, D.D.; Malmstrom, R.R.; Morgan, R.D.; Posfai, J.; Singh, K.; Visel, A.; Wetmore, K.; Zhao, Z.; Rubin, E.M.; Korlach, J.; Pennacchio, L.A.; Roberts, R.J. The epigenomic landscape of prokaryotes. PLoS Genet., 2016, 12(2)e1005854
[http://dx.doi.org/10.1371/journal.pgen.1005854] [PMID: 26870957]

Fu, Y.; Luo, G-Z.; Chen, K.; Deng, X.; Yu, M.; Han, D.; Hao, Z.; Liu, J.; Lu, X.; Dore, L.C.; Weng, X.; Ji, Q.; Mets, L.; He, C. N6-methyldeoxyadenosine marks active transcription start sites in Chlamydomonas. Cell, 2015, 161(4), 879-892.
[http://dx.doi.org/10.1016/j.cell.2015.04.010] [PMID: 25936837]

Lyko, F. The DNA methyltransferase family: a versatile toolkit for epigenetic regulation. Nat. Rev. Genet., 2018, 19(2), 81-92.
[http://dx.doi.org/10.1038/nrg.2017.80] [PMID: 29033456]

Hattman, S. DNA-[adenine] methylation in lower eukaryotes. Biochemistry (Mosc.), 2005, 70(5), 550-558.
[http://dx.doi.org/10.1007/s10541-005-0148-6] [PMID: 15948708]

Wei, L.; Luan, S.; Nagai, L.A.E.; Su, R.; Zou, Q. Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species. Bioinformatics, 2019, 35(8), 1326-1333.
[http://dx.doi.org/10.1093/bioinformatics/bty824] [PMID: 30239627]

Linn, S.; Arber, W. Host specificity of DNA produced by Escherichia coli, X. In vitro restriction of phage fd replicative form. Proc. Natl. Acad. Sci. USA, 1968, 59(4), 1300-1306.
[http://dx.doi.org/10.1073/pnas.59.4.1300] [PMID: 4870862]

Campbell, J.L.; Kleckner, N.E. coli oriC and the DNA a gene promoter are sequestered from dam methyltransferase following the passage of the chromosomal replication fork. Cell, 1990, 62(5), 967-979.
[http://dx.doi.org/10.1016/0092-8674(90)90271-F] [PMID: 1697508]

Vasu, K.; Nagaraja, V. Diverse functions of restriction-modification systems in addition to cellular defense. Microb. Mol. Biol. Rev., 2013, 77(1), 53-72.

Tao, Y.; Xi, S.; Shan, J.; Maunakea, A.; Che, A.; Briones, V.; Lee, E.Y.; Geiman, T.; Huang, J.; Stephens, R.; Leighty, R.M. Lsh, chromatin remodeling family member, modulates genome-wide cytosine methylation patterns at nonrepeat sequences. Proceed Natl. Acad. Sci., 2011, 108(14), 5626-5631.
[http://dx.doi.org/10.1073/pnas.1017000108]

Scarano, M.I.; Strazzullo, M.; Matarazzo, M.R.; D’Esposito, M. DNA methylation 40 years later: Its role in human health and disease. J. Cell. Physiol., 2005, 204(1), 21-35.

Alvarez, J.R.; Skachkov, D.; Massey, S.E.; Kalitsov, A.; Velev, J.P. Mapping Base Modifications in DNA by Transverse-Current Sequencing. Phys. Rev. Appl., 2018, 9(2)
[http://dx.doi.org/10.1103/PhysRevApplied.9.024024]

Wang, X.; Song, Y.; Song, M.; Wang, Z.; Li, T.; Wang, H. Fluorescence polarization combined capillary electrophoresis immunoassay for the sensitive detection of genomic DNA methylation. Anal. Chem., 2009, 81(19), 7885-7891.
[http://dx.doi.org/10.1021/ac901681k] [PMID: 19788313]

Ziller, M.J.; Hansen, K.D.; Meissner, A.; Aryee, M.J. Coverage recommendations for methylation analysis by whole-genome bisulfite sequencing. Nat. Methods, 2015, 12(3), 230-232.
[http://dx.doi.org/10.1038/nmeth.3152] [PMID: 25362363]

Meissner, A.; Gnirke, A.; Bell, G.W.; Ramsahoye, B.; Lander, E.S.; Jaenisch, R. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res., 2005, 33(18), 5868-5877.
[http://dx.doi.org/10.1093/nar/gki901] [PMID: 16224102]

Krais, A.M.; Cornelius, M.G.; Schmeiser, H.H. Genomic N6-methyladenine determination by MEKC with LIF. Electrophoresis, 2010, 31(21), 3548-3551.
[http://dx.doi.org/10.1002/elps.201000357]

Clark, S.J.; Smallwood, S.A.; Lee, H.J.; Krueger, F.; Reik, W.; Kelsey, G. Genome-wide base-resolution mapping of DNA methylation in single cells using single-cell bisulfite sequencing (scBS-seq). Nat. Protoc., 2017, 12(3), 534-547.
[http://dx.doi.org/10.1038/nprot.2016.187] [PMID: 28182018]

Taiwo, O.; Wilson, G.A.; Morris, T.; Seisenberger, S.; Reik, W.; Pearce, D.; Beck, S.; Butcher, L.M. Methylome analysis using MeDIP-seq with low DNA concentrations. Nat. Protoc., 2012, 7(4), 617-636.
[http://dx.doi.org/10.1038/nprot.2012.012] [PMID: 22402632]

Flusberg, B.A.; Webster, D.R.; Lee, J.H.; Travers, K.J.; Olivares, E.C.; Clark, T.A.; Korlach, J.; Turner, S.W. Direct detection of DNA methylation during single-molecule, real-time sequencing. Nat. Methods, 2010, 7(6), 461-465.
[http://dx.doi.org/10.1038/nmeth.1459] [PMID: 20453866]

Grunau, C.; Renault, E.; Rosenthal, A.; Roizes, G.; Meth, D.B. MethDB-a public database for DNA methylation data. Nucleic Acids Res., 2001, 29(1), 270-274.
[http://dx.doi.org/10.1093/nar/29.1.270] [PMID: 11125109]

Amoreira, C.; Hindermann, W.; Grunau, C. An improved version of the DNA Methylation database (MethDB). Nucleic Acids Res., 2003, 31(1), 75-77.
[http://dx.doi.org/10.1093/nar/gkg093] [PMID: 12519951]

Ye, P.; Luan, Y.; Chen, K.; Liu, Y.; Xiao, C.; Xie, Z. MethSMRT: an integrative database for DNA N6-methyladenine and N4-methylcytosine generated by single-molecular real-time sequencing. Nucleic Acids Res., 2017, 45(D1), D85-D89.
[http://dx.doi.org/10.1093/nar/gkw950] [PMID: 27924023]

Liu, Z-Y.; Xing, J-F.; Chen, W.; Luan, M-W.; Xie, R.; Huang, J.; Xie, S-Q.; Xiao, C-L. MDR: an integrative DNA N6-methyladenine and N4-methylcytosine modification database for Rosaceae. Hortic. Res., 2019, 6, 78.
[http://dx.doi.org/10.1038/s41438-019-0160-4] [PMID: 31240103]

Sood, A.J.; Viner, C.; Hoffman, M.M. DNAmod: the DNA modification database. J. Cheminform., 2019, 11(1), 30.
[http://dx.doi.org/10.1186/s13321-019-0349-4] [PMID: 31016417]

Xin, Y.; Chanrion, B.; O’Donnell, A.H.; Milekic, M.; Costa, R.; Ge, Y.; Haghighi, F.G.; Methylome, D.B. MethylomeDB: a database of DNA methylation profiles of the brain. Nucleic Acids Res., 2012, 40(Database issue), D1245-D1249.
[http://dx.doi.org/10.1093/nar/gkr1193] [PMID: 22140101]

Chen, W.; Yang, H.; Feng, P.; Ding, H.; Lin, H. iDNA4mC: identifying DNA N4-methylcytosine sites based on nucleotide chemical properties. Bioinformatics, 2017, 33(22), 3518-3523.
[http://dx.doi.org/10.1093/bioinformatics/btx479] [PMID: 28961687]

He, W.; Jia, C.; Zou, Q. 4mCPred: machine learning methods for DNA N4-methylcytosine sites prediction. Bioinformatics, 2019, 35(4), 593-601.
[http://dx.doi.org/10.1093/bioinformatics/bty668] [PMID: 30052767]

Manavalan, B.; Basith, S.; Shin, T.H.; Wei, L.; Lee, G. Meta-4mCpred: A sequence-based meta-predictor for accurate DNA 4mC site prediction using effective feature representation. Mol. Ther. Nucleic Acids, 2019, 16, 733-744.
[http://dx.doi.org/10.1016/j.omtn.2019.04.019] [PMID: 31146255]

Manavalan, B.; Basith, S.; Shin, T.H.; Lee, D.Y.; Wei, L.; Lee, G. 4mCpred-EL: an ensemble learning framework for identification of DNA N4-methylcytosine sites in the mouse genome. Cells, 2019, 8(11)E1332
[http://dx.doi.org/10.3390/cells8111332] [PMID: 31661923]

Wu, X.; Wei, Y.; Jiang, T.; Wang, Y.; Jiang, S. A Micro-aggregation algorithm based on density partition method for anonymizing biomedical data. Curr. Bioinform., 2019, 14(7), 667-675.
[http://dx.doi.org/10.2174/1574893614666190416152025]

Hasan, M.M.; Manavalan, B.; Khatun, M.S.; Kurata, H. i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome. Int. J. Biol. Macromol., 2020, 157, 752-758.
[http://dx.doi.org/10.1016/j.ijbiomac.2019.12.009] [PMID: 31805335]

Hasan, M.M.; Manavalan, B.; Shoombuatong, W.; Khatun, M.S.; Kurata, H. i4mC-Mouse: improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes. Comput. Struct. Biotechnol. J., 2020, 18, 906-912.
[http://dx.doi.org/10.1016/j.csbj.2020.04.001] [PMID: 32322372]

Yang, J.; Lang, K.; Zhang, G.; Fan, X.; Chen, Y.; Pian, C. SOMM4mC: a second-order Markov model for DNA N4-methylcytosine site prediction in six species. Bioinformatics, 2020, 36(14), 4103-4105.
[http://dx.doi.org/10.1093/bioinformatics/btaa507] [PMID: 32413127]

Lv, Z.; Wang, D.; Ding, H.; Zhong, B.; Xu, L. Escherichia coli, D.N.A. N-4-methycytosine site prediction accuracy improved by light gradient boosting machine feature selection technology. IEEE Access, 2020, 8, 14851-14859.
[http://dx.doi.org/10.1109/ACCESS.2020.2966576]

Tang, Q.; Kang, J.; Yuan, J.; Tang, H.; Li, X.; Lin, H.; Huang, J.; Chen, W. DNA4mC-LIP: a linear integration method to identify N4-methylcytosine site in multiple species. Bioinformatics, 2020, 36(11), 3327-3335.
[http://dx.doi.org/10.1093/bioinformatics/btaa143] [PMID: 32108866]

Khanal, J.; Nazari, I.; Tayara, H.; Chong, K.T. 4mCCNN: Identification of N4-methylcytosine sites in prokaryotes using convolutional neural network. IEEE Access, 2019, 7, 145455-145461.
[http://dx.doi.org/10.1109/ACCESS.2019.2943169]

Zeng, R.; Liao, M. Developing a multi-layer deep learning based predictive model to identify DNA N4-methylcytosine modifications. Front. Bioeng. Biotechnol., 2020, 8, 274.
[http://dx.doi.org/10.3389/fbioe.2020.00274] [PMID: 32373597]

Zeng, F.; Fang, G.; Yao, L. A deep neural network for identifying DNA N4-methylcytosine sites. Front. Genet., 2020, 11, 209.
[http://dx.doi.org/10.3389/fgene.2020.00209] [PMID: 32211035]

Chen, W.; Lv, H.; Nie, F.; Lin, H. i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome. Bioinformatics, 2019, 35(16), 2796-2800.
[http://dx.doi.org/10.1093/bioinformatics/btz015] [PMID: 30624619]

Basith, S.; Manavalan, B.; Shin, T.H.; Lee, G. SDM6A: a web-based integrative machine-learning framework for predicting 6mA sites in the rice genome. Mol. Ther. Nucleic Acids, 2019, 18, 131-141.
[http://dx.doi.org/10.1016/j.omtn.2019.08.011] [PMID: 31542696]

Kong, L.; Zhang, L. i6mA-DNCP: computational identification of DNA N6-methyladenine sites in the rice genome using optimized dinucleotide-based features. Genes (Basel), 2019, 10(10)E828
[http://dx.doi.org/10.3390/genes10100828] [PMID: 31635172]

Le, N.Q.K. iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou’s 5-step rule. Mol. Genet. Genomics, 2019, 294(5), 1173-1182.
[http://dx.doi.org/10.1007/s00438-019-01570-y] [PMID: 31055655]

Lv, H.; Dao, F-Y.; Guan, Z-X.; Zhang, D.; Tan, J-X.; Zhang, Y.; Chen, W.; Lin, H. iDNA6mA-rice: a computational tool for detecting N6-methyladenine sites in rice. Front. Genet., 2019, 10, 793.
[http://dx.doi.org/10.3389/fgene.2019.00793] [PMID: 31552096]

Tahir, M.; Tayara, H.; Chong, K.T. iDNA6mA (5-step rule): identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model via Chou’s 5-step rule. Chemom. Intell. Lab. Syst., 2019, 189, 96-101.
[http://dx.doi.org/10.1016/j.chemolab.2019.04.007]

Yu, H.; Dai, Z. SNNRice6mA: a deep learning method for predicting DNA N6-methyladenine sites in rice genome. Front. Genet., 2019, 10, 1071.
[http://dx.doi.org/10.3389/fgene.2019.01071] [PMID: 31681441]

Huang, Q.; Zhang, J.; Wei, L.; Guo, F.; Zou, Q. 6mA-RicePred: A method for identifying DNA N6-methyladenine sites in the rice genome based on feature fusion. Front. Plant Sci., 2020, 11, 4.
[http://dx.doi.org/10.3389/fpls.2020.00004] [PMID: 32076430]

Pian, C.; Zhang, G.; Li, F.; Fan, X. MM-6mAPred: identifying DNA N6-methyladenine sites based on Markov model. Bioinformatics, 2020, 36(2), 388-392.
[PMID: 31297537]

Liu, Z.; Dong, W.; Jiang, W.; He, Z. csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou’s 5-step rule. Sci. Rep., 2019, 9(1), 13109.
[http://dx.doi.org/10.1038/s41598-019-49430-4] [PMID: 31511570]

Wahab, A.; Ali, S.D.; Tayara, H.; Chong, K.T. iIM-CNN: intelligent identifier of 6mA sites on different species by using convolution neural network. IEEE Access, 2019, 7, 178577-178583.
[http://dx.doi.org/10.1109/ACCESS.2019.2958618]

Luo, Y.; Liao, X.; Wu, F-X.; Wang, J. Computational approaches for transcriptome assembly based on sequencing technologies. Curr. Bioinform., 2020, 15(1), 2-16.
[http://dx.doi.org/10.2174/1574893614666190410155603]

Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chen, W.; Chou, K-C. iDNA6mA-PseKNC: Identifying DNA N6-methyla-denosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics, 2019, 111(1), 96-102.
[http://dx.doi.org/10.1016/j.ygeno.2018.01.005] [PMID: 29360500]

Hasan, M.M.; Manavalan, B.; Shoombuatong, W.; Khatun, M.S.; Kurata, H. i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation. Plant Mol. Biol., 2020, 103(1-2), 225-234.
[http://dx.doi.org/10.1007/s11103-020-00988-y] [PMID: 32140819]

Xu, H.; Hu, R.; Jia, P.; Zhao, Z. 6mA-Finder: a novel online tool for predicting DNA N6-methyladenine sites in genomes. Bioinformatics, 2020, 36(10), 3257-3259.
[http://dx.doi.org/10.1093/bioinformatics/btaa113] [PMID: 32091591]

Down, T.A.; Rakyan, V.K.; Turner, D.J.; Flicek, P.; Li, H.; Kulesha, E.; Gräf, S.; Johnson, N.; Herrero, J.; Tomazou, E.M.; Thorne, N.P.; Bäckdahl, L.; Herberth, M.; Howe, K.L.; Jackson, D.K.; Miretti, M.M.; Marioni, J.C.; Birney, E.; Hubbard, T.J.P.; Durbin, R.; Tavaré, S.; Beck, S. A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat. Biotechnol., 2008, 26(7), 779-785.
[http://dx.doi.org/10.1038/nbt1414] [PMID: 18612301]

Aryee, M.J.; Jaffe, A.E.; Corrada-Bravo, H.; Ladd-Acosta, C.; Feinberg, A.P.; Hansen, K.D.; Irizarry, R.A. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics, 2014, 30(10), 1363-1369.
[http://dx.doi.org/10.1093/bioinformatics/btu049] [PMID: 24478339]

Bhasin, M.; Zhang, H.; Reinherz, E.L.; Reche, P.A. Prediction of methylated CpGs in DNA sequences using a support vector machine. FEBS Lett., 2005, 579(20), 4302-4308.
[http://dx.doi.org/10.1016/j.febslet.2005.07.002] [PMID: 16051225]

Fang, F.; Fan, S.; Zhang, X.; Zhang, M.Q. Predicting methylation status of CpG islands in the human brain. Bioinformatics, 2006, 22(18), 2204-2209.
[http://dx.doi.org/10.1093/bioinformatics/btl377] [PMID: 16837523]

Liu, Z.; Xiao, X.; Qiu, W-R.; Chou, K-C. iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition. Anal. Biochem., 2015, 474, 69-77.
[http://dx.doi.org/10.1016/j.ab.2014.12.009] [PMID: 25596338]

Pan, G.; Jiang, L.; Tang, J.; Guo, F. A novel computational method for detecting DNA methylation sites with DNA sequence information and physicochemical properties. Int. J. Mol. Sci., 2018, 19(2), 511.
[http://dx.doi.org/10.3390/ijms19020511] [PMID: 29419752]

Angermueller, C.; Lee, H.J.; Reik, W.; Stegle, O. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol., 2017, 18(1), 67.
[http://dx.doi.org/10.1186/s13059-017-1189-z] [PMID: 28395661]

Zhang, L.; Xiao, X.; Xu, Z-C. iPromoter-5mC: a novel fusion decision predictor for the identification of 5-methylcytosine sites in genome-wide DNA promoters. Front. Cell Dev. Biol., 2020, 8, 614-614.
[http://dx.doi.org/10.3389/fcell.2020.00614] [PMID: 32850787]

Sharma, P.; Gupta, A.; Aggarwal, A.; Gupta, D.; Khanna, A.; Hassanien, A.E.; de Albuquerque, V.H.C. The health of things for classification of protein structure using improved grey wolf optimization. J. Supercomput., 2020, 76(2), 1226-1241.
[http://dx.doi.org/10.1007/s11227-018-2639-4]

Kustatscher, G.; Grabowski, P.; Schrader, T.A.; Passmore, J.B.; Schrader, M.; Rappsilber, J. Co-regulation map of the human proteome enables identification of protein functions. Nat. Biotechnol., 2019, 37(11), 1361-1371.
[http://dx.doi.org/10.1038/s41587-019-0298-5] [PMID: 31690884]

Liu, K.; Chen, W. iMRM: a platform for simultaneously identifying multiple kinds of RNA modifications. Bioinformatics, 2020, 36(11), 3336-3342.
[http://dx.doi.org/10.1093/bioinformatics/btaa155] [PMID: 32134472]

Liu, Q.; Chen, J.; Wang, Y.; Li, S.; Jia, C.; Song, J.; Li, F. DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites. Brief. Bioinform., 2021, 22(3), bbaa124.
[PMID: 32608476]

Breiman, L. Random forests. Mach. Learn., 2001, 45(1), 5-32.
[http://dx.doi.org/10.1023/A:1010933404324]

Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn., 1995, 20(3), 273-297.
[http://dx.doi.org/10.1007/BF00994018]

Quinlau, R. Induction of decision trees. Mach. Learn., 1986, 1(1), S1-S106.

LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature, 2015, 521(7553), 436-444.
[http://dx.doi.org/10.1038/nature14539] [PMID: 26017442]

Burges, C.J.C. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov., 1998, 2(2), 121-167.
[http://dx.doi.org/10.1023/A:1009715923555]

Liu, Y.; Wang, M.; Xi, J.; Luo, F.; Li, A. PTM-ssMP: a web server for predicting different types of post-translational modification sites using novel site-specific modification profile. Int. J. Biol. Sci., 2018, 14(8), 946-956.
[http://dx.doi.org/10.7150/ijbs.24121] [PMID: 29989096]

Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K-C. pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. J. Theor. Biol., 2016, 394, 223-230.
[http://dx.doi.org/10.1016/j.jtbi.2016.01.020] [PMID: 26807806]

Sun, X.; Jin, T.; Chen, C.; Cui, X.; Ma, Q.; Yu, B. RBPro-RF: Use Chou’s 5-steps rule to predict RNA-binding proteins via random forest with elastic net. Chemom. Intell. Lab. Syst., 2020, 197103919
[http://dx.doi.org/10.1016/j.chemolab.2019.103919]

Liaw, A.; Wiener, M. Classification and regression by randomForest. R News, 2002, 2(3), 18-22.

Li, J.; Liu, L.; Cui, Q.; Zhou, Y. Comparisons of MicroRNA set enrichment analysis tools on cancer de-regulated miRNAs from TCGA expression datasets. Curr. Bioinform., 2020, 15(10), 1104-1112.
[http://dx.doi.org/10.2174/1574893615666200224095041]

Szulwach, K.E.; Jin, P. Integrating DNA methylation dynamics into a framework for understanding epigenetic codes. BioEssays, 2014, 36(1), 107-117.
[http://dx.doi.org/10.1002/bies.201300090]

Bakusic, J.; Schaufeli, W.; Claes, S.; Godderis, L. Stress, burnout and depression: a systematic review on DNA methylation mechanisms. J. Psychosom. Res., 2017, 92, 34-44.
[http://dx.doi.org/10.1016/j.jpsychores.2016.11.005] [PMID: 27998510]

Dong, C.; Chen, J.; Zheng, J.; Liang, Y.; Yu, T.; Liu, Y.; Gao, F.; Long, J.; Chen, H.; Zhu, Q.; He, Z.; Hu, S.; He, C.; Lin, J.; Tang, Y.; Zhu, H. 5-Hydroxymethylcytosine signatures in circulating cell-free DNA as diagnostic and predictive biomarkers for coronary artery disease. Clin. Epigenetics, 2020, 12(1), 17.
[http://dx.doi.org/10.1186/s13148-020-0810-2] [PMID: 31964422]

Armstrong, M.J.; Jin, Y.; Allen, E.G.; Jin, P. Diverse and dynamic DNA modifications in brain and diseases. Hum. Mol. Genet., 2019, 28(R2), R241-R253.
[PMID: 31348493]

Xiao, C-L.; Zhu, S.; He, M.; Chen, D.; Zhang, Q.; Chen, Y.; Yu, G.; Liu, J.; Xie, S-Q.; Luo, F.; Liang, Z.; Wang, D-P.; Bo, X-C.; Gu, X-F.; Wang, K.; Yan, G-R. N6-Methyladenine DNA Modification in the Human Genome. Mol. Cell, 2018, 71(2), 306-318.
[http://dx.doi.org/10.1016/j.molcel.2018.06.015] [PMID: 30017583]

Xie, Q.; Wu, T.P.; Gimple, R.C.; Li, Z.; Prager, B.C.; Wu, Q.; Yu, Y.; Wang, P.; Wang, Y.; Gorkin, D.U.; Zhang, C.; Dowiak, A.V.; Lin, K.; Zeng, C.; Sui, Y.; Kim, L.J.Y.; Miller, T.E.; Jiang, L.; Lee, C.H.; Huang, Z.; Fang, X.; Zhai, K.; Mack, S.C.; Sander, M.; Bao, S.; Kerstetter-Fogle, A.E.; Sloan, A.E.; Xiao, A.Z.; Rich, J.N. N6-methyladenine DNA Modification in Glioblastoma. Cell, 2018, 175(5), 1228-1243.
[http://dx.doi.org/10.1016/j.cell.2018.10.006] [PMID: 30392959]

Kumamoto, T.; Yamada, K.; Yoshida, S.; Aoki, K.; Hirooka, S.; Eto, K.; Yanaga, K.; Yoshida, K. Impairment of DYRK2 by DNMT1 mediated transcription augments carcinogenesis in human colorectal cancer. Int. J. Oncol., 2020, 56(6), 1529-1539.
[http://dx.doi.org/10.3892/ijo.2020.5020] [PMID: 32236621]

Zhang, T.; Wei, X.; Li, Z.; Shi, F.; Xia, Z.; Lian, M.; Chen, L.; Zhang, H. Natural scene nutrition information acquisition and analysis based on deep learning. Curr. Bioinform., 2020, 15(7), 662-670.
[http://dx.doi.org/10.2174/1574893614666190723121610]

Friso, S.; Carvajal, C.A.; Fardella, C.E.; Olivieri, O. Epigenetics and arterial hypertension: the challenge of emerging evidence. Transl. Res., 2015, 165(1), 154-165.
[http://dx.doi.org/10.1016/j.trsl.2014.06.007] [PMID: 25035152]

Kundu, A.; Anand, A. Computational study of ADD1 gene polymorphism associated with hypertension. Cell Biochem. Biophys., 2013, 65(1), 13-19.
[http://dx.doi.org/10.1007/s12013-012-9398-2] [PMID: 22810272]

Zhang, L-N.; Liu, P-P.; Wang, L.; Yuan, F.; Xu, L.; Xin, Y.; Fei, L-J.; Zhong, Q-L.; Huang, Y.; Xu, L.; Hao, L-M.; Qiu, X-J.; Le, Y.; Ye, M.; Duan, S. Lower ADD1 gene promoter DNA methylation increases the risk of essential hypertension. PLoS One, 2013, 8(5)e63455
[http://dx.doi.org/10.1371/journal.pone.0063455] [PMID: 23691048]

Kato, N.; Loh, M.; Takeuchi, F.; Verweij, N.; Wang, X.; Zhang, W.; Kelly, T.N.; Saleheen, D.; Lehne, B.; Leach, I.M.; Drong, A.W.; Abbott, J.; Wahl, S.; Tan, S-T.; Scott, W.R.; Campanella, G.; Chadeau-Hyam, M.; Afzal, U.; Ahluwalia, T.S.; Bonder, M.J.; Chen, P.; Dehghan, A.; Edwards, T.L.; Esko, T.; Go, M.J.; Harris, S.E.; Hartiala, J.; Kasela, S.; Kasturiratne, A.; Khor, C-C.; Kleber, M.E.; Li, H.; Yu Mok, Z.; Nakatochi, M.; Sapari, N.S.; Saxena, R.; Stewart, A.F.R.; Stolk, L.; Tabara, Y.; Teh, A.L.; Wu, Y.; Wu, J-Y.; Zhang, Y.; Aits, I.; Da Silva Couto Alves, A.; Das, S.; Dorajoo, R.; Hopewell, J.C.; Kim, Y.K.; Koivula, R.W.; Luan, J.; Lyytikäinen, L-P.; Nguyen, Q.N.; Pereira, M.A.; Postmus, I.; Raitakari, O.T.; Scannell Bryan, M.; Scott, R.A.; Sorice, R.; Tragante, V.; Traglia, M.; White, J.; Yamamoto, K.; Zhang, Y.; Adair, L.S.; Ahmed, A.; Akiyama, K.; Asif, R.; Aung, T.; Barroso, I.; Bjonnes, A.; Braun, T.R.; Cai, H.; Chang, L-C.; Chen, C-H.; Cheng, C-Y.; Chong, Y-S.; Collins, R.; Courtney, R.; Davies, G.; Delgado, G.; Do, L.D.; Doevendans, P.A.; Gansevoort, R.T.; Gao, Y-T.; Grammer, T.B.; Grarup, N.; Grewal, J.; Gu, D.; Wander, G.S.; Hartikainen, A-L.; Hazen, S.L.; He, J.; Heng, C-K.; Hixson, J.E.; Hofman, A.; Hsu, C.; Huang, W.; Husemoen, L.L.N.; Hwang, J-Y.; Ichihara, S.; Igase, M.; Isono, M.; Justesen, J.M.; Katsuya, T.; Kibriya, M.G.; Kim, Y.J.; Kishimoto, M.; Koh, W-P.; Kohara, K.; Kumari, M.; Kwek, K.; Lee, N.R.; Lee, J.; Liao, J.; Lieb, W.; Liewald, D.C.M.; Matsubara, T.; Matsushita, Y.; Meitinger, T.; Mihailov, E.; Milani, L.; Mills, R.; Mononen, N.; Müller-Nurasyid, M.; Nabika, T.; Nakashima, E.; Ng, H.K.; Nikus, K.; Nutile, T.; Ohkubo, T.; Ohnaka, K.; Parish, S.; Paternoster, L.; Peng, H.; Peters, A.; Pham, S.T.; Pinidiyapathirage, M.J.; Rahman, M.; Rakugi, H.; Rolandsson, O.; Ann Rozario, M.; Ruggiero, D.; Sala, C.F.; Sarju, R.; Shimokawa, K.; Snieder, H.; Sparsø, T.; Spiering, W.; Starr, J.M.; Stott, D.J.; Stram, D.O.; Sugiyama, T.; Szymczak, S.; Tang, W.H.W.; Tong, L.; Trompet, S.; Turjanmaa, V.; Ueshima, H.; Uitterlinden, A.G.; Umemura, S.; Vaarasmaki, M.; van Dam, R.M.; van Gilst, W.H.; van Veldhuisen, D.J.; Viikari, J.S.; Waldenberger, M.; Wang, Y.; Wang, A.; Wilson, R.; Wong, T-Y.; Xiang, Y-B.; Yamaguchi, S.; Ye, X.; Young, R.D.; Young, T.L.; Yuan, J-M.; Zhou, X.; Asselbergs, F.W.; Ciullo, M.; Clarke, R.; Deloukas, P.; Franke, A.; Franks, P.W.; Franks, S.; Friedlander, Y.; Gross, M.D.; Guo, Z.; Hansen, T.; Jarvelin, M-R.; Jørgensen, T.; Jukema, J.W.; Kähönen, M.; Kajio, H.; Kivimaki, M.; Lee, J-Y.; Lehtimäki, T.; Linneberg, A.; Miki, T.; Pedersen, O.; Samani, N.J.; Sørensen, T.I.A.; Takayanagi, R.; Toniolo, D.; Ahsan, H.; Allayee, H.; Chen, Y-T.; Danesh, J.; Deary, I.J.; Franco, O.H.; Franke, L.; Heijman, B.T.; Holbrook, J.D.; Isaacs, A.; Kim, B-J.; Lin, X.; Liu, J.; März, W.; Metspalu, A.; Mohlke, K.L.; Sanghera, D.K.; Shu, X-O.; van Meurs, J.B.J.; Vithana, E.; Wickremasinghe, A.R.; Wijmenga, C.; Wolffenbuttel, B.H.W.; Yokota, M.; Zheng, W.; Zhu, D.; Vineis, P.; Kyrtopoulos, S.A.; Kleinjans, J.C.S.; McCarthy, M.I.; Soong, R.; Gieger, C.; Scott, J.; Teo, Y-Y.; He, J.; Elliott, P.; Tai, E.S.; van der Harst, P.; Kooner, J.S.; Chambers, J.C. Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation. Nat. Genet., 2015, 47(11), 1282-1293.
[http://dx.doi.org/10.1038/ng.3405] [PMID: 26390057]

Meric, M.; Soylu, K.; Avci, B.; Yuksel, S.; Gulel, O.; Yenercag, M.; Coksevim, M.; Uzun, A. Evaluation of plasma chemerin levels in patients with non-dipper blood pressure patterns. Med. Sci. Monit., 2014, 20, 698-705.
[http://dx.doi.org/10.12659/MSM.890784] [PMID: 24769499]

Guo, Y.; Pei, Y.; Li, K.; Cui, W.; Zhang, D. DNA N6-methyladenine modification in hypertension. Aging (Albany NY), 2020, 12(7), 6276-6291.
[http://dx.doi.org/10.18632/aging.103023] [PMID: 32283543]

Van Cauwenberghe, C.; Van Broeckhoven, C.; Sleegers, K. The genetic landscape of Alzheimer disease: clinical implications and perspectives. Genet. Med., 2016, 18(5), 421-430.
[http://dx.doi.org/10.1038/gim.2015.117] [PMID: 26312828]

Keogh-Brown, M.R.; Jensen, H.T.; Arrighi, H.M.; Smith, R.D. The impact of Alzheimer’s disease on the Chinese economy. EBioMedicine, 2015, 4, 184-190.
[http://dx.doi.org/10.1016/j.ebiom.2015.12.019] [PMID: 26981556]

Chouliaras, L.; Mastroeni, D.; Delvaux, E.; Grover, A.; Kenis, G.; Hof, P.R.; Steinbusch, H.W.M.; Coleman, P.D.; Rutten, B.P.F.; van den Hove, D.L.A. Consistent decrease in global DNA methylation and hydroxymethylation in the hippocampus of Alzheimer’s disease patients. Neurobiol. Aging, 2013, 34(9), 2091-2099.
[http://dx.doi.org/10.1016/j.neurobiolaging.2013.02.021] [PMID: 23582657]

Coppieters, N.; Dieriks, B.V.; Lill, C.; Faull, R.L.M.; Curtis, M.A.; Dragunow, M. Global changes in DNA methylation and hydroxymethylation in Alzheimer’s disease human brain. Neurobiol. Aging, 2014, 35(6), 1334-1344.
[http://dx.doi.org/10.1016/j.neurobiolaging.2013.11.031] [PMID: 24387984]

Bradley-Whitman, M.A.; Lovell, M.A. Epigenetic changes in the progression of Alzheimer’s disease. Mech. Ageing Dev., 2013, 134(10), 486-495.
[http://dx.doi.org/10.1016/j.mad.2013.08.005] [PMID: 24012631]

Lashley, T.; Gami, P.; Valizadeh, N.; Li, A.; Revesz, T.; Balazs, R. Alterations in global DNA methylation and hydroxymethylation are not detected in Alzheimer’s disease. Neuropathol. Appl. Neurobiol., 2015, 41(4), 497-506.
[http://dx.doi.org/10.1111/nan.12183]

Ellison, E.M.; Abner, E.L.; Lovell, M.A. Multiregional analysis of global 5-methylcytosine and 5-hydroxymethylcytosine throughout the progression of Alzheimer’s disease. J. Neurochem., 2017, 140(3), 383-394.

Bakulski, K.M.; Dolinoy, D.C.; Sartor, M.A.; Paulson, H.L.; Konen, J.R.; Lieberman, A.P.; Albin, R.L.; Hu, H.; Rozek, L.S. Genome-wide DNA methylation differences between late-onset Alzheimer’s disease and cognitively normal controls in human frontal cortex. J. Alzheimers Dis., 2012, 29(3), 571-588.
[http://dx.doi.org/10.3233/JAD-2012-111223] [PMID: 22451312]

De Jager, P.L.; Srivastava, G.; Lunnon, K.; Burgess, J.; Schalkwyk, L.C.; Yu, L.; Eaton, M.L.; Keenan, B.T.; Ernst, J.; McCabe, C.; Tang, A.; Raj, T.; Replogle, J.; Brodeur, W.; Gabriel, S.; Chai, H.S.; Younkin, C.; Younkin, S.G.; Zou, F.; Szyf, M.; Epstein, C.B.; Schneider, J.A.; Bernstein, B.E.; Meissner, A.; Ertekin-Taner, N.; Chibnik, L.B.; Kellis, M.; Mill, J.; Bennett, D.A. Alzheimer’s disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat. Neurosci., 2014, 17(9), 1156-1163.
[http://dx.doi.org/10.1038/nn.3786] [PMID: 25129075]

Humphries, C.E.; Kohli, M.A.; Nathanson, L.; Whitehead, P.; Beecham, G.; Martin, E.; Mash, D.C.; Pericak-Vance, M.A.; Gilbert, J. Integrated whole transcriptome and DNA methylation analysis identifies gene networks specific to late-onset Alzheimer’s disease. J. Alzheimers Dis., 2015, 44(3), 977-987.
[http://dx.doi.org/10.3233/JAD-141989] [PMID: 25380588]

Watson, C.T.; Roussos, P.; Garg, P.; Ho, D.J.; Azam, N.; Katsel, P.L.; Haroutunian, V.; Sharp, A.J. Genome-wide DNA methylation profiling in the superior temporal gyrus reveals epigenetic signatures associated with Alzheimer’s disease. Genome Med., 2016, 8(1), 5.
[http://dx.doi.org/10.1186/s13073-015-0258-8] [PMID: 26803900]

de Mello, V.D.F.; Pulkkinen, L.; Lalli, M.; Kolehmainen, M.; Pihlajamäki, J.; Uusitupa, M. DNA methylation in obesity and type 2 diabetes. Ann. Med., 2014, 46(3), 103-113.
[http://dx.doi.org/10.3109/07853890.2013.857259] [PMID: 24779963]

Murea, M.; Ma, L.; Freedman, B.I. Genetic and environmental factors associated with type 2 diabetes and diabetic vascular complications. Rev. Diabet. Stud., 2012, 9(1), 6-22.
[http://dx.doi.org/10.1900/RDS.2012.9.6] [PMID: 22972441]

Poirier, L.A.; Brown, A.T.; Fink, L.M.; Wise, C.K.; Randolph, C.J.; Delongchamp, R.R.; Fonseca, V.A. Blood S-adenosylmethionine concentrations and lymphocyte methylenetetrahydrofolate reductase activity in diabetes mellitus and diabetic nephropathy. Metabolism, 2001, 50(9), 1014-1018.
[http://dx.doi.org/10.1053/meta.2001.25655] [PMID: 11555831]

Volkmar, M.; Dedeurwaerder, S.; Cunha, D.A.; Ndlovu, M.N.; Defrance, M.; Deplus, R.; Calonne, E.; Volkmar, U.; Igoillo-Esteve, M.; Naamane, N.; Del Guerra, S.; Masini, M.; Bugliani, M.; Marchetti, P.; Cnop, M.; Eizirik, D.L.; Fuks, F. DNA methylation profiling identifies epigenetic dysregulation in pancreatic islets from type 2 diabetic patients. EMBO J., 2012, 31(6), 1405-1426.
[http://dx.doi.org/10.1038/emboj.2011.503]

Arroyo-Jousse, V.; García-Díaz, D.F.; Pérez-Bravo, F. Global DNA methylation and homocysteine levels are lower in type 1 diabetes patients. Rev. Med. Chil., 2015, 143(5), 562-568.
[http://dx.doi.org/10.4067/S0034-98872015000500002] [PMID: 26203566]

Kim, M. DNA methylation: a cause and consequence of type 2 diabetes. Genomics Inform., 2019, 17(4), e38-e38.
[http://dx.doi.org/10.5808/GI.2019.17.4.e38] [PMID: 31896238]

Shridhar, K.; Walia, G.K.; Aggarwal, A.; Gulati, S.; Geetha, A.V.; Prabhakaran, D.; Dhillon, P.K.; Rajaraman, P. DNA methylation markers for oral pre-cancer progression: a critical review. Oral Oncol., 2016, 53, 1-9.
[http://dx.doi.org/10.1016/j.oraloncology.2015.11.012] [PMID: 26690652]

Balgkouranidou, I.; Liloglou, T.; Lianidou, E.S. Lung cancer epigenetics: emerging biomarkers. Biomarkers Med., 2013, 7(1), 49-58.
[http://dx.doi.org/10.2217/bmm.12.111]

Ooki, A.; Yamashita, K.; Kikuchi, S.; Sakuramoto, S.; Katada, N.; Kokubo, K.; Kobayashi, H.; Kim, M.S.; Sidransky, D.; Watanabe, M. Potential utility of HOP homeobox gene promoter methylation as a marker of tumor aggressiveness in gastric cancer. Oncogene, 2010, 29(22), 3263-3275.
[http://dx.doi.org/10.1038/onc.2010.76] [PMID: 20228841]

Dong, W.; Wang, L.; Chen, X.; Sun, P.; Wu, Y.J.D.d. sciences, Upregulation and CpG island hypomethylation of the TRF2 gene in human gastric cancer. Biomarkers Med., 2010, 55(4), 997-1003.

Kwon, O-H.; Park, J-L.; Kim, M.; Kim, J-H.; Lee, H-C.; Kim, H-J.; Noh, S-M.; Song, K-S.; Yoo, H-S.; Paik, S-G.; Kim, S-Y.; Kim, Y.S. Aberrant up-regulation of LAMB3 and LAMC2 by promoter demethylation in gastric cancer. Biochem. Biophys. Res. Commun., 2011, 406(4), 539-545.
[http://dx.doi.org/10.1016/j.bbrc.2011.02.082] [PMID: 21345334]

Li, L-C.; Okino, S.T.; Dahiya, R. DNA methylation in prostate cancer. Biochim. Biophys. Acta (BBA) –. Rev. Can., 2004, 1704(2), 87-102.

Yegnasubramanian, S.; Kowalski, J.; Gonzalgo, M.L.; Zahurak, M.; Piantadosi, S.; Walsh, P.C.; Bova, G.S.; De Marzo, A.M.; Isaacs, W.B.; Nelson, W.G.J.C.R. Hypermethylation of CpG islands in primary and metastatic human prostate cancer. Cancer Res.,, 2004, 64(6), 1975-1986.
[http://dx.doi.org/10.1158/0008-5472.CAN-03-3972]

Chen, H.; Ye, F.; Zhang, J.; Lu, W.; Cheng, Q.; Xie, X. Loss of OPCML expression and the correlation with CpG island methylation and LOH in ovarian serous carcinoma. Eur. J. Gynaecol. Oncol., 2007, 28(6), 464-467.