DNA4mC-LIP: a linear integration method to identify N4-methylcytosine site in multiple species

MOTIVATION DNA N4-methylcytosine (4mC) is a crucial epigenetic modification. However, the knowledge about its biological functions is limited. Effective and accurate identification of 4mC sites will be helpful to reveal its biological functions and mechanisms. Since experimental methods are cost and ineffective, a number of machine learning based approaches have been proposed to detect 4mC sites. Although these methods yielded acceptable accuracy, there is still room for the improvement of the prediction performance and the stability of existing methods in practical applications. RESULTS In this work, we first systematically assessed the existing methods based on an independent dataset. And then, we proposed DNA4mC-LIP, a linear integration method by combining existing predictors to identify 4mC sites in multiple species. The results obtained from independent dataset demonstrated that DNA4mC-LIP outperformed existing methods for identifying 4mC sites. To facilitate the scientific community, a web server for DNA4mC-LIP was developed. We anticipated that DNA4mC-LIP could serve as a powerful computational technique for identifying 4mC sites and facilitate the interpretation of 4mC mechanism. AVAILABILITY http://i.uestc.edu.cn/DNA4mC-LIP/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

[1]  Virapong Prachayasittikul,et al.  Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation , 2019, International journal of molecular sciences.

[2]  Nancy Kleckner,et al.  SeqA: A negative modulator of replication initiation in E. coli , 1994, Cell.

[3]  Zhi Xie,et al.  MethSMRT: an integrative database for DNA N6-methyladenine and N4-methylcytosine generated by single-molecular real-time sequencing , 2016, Nucleic Acids Res..

[4]  Zhiqiang Gao,et al.  DNA Methyltransferase Activity Assays: Advances and Challenges , 2016, Theranostics.

[5]  Xiaodong Cheng,et al.  DNA modification by methyltransferases. , 1995, Current opinion in structural biology.

[6]  Robert J. Schmitz,et al.  Base-resolution detection of N4-methylcytosine in genomic DNA using 4mC-Tet-assisted-bisulfite- sequencing , 2015, Nucleic acids research.

[7]  P. Modrich,et al.  Mechanisms and biological effects of mismatch repair. , 1991, Annual review of genetics.

[8]  K. Zhao,et al.  Lsh, chromatin remodeling family member, modulates genome-wide cytosine methylation patterns at nonrepeat sequences , 2011, Proceedings of the National Academy of Sciences.

[9]  Rudolf Jaenisch,et al.  Role for DNA methylation in genomic imprinting , 1993, Nature.

[10]  W. Messer,et al.  Timing and targeting: The biological functions of Dam methylation in E. coli , 1988, Cell.

[11]  Howard Cedar,et al.  Epigenetic mechanisms that regulate antigen receptor gene expression. , 2003, Current opinion in immunology.

[12]  Leyi Wei,et al.  Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation , 2019, Molecular therapy. Nucleic acids.

[13]  Juanjuan Kang,et al.  NeuroPP: A Tool for the Prediction of Neuropeptide Precursors Based on Optimal Sequence Composition , 2019, Interdisciplinary Sciences: Computational Life Sciences.

[14]  Fu-Ying Dao,et al.  A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae , 2019, Briefings Bioinform..

[15]  Tobias Bergmiller,et al.  Bacterial Autoimmunity Due to a Restriction-Modification System , 2016, Current Biology.

[16]  H. Schweizer.  Bacterial genetics: past achievements, present state of the field, and future challenges. , 2008, BioTechniques.

[17]  Maria Strazzullo,et al.  DNA methylation 40 years later: Its role in human health and disease , 2005, Journal of cellular physiology.

[18]  Chih-Fong Tsai,et al.  SVM and SVM Ensembles in Breast Cancer Prediction , 2017, PloS one.

[19]  Fan Liang,et al.  DNA N6-Adenine Methylation in Arabidopsis thaliana. , 2018, Developmental cell.

[20]  David T. F. Dryden,et al.  Type III restriction-modification enzymes: a historical perspective , 2013, Nucleic acids research.

[21]  Peter A. Jones.  Functions of DNA methylation: islands, start sites, gene bodies and beyond , 2012, Nature Reviews Genetics.

[22]  Yang Shi,et al.  DNA N6-methyladenine: a new epigenetic mark in eukaryotes? , 2015, Nature Reviews Molecular Cell Biology.

[23]  Peter A. C. 't Hoen,et al.  Phd7faster: Predicting Clones Propagating Faster from the pH.D.-7 phage Display peptide Library , 2014, J. Bioinform. Comput. Biol..

[24]  Preeti Rathi,et al.  Selective recognition of N4-methylcytosine in DNA by engineered transcription-activator-like effectors , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[25]  Epigenetic Gene Regulation in the Bacterial World , 2006, Microbiology and Molecular Biology Reviews.

[26]  A. Bird,et al.  Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals , 2003, Nature Genetics.

[27]  Chuan He,et al.  Nucleic Acid Modifications in Regulation of Gene Expression. , 2016, Cell chemical biology.

[28]  Bing Tian,et al.  N4-Cytosine DNA Methylation Is Involved in the Maintenance of Genomic Stability in Deinococcus radiodurans , 2019, Front. Microbiol..

[29]  Tyson A. Clark,et al.  Direct detection of DNA methylation during single-molecule, real-time sequencing , 2010, Nature Methods.

[30]  B. Vanyushin,et al.  5-Methylcytosine and 6-Methylaminopurine in Bacterial DNA , 1968, Nature.

[31]  Yong Luo,et al.  Cost-Sensitive Feature Selection by Optimizing F-Measures , 2018, IEEE Transactions on Image Processing.

[32]  M. Esteller,et al.  DNA methylation and cancer. , 2010, Advances in genetics.

[33]  Wei Chen,et al.  iDNA4mC: identifying DNA N4‐methylcytosine sites based on nucleotide chemical properties , 2017, Bioinform..

[34]  Jianzhong Su,et al.  Homeobox oncogene activation by pan-cancer DNA hypermethylation , 2018, Genome Biology.

[35]  M. Esteller,et al.  An Adenine Code for DNA: A Second Life for N6-Methyladenine , 2015, Cell.

[36]  Wei Chen,et al.  iProEP: A Computational Predictor for Predicting Promoter , 2019, Molecular therapy. Nucleic acids.

[37]  Lucy Shapiro,et al.  A DNA methylation ratchet governs progression through a bacterial cell cycle , 2007, Proceedings of the National Academy of Sciences.

[38]  Ran Su,et al.  Iterative feature representations improve N4-methylcytosine site prediction , 2019, Bioinform..

[39]  Janusz M. Bujnicki,et al.  MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins , 2012, BMC Bioinformatics.

[40]  Q. Cui,et al.  Benchmark of computational methods for predicting microRNA-disease associations , 2019, Genome Biology.

[41]  G. Fan,et al.  DNA Methylation and Its Basic Function , 2013, Neuropsychopharmacology.

[42]  Yukio Nakatani,et al.  Frequent promoter hypermethylation associated with human papillomavirus infection in pharyngeal cancer. , 2017, Cancer letters.

[43]  Hui Ding,et al.  NIEluter: Predicting peptides eluted from HLA class I molecules. , 2015, Journal of immunological methods.

[44]  M. Pellegrini,et al.  Genome-wide High-Resolution Mapping and Functional Analysis of DNA Methylation in Arabidopsis , 2006, Cell.

[45]  S. Baylin,et al.  Increased cytosine DNA-methyltransferase activity during colon cancer progression. , 1993, Journal of the National Cancer Institute.

[46]  Harry Venner,et al.  Nachweis von Minoritätsbasen in Sperma-Desoxyribonucleinsäure , 1966 .

[47]  P. Molloy,et al.  DNA hypomethylation and human diseases. , 2007, Biochimica et biophysica acta.

[48]  A. Jeltsch,et al.  Structure and Function of Mammalian DNA Methyltransferases , 2011, Chembiochem : a European journal of chemical biology.

[49]  B. Vanyushin,et al.  Rare Bases in Animal DNA , 1970, Nature.

[50]  Balachandran Manavalan,et al.  4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N4-Methylcytosine Sites in the Mouse Genome , 2019, Cells.

[51]  Hao Lv,et al.  iRNA-m7G: Identifying N7-methylguanosine Sites by Fusing Multiple Features , 2019, Molecular therapy. Nucleic acids.

[52]  A. Bart,et al.  Direct detection of methylation in genomic DNA , 2005, Nucleic acids research.

[53]  Rudolf Jaenisch,et al.  Synergism of Xist Rna, DNA Methylation, and Histone Hypoacetylation in Maintaining X Chromosome Inactivation , 2001, The Journal of cell biology.

[54]  David T. F. Dryden,et al.  Type I restriction enzymes and their relatives , 2013, Nucleic acids research.

[55]  Cangzhi Jia,et al.  4mCPred: machine learning methods for DNA N4‐methylcytosine sites prediction , 2018, Bioinform..

[56]  Zachary D. Smith,et al.  DNA methylation: roles in mammalian development , 2013, Nature Reviews Genetics.

[57]  Frank Lyko,et al.  DNA methyltransferase inhibitors and the development of epigenetic cancer therapies. , 2005, Journal of the National Cancer Institute.

[58]  Alfred Pingoud,et al.  Type II restriction endonucleases—a historical perspective and more , 2014, Nucleic acids research.

[59]  D. Wion,et al.  N6-methyl-adenine: an epigenetic signal for DNA–protein interactions , 2006, Nature Reviews Microbiology.

[60]  Wei Chen,et al.  iRNAD: a computational tool for identifying D modification sites in RNA sequence , 2019, Bioinform..

[61]  Ran Su,et al.  Exploring sequence‐based features for the improved prediction of DNA N4‐methylcytosine sites in multiple species , 2018, Bioinform..

[62]  François Berger,et al.  N6-methyladenine: the other methylated base of DNA. , 2006, BioEssays : news and reviews in molecular, cellular and developmental biology.