i6mA-DNCP: Computational Identification of DNA N6-Methyladenine Sites in the Rice Genome Using Optimized Dinucleotide-Based Features
暂无分享,去创建一个
[1] Cangzhi Jia,et al. 4mCPred: machine learning methods for DNA N4‐methylcytosine sites prediction , 2018, Bioinform..
[2] Zachary D. Smith,et al. DNA methylation: roles in mammalian development , 2013, Nature Reviews Genetics.
[3] Kil To Chong,et al. iDNA6mA (5-step rule): Identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model via Chou's 5-step rule , 2019, Chemometrics and Intelligent Laboratory Systems.
[4] Q. Cui,et al. SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features , 2016, Nucleic acids research.
[5] Wei Chen,et al. PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions , 2015, Bioinform..
[6] Yang Shi,et al. DNA N6-methyladenine: a new epigenetic mark in eukaryotes? , 2015, Nature Reviews Molecular Cell Biology.
[7] Liang Kong,et al. iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou's pseudo components. , 2018, Journal of theoretical biology.
[8] Peng Jin,et al. DNA N6-methyladenine is dynamically regulated in the mouse brain following environmental stress , 2017, Nature Communications.
[9] K. Chou,et al. iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. , 2018, Genomics.
[10] L. Aravind,et al. DNA Methylation on N6-Adenine in C. elegans , 2015, Cell.
[11] Shanxin Zhang,et al. Prediction of DNase I hypersensitive sites in plant genome using multiple modes of pseudo components. , 2018, Analytical biochemistry.
[12] Wei Chen,et al. i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome , 2019, Bioinform..
[13] J. Cadet,et al. High-performance liquid chromatography--tandem mass spectrometry measurement of radiation-induced base damage to isolated and cellular DNA. , 2000, Chemical research in toxicology.
[14] Andrew V. Colasanti,et al. A novel roll-and-slide mechanism of DNA folding in chromatin: implications for nucleosome positioning. , 2007, Journal of molecular biology.
[15] Chuan-Le Xiao,et al. MDR: an integrative DNA N6-methyladenine and N4-methylcytosine modification database for Rosaceae , 2019, Horticulture Research.
[16] Liang Kong,et al. iRSpot-PDI: Identification of recombination spots by incorporating dinucleotide property diversity information into Chou's pseudo components. , 2019, Genomics.
[17] James A. Swenberg,et al. DNA methylation on N6-adenine in mammalian embryonic stem cells , 2016, Nature.
[18] T. Richmond,et al. The structure of DNA in the nucleosome core , 2003, Nature.
[19] Chuan He,et al. Abundant DNA 6mA methylation during early embryogenesis of zebrafish and pig , 2016, Nature Communications.
[20] Tyson A. Clark,et al. Direct detection of DNA methylation during single-molecule, real-time sequencing , 2010, Nature Methods.
[21] L. Doré,et al. N 6-Methyldeoxyadenosine Marks Active Transcription Start Sites in Chlamydomonas , 2015, Cell.
[22] Yu Zhao,et al. Identification and analysis of adenine N6-methylation sites in the rice genome , 2018, Nature Plants.
[23] Peter A. Jones. Functions of DNA methylation: islands, start sites, gene bodies and beyond , 2012, Nature Reviews Genetics.
[24] Shanxin Zhang,et al. pDHS-DSET: Prediction of DNase I hypersensitive sites in plant genome using DS evidence theory. , 2019, Analytical biochemistry.
[25] Wei Chen,et al. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition , 2013, Nucleic acids research.
[26] Shunmin He,et al. N6-Methyladenine DNA Modification in Drosophila , 2015, Cell.
[27] Charles R. Bradshaw,et al. Identification of methylated deoxyadenosines in vertebrates reveals diversity in DNA modifications , 2015, Nature Structural &Molecular Biology.
[28] Zhen Xu,et al. pDHS-ELM: computational predictor for plant DNase I hypersensitive sites based on extreme learning machines , 2018, Molecular Genetics and Genomics.
[29] François Berger,et al. N6-methyladenine: the other methylated base of DNA. , 2006, BioEssays : news and reviews in molecular, cellular and developmental biology.
[30] Fan Liang,et al. DNA N6-adenine methylation in Arabidopsis thaliana , 2017, Mechanisms of Development.
[31] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[32] Xing Gao,et al. Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites , 2019, Neurocomputing.
[33] Hao Lin,et al. iDNA6mA-Rice: A Computational Tool for Detecting N6-Methyladenine Sites in Rice , 2019, Front. Genet..
[34] Zhengwei Zhu,et al. CD-HIT: accelerated for clustering the next-generation sequencing data , 2012, Bioinform..
[35] Wei Chen,et al. Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines , 2017, Scientific Reports.
[36] Hongkun Zheng,et al. N6-Methyladenine DNA Methylation in Japonica and Indica Rice Genomes and Its Association with Gene Expression, Plant Development, and Stress Responses. , 2018, Molecular plant.
[37] B. Liu,et al. iRSpot-DACC: a computational predictor for recombination hot/cold spots identification based on dinucleotide-based auto-cross covariance , 2016, Scientific Reports.
[38] K. Chou,et al. iRNA-Methyl: Identifying N(6)-methyladenosine sites using pseudo nucleotide composition. , 2015, Analytical biochemistry.
[39] Yong-qiang Xing,et al. Using weighted features to predict recombination hotspots in Saccharomyces cerevisiae. , 2015, Journal of theoretical biology.
[40] K. Chou,et al. PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. , 2014, Analytical biochemistry.
[41] P. Modrich,et al. Extent of equilibrium perturbation of the DNA helix upon enzymatic methylation of adenine residues. , 1985, The Journal of biological chemistry.