im6A-TS-CNN: Identifying the N6-Methyladenine Site in Multiple Tissues by Using the Convolutional Neural Network

N6-methyladenosine (m6A) is the most abundant post-transcriptional modification and involves a series of important biological processes. Therefore, accurate detection of the m6A site is very important for revealing its biological functions and impacts on diseases. Although both experimental and computational methods have been proposed for identifying m6A sites, few of them are able to detect m6A sites in different tissues. With the consideration of the spatial specificity of m6A modification, it is necessary to develop methods able to detect the m6A site in different tissues. In this work, by using the convolutional neural network (CNN), we proposed a new method, called im6A-TS-CNN, that can identify m6A sites in brain, liver, kidney, heart, and testis of Homo sapiens, Mus musculus, and Rattus norvegicus. In im6A-TS-CNN, the samples were encoded by using the one-hot encoding scheme. The results from both a 5-fold cross-validation test and independent dataset test demonstrate that im6A-TS-CNN is better than the existing method for the same purpose. The command-line version of im6A-TS-CNN is available at https://github.com/liukeweiaway/DeepM6A_cnn.

[1]  Wei Chen,et al.  iMRM: a platform for simultaneously identifying multiple kinds of RNA modifications , 2020, Bioinform..

[2]  Adam Godzik,et al.  Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences , 2006, Bioinform..

[3]  Michiaki Hamada,et al.  DeepM6ASeq: prediction and characterization of m6A-containing sequences using deep learning , 2018, BMC Bioinformatics.

[4]  Chuan He,et al.  N 6 -methyladenosine Modulates Messenger RNA Translation Efficiency , 2015, Cell.

[5]  Jionglong Su,et al.  WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach , 2019, Nucleic acids research.

[6]  Peter Kraft,et al.  Association of type 2 diabetes susceptibility variants with advanced prostate cancer risk in the Breast and Prostate Cancer Cohort Consortium. , 2012, American journal of epidemiology.

[7]  Hiroyuki Kurata,et al.  i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation , 2020, Plant Molecular Biology.

[8]  Hilal Tayara,et al.  iPseU-CNN: Identifying RNA Pseudouridine Sites Using Convolutional Neural Networks , 2019, Molecular therapy. Nucleic acids.

[9]  Yufei Huang,et al.  A protocol for RNA methylation differential analysis with MeRIP-Seq data and exomePeak R/Bioconductor package. , 2014, Methods.

[10]  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.

[11]  Zhang Zhang,et al.  Single-base mapping of m6A by an antibody-independent method , 2019, Science Advances.

[12]  Chenhui Yang,et al.  Exploiting Discriminative Regions of Brain Slices Based on 2D CNNs for Alzheimer’s Disease Classification , 2019, IEEE Access.

[13]  Yizhen Wang,et al.  N6-Methyladenosine (m6A) Methylation in mRNA with A Dynamic and Reversible Epigenetic Modification , 2016, Molecular Biotechnology.

[14]  Hiroyuki Kurata,et al.  i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes , 2020, Computational and structural biotechnology journal.

[15]  Cai-Guang Yang,et al.  Single-base mapping of m6A by an antibody-independent method , 2019, Science Advances.

[16]  Jiangning Song,et al.  Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences , 2019, Briefings Bioinform..

[17]  Yang Wang,et al.  N6-methyladenosine modification destabilizes developmental regulators in embryonic stem cells , 2014, Nature Cell Biology.

[18]  Tao Pan,et al.  High-resolution N(6) -methyladenosine (m(6) A) map using photo-crosslinking-assisted m(6) A sequencing. , 2015, Angewandte Chemie.

[19]  Arne Klungland,et al.  A majority of m6A residues are in the last exons, allowing the potential for 3′ UTR regulation , 2015, Genes & development.

[20]  J. Bokar.  The biosynthesis and functional roles of methylated nucleosides in eukaryotic mRNA , 2005 .

[21]  Chuan He,et al.  Dynamics of Human and Viral RNA Methylation during Zika Virus Infection. , 2016, Cell host & microbe.

[22]  Yasubumi Sakakibara,et al.  Convolutional neural networks for classification of alignments of non-coding RNA sequences , 2018, Bioinform..

[23]  Q. Zou,et al.  Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA , 2018, RNA.

[24]  O. Elemento,et al.  Comprehensive Analysis of mRNA Methylation Reveals Enrichment in 3′ UTRs and near Stop Codons , 2012, Cell.

[25]  M. Kupiec,et al.  Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq , 2012, Nature.

[26]  Horst Zitzelsberger,et al.  Novel candidate genes of thyroid tumourigenesis identified in Trk-T1 transgenic mice. , 2012, Endocrine-related cancer.

[27]  Chengqi Yi,et al.  N6-Methyladenosine in Nuclear RNA is a Major Substrate of the Obesity-Associated FTO , 2011, Nature chemical biology.

[28]  B. Turnbull,et al.  NONPARAMETRIC AND SEMIPARAMETRIC ESTIMATION OF THE RECEIVER OPERATING CHARACTERISTIC CURVE , 1996 .

[29]  G E Tomlinson,et al.  WTAP is a novel oncogenic protein in acute myeloid leukemia , 2014, Leukemia.

[30]  Lei Li,et al.  BERMP: a cross-species classifier for predicting m6A sites by integrating a deep learning algorithm and a random forest approach , 2018, International journal of biological sciences.

[31]  Fu-Ying Dao,et al.  Computational identification of N6-methyladenosine sites in multiple tissues of mammals , 2020, Computational and structural biotechnology journal.

[32]  Leyi Wei,et al.  AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees , 2019, Computational and structural biotechnology journal.