RAMPred: identifying the N1-methyladenosine sites in eukaryotic transcriptomes

N1-methyladenosine (m1A) is a prominent RNA modification involved in many biological processes. Accurate identification of m1A site is invaluable for better understanding the biological functions of m1A. However, limitations in experimental methods preclude the progress towards the identification of m1A site. As an excellent complement of experimental methods, a support vector machine based-method called RAMPred is proposed to identify m1A sites in H. sapiens, M. musculus and S. cerevisiae genomes for the first time. In this method, RNA sequences are encoded by using nucleotide chemical property and nucleotide compositions. RAMPred achieves promising performances in jackknife tests, cross cell line tests and cross species tests, indicating that RAMPred holds very high potential to become a useful tool for identifying m1A sites. For the convenience of experimental scientists, a web-server based on the proposed model was constructed and could be freely accessible at http://lin.uestc.edu.cn/server/RAMPred.

[1]  Ho-Jin Choi,et al.  DNA Encoding for Splice Site Prediction in Large DNA Sequence , 2013, DASFAA Workshops.

[2]  Zhengwei Zhu,et al.  CD-HIT: accelerated for clustering the next-generation sequencing data , 2012, Bioinform..

[3]  E. Cundliffe,et al.  Site-specific methylation of 16S rRNA caused by pct, a pactamycin resistance determinant from the producing organism, Streptomyces pactum , 1991, Journal of bacteriology.

[4]  Wei Chen,et al.  Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome , 2015, Scientific Reports.

[5]  J. Sussman,et al.  Crystal structure of a eukaryotic initiator tRNA , 1979, Nature.

[6]  Wei Chen,et al.  MethyRNA: a web server for identification of N6-methyladenosine sites , 2017, Journal of biomolecular structure & dynamics.

[7]  Wei Chen,et al.  iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. , 2014, Analytical biochemistry.

[8]  angesichts der Corona-Pandemie,et al.  UPDATE , 1973, The Lancet.

[9]  Gideon Rechavi,et al.  The dynamic N1-methyladenosine methylome in eukaryotic messenger RNA , 2016, Nature.

[10]  Marcin Feder,et al.  MODOMICS: a database of RNA modification pathways , 2005, Nucleic Acids Res..

[11]  Ian H. Witten,et al.  Data mining in bioinformatics using Weka , 2004, Bioinform..

[12]  K. Chou Some remarks on protein attribute prediction and pseudo amino acid composition , 2010, Journal of Theoretical Biology.

[13]  Chengqi Yi,et al.  Transcriptome-wide mapping reveals reversible and dynamic N(1)-methyladenosine methylome. , 2016, Nature chemical biology.

[14]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[15]  Xiaolong Wang,et al.  Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection , 2013, Bioinform..

[16]  Wei Chen,et al.  Prediction of CpG island methylation status by integrating DNA physicochemical properties. , 2014, Genomics.

[17]  H. Ding,et al.  Identification of mitochondrial proteins of malaria parasite using analysis of variance , 2014, Amino Acids.

[18]  Karl Rihaczek,et al.  1. WHAT IS DATA MINING? , 2019, Data Mining for the Social Sciences.

[19]  Wei Chen,et al.  Predicting the Types of J-Proteins Using Clustered Amino Acids , 2014, BioMed research international.

[20]  D. B. Dunn,et al.  The occurrence of 1-methyladenine in ribonucleic acid. , 1961, Biochimica et biophysica acta.

[21]  P. Ruoff,et al.  Structural basis for enzymatic excision of N1‐methyladenine and N3‐methylcytosine from DNA , 2007, The EMBO journal.

[22]  Christian Peifer,et al.  Yeast Rrp8p, a novel methyltransferase responsible for m1A 645 base modification of 25S rRNA , 2012, Nucleic acids research.

[23]  Manish Kumar,et al.  Prediction of β-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine. , 2015, Journal of theoretical biology.

[24]  Mark Helm,et al.  Posttranscriptional RNA Modifications: playing metabolic games in a cell's chemical Legoland. , 2014, Chemistry & biology.

[25]  B. Liu,et al.  Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy. , 2015, Journal of theoretical biology.

[26]  Tao Pan,et al.  Genome-wide analysis of N1-methyl-adenosine modification in human tRNAs. , 2010, RNA.

[27]  K. Chou,et al.  iRNA-Methyl: Identifying N(6)-methyladenosine sites using pseudo nucleotide composition. , 2015, Analytical biochemistry.

[28]  J. Bujnicki,et al.  MODOMICS: a database of RNA modification pathways—2013 update , 2012, Nucleic Acids Res..

[29]  Clement T Y Chan,et al.  A Quantitative Systems Approach Reveals Dynamic Control of tRNA Modifications during Cellular Stress , 2010, PLoS genetics.

[30]  Wei Chen,et al.  PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions , 2015, Bioinform..

[31]  Hui Ding,et al.  AcalPred: A Sequence-Based Tool for Discriminating between Acidic and Alkaline Enzymes , 2013, PloS one.

[32]  Wei Chen,et al.  iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition , 2013, Nucleic acids research.

[33]  K. Chou,et al.  PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. , 2014, Analytical biochemistry.

[34]  Judith P Klinman,et al.  A 21st century revisionist's view at a turning point in enzymology. , 2009, Nature chemical biology.