Login Register Cart 0
Generic placeholder image

Current Pharmaceutical Design

Editor-in-Chief

ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
General Review Article

Comparison and Analysis of Computational Methods for Identifying N6-Methyladenosine Sites in Saccharomyces cerevisiae

Author(s): Pengmian Feng*, Lijing Feng and Chaohui Tang

Volume 27, Issue 9, 2021

Published on: 09 November, 2020

Page: [1219 - 1229] Pages: 11

DOI: 10.2174/1381612826666201109110703

Price: $65

Open Access Journals Promotions 2
Abstract

Background: N6-methyladenosine (m6A) plays critical roles in a broad range of biological processes. Knowledge about the precise location of m6A site in the transcriptome is vital for deciphering its biological functions. Although experimental techniques have made substantial contributions to identify m6A, they are still labor intensive and time consuming. As complement to experimental methods, in the past few years, a series of computational approaches have been proposed to identify m6A sites.

Methods: In order to facilitate researchers to select appropriate methods for identifying m6A sites, it is necessary to conduct a comprehensive review and comparison of existing methods.

Results: Since research works on m6A in Saccharomyces cerevisiae are relatively clear, in this review, we summarized recent progress of computational prediction of m6A sites in S. cerevisiae and assessed the performance of existing computational methods. Finally, future directions of computationally identifying m6A sites are presented.

Conclusion: Taken together, we anticipate that this review will serve as an important guide for computational analysis of m6A modifications.

Keywords: RNA modification, N6-methyladenosine, epitranscriptome, machine learning, feature representation, web server.

« Previous
[1]
Desrosiers R, Friderici K, Rottman F. Identification of methylated nucleosides in messenger RNA from Novikoff hepatoma cells. Proc Natl Acad Sci USA 1974; 71(10): 3971-5.
[http://dx.doi.org/10.1073/pnas.71.10.3971] [PMID: 4372599]
[2]
Liu J, Jia G. Methylation modifications in eukaryotic messenger RNA. J Genet Genomics 2014; 41(1): 21-33.
[http://dx.doi.org/10.1016/j.jgg.2013.10.002] [PMID: 24480744]
[3]
Bokar JA, Rath-Shambaugh ME, Ludwiczak R, Narayan P, Rottman F. Characterization and partial purification of mRNA N6-adenosine methyltransferase from HeLa cell nuclei. Internal mRNA methylation requires a multisubunit complex. J Biol Chem 1994; 269(26): 17697-704.
[PMID: 8021282]
[4]
Liu J, Yue Y, Han D, et al. A METTL3-METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation. Nat Chem Biol 2014; 10(2): 93-5.
[http://dx.doi.org/10.1038/nchembio.1432] [PMID: 24316715]
[5]
Ping XL, Sun BF, Wang L, et al. Mammalian WTAP is a regulatory subunit of the RNA N6-methyladenosine methyltransferase. Cell Res 2014; 24(2): 177-89.
[http://dx.doi.org/10.1038/cr.2014.3] [PMID: 24407421]
[6]
Jia G, Fu Y, Zhao X, et al. N6-methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO. Nat Chem Biol 2011; 7(12): 885-7.
[http://dx.doi.org/10.1038/nchembio.687] [PMID: 22002720]
[7]
Zheng G, Dahl JA, Niu Y, et al. ALKBH5 is a mammalian RNA demethylase that impacts RNA metabolism and mouse fertility. Mol Cell 2013; 49(1): 18-29.
[http://dx.doi.org/10.1016/j.molcel.2012.10.015] [PMID: 23177736]
[8]
Liu N, Dai Q, Zheng G, He C, Parisien M, Pan TN. (6)-methyladenosine-dependent RNA structural switches regulate RNA-protein interactions. Nature 2015; 518(7540): 560-4.
[http://dx.doi.org/10.1038/nature14234] [PMID: 25719671]
[9]
Zhou KI, Parisien M, Dai Q, et al. N(6)-Methyladenosine Modification in a Long Noncoding RNA Hairpin Predisposes Its Conformation to Protein Binding. J Mol Biol 2016; 428(5 Pt A): 822-33.
[http://dx.doi.org/10.1016/j.jmb.2015.08.021] [PMID: 26343757]
[10]
Cao G, Li HB, Yin Z, Flavell RA. Recent advances in dynamic m6A RNA modification. Open Biol 2016; 6(4)160003
[http://dx.doi.org/10.1098/rsob.160003] [PMID: 27249342]
[11]
Zhang Z, Theler D, Kaminska KH, et al. The YTH domain is a novel RNA binding domain. J Biol Chem 2010; 285(19): 14701-10.
[http://dx.doi.org/10.1074/jbc.M110.104711] [PMID: 20167602]
[12]
Xu C, Wang X, Liu K, et al. Structural basis for selective binding of m6A RNA by the YTHDC1 YTH domain. Nat Chem Biol 2014; 10(11): 927-9.
[http://dx.doi.org/10.1038/nchembio.1654] [PMID: 25242552]
[13]
Luo S, Tong L. Molecular basis for the recognition of methylated adenines in RNA by the eukaryotic YTH domain. Proc Natl Acad Sci USA 2014; 111(38): 13834-9.
[http://dx.doi.org/10.1073/pnas.1412742111] [PMID: 25201973]
[14]
Roundtree IA, He C. RNA epigenetics-chemical messages for posttranscriptional gene regulation. Curr Opin Chem Biol 2016; 30: 46-51.
[http://dx.doi.org/10.1016/j.cbpa.2015.10.024] [PMID: 26625014]
[15]
Sergiev PV, Golovina AY, Osterman IA, et al. N6-Methylated Adenosine in RNA: From Bacteria to Humans. J Mol Biol 2016; 428(10 Pt B): 2134-45.
[http://dx.doi.org/10.1016/j.jmb.2015.12.013] [PMID: 26707202]
[16]
Wang X, Lu Z, Gomez A, et al. N6-methyladenosine-dependent regulation of messenger RNA stability. Nature 2014; 505(7481): 117-20.
[http://dx.doi.org/10.1038/nature12730] [PMID: 24284625]
[17]
Fustin JM, Doi M, Yamaguchi Y, et al. RNA-methylation-dependent RNA processing controls the speed of the circadian clock. Cell 2013; 155(4): 793-806.
[http://dx.doi.org/10.1016/j.cell.2013.10.026] [PMID: 24209618]
[18]
Chen T, Hao YJ, Zhang Y, et al. m(6)A RNA methylation is regulated by microRNAs and promotes reprogramming to pluripotency. Cell Stem Cell 2015; 16(3): 289-301.
[http://dx.doi.org/10.1016/j.stem.2015.01.016] [PMID: 25683224]
[19]
Geula S, Moshitch-Moshkovitz S, Dominissini D, et al. Stem cells. m6A mRNA methylation facilitates resolution of naïve pluripotency toward differentiation. Science 2015; 347(6225): 1002-6.
[http://dx.doi.org/10.1126/science.1261417] [PMID: 25569111]
[20]
Karikó K, Buckstein M, Ni H, Weissman D. Suppression of RNA recognition by Toll-like receptors: the impact of nucleoside modification and the evolutionary origin of RNA. Immunity 2005; 23(2): 165-75.
[http://dx.doi.org/10.1016/j.immuni.2005.06.008] [PMID: 16111635]
[21]
Shen F, Huang W, Huang JT, et al. Decreased N(6)-methyladenosine in peripheral blood RNA from diabetic patients is associated with FTO expression rather than ALKBH5. J Clin Endocrinol Metab 2015; 100(1): E148-54.
[http://dx.doi.org/10.1210/jc.2014-1893] [PMID: 25303482]
[22]
Yang Y, Huang W, Huang JT, et al. Increased N6-methyladenosine in Human Sperm RNA as a Risk Factor for Asthenozoospermia. Sci Rep 2016; 6: 24345.
[http://dx.doi.org/10.1038/srep24345] [PMID: 27072590]
[23]
Tsai K, Courtney DG, Cullen BR. Addition of m6A to SV40 late mRNAs enhances viral structural gene expression and replication. PLoS Pathog 2018; 14(2)e1006919
[http://dx.doi.org/10.1371/journal.ppat.1006919] [PMID: 29447282]
[24]
Meyer KD, Saletore Y, Zumbo P, Elemento O, Mason CE, Jaffrey SR. Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell 2012; 149(7): 1635-46.
[http://dx.doi.org/10.1016/j.cell.2012.05.003] [PMID: 22608085]
[25]
Dominissini D, Moshitch-Moshkovitz S, Salmon-Divon M, Amariglio N, Rechavi G. Transcriptome-wide mapping of N(6)-methyladenosine by m(6)A-seq based on immunocapturing and massively parallel sequencing. Nat Protoc 2013; 8(1): 176-89.
[http://dx.doi.org/10.1038/nprot.2012.148] [PMID: 23288318]
[26]
Linder B, Grozhik AV, Olarerin-George AO, Meydan C, Mason CE, Jaffrey SR. Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nat Methods 2015; 12(8): 767-72.
[http://dx.doi.org/10.1038/nmeth.3453] [PMID: 26121403]
[27]
Xuan JJ, Sun WJ, Lin PH, et al. RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data. Nucleic Acids Res 2018; 46(D1): D327-34.
[http://dx.doi.org/10.1093/nar/gkx934] [PMID: 29040692]
[28]
Zheng Y, Nie P, Peng D, et al. m6AVar: a database of functional variants involved in m6A modification. Nucleic Acids Res 2018; 46(D1): D139-45.
[http://dx.doi.org/10.1093/nar/gkx895] [PMID: 29036329]
[29]
Schwartz S, Agarwala SD, Mumbach MR, et al. High-resolution mapping reveals a conserved, widespread, dynamic mRNA methylation program in yeast meiosis. Cell 2013; 155(6): 1409-21.
[http://dx.doi.org/10.1016/j.cell.2013.10.047] [PMID: 24269006]
[30]
Chen W, Tran H, Liang Z, Lin H, Zhang L. Identification and analysis of the N(6)-methyladenosine in the Saccharomyces cerevisiae transcriptome. Sci Rep 2015; 5: 13859.
[http://dx.doi.org/10.1038/srep13859] [PMID: 26343792]
[31]
Chen W, Feng P, Ding H, Lin H, Chou KC. iRNA-Methyl: Identifying N(6)-methyladenosine sites using pseudo nucleotide composition. Anal Biochem 2015; 490: 26-33.
[http://dx.doi.org/10.1016/j.ab.2015.08.021] [PMID: 26314792]
[32]
Zhou Y, Zeng P, Li YH, Zhang Z, Cui Q. SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features. Nucleic Acids Res 2016; 44(10)e91
[http://dx.doi.org/10.1093/nar/gkw104] [PMID: 26896799]
[33]
Liu Z, Xiao X, Yu DJ, Jia J, Qiu WR, Chou KC. pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties. Anal Biochem 2016; 497: 60-7.
[http://dx.doi.org/10.1016/j.ab.2015.12.017] [PMID: 26748145]
[34]
Chen W, Xing P, Zou Q. Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines. Sci Rep 2017; 7: 40242.
[http://dx.doi.org/10.1038/srep40242] [PMID: 28079126]
[35]
Jia CZ, Zhang JJ, Gu WZ. RNA-MethylPred: A high-accuracy predictor to identify N6-methyladenosine in RNA. Anal Biochem 2016; 510: 72-5.
[http://dx.doi.org/10.1016/j.ab.2016.06.012] [PMID: 27338301]
[36]
Chen K, Wei Z, Zhang Q, et al. WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach. Nucleic Acids Res 2019; 47(7)e41
[http://dx.doi.org/10.1093/nar/gkz074] [PMID: 30993345]
[37]
Chou KC, Shen HB. Recent progress in protein subcellular location prediction. Anal Biochem 2007; 370(1): 1-16.
[http://dx.doi.org/10.1016/j.ab.2007.07.006] [PMID: 17698024]
[38]
Chen W, Lei TY, Jin DC, Lin H, Chou KC. PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. Anal Biochem 2014; 456: 53-60.
[http://dx.doi.org/10.1016/j.ab.2014.04.001] [PMID: 24732113]
[39]
Chen W, Lin H, Chou KC. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol Biosyst 2015; 11(10): 2620-34.
[http://dx.doi.org/10.1039/C5MB00155B] [PMID: 26099739]
[40]
Chou KC. Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol 2011; 273(1): 236-47.
[http://dx.doi.org/10.1016/j.jtbi.2010.12.024] [PMID: 21168420]
[41]
Su ZD, Huang Y, Zhang ZY, et al. iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics 2018; 34(24): 4196-204.
[http://dx.doi.org/10.1093/bioinformatics/bty508] [PMID: 29931187]
[42]
Feng P, Yang H, Ding H, Lin H, Chen W, Chou KC. iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine 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]
[43]
Chen W, Feng P, Yang H, Ding H, Lin H, Chou KC. iRNA-3typeA: identifying 3-types of modification at RNA’s adenosine sites. Mol Ther Nucleic Acids 2018; 11: 468-74.
[http://dx.doi.org/10.1016/j.omtn.2018.03.012] [PMID: 29858081]
[44]
Yang H, Qiu WR, Liu G, et al. iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Int J Biol Sci 2018; 14(8): 883-91.
[http://dx.doi.org/10.7150/ijbs.24616] [PMID: 29989083]
[45]
Feng PM, Chen W, Lin H, Chou KC. iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal Biochem 2013; 442(1): 118-25.
[http://dx.doi.org/10.1016/j.ab.2013.05.024] [PMID: 23756733]
[46]
Chen X-X, Tang H, Li W-C, et al. Identification of Bacterial Cell Wall Lyases via Pseudo Amino Acid Composition. BioMed Res Int 2016.20161654623
[http://dx.doi.org/10.1155/2016/1654623] [PMID: 27437396]
[47]
Chou KC, Cai YD. Using functional domain composition and support vector machines for prediction of protein subcellular location. J Biol Chem 2002; 277(48): 45765-9.
[http://dx.doi.org/10.1074/jbc.M204161200] [PMID: 12186861]
[48]
Cai YD, Zhou GP, Chou KC. Support vector machines for predicting membrane protein types by using functional domain composition. Biophys J 2003; 84(5): 3257-63.
[http://dx.doi.org/10.1016/S0006-3495(03)70050-2] [PMID: 12719255]
[49]
Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press 2000.
[http://dx.doi.org/10.1017/CBO9780511801389]
[50]
Breiman L. Random forests. Mach Learn 2001; 45: 5-32.
[http://dx.doi.org/10.1023/A:1010933404324]
[51]
Chen J, Liu H, Yang J, Chou KC. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids 2007; 33(3): 423-8.
[http://dx.doi.org/10.1007/s00726-006-0485-9] [PMID: 17252308]
[52]
Chen W, Feng PM, Lin H, Chou KC. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res 2013; 41(6)e68
[http://dx.doi.org/10.1093/nar/gks1450] [PMID: 23303794]
[53]
Xu Y, Shao XJ, Wu LY, Deng NY, Chou KC. .iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ 2013; 1e171.
[http://dx.doi.org/10.7717/peerj.171] [PMID: 24109555]
[54]
Basith S, Manavalan B, Hwan Shin T, Lee G. Machine intelligence in peptide therapeutics: A next-generation tool for rapid disease screening. Med Res Rev 2020; 40(4): 1276-314.
[http://dx.doi.org/10.1002/med.21658] [PMID: 31922268]
[55]
Manavalan B, Basith S, Shin TH, Wei L, Lee G. AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees. Comput Struct Biotechnol J 2019; 17: 972-81.
[http://dx.doi.org/10.1016/j.csbj.2019.06.024] [PMID: 31372196]
[56]
Lin H, Deng EZ, Ding H, Chen W, Chou KC. iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res 2014; 42(21): 12961-72.
[http://dx.doi.org/10.1093/nar/gku1019] [PMID: 25361964]
[57]
Chen W, Ding H, Feng P, Lin H, Chou KC. iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget 2016; 7(13): 16895-909.
[http://dx.doi.org/10.18632/oncotarget.7815] [PMID: 26942877]
[58]
Liu B, Yang F, Chou KC. 2L-piRNA: A two-layer ensemble classifier for identifying piwi-interacting RNAs and their function. Mol Ther Nucleic Acids 2017; 7: 267-77.
[http://dx.doi.org/10.1016/j.omtn.2017.04.008] [PMID: 28624202]
[59]
Liu B, Wang S, Long R, Chou KC. iRSpot-EL: identify recombination spots with an ensemble learning approach. Bioinformatics 2017; 33(1): 35-41.
[http://dx.doi.org/10.1093/bioinformatics/btw539] [PMID: 27531102]
[60]
Chen W, Feng P, Yang H, Ding H, Lin H, Chou KC. iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget 2017; 8(3): 4208-17.
[http://dx.doi.org/10.18632/oncotarget.13758] [PMID: 27926534]
[61]
Meher PK, Sahu TK, Saini V, Rao AR. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Sci Rep 2017; 7: 42362.
[http://dx.doi.org/10.1038/srep42362] [PMID: 28205576]
[62]
Feng P, Ding H, Yang H, Chen W, Lin H, Chou KC. iRNA-PseColl: Identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into PseKNC. Mol Ther Nucleic Acids 2017; 7: 155-63.
[http://dx.doi.org/10.1016/j.omtn.2017.03.006] [PMID: 28624191]
[63]
Liu B, Yang F, Huang DS, Chou KC. iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics 2018; 34(1): 33-40.
[http://dx.doi.org/10.1093/bioinformatics/btx579] [PMID: 28968797]
[64]
Chen W, Ding H, Zhou X, Lin H, Chou KC. iRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition. Anal Biochem 2018; 561-562: 59-65.
[http://dx.doi.org/10.1016/j.ab.2018.09.002] [PMID: 30201554]
[65]
Chou KC, Zhang CT. Prediction of protein structural classes. Crit Rev Biochem Mol Biol 1995; 30(4): 275-349.
[http://dx.doi.org/10.3109/10409239509083488] [PMID: 7587280]
[66]
Mohabatkar H. Prediction of cyclin proteins using Chou’s pseudo amino acid composition. Protein Pept Lett 2010; 17(10): 1207-14.
[http://dx.doi.org/10.2174/092986610792231564] [PMID: 20450487]
[67]
Zhou GP, Doctor K. Subcellular location prediction of apoptosis proteins. Proteins 2003; 50(1): 44-8.
[http://dx.doi.org/10.1002/prot.10251] [PMID: 12471598]
[68]
Sahu SS, Panda G. A novel feature representation method based on Chou’s pseudo amino acid composition for protein structural class prediction. Comput Biol Chem 2010; 34(5-6): 320-7.
[http://dx.doi.org/10.1016/j.compbiolchem.2010.09.002] [PMID: 21106461]
[69]
Zia-Ur-Rehman. Khan A. Identifying GPCRs and their types with Chou’s pseudo amino acid composition: an approach from multi-scale energy representation and position specific scoring matrix. Protein Pept Lett 2012; 19(8): 890-903.
[http://dx.doi.org/10.2174/092986612801619589] [PMID: 22316312]
[70]
Fan GL, Li QZ. Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou’s pseudo amino acid composition. J Theor Biol 2013; 334: 45-51.
[http://dx.doi.org/10.1016/j.jtbi.2013.06.003] [PMID: 23770403]
[71]
Huang C, Yuan JQ. Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou’s pseudo amino acid compositions. J Theor Biol 2013; 335: 205-12.
[http://dx.doi.org/10.1016/j.jtbi.2013.06.034] [PMID: 23850480]
[72]
Hajisharifi Z, Piryaiee M, Mohammad Beigi M, Behbahani M, Mohabatkar H. Predicting anticancer peptides with Chou’s pseudo amino acid composition and investigating their mutagenicity via Ames test. J Theor Biol 2014; 341: 34-40.
[http://dx.doi.org/10.1016/j.jtbi.2013.08.037] [PMID: 24035842]
[73]
Chen W, Lv H, Nie F, Lin H. i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome. Bioinformatics 2019; 35(16): 2796-800.
[http://dx.doi.org/10.1093/bioinformatics/btz015] [PMID: 30624619]
[74]
Feng CQ, Zhang ZY, Zhu XJ, et al. iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators. Bioinformatics 2019; 35(9): 1469-77.
[http://dx.doi.org/10.1093/bioinformatics/bty827] [PMID: 30247625]
[75]
Chou KC, Shen HB. Recent advances in developing web-servers for predicting protein attributes. Nat Sci 2009; 1: 63-92.
[http://dx.doi.org/10.4236/ns.2009.12011]
[76]
Lai H-Y, Chen X-X, Chen W, Tang H, Lin H. Sequence-based predictive modeling to identify cancerlectins. Oncotarget 2017; 8(17): 28169-75.
[http://dx.doi.org/10.18632/oncotarget.15963] [PMID: 28423655]
[77]
Xing P, Su R, Guo F, Wei L. Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine. Sci Rep 2017; 7: 46757.
[http://dx.doi.org/10.1038/srep46757] [PMID: 28440291]
[78]
Zhang M, Sun JW, Liu Z, Ren MW, Shen HB, Yu DJ. Improving N(6)-methyladenosine site prediction with heuristic selection of nucleotide physical-chemical properties. Anal Biochem 2016; 508: 104-13.
[http://dx.doi.org/10.1016/j.ab.2016.06.001] [PMID: 27293216]
[79]
Wei L, Su R, Wang B, Li X, Zou Q. Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites. Neurocomputing 2019; 324: 3-9.
[80]
Spitale RC, Flynn RA, Zhang QC, et al. Structural imprints in vivo decode RNA regulatory mechanisms. Nature 2015; 519(7544): 486-90.
[http://dx.doi.org/10.1038/nature14263] [PMID: 25799993]
[81]
Chen W, Zhang L. The pattern of DNA cleavage intensity around indels. Sci Rep 2015; 5: 8333.
[http://dx.doi.org/10.1038/srep08333] [PMID: 25660536]
[82]
Chen W, Zhang X, Brooker J, Lin H, Zhang L, Chou KC. PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics 2015; 31(1): 119-20.
[http://dx.doi.org/10.1093/bioinformatics/btu602] [PMID: 25231908]
[83]
Xia T, SantaLucia J Jr, Burkard ME, et al. Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson-Crick base pairs. Biochemistry 1998; 37(42): 14719-35.
[http://dx.doi.org/10.1021/bi9809425] [PMID: 9778347]
[84]
Freier SM, Kierzek R, Jaeger JA, et al. Improved free-energy parameters for predictions of RNA duplex stability. Proc Natl Acad Sci USA 1986; 83(24): 9373-7.
[http://dx.doi.org/10.1073/pnas.83.24.9373] [PMID: 2432595]
[85]
Ghandi M, Mohammad-Noori M, Ghareghani N, Lee D, Garraway L, Beer MA. gkmSVM: an R package for gapped-kmer SVM. Bioinformatics 2016; 32(14): 2205-7.
[http://dx.doi.org/10.1093/bioinformatics/btw203] [PMID: 27153639]
[86]
Jia C, Zuo Y. S-SulfPred: A sensitive predictor to capture S-sulfenylation sites based on a resampling one-sided selection undersampling-synthetic minority oversampling technique. J Theor Biol 2017; 422: 84-9.
[http://dx.doi.org/10.1016/j.jtbi.2017.03.031] [PMID: 28411111]
[87]
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 2002; 16: 321-57.
[http://dx.doi.org/10.1613/jair.953]
[88]
Tomek I. Two Modifications of CNN. IEEE Trans Syst Man Cybern 1976; 6: 769-72.
[89]
Chen T, Guestrin C. XGBoost:A Scalable Tree Boosting System. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785-94.
[http://dx.doi.org/10.1145/2939672.2939785]
[90]
Lorenz R, Bernhart SH, Höner Zu Siederdissen C, et al. ViennaRNA Package 2.0. Algorithms Mol Biol 2011; 6: 26.
[http://dx.doi.org/10.1186/1748-7188-6-26] [PMID: 22115189]
[91]
Peng H, Long F, Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 2005; 27(8): 1226-38.
[http://dx.doi.org/10.1109/TPAMI.2005.159] [PMID: 16119262]
[92]
Zou Q, Zeng JC, Cao LJ, Zeng XX. A Novel Features Ranking Metric with Application to Scalable Visual and Bioinformatics Data Classification. Neurocomputing 2016; 173: 346-54.
[http://dx.doi.org/10.1016/j.neucom.2014.12.123]
[93]
Lin H, Liu WX, He J, Liu XH, Ding H, Chen W. Predicting cancerlectins by the optimal g-gap dipeptides. Sci Rep 2015; 5: 16964.
[http://dx.doi.org/10.1038/srep16964] [PMID: 26648527]
[94]
Manavalan B, Basith S, Shin TH, Lee DY, 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): 8.
[http://dx.doi.org/10.3390/cells8111332] [PMID: 31661923]
[95]
Feng PM, Ding H, Chen W, Lin H. Naïve Bayes classifier with feature selection to identify phage virion proteins. Comput Math Methods Med 2013.2013530696
[http://dx.doi.org/10.1155/2013/530696] [PMID: 23762187]
[96]
Manavalan B, Basith S, Shin TH, Wei L, Lee G. mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation. Bioinformatics 2019; 35(16): 2757-65.
[http://dx.doi.org/10.1093/bioinformatics/bty1047] [PMID: 30590410]
[97]
Charette M, Gray MW. Pseudouridine in RNA: what, where, how, and why. IUBMB Life 2000; 49(5): 341-51.
[http://dx.doi.org/10.1080/152165400410182] [PMID: 10902565]
[98]
Dominissini D, Nachtergaele S, Moshitch-Moshkovitz S, et al. The dynamic N(1)-methyladenosine methylome in eukaryotic messenger RNA. Nature 2016; 530(7591): 441-6.
[http://dx.doi.org/10.1038/nature16998] [PMID: 26863196]
[99]
Wu Y, Tang H, Chen W, Lin H. Predicting Human Enzyme Family Classes by Using Pseudo Amino Acid Composition. Curr Proteomics 2016; 13: 99-104.
[http://dx.doi.org/10.2174/157016461302160514003437]
[100]
Squires JE, Patel HR, Nousch M, et al. Widespread occurrence of 5-methylcytosine in human coding and non-coding RNA. Nucleic Acids Res 2012; 40(11): 5023-33.
[http://dx.doi.org/10.1093/nar/gks144] [PMID: 22344696]

Rights & Permissions Print Cite
Article Metrics
32
1
Wayfinder Image
Open Access Journals Promotions
World Antimicrobial Awareness Week
© 2024 Bentham Science Publishers | Privacy Policy