iRNA-PseTNC: identification of RNA 5-methylcytosine sites using hybrid vector space of pseudo nucleotide composition

Abstract

RNA 5-methylcytosine (m⁵C) sites perform a major role in numerous biological processes and commonly reported in both DNA and RNA cellular. The enzymatic mechanism and biological functions of m⁵C sites in DNA remain the focusing area of researchers for last few decades. Likewise, the investigators also targeted m⁵C sites in RNA due to its cellular functions, positioning and formation mechanism. Currently, several rudimentary roles of the m⁵C in RNA have been explored, but a lot of improvements are still under consideration. Initially, the identification of RNA methylcytosine sites was carried out via experimental methods, which were very hard, erroneous and time consuming owing to partial availability of recognized structures. Looking at the significance of m⁵C role in RNA, scientists have diverted their attention from structure to sequence-based prediction. In this regards, an intelligent computational model is proposed in order to identify m⁵C sites in RNA with high precision. Three RNA sequences formulation methods namely: pseudo dinucleotide composition,pseudo trinucleotide composition and pseudo tetra nucleotide composition are applied to extract variant and high profound numerical features. In a sequel, the vector spaces are fused to build a hybrid space in order to compensate the weakness of each other. Various learning hypotheses are examined to select the best operational engine, which can truly identify the pattern of the target class. The strength and generalization of the proposed model are measured using two different cross validation tests. The reported outcomes reveal that the proposed model achieved 3% better accuracy than that of the highest present approach in the literature so far.

Keywords

methylcytosine sites / PseTNC / PseTetraNC / hybrid features / SVM / cross validation test

References

[1] Yue Y, Liu J, He C. RNA N6-methyladenosine methylation in posttranscriptional gene expression regulation. Genes & Development, 2015, 29(13): 1343–1355

[2] Edelheit S, Schwartz S, Mumbach M R, Wurtzel O, Sorek R. Transcriptome-wide mapping of 5-methylcytidine RNA modifications in bacteria, archaea, and yeast reveals m5C within archaeal mRNAs. PLoS Genetics, 2013, 9(6): e1003602

[3] Feng P, Ding H, Chen W, Lin H. Identifying RNA 5-methylcytosine sites via pseudo nucleotide compositions. Molecular BioSystems, 2016, 12(11): 3307–3311

[4] Agris P F. Bringing order to translation: the contributions of transfer RNA anticodon-domain modifications. EMBO Reports, 2008, 9(7): 629–635

[5] Helm M. Post-transcriptional nucleotide modification and alternative folding of RNA. Nucleic Acids Research, 2006, 34(2): 721–733

[6] Motorin Y, Helm M. tRNA stabilization by modified nucleotides. Biochemistry, 2010, 49(24): 4934–4944

[7] Schaefer M, Pollex T, Hanna K, Lyko F. RNA cytosine methylation analysis by bisulfite sequencing. Nucleic Acids Research, 2008, 37(2): e12

[8] Hussain S, Sajini A A, Blanco S, Dietmann S, Lombard P, Sugimoto Y, Paramor M, Gleeson J G, Odom D T, Ule J. NSun2-mediated cytosine-5 methylation of vault noncoding RNA determines its processing into regulatory small RNAs. Cell Reports, 2013, 4(2): 255–261

[9] Zou Q, Guo J, Ju Y, Wu M, Zeng X, Hong Z. Improving tRNAscan- SE annotation results via ensemble classifiers. Molecular Informatics, 2015, 34(11–12): 761–770

[10] Khoddami V, Cairns B R. Identification of direct targets and modified bases of RNA cytosine methyltransferases. Nature Biotechnology, 2013, 31(5): 458–464

[11] Feng P, Ding H, Yang H, Chen W, Lin H, Chou K C. iRNA-PseColl: identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into PseKNC. Molecular Therapy-Nucleic Acids, 2017, 7: 155–163

[12] Wan S, Duan Y, Zou Q. HPSLPred: an ensemble multi-label classifier for human protein subcellular location prediction with imbalanced source. Proteomics, 2017, 17(17–18): 1700262

[13] Liao Z, Ju Y, Zou Q. Prediction of G protein-coupled receptors with SVM-prot features and random forest. Scientifica, 2016, 2016: 8309253

[14] Chen W, Xing P, Zou Q. Detecting N 6-methyladenosine sites from RNA transcriptomes using ensemble support vector machines. Scientific Reports, 2017, 7: 40242

[15] Lin C, Zou Y, Qin J, Liu X, Jiang Y, Ke C, Zou Q. Hierarchical classification of protein folds using a novel ensemble classifier. PLoS One, 2013, 8(2): e56499

[16] Zhang M, Xu Y, Li L, Liu Z, Yang X, Yu D J. Accurate RNA 5- methylcytosine site prediction based on heuristic physical-chemical properties reduction and classifier ensemble. Analytical Biochemistry, 2018, 550: 41–48

[17] Qiu W R, Jiang S Y, Xu Z C, Xiao X, Chou K C. iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physicalchemical properties into pseudo dinucleotide composition. Oncotarget, 2017, 8(25): 41178

[18] Iqbal M, Hayat M. “iSS-Hyb-mRMR”: identification of splicing sites using hybrid space of pseudo trinucleotide and pseudo tetranucleotide composition. Computer Methods and Programs in Biomedicine, 2016, 128: 1–11

[19] Squires J E, Patel H R, Nousch M, Sibbritt T, Humphreys D T, Parker B J, Suter C M, Preiss T. Widespread occurrence of 5-methylcytosine in human coding and non-coding RNA. Nucleic Acids Research, 2012, 40(11): 5023–5033

[20] Sun W J, Li J H, Liu S, Wu J, Zhou H, Qu L H, Yang J H. RMBase: a resource for decoding the landscape of RNA modifications from highthroughput sequencing data. Nucleic Acids Research, 2015, 44(D1): D259–D265

[21] Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics, 2012, 28(23): 3150–3152

[22] Akbar S, Hayat M, Iqbal M, Jan M A. iACP-GAEnsC: evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space. Artificial Intelligence in Medicine, 2017, 79: 62–70

[23] Hayat M, Khan A. Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition. Journal of Theoretical Biology, 2011, 271(1): 10–17

[24] Kabir M, Yu D J. Predicting DNase I hypersensitive sites via un-biased pseudo trinucleotide composition. Chemometrics and Intelligent Laboratory Systems, 2017, 167: 78–84

[25] Tahir M, Hayat M, Kabir M. Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou’s trinucleotide composition. Computer Methods and Programs in Biomedicine, 2017, 146: 69–75

[26] Liu Z, Xiao X, Qiu W R, Chou K C. iDNA-methyl: identifying DNA methylation sites via pseudo trinucleotide composition. Analytical Biochemistry, 2015, 474: 69–77

[27] Kabir M, Hayat M. iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples. Molecular Genetics and Genomics, 2016, 291(1): 285–296

[28] Chen W, Lei T Y, Jin D C, Lin H, Chou K C. PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. Analytical Biochemistry, 2014, 456: 53–60

[29] Hayat M, Khan A.WRF-TMH: predicting transmembrane helix by fusing composition index and physicochemical properties of amino acids. Amino Acids, 2013, 44(5): 1317–1328

[30] Ali F, Hayat M. Classification of membrane protein types using voting feature interval in combination with Chou’s pseudo amino acid composition. Journal of Theoretical Biology, 2015, 384: 78–83

[31] Akbar S, Hayat M. iMethyl-STTNC: identification of N6-methyladenosine sites by extending the idea of SAAC into Chou’s PseAAC to formulate RNA sequences. Journal of Theoretical Biology, 2018, 455: 205–211

[32] Khan A, Majid A, Hayat M. CE-PLoc: an ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition. Computational Biology and Chemistry, 2011, 35(4): 218–229

[33] Hu J, Han K, Li Y, Yang J Y, Shen H B, Yu D J. TargetCrys: protein crystallization prediction by fusing multi-view features with twolayered SVM. Amino Acids, 2016, 48(11): 2533–2547

[34] Hayat M, Khan A. Discriminating outer membrane proteins with fuzzy K-nearest neighbor algorithms based on the general form of Chou’s PseAAC. Protein and Peptide Letters, 2012, 19(4): 411–421

[35] Ahmad S, Kabir M, Hayat M. Identification of heat shock protein families and J-protein types by incorporating dipeptide composition into Chou’s general PseAAC. Computer Methods and Programs in Biomedicine, 2015, 122(2): 165–174

[36] Liu B, Wang S, Long R, Chou K C. iRSpot-EL: identify recombination spots with an ensemble learning approach. Bioinformatics, 2016, 33(1): 35–41

[37] Xiao X, Min J L, Lin W Z, Liu Z, Cheng X, Chou K C. iDrugtarget: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach. Journal of Biomolecular Structure and Dynamics, 2015, 33(10): 2221–2233

[38] Akbar S, Hayat M, Kabir M, Iqbal M. iAFP-gap-SMOTE: an efficient feature extraction scheme gapped dipeptide composition is coupled with an oversampling technique for identification of antifreeze proteins. Letters in Organic Chemistry, 2019, 16(4): 294–302

[39] Lin W Z, Fang J A, Xiao X, Chou K C. iDNA-Prot: identification of DNA binding proteins using random forest with grey model. PLoS One, 2011, 6(9): e24756

[40] Huang Y F, Chiu L Y, Huang C C, Huang C K. Predicting RNAbinding residues from evolutionary information and sequence conservation. BMC Genomics, 2010, 11(4): S2

[41] Chen W, Ding H, Feng P, Lin H, Chou K C. iACP: a sequencebased tool for identifying anticancer peptides. Oncotarget, 2016, 7(13): 16895

[42] Akbar S, Ahmad A, Hayat M, Ali F. Face recognition using hybrid feature space in conjunction with support vector machine. Journal of Applied Environmental and Biological Sciences, 2015, 5(7): 28–36

[43] Hu J, Yan X. BS-KNN: an effective algorithm for predicting protein subchloroplast localization. Evolutionary Bioinformatics Online, 2012, 8: 79

[44] Arlot S, Celisse A. A survey of cross-validation procedures for model selection. Statistics Surveys, 2010, 4: 40–79

[45] Ng A Y. Preventing “overfitting” of cross-validation data. In: Proceedings of the 14th International Conference on Machine Learning. 1997, 245–253

[46] Vehtari A, Gelman A, Gabry J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 2017, 27(5): 1413–1432

[47] Ahmad J, Javed F, Hayat M. Intelligent computational model for classification of sub-Golgi protein using oversampling and fisher feature selection methods. Artificial Intelligence in Medicine, 2017, 78: 14–22

[48] Tahir M, Hayat M. Machine learning based identification of proteinprotein interactions using derived features of physiochemical properties and evolutionary profiles. Artificial Intelligence in Medicine, 2017, 78: 61–71

[49] Zhang W, Robbins K, Wang Y, Bertrand K, Rekaya R. A jackknife-like method for classification and uncertainty assessment of multi-category tumor samples using gene expression information. BMC Genomics, 2010, 11(1): 273

[50] Elloumi M, Iliopoulos C, Wang J T, Zomaya A Y. Pattern Recognition in Computational Molecular Biology: Techniques and Approaches. John Wiley & Sons, 2015

[51] Wasserman L. All of Statistics: a Concise course in Statistical Inference. Springer Science & Business Media, 2013

[52] Bengio Y, Grandvalet Y. No unbiased estimator of the variance of Kfold cross-validation. Journal of Machine Learning Research, 2004, 5(Sep): 1089–1105

[53] Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intellgence-Volum 2. 1995, 1137–1145

[54] Fushiki T. Estimation of prediction error by using K-fold crossvalidation. Statistics and Computing, 2011, 21(2): 137–146

[55] Doreswamy H K. Performance evaluation of predictive classifiers for knowledge discovery from engineering materials data sets. 2012, arXiv preprint arXiv:1209.2501

[56] Qiu W R, Xiao X, Lin W Z, Chou K C. iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approach. BioMed Research International, 2014, 2014: 947416

[57] Xiao X, Wang P, Chou K C. iNR-PhysChem: a sequence-based predictor for identifying nuclear receptors and their subfamilies via physicalchemical property matrix. PLoS One, 2012, 7(2): e30869

[58] Xiao X, Wang P, Lin W Z, Jia J H, Chou K C. iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Analytical Biochemistry, 2013, 436(2): 168–177

[59] Feng P, Yang H, Ding H, Lin H, Chen W, Chou K C. iDNA6mAPseKNC: identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics, 2019, 111(1): 96–102

[60] Chen W, Yang H, Feng P, Ding H, Lin H. iDNA4mC: identifying DNA N4-methylcytosine sites based on nucleotide chemical properties. Bioinformatics, 2017, 33(22): 3518–3523

[61] Zhao Y W, Su Z D, Yang W, Lin H, Chen W, Tang H. IonchanPred 2.0: a tool to predict Ion channels and their types. International Journal of Molecular Sciences, 2017, 18(9): 1838

[62] Dao F Y, Yang H, Su Z D, Yang W, Wu Y, Hui D, Chen W, Tang H, Lin H. Recent advances in conotoxin classification by using machine learning methods. Molecules, 2017, 22(7): 1057

RIGHTS & PERMISSIONS

2019 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature