Skip to main content
SpringerLink
Log in

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

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Yue Y, Liu J, He C. RNA N6-mefhyladenosine methylation in post-transcriptional gene expression regulation. Genes & Development, 2015, 29(29): 1343–1355

    Google Scholar 

  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 m C within archaeal mRNAs. PLoS Genetics, 2013, 9(9): el003602

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  6. Motorin Y, Helm M. tRNA stabilization by modified nucleotides. Bio chemistry, 2010, 49(49): 4934 1944

    Google Scholar 

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

    Google Scholar 

  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(4): 255–261

    Google Scholar 

  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

    Google Scholar 

  10. Khoddami V, Cairns B R. Identification of direct targets and modified bases of RNA cytosine methyltransferases. Nature Biotechnology, 2013, 31(31): 458 164

    Google Scholar 

  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 in corporating collective effects of nucleotides into PseKNC Molecular Therapy-Nucleic Acids, 2017, 7: 155–163

    Google Scholar 

  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

    Google Scholar 

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

    Google Scholar 

  14. Chen W, Xing P, Zou Q. Detecting N 6-mefhyladenosine sites from RNA transcriptomes using ensemble support vector machines. Scien tific Reports, 2017, 7: 40242

    Google Scholar 

  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(8): e56499

    Google Scholar 

  16. Zhang M, 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

    Google Scholar 

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

    Google Scholar 

  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

    Google Scholar 

  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-mefhylcytosine in human coding and non-coding RNA. Nucleic Acids Research, 2012, 40(40): 5023–5033

    Google Scholar 

  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 high- throughput sequencing data. Nucleic Acids Research, 2015, 44(D1): D259–D265

    Google Scholar 

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

    Google Scholar 

  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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  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(291): 285–296

    Google Scholar 

  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. Analyti cal Biochemistry, 2014, 456: 53–60

    Google Scholar 

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

    Google Scholar 

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

    MATH  Google Scholar 

  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

    MATH  Google Scholar 

  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 Chem istry, 2011, 35(35): 218–229

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  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(19): 411–421

    Google Scholar 

  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(122): 165–174

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  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 pro teins. Letters in Organic Chemistry, 2019, 16(16): 294–302

    Google Scholar 

  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

    Google Scholar 

  40. Huang Y F, Chiu L Y, Huang C C, Huang C K. Predicting RNA- binding residues from evolutionary information and sequence conser vation. BMC Genomics, 2010, 11(11): S2

    Google Scholar 

  41. Chen W, Ding H, Feng P, Lin H, Chou K C. iACP: a sequence- based tool for identifying anticancer peptides. Oncotarget, 2016, 7(7): 16895

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  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(11): 273

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  57. Xiao X, Wang P, Chou K C. iNR-PhysChem: a sequence-based predic tor for identifying nuclear receptors and their subfamilies via physical- chemical property matrix. PLoS One, 2012, 7(7): e30869

    Google Scholar 

  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(436): 168–177

    Google Scholar 

  59. Feng P, Yang H, Ding H, Lin H, Chen W, Chou K C. iDNA6mA- PseKNC: identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC Genomics, 2019, 111(111): 96–102

    Google Scholar 

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

    Google Scholar 

  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(18): 1838

    Google Scholar 

  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(22): 1057

    Google Scholar 

Download references

Acknowledgments

We thank to the anonymous reviewers for their careful reading of our manuscript and their useful comments and suggestions.

Author information

Authors and Affiliations

  1. Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, KP 23200, Pakistan

    Shahid Akbar, Maqsood Hayat, Muhammad Iqbal & Muhammad Tahir

Authors
  1. Shahid Akbar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maqsood Hayat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Iqbal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Muhammad Tahir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maqsood Hayat.

Additional information

Shahid Akbar received his Bachelor degree in Computer Science & Information Technology from Islamic University of Technology, Bangladesh in 2011. He received his MS degree in Computer Science from Abdul Wali Khan University (AWKU), Pakistan in 2015. He is currently pursuing his PhD in Computer Science from Abdul Wali Khan University (AWKU), Pakistan. His research interests include bioinformatics, pattern recognition and machine learning.

Maqsood Hayat received his MCS degree from Gomal University, Pakistan in 2004 and his MS degree in Software & System Engineering from Mohammad Ali Jinnah University (MAJU), Pakistan in 2009. He received his PhD degree from Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Pakistan. He is working as an assistant professor since august 2012. His main research includes machine learning, pattern recognition, evolutionary computing and its application in bioinformatics.

Muhammad Iqbal received his Bachelor degree in Computer Science from Islamia College University, Pakistan in 2012. He is currently pursuing his MS degree in Computer Science from Abdul Wali Khan University (AWKUM), Pakistan. His research interests include bioinformatics, pattern recognition and machine learning.

Muhammad Tahir received his PhD degree in Computer Science from Abdul Wali Khan University (AWKUM), Pakistan in 2017. He is working as a lecturer since August 2010. His main research includes pattern recognition, bioinformatics and machine learning.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akbar, S., Hayat, M., Iqbal, M. et al. iRNA-PseTNC: identification of RNA 5-methylcytosine sites using hybrid vector space of pseudo nucleotide composition. Front. Comput. Sci. 14, 451–460 (2020). https://doi.org/10.1007/s11704-018-8094-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-018-8094-9

Keywords

Search

Navigation