iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou’s PseAAC to pseudo-tri-nucleotide composition

  • Muhammad Tahir
  • Maqsood Hayat
  • Sher Afzal Khan
Original Article


Nucleosome is a central element of eukaryotic chromatin, which composes of histone proteins and DNA molecules. It performs vital roles in many eukaryotic intra-nuclear processes, for instance, chromatin structure and transcriptional regulation formation. Identification of nucleosome positioning via wet lab is difficult; so, the attention is diverted towards the accurate intelligent automated prediction. In this regard, a novel intelligent automated model “iNuc-ext-PseTNC” is developed to identify the nucleosome positioning in genomes accurately. In this predictor, the sequences of DNA are mathematically represented by two different discrete feature extraction techniques, namely pseudo-tri-nucleotide composition (PseTNC) and pseudo-di-nucleotide composition. Several contemporary machine learning algorithms were examined. Further, the predictions of individual classifiers were integrated through an evolutionary genetic algorithm. The success rates of the ensemble model are higher than individual classifiers. After analyzing the prediction results, it is noticed that iNuc-ext-PseTNC model has achieved better performance in combination with PseTNC feature space, which are 94.3%, 93.14%, and 88.60% of accuracies using six-fold cross-validation test for the three benchmark datasets S1, S2, and S3, respectively. The achieved outcomes exposed that the results of iNuc-ext-PseTNC model are prominent compared to the existing methods so far notifiable in the literature. It is ascertained that the proposed model might be more fruitful and a practical tool for rudimentary academia and research.


DNA SVM GA Tri-nucleotide composition 


Compliance with ethical standards

Conflict of interest

The authors have no conflict of interest.


  1. Ahmad J, Javed F, Hayat M (2017) Intelligent computational model for classification of sub-Golgi protein using oversampling and fisher feature selection methods. Artif Intell Med 78:14–22CrossRefGoogle Scholar
  2. Athey BD, Smith MF, Rankert DA, Williams SP, Langmore JP (1990) The diameters of frozen-hydrated chromatin fibers increase with DNA linker length: evidence in support of variable diameter models for chromatin. J Cell Biol 111:795–806CrossRefGoogle Scholar
  3. Awazu A (2017) Prediction of nucleosome positioning by the incorporation of frequencies and distributions of three different nucleotide segment lengths into a general pseudo k-tuple nucleotide composition. Bioinformatics 33:42–48CrossRefGoogle Scholar
  4. Berbenetz NM, Nislow C, Brown GW (2010) Diversity of eukaryotic DNA replication origins revealed by genome-wide analysis of chromatin structure. PLoS Genet 6:e1001092CrossRefGoogle Scholar
  5. Cao D-S, Xu Q-S, Liang Y-Z (2013) Propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics 29:960–962CrossRefGoogle Scholar
  6. Che Y, Ju Y, Xuan P, Long R, Xing F (2016) Identification of multi-functional enzyme with multi-label classifier. PLoS One 11:e0153503CrossRefGoogle Scholar
  7. Chen Y-K, Li K-B (2013) Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou’s pseudo amino acid composition. J Theor Biol 318:1–12CrossRefGoogle Scholar
  8. Chen W, Feng P-M, Lin H, Chou K-C (2013a) iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res 41(6):e68CrossRefGoogle Scholar
  9. Chen W, Feng P, Lin H, Chou K (2013b) iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res gks1450Google Scholar
  10. Chen W, Lei T-Y, Jin D-C, Lin H, Chou K-C (2014) PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. Anal Biochem 456:53–60CrossRefGoogle Scholar
  11. Chen W, Zhang X, Brooker J, Lin H, Zhang L, Chou K-C (2015) PseKNC-general: a cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics 31:119–120CrossRefGoogle Scholar
  12. Chen W, Ding H, Feng P, Lin H, Chou K-C (2016) iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget 7:16895PubMedPubMedCentralGoogle Scholar
  13. Chen W, Feng P, Yang H, Ding H, Lin H, Chou K-C (2017) iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget 8:4208PubMedGoogle Scholar
  14. Cheng X, Xiao X, Chou K-C (2017a) pLoc-mGneg: predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. Genomics 110:231–239CrossRefGoogle Scholar
  15. Cheng X, Xiao X, Chou K-C (2017b) pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics 34:1448–1456CrossRefGoogle Scholar
  16. Cheng X, Xiao X, Chou K-C (2017c) pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC. Mol Biosyst 13:1722–1727CrossRefGoogle Scholar
  17. Cheng X, Xiao X, Chou K-C (2017d) pLoc-mVirus: predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene 628:315–321CrossRefGoogle Scholar
  18. Cheng X, Xiao X, Chou K-C (2018) pLoc-mEuk: predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics 110:50–58CrossRefGoogle Scholar
  19. Chou KC (2001a) Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins Struct Funct Bioinform 43:246–255CrossRefGoogle Scholar
  20. Chou K-C (2001b) Prediction of signal peptides using scaled window. Peptides 22:1973–1979CrossRefGoogle Scholar
  21. Chou K-C (2005) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21:10–19CrossRefGoogle Scholar
  22. Chou K-C (2015) Impacts of bioinformatics to medicinal chemistry. Med Chem 11:218–234CrossRefGoogle Scholar
  23. Chou K-C (2017) An unprecedented revolution in medicinal chemistry driven by the progress of biological science. Curr Top Med Chem 17:2337–2358CrossRefGoogle Scholar
  24. Chou K-C, Shen H-B (2007a) Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. J Proteome Res 6:1728–1734CrossRefGoogle Scholar
  25. Chou K-C, Shen H-B (2007b) Recent progress in protein subcellular location prediction. Anal Biochem 370:1–16CrossRefGoogle Scholar
  26. Chou K-C, Shen H-B (2007c) Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. Biochem Biophys Res Commun 357:633–640CrossRefGoogle Scholar
  27. Dong C, Yuan Y-Z, Zhang F-Z, Hua H-L, Ye Y-N, Labena AA, Lin H, Chen W, Guo F-B (2016) Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots. Mol BioSyst 12:2893–2900CrossRefGoogle Scholar
  28. Eddy SR (1996) Hidden markov models. Curr Opin Struct Biol 6:361–365CrossRefGoogle Scholar
  29. Ehsan A, Mahmood K, Khan YD, Khan SA, Chou K-C (2018) A novel modeling in mathematical biology for classification of signal peptides. Sci Rep 8:1039CrossRefGoogle Scholar
  30. Feng P, Ding H, Yang H, Chen W, Lin H, Chou K-C (2017) iRNA-PseColl: identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into PseKNC. Mol Ther Nucleic Acids 7:155–163CrossRefGoogle Scholar
  31. Feng P, Yang H, Ding H, Lin H, Chen W, Chou K-C (2018) iDNA6mA-PseKNC: identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics. CrossRefPubMedGoogle Scholar
  32. Field Y, Kaplan N, Fondufe-Mittendorf Y, Moore IK, Sharon E, Lubling Y, Widom J, Segal E (2008) Distinct modes of regulation by chromatin encoded through nucleosome positioning signals. PLoS Comput Biol 4:e1000216CrossRefGoogle Scholar
  33. Gabdank I, Barash D, Trifonov EN (2010) Single-base resolution nucleosome mapping on DNA sequences. J Biomol Struct Dyn 28:107–121CrossRefGoogle Scholar
  34. Goñi JR, Fenollosa C, Pérez A, Torrents D, Orozco M (2008) DNAlive: a tool for the physical analysis of DNA at the genomic scale. Bioinformatics 24:1731–1732CrossRefGoogle Scholar
  35. Guo S-H, Deng E-Z, Xu L-Q, Ding H, Lin H, Chen W, Chou K-C (2014) iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics 30(11):1522–1529CrossRefGoogle Scholar
  36. Hayat M, Khan A (2012) Discriminating outer membrane proteins with fuzzy K-nearest neighbor algorithms based on the general form of Chou’s PseAAC. Protein Pept Lett 19:411–421CrossRefGoogle Scholar
  37. Hayat M, Tahir M (2015) PSOFuzzySVM-TMH: identification of transmembrane helix segments using ensemble feature space by incorporated fuzzy support vector machine. Mol BioSyst 11:2255–2262CrossRefGoogle Scholar
  38. Ioshikhes I, Bolshoy A, Derenshteyn K, Borodovsky M, Trifonov EN (1996) Nucleosome DNA sequence pattern revealed by multiple alignment of experimentally mapped sequences. J Mol Biol 262:129–139CrossRefGoogle Scholar
  39. Isami S, Sakamoto N, Nishimori H, Awazu A (2015) Simple elastic network models for exhaustive analysis of long double-stranded DNA dynamics with sequence geometry dependence. PLoS One 10:e0143760CrossRefGoogle Scholar
  40. Jia J, Liu Z, Xiao X, Liu B, Chou K-C (2016) pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. J Theor Biol 394:223–230CrossRefGoogle Scholar
  41. Kabir M, Hayat M (2016) iRSpot-GAEnsC: identifying recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples. Mol Genet Genom 291:285–296CrossRefGoogle Scholar
  42. Kaplan N, Moore IK, Fondufe-Mittendorf Y, Gossett AJ, Tillo D, Field Y, LeProust EM, Hughes TR, Lieb JD, Widom J (2009) The DNA-encoded nucleosome organization of a eukaryotic genome. Nature 458:362–366CrossRefGoogle Scholar
  43. Levitsky VG (2004) RECON: a program for prediction of nucleosome formation potential. Nucleic Acids Res 32:W346–W349CrossRefGoogle Scholar
  44. Li W-C, Deng E-Z, Ding H, Chen W, Lin H (2015) iORI-PseKNC: a predictor for identifying origin of replication with pseudo k-tuple nucleotide composition. Chemom Intell Lab Syst 141:100–106CrossRefGoogle Scholar
  45. Li D, Luo L, Zhang W, Liu F, Luo F (2016) A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs. BMC Bioinform 17:329CrossRefGoogle Scholar
  46. Lin H, Deng E-Z, Ding H, Chen W, Chou K-C (2014) iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res 42:12961–12972CrossRefGoogle Scholar
  47. Liu B, Zhang D, Xu R, Xu J, Wang X, Chen Q, Dong Q, Chou K-C (2014a) Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. Bioinformatics 30:472–479CrossRefGoogle Scholar
  48. Liu B, Xu J, Lan X, Xu R, Zhou J, Wang X, Chou K-C (2014b) iDNA-Prot| dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition. PLoS One 9:e106691CrossRefGoogle Scholar
  49. Liu B, Liu F, Fang L, Wang X, Chou K-C (2015a) repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects. Bioinformatics 31:1307–1309CrossRefGoogle Scholar
  50. Liu Z, Xiao X, Qiu W-R, Chou K-C (2015c) iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition. Anal Biochem 474:69–77CrossRefGoogle Scholar
  51. Liu B, Fang L, Liu F, Wang X, Chen J, Chou K-C (2015d) Identification of real microRNA precursors with a pseudo structure status composition approach. PLoS One 10:e0121501CrossRefGoogle Scholar
  52. Liu G-H, Shen H-B, Yu D-J (2016a) Prediction of protein–protein interaction sites with machine-learning-based data-cleaning and post-filtering procedures. J Membr Biol 249:141–153CrossRefGoogle Scholar
  53. Liu B, Long R, Chou K-C (2016b) iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework. Bioinformatics 32(16):2411–2418CrossRefGoogle Scholar
  54. Liu B, Wang S, Long R, Chou K-C (2016c) iRSpot-EL: identify recombination spots with an ensemble learning approach. Bioinformatics 33:35–41CrossRefGoogle Scholar
  55. Liu B, Yang F, Huang D-S, Chou K-C (2017a) iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics 34:33–40CrossRefGoogle Scholar
  56. Liu B, Yang F, Chou K-C (2017b) 2L-piRNA: a two-layer ensemble classifier for identifying Piwi-interacting RNAs and their function. Mol Ther Nucleic Acids 7:267–277CrossRefGoogle Scholar
  57. Liu B, Wu H, Zhang D, Wang X, Chou K-C (2017c) Pse-Analysis: a python package for DNA/RNA and protein/peptide sequence analysis based on pseudo components and kernel methods. Oncotarget 8:13338PubMedPubMedCentralGoogle Scholar
  58. Liu B, Li K, Huang D-S, Chou K-C (2018) iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach. Bioinformatics. CrossRefPubMedPubMedCentralGoogle Scholar
  59. Luo L, Li D, Zhang W, Tu S, Zhu X, Tian G (2016) Accurate prediction of transposon-derived piRNAs by integrating various sequential and physicochemical features. PLoS One 11:e0153268CrossRefGoogle Scholar
  60. Manavalan B, Shin TH, Lee G (2018) PVP-SVM: sequence-based prediction of phage virion proteins using a support vector machine. Front Microbiol 9:476CrossRefGoogle Scholar
  61. Mavrich TN, Jiang C, Ioshikhes IP, Li X, Venters BJ, Zanton SJ, Tomsho LP, Qi J, Glaser RL, Schuster SC (2008a) Nucleosome organization in the Drosophila genome. Nature 453:358–362CrossRefGoogle Scholar
  62. Mavrich TN, Ioshikhes IP, Venters BJ, Jiang C, Tomsho LP, Qi J, Schuster SC, Albert I, Pugh BF (2008b) A barrier nucleosome model for statistical positioning of nucleosomes throughout the yeast genome. Genome Res 18:1073–1083CrossRefGoogle Scholar
  63. Mavrich TN, Ioshikhes IP, Venters BJ, Jiang C, Tomsho LP, Qi J, Schuster SC, Albert I, Pugh BF (2008c) A barrier nucleosome model for statistical positioning of nucleosomes throughout the yeast genome. Genome ResGoogle Scholar
  64. Nikolaou C, Althammer S, Beato M, Guigó R (2010) Structural constraints revealed in consistent nucleosome positions in the genome of S. cerevisiae. Epigenetics Chromatin 3:20CrossRefGoogle Scholar
  65. Peckham HE, Thurman RE, Fu Y, Stamatoyannopoulos JA, Noble WS, Struhl K, Weng Z (2007) Nucleosome positioning signals in genomic DNA. Genome Res 17:1170–1177CrossRefGoogle Scholar
  66. Qiu W-R, Xiao X, Chou K-C (2014) iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components. Int J Mol Sci 15:1746–1766CrossRefGoogle Scholar
  67. Satchwell SC, Drew HR, Travers AA (1986) Sequence periodicities in chicken nucleosome core DNA. J Mol Biol 191:659–675CrossRefGoogle Scholar
  68. Schwartz S, Meshorer E, Ast G (2009) Chromatin organization marks exon–intron structure. Nat Struct Mol Biol 16:990CrossRefGoogle Scholar
  69. Segal E, Fondufe-Mittendorf Y, Chen L, Thåström A, Field Y, Moore IK, Wang J-PZ, Widom J (2006) A genomic code for nucleosome positioning. Nature 442:772–778CrossRefGoogle Scholar
  70. Stolz RC, Bishop TC (2010) ICM Web: the interactive chromatin modeling web server. Nucleic Acids Res 38:W254–W261CrossRefGoogle Scholar
  71. Tahir M, Hayat M (2016) iNuc-STNC: a sequence-based predictor for identification of nucleosome positioning in genomes by extending the concept of SAAC and Chou’s PseAAC. Mol BioSyst 12:2587–2593CrossRefGoogle Scholar
  72. Thoma F, Koller T, Klug A (1979) Involvement of histone H1 in the organization of the nucleosome and of the salt-dependent superstructures of chromatin. J Cell Biol 83:403–427CrossRefGoogle Scholar
  73. Tian K, Yang X, Kong Q, Yin C, He RL, Yau SS-T (2015) Two dimensional Yau-hausdorff distance with applications on comparison of DNA and protein sequences. PLoS One 10:e0136577CrossRefGoogle Scholar
  74. Tolstorukov MY, Choudhary V, Olson WK, Zhurkin VB, Park PJ (2008) nuScore: a web-interface for nucleosome positioning predictions. Bioinformatics 24:1456–1458CrossRefGoogle Scholar
  75. Xi L, Fondufe-Mittendorf Y, Xia L, Flatow J, Widom J, Wang J-P (2010) Predicting nucleosome positioning using a duration Hidden Markov Model. BMC Bioinform 11:1CrossRefGoogle Scholar
  76. Xiang S, Liu K, Yan Z, Zhang Y, Sun Z (2016) RNAMethPre: a web server for the prediction and query of mRNA m 6 A sites. PLoS One 11:e0162707CrossRefGoogle Scholar
  77. Xiao X, Wang P, Lin W-Z, Jia J-H, Chou K-C (2013) iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal Biochem 436:168–177CrossRefGoogle Scholar
  78. Xiao X, Cheng X, Su S, Mao Q, Chou K-C (2017) pLoc-mGpos: incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins. Nat Sci 9:330Google Scholar
  79. Xiao X, Cheng X, Chen G, Mao Q, Chou K-C (2018) pLoc-mGpos: predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. Genomics. CrossRefPubMedGoogle Scholar
  80. Xu Y, Shao X-J, Wu L-Y, Deng N-Y, Chou K-C (2013a) iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ 1:e171CrossRefGoogle Scholar
  81. Xu Y, Ding J, Wu L-Y, Chou K-C (2013b) iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. PLoS One 8:e55844CrossRefGoogle Scholar
  82. Xu Y, Wen X, Wen L-S, Wu L-Y, Deng N-Y, Chou K-C (2014) iNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS One 9:e105018CrossRefGoogle Scholar
  83. Yasuda T, Sugasawa K, Shimizu Y, Iwai S, Shiomi T, Hanaoka F (2005) Nucleosomal structure of undamaged DNA regions suppresses the non-specific DNA binding of the XPC complex. DNA Repair 4:389–395CrossRefGoogle Scholar
  84. YongE F, GaoShan K (2015) Identify beta-hairpin motifs with quadratic discriminant algorithm based on the chemical shifts. PLoS One 10:e0139280CrossRefGoogle Scholar
  85. Yuan G-C, Liu JS (2008) Genomic sequence is highly predictive of local nucleosome depletion. PLoS Comput Biol 4:e13CrossRefGoogle Scholar
  86. Yuan G-C, Liu Y-J, Dion MF, Slack MD, Wu LF, Altschuler SJ, Rando OJ (2005) Genome-scale identification of nucleosome positions in S. cerevisiae. Science 309:626–630CrossRefGoogle Scholar
  87. Zhang W, Niu Y, Xiong Y, Zhao M, Yu R, Liu J (2012) Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning. PLoS One 7:e43575CrossRefGoogle Scholar
  88. Zhang W, Liu F, Luo L, Zhang J (2015a) Predicting drug side effects by multi-label learning and ensemble learning. BMC Bioinform 16:365CrossRefGoogle Scholar
  89. Zhang W, Niu Y, Zou H, Luo L, Liu Q, Wu W (2015b) Accurate prediction of immunogenic T-cell epitopes from epitope sequences using the genetic algorithm-based ensemble learning. PLoS one 10:e0128194CrossRefGoogle Scholar
  90. Zhang W, Zou H, Luo L, Liu Q, Wu W, Xiao W (2016a) Predicting potential side effects of drugs by recommender methods and ensemble learning. Neurocomputing 173:979–987CrossRefGoogle Scholar
  91. Zhang C-J, Tang H, Li W-C, Lin H, Chen W, Chou K-C (2016b) iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition. Oncotarget 7:69783PubMedPubMedCentralGoogle Scholar
  92. Zhang W, Shi J, Tang G, Wu W, Yue X, Li D (2017) Predicting small RNAs in bacteria via sequence learning ensemble method. In: Bioinformatics and biomedicine (BIBM), 2017 IEEE international conference on, IEEE, pp 643–647Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Muhammad Tahir
    • 1
  • Maqsood Hayat
    • 1
  • Sher Afzal Khan
    • 1
  1. 1.Department of Computer ScienceAbdul Wali Khan University MardanMardanPakistan

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