Molecular Genetics and Genomics

, Volume 294, Issue 1, pp 69–84 | Cite as

iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features

  • Md. Siddiqur Rahman
  • Usma Aktar
  • Md Rafsan Jani
  • Swakkhar ShatabdaEmail author
Original Article


In bacterial DNA, there are specific sequences of nucleotides called promoters that can bind to the RNA polymerase. Sigma70 (\(\sigma ^{70}\)) is one of the most important promoter sequences due to its presence in most of the DNA regulatory functions. In this paper, we identify the most effective and optimal sequence-based features for prediction of \(\sigma ^{70}\) promoter sequences in a bacterial genome. We used both short-range and long-range DNA sequences in our proposed method. A very small number of effective features are selected from a large number of the extracted features using multi-window of different sizes within the DNA sequences. We call our prediction method iPro70-FMWin and made it freely accessible online via a web application established at for the sake of convenience of the researchers. We have tested our method using a standard benchmark dataset. In the experiments, iPro70-FMWin has achieved an area under the curve of the receiver operating characteristic and accuracy of 0.959 and 90.57%, respectively, which significantly outperforms the state-of-the-art predictors.


\(\sigma ^{70}\) promoter Prokaryote Sequence-based features Multi-windowing Feature selection 


Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Aggarwala V, Voight BF (2015) An expanded sequence context model broadly explains variability in polymorphism levels across the human genome. Nat Genet 47(3):349Google Scholar
  2. Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185Google Scholar
  3. Arif M, Hayat M, Jan Z (2018) iMem-2LSAAC: a two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou’s pseudo amino acid composition. J Theor Biol 442:11–21Google Scholar
  4. Audic S, Claverie JM (1997) Detection of eukaryotic promoters using Markov transition matrices. Comput Chem 21(4):223–227Google Scholar
  5. Bermingham ML, Pong-Wong R, Spiliopoulou A, Hayward C, Rudan I, Campbell H, Wright AF, Wilson JF, Agakov F, Navarro P (2015) Application of high-dimensional feature selection: evaluation for genomic prediction in man. Sci Rep 5:10312Google Scholar
  6. Boyle AP, Davis S, Shulha HP, Meltzer P, Margulies EH, Weng Z, Furey TS, Crawford GE (2008) High-resolution mapping and characterization of open chromatin across the genome. Cell 132(2):311–322Google Scholar
  7. Chen W, Feng PM, Lin H, Chou KC (2013) iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res 41(6):e68–e68Google Scholar
  8. Chen W, Lei TY, Jin DC, Lin H, Chou KC (2014) PseKNC: a flexible web server for generating pseudo k-tuple nucleotide composition. Anal Biochem 456:53–60Google Scholar
  9. Chen W, Lin H, Chou KC (2015) Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol BioSyst 11(10):2620–2634Google Scholar
  10. Chen W, Feng P, Yang H, Ding H, Lin H, Chou KC (2018) iRNA-3typeA: identifying three types of modification at RNAs adenosine sites. Mol Ther Nucleic Acids 11:468–474. Google Scholar
  11. Chen XX, Tang H, Li WC, Wu H, Chen W, Ding H, Lin H (2016) Identification of bacterial cell wall lyases via pseudo amino acid composition. BioMed Res Int 2016:2016Google Scholar
  12. Chou KC (2001a) Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins Struct Funct Bioinf 43(3):246–255Google Scholar
  13. Chou KC (2001b) Prediction of signal peptides using scaled window. Peptides 22(12):1973–1979Google Scholar
  14. Chou KC (2001c) Using subsite coupling to predict signal peptides. Protein Eng 14(2):75–79Google Scholar
  15. Chou KC (2004) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21(1):10–19Google Scholar
  16. Chou KC (2009) Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr Proteom 6(4):262–274Google Scholar
  17. Chou KC (2011a) Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol 273(1):236–247Google Scholar
  18. Chou KC (2011b) Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol 273(1):236–247Google Scholar
  19. Chou KC (2013) Some remarks on predicting multi-label attributes in molecular biosystems. Mol BioSyst 9(6):1092–1100Google Scholar
  20. Chou KC (2015) Impacts of bioinformatics to medicinal chemistry. Med Chem 11(3):218–234Google Scholar
  21. Chou KC (2017) An unprecedented revolution in medicinal chemistry driven by the progress of biological science. Curr Top Med Chem 17(21):2337–2358Google Scholar
  22. Chou KC, Shen HB (2009) Recent advances in developing web-servers for predicting protein attributes. Nat Sci 1(02):63Google Scholar
  23. Compeau PE, Pevzner PA, Tesler G (2011) How to apply de Bruijn graphs to genome assembly. Nat Biotechnol 29(11):987Google Scholar
  24. Contreras-Torres E (2018) Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou’s pseaac. J Theor Biol. Google Scholar
  25. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297Google Scholar
  26. Coussement K, Van den Poel D (2008) Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques. Exp Syst Appl 34(1):313–327Google Scholar
  27. Crawford GE, Holt IE, Whittle J, Webb BD, Tai D, Davis S, Margulies EH, Chen Y, Bernat JA, Ginsburg D (2006) Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS). Genome Res 16(1):123–131Google Scholar
  28. Dash M, Liu H (1997) Feature selection for classification. Int Data Anal 1(3):131–156Google Scholar
  29. Demeler B, Zhou G (1991) Neural network optimization for E. coli promoter prediction. Nucleic Acids Res 19(7):1593–1599Google Scholar
  30. El Hassan M, Calladine C (1996) Propeller-twisting of base-pairs and the conformational mobility of dinucleotide steps in DNA. J Mol Biol 259(1):95–103Google Scholar
  31. Feng P, Yang H, Ding H, Lin H, Chen W, Chou KC (2018) iDNA6mA-PseKNC: identifying dna n6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics. Google Scholar
  32. Fickett JW, Hatzigeorgiou AG (1997) Eukaryotic promoter recognition. Genome Res 7(9):861–878Google Scholar
  33. Gama-Castro S, Salgado H, Santos-Zavaleta A, Ledezma-Tejeida D, Muñiz-Rascado L, García-Sotelo JS, Alquicira-Hernández K, Martínez-Flores I, Pannier L, Castro-Mondragón JA (2015) RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond. Nucleic Acids Res 44(D1):D133–D143Google Scholar
  34. Gan Y, Guan J, Zhou S (2012) A comparison study on feature selection of DNA structural properties for promoter prediction. BMC Bioinf 13(1):4Google Scholar
  35. Ginno PA, Lim YW, Lott PL, Korf I, Chédin F (2013) Gc skew at the 5’ and 3’ ends of human genes links r-loop formation to epigenetic regulation and transcription termination. Genome Res 23(10):1590–1600Google Scholar
  36. Gordon JJ, Towsey MW, Hogan JM, Mathews SA, Timms P (2005) Improved prediction of bacterial transcription start sites. Bioinformatics 22(2):142–148Google Scholar
  37. Gordon L, Chervonenkis AY, Gammerman AJ, Shahmuradov IA, Solovyev VV (2003) Sequence alignment kernel for recognition of promoter regions. Bioinformatics 19(15):1964–1971Google Scholar
  38. Grech B, Maetschke S, Mathews S, Timms P (2007) Genome-wide analysis of chlamydiae for promoters that phylogenetically footprint. Res Micro 158(8–9):685–693Google Scholar
  39. Gruber TM, Gross CA (2003) Multiple sigma subunits and the partitioning of bacterial transcription space. Ann Rev Micro 57(1):441–466Google Scholar
  40. Guo SH, Deng EZ, Xu LQ, Ding H, Lin H, Chen W, Chou KC (2014) iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics 30(11):1522–1529Google Scholar
  41. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36Google Scholar
  42. Hodge V, Austin J (2004) A survey of outlier detection methodologies. Artif Int Rev 22(2):85–126Google Scholar
  43. Hosmer DW Jr, Lemeshow S, Sturdivant RX (2013) Applied logistic regression, vol 398. Wiley, OxfordGoogle Scholar
  44. Huerta AM, Collado-Vides J (2003) Sigma70 promoters in Escherichia coli: specific transcription in dense regions of overlapping promoter-like signals. J Mol Biol 333(2):261–278Google Scholar
  45. James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning, vol 112. Springer, BerlinGoogle Scholar
  46. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI Montreal Can 14:1137–1145Google Scholar
  47. Krishnan SM (2018) Using Chou’s general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains. J Theor Biol 445:62–74Google Scholar
  48. Li FM, Li QZ (2008) Using pseudo amino acid composition to predict protein subnuclear location with improved hybrid approach. Amino Acids 34(1):119–125Google Scholar
  49. Li QZ, Lin H (2006) The recognition and prediction of \(\sigma\)70 promoters in Escherichia coli k-12. J Theor Biol 242(1):135–141Google Scholar
  50. Liang ZY, Lai HY, Yang H, Zhang CJ, Yang H, Wei HH, Chen XX, Zhao YW, Su ZD, Li WC et al (2017) Pro54db: a database for experimentally verified sigma-54 promoters. Bioinformatics 33(3):467–469Google Scholar
  51. Lin H, Li QZ (2011) Eukaryotic and prokaryotic promoter prediction using hybrid approach. Theory Biosci 130(2):91–100Google Scholar
  52. Lin H, Deng EZ, Ding H, Chen W, Chou KC (2014) iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res 42(21):12961–12972Google Scholar
  53. Lin H, Liang Z, Tang H, Chen W (2017) Identifying sigma70 promoters with novel pseudo nucleotide composition. IEEE ACM Trans Comput Biol Bioinf 2017:10Google Scholar
  54. Liu B, Liu F, Wang X, Chen J, Fang L, Chou KC (2015) Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res 43(W1):W65–W71Google Scholar
  55. Liu B, Wu H, Chou KC (2017a) Pse-in-One 2.0: an improved package of web servers for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nat Sci 9(04):67Google Scholar
  56. Liu B, Yang F, Huang DS, Chou KC (2017b) iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics 34(1):33–40Google Scholar
  57. Liu B, Li K, Huang DS, Chou KC (2018a) iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach. Bioinformatics. Google Scholar
  58. Liu B, Weng F, Huang DS, Chou KC (2018b) iRO-3wPseKNC: Identify DNA replication origins by three-window-based PseKNC. Bioinformatics 1:8Google Scholar
  59. Lobry J (1996) Asymmetric substitution patterns in the two DNA strands of bacteria. Mol Biol Evol 13(5):660–665Google Scholar
  60. Lukashin A, Anshelevich V, Amirikyan B, Gragerov A, Frank-Kamenetskii M (1989) Neural network models for promoter recognition. J Biomol Struct Dyn 6(6):1123–1133Google Scholar
  61. Mallios RR, Ojcius DM, Ardell DH (2009) An iterative strategy combining biophysical criteria and duration hidden Markov models for structural predictions of Chlamydia trachomatis \(\sigma\) 66 promoters. BMC Bioinf 10(1):271Google Scholar
  62. Mei J, Zhao J (2018a) Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou’s general pseudo amino acid composition and motif features. J Theor Biol 447:147–153Google Scholar
  63. Mei J, Zhao J (2018b) Prediction of HIV-1 and HIV-2 proteins by using Chous pseudo amino acid compositions and different classifiers. Sci Rep 8(1):2359Google Scholar
  64. Mika S, Ratsch G, Weston J, Scholkopf B, Mullers KR (1999) Fisher discriminant analysis with kernels. In: Neural networks for signal processing IX, 1999. Proceedings of the 1999 IEEE signal processing society workshop, IEEE, pp 41–48Google Scholar
  65. Murphy KP (2006) Naive Bayes classifiers. University of British Columbia, Vancouver, p 18Google Scholar
  66. Olson DG, Maloney M, Lanahan AA, Hon S, Hauser LJ, Lynd LR (2015) Identifying promoters for gene expression in Clostridium thermocellum. Metab Eng Commun 2:23–29Google Scholar
  67. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12(Oct):2825–2830Google Scholar
  68. Rahman MS, Shatabda S, Saha S, Kaykobad M, Rahman MS (2018a) DPP-PseAAC: a DNA-binding protein prediction model using Chous general PseAAC. J Theor Biol 452:22–34Google Scholar
  69. Rahman S, Aktar U, Jani R, Shatabda S (2018b) iPromoter-FSEn: identification of bacterial \(\sigma\)70 promoter sequences using feature subspace based ensemble classifier. Genomics. Google Scholar
  70. Rayhan F, Ahmed S, Shatabda S, Farid DM, Mousavian Z, Dehzangi A, Rahman MS (2017) idti-esboost: identification of drug target interaction using evolutionary and structural features with boosting. Sci Rep 7(1):17731Google Scholar
  71. Sabooh MF, Iqbal N, Khan M, Khan M, Maqbool H (2018) Identifying 5-methylcytosine sites in rna sequence using composite encoding feature into Chou’s PseKNC. J Theor Biol 452:1–9Google Scholar
  72. Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674Google Scholar
  73. Samocha KE, Robinson EB, Sanders SJ, Stevens C, Sabo A, McGrath LM, Kosmicki JA, Rehnström K, Mallick S, Kirby A (2014) A framework for the interpretation of de novo mutation in human disease. Nat Genet 46(9):944Google Scholar
  74. Shen L, Bai L (2004) AdaBoost Gabor feature selection for classification. In: Proceedings of image and vision computing, New Zealand, pp 77–83Google Scholar
  75. Shin J, Noireaux V (2010) Efficient cell-free expression with the endogenous E. coli RNA polymerase and sigma factor 70. J Biol Eng 4(1):8Google Scholar
  76. e Silva SDA, Forte F, Sartor IT, Andrighetti T, Gerhardt GJ, Delamare APL, Echeverrigaray S (2014) Dna duplex stability as discriminative characteristic for Escherichia coli \(\sigma\)54-and \(\sigma\)28-dependent promoter sequences. Biologicals 42(1):22–28Google Scholar
  77. Song K (2011) Recognition of prokaryotic promoters based on a novel variable-window z-curve method. Nucleic Acids Res 40(3):963–971Google Scholar
  78. Stormo GD (2000) Dna binding sites: representation and discovery. Bioinformatics 16(1):16–23Google Scholar
  79. Su ZD, Huang Y, Zhang ZY, Zhao YW, Wang D, Chen W, Chou KC, Lin H (2018) iLoc-lncRNA: predict the subcellular location of lncrnas by incorporating octamer composition into general PseKNC. Bioinformatics. Google Scholar
  80. Tang H, Chen W, Lin H (2016) Identification of immunoglobulins using Chou’s pseudo amino acid composition with feature selection technique. Mol BioSyst 12(4):1269–1275Google Scholar
  81. Tang H, Zhao YW, Zou P, Zhang CM, Chen R, Huang P, Lin H (2018) Hbpred: a tool to identify growth hormone-binding proteins. Int J Biol Sci 14(8):957–964Google Scholar
  82. Towsey M, Timms P, Hogan J, Mathews SA (2008) The cross-species prediction of bacterial promoters using a support vector machine. Comput Biol Chem 32(5):359–366Google Scholar
  83. Williamson DF, Parker RA, Kendrick JS (1989) The box plot: a simple visual method to interpret data. Ann Intern Med 110(11):916–921Google Scholar
  84. Yamagishi H (1974) Nucleotide distribution in bacterial DNA’s differing in g+ c content. J Mol Evol 3(3):239–242Google Scholar
  85. Yang H, Tang H, Chen XX, Zhang CJ, Zhu PP, Ding H, Chen W, Lin H (2016) Identification of secretory proteins in mycobacterium tuberculosis using pseudo amino acid composition. BioMed Res IntGoogle Scholar
  86. Yang H, Qiu WR, Liu G, Guo FB, Chen W, Chou KC, Lin H (2018) iRSpot-Pse6NC: identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Int J Biol Sci 14(8):883Google Scholar
  87. Zhang CJ, Tang H, Li WC, Lin H, Chen W, Chou KC (2016) iOri-human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition. Oncotarget 7(43):69783Google Scholar
  88. Zhang S, Duan X (2018) Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC. J Theor Biol 437:239–250Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUnited International UniversityDhakaBangladesh

Personalised recommendations