Skip to main content

Advertisement

SpringerLink
Log in

Splicing sites prediction of human genome using machine learning techniques

  • 1155T: Advanced machine learning algorithms for biomedical data and imaging
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The accurate splice site prediction has several applications in the field of medical sciences and biochemistry. For instance, any mutation affecting the splice site will lead to genetic diseases and cancer such as Lynch syndrome and breast cancer. For this purpose, collecting the Ribonucleic Acid (RNA) samples is an efficient and convenient method to detect the involvement of splicing defects in disease formation. Therefore, the present study aims to develop an accurate and robust Computer-Aided Diagnosis (CAD) method for swift and precise targeting of splice site sequences. A composite features-based model is proposed by integrating three different sample representation methods i.e., Dinucleotide Composition (DNC), Trinucleotide Composition (TNC) and Tetranucleotide Composition (TetraNC) for precise splice site prediction after converting the DNA sequences into numerical descriptors. The precision and accuracy of these features are analyzed by applying different machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB). Results show that the proposed model of composite features vector with SVM classifier achieved an accuracy of 95.20% and 97.50% for donor and acceptor sites datasets, respectively.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. Ali F, Hayat M (2016) Machine learning approaches for discrimination of extracellular matrix proteins using hybrid feature space. J Theor Biol 403:30–37

    Article  MathSciNet  MATH  Google Scholar 

  2. Angermueller C, Lee HJ, Reik W, Stegle O (2017) DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol 18:67

    Article  Google Scholar 

  3. Burge C, Karlin S (1997) Prediction of complete gene structures in human genomic DNA1. J Mol Biol 268:78–94

    Article  Google Scholar 

  4. Burke B, Stewart CL (2014) Functional architecture of the cell's nucleus in development, aging, and disease. Curr Top Dev Biol 109, Elsevier:1–52

    Article  Google Scholar 

  5. Cai Y-D, Zhou G-P, Chou K-C (2003) Support vector machines for predicting membrane protein types by using functional domain composition. Biophys J 84:3257–3263

    Article  Google Scholar 

  6. Cao D-S, Xu Q-S, Liang Y-Z (2013) Propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics 29:960–962

    Article  Google Scholar 

  7. Cartegni L, Wang J, Zhu Z, Zhang MQ, Krainer AR (2003) ESEfinder: a web resource to identify exonic splicing enhancers. Nucleic Acids Res 31:3568–3571

    Article  Google Scholar 

  8. Chaki J, Dey N (2019) Pattern analysis of genetics and genomics: a survey of the state-of-art. Multimed Tools Appl 1–32

  9. Chen W, Feng P-M, Lin H, Chou K-C (2013) iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res 41:e68–e68

    Article  Google Scholar 

  10. Chen W, Feng P-M, Lin H, Chou K-C (2014) iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. Biomed Res Int 2014:623149–623149

    Google Scholar 

  11. Chen W, Zhang X, Brooker J, Lin H, Zhang L, Chou K-C (2014) PseKNC-general: a cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics 31:119–120

    Article  Google Scholar 

  12. Chen W, Feng P-M, Deng E-Z, Lin H, Chou K-C (2014) iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal Biochem 462:76–83

    Article  Google Scholar 

  13. Chen W, Lin H, Chou K-C (2015) Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol BioSyst 11:2620–2634

    Article  Google Scholar 

  14. Chou KC (2001) Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins Struct Funct Bioinforma 43:246–255

    Article  Google Scholar 

  15. Chou KC (2001) Prediction of protein signal sequences and their cleavage sites. Proteins Struct Funct Bioinforma 42:136–139

    Article  Google Scholar 

  16. Chou K-C (2004) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21:10–19

    Article  Google Scholar 

  17. Chou K-C (2009) Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr Proteome 6:262–274

    Article  Google Scholar 

  18. Chou K-C, Shen H-B (2007) Recent progress in protein subcellular location prediction. Anal Biochem 370:1–16

    Article  Google Scholar 

  19. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297

    Article  MATH  Google Scholar 

  20. Cui Y, Han J, Zhong D, Liu R (2013) A novel computational method for the identification of plant alternative splice sites. Biochem Biophys Res Commun 431:221–224

    Article  Google Scholar 

  21. Du P, Gu S, Jiao Y (2014) PseAAC-general: fast building various modes of general form of Chou’s pseudo-amino acid composition for large-scale protein datasets. Int J Mol Sci 15:3495–3506

    Article  Google Scholar 

  22. Feng P-M, Chen W, Lin H, Chou K-C (2013) iHSP-PseRAAAC: identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal Biochem 442:118–125

    Article  Google Scholar 

  23. Fernandez M, Miranda-Saavedra D (2012) Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines. Nucleic Acids Res 40:e77–e77

    Article  Google Scholar 

  24. Firpi HA, Ucar D, Tan K (2010) Discover regulatory DNA elements using chromatin signatures and artificial neural network. Bioinformatics 26:1579–1586

    Article  Google Scholar 

  25. Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29:131–163

    Article  MATH  Google Scholar 

  26. Garhwal AS, Yan WQ (2019) BIIIA: a bioinformatics-inspired image identification approach. Multimed Tools Appl 78:9537–9552

    Article  Google Scholar 

  27. Goel N, Singh S, Aseri TC (2015) An improved method for splice site prediction in DNA sequences using support vector machines. Procedia Comput Sci 57:358–367

    Article  Google Scholar 

  28. Guo S-H, Deng E-Z, Xu L-Q, 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 30:1522–1529

    Article  Google Scholar 

  29. Henderson J, Salzberg S, Fasman KH (1997) Finding genes in DNA with a hidden Markov model. J Comput Biol 4:127–141

    Article  Google Scholar 

  30. Hill ST, Kuintzle R, Teegarden A, Merrill E III, Danaee P, Hendrix DA (2018) A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential. Nucleic Acids Res 46:8105–8113

    Article  Google Scholar 

  31. Hoang T, Yin C, Yau SS-T (2020) Splice sites detection using chaos game representation and neural network. Genomics 112:1847–1852

    Article  Google Scholar 

  32. Iqbal M, Hayat M (2016) “iSS-Hyb-mRMR”: identification of splicing sites using hybrid space of pseudo trinucleotide and pseudo tetranucleotide composition. Comput Methods Prog Biomed 128:1–11

    Article  Google Scholar 

  33. Jian X, Boerwinkle E, Liu X (2014) In silico tools for splicing defect prediction: a survey from the viewpoint of end users. Genet Med 16:497–503

    Article  Google Scholar 

  34. Kabir M, Yu D-J (2017) Predicting DNase I hypersensitive sites via un-biased pseudo trinucleotide composition. Chemom Intell Lab Syst 167:78–84

    Article  Google Scholar 

  35. Kabir M, Iqbal M, Ahmad S, Hayat M (2015) iTIS-PseKNC: identification of translation initiation site in human genes using pseudo k-tuple nucleotides composition. Comput Biol Med 66:252–257

    Article  Google Scholar 

  36. Kandaswamy KK, Chou K-C, Martinetz T, Möller S, Suganthan P, Sridharan S et al (2011) AFP-Pred: a random forest approach for predicting antifreeze proteins from sequence-derived properties. J Theor Biol 270:56–62

    Article  Google Scholar 

  37. Kulakovskiy IV, Medvedeva YA, Schaefer U, Kasianov AS, Vorontsov IE, Bajic VB et al (2012) HOCOMOCO: a comprehensive collection of human transcription factor binding sites models. Nucleic Acids Res 41:D195–D202

    Article  Google Scholar 

  38. Li W, Jaroszewski L, Godzik A (2002) Tolerating some redundancy significantly speeds up clustering of large protein databases. Bioinformatics 18:77–82

    Article  Google Scholar 

  39. Li C, Li X, Lin Y-X (2016) Numerical characterization of protein sequences based on the generalized Chou’s pseudo amino acid composition. Appl Sci 6:406

    Article  Google Scholar 

  40. Li W, Li J, Huo L, Li W, Du X (2017) Prediction of splice site using support vector machine with feature selection. In: Proceedings of the International Conference on Bioinformatics and Computational Intelligence (pp. 1–5)

  41. Lin S-X, Lapointe J (2013) Theoretical and experimental biology in one—a symposium in honour of professor Kuo-Chen Chou’s 50th anniversary and professor Richard Giegé’s 40th anniversary of their scientific careers. J Biomed Sci Eng 6:435–442

    Article  Google Scholar 

  42. 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 42:12961–12972

    Article  Google Scholar 

  43. Liu B (2016) iEnhancer-PsedeKNC: identification of enhancers and their subgroups based on Pseudo degenerate kmer nucleotide composition. Neurocomputing 217:46–52

    Article  Google Scholar 

  44. Liu B, Liu F, Wang X, Chen J, Fang L, Chou K-C (2015) Pse-in-one: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res 43:W65–W71

    Article  Google Scholar 

  45. Maji S, Garg D (2014) Hybrid approach using SVM and MM2 in splice site junction identification. Curr Bioinforma 9:76–85

    Article  Google Scholar 

  46. Maji S, Kanrar S (2019) SpliceCombo: A hybrid technique efficiently use for principal component analysis of splice site prediction. arXiv preprint arXiv:1907.09401

  47. Meher PK, Sahu TK, Rao A, Wahi S (2016) Identification of donor splice sites using support vector machine: a computational approach based on positional, compositional and dependency features. Algorithm Mol Biol 11:16

    Article  Google Scholar 

  48. Moles-Fernández A, Duran-Lozano L, Montalban G, Bonache S, López-Perolio I, Menéndez M, Santamariña M, Behar R, Blanco A, Carrasco E, López-Fernández A, Stjepanovic N, Balmaña J, Capellá G, Pineda M, Vega A, Lázaro C, de la Hoya M, Diez O, Gutiérrez-Enríquez S (2018) Computational tools for splicing defect prediction in breast/ovarian cancer genes: how efficient are they at predicting RNA alterations? Front Genet 9:366

    Article  Google Scholar 

  49. Naito T (2019) Predicting the impact of single nucleotide variants on splicing via sequence-based deep neural networks and genomic features. Hum Mutat 40:1261–1269

    Article  Google Scholar 

  50. Nanni L, Lumini A (2006) An ensemble of K-local hyperplanes for predicting protein–protein interactions. Bioinformatics 22:1207–1210

    Article  Google Scholar 

  51. Nazari I, Tahir M, Tayara H, Chong KT (2019) iN6-methyl (5-step): identifying RNA N6-methyladenosine sites using deep learning mode via Chou's 5-step rules and Chou's general PseKNC. Chemom Intell Lab Syst 193:103811

    Article  Google Scholar 

  52. Norouzi B, Mirzakuchaki S (2017) An image encryption algorithm based on DNA sequence operations and cellular neural network. Multimed Tools Appl 76:13681–13701

    Article  Google Scholar 

  53. Ogura H, Agata H, Xie M, Odaka T, Furutani H (1997) A study of learning splice sites of DNA sequence by neural networks. Comput Biol Med 27:67–75

    Article  Google Scholar 

  54. Pashaei E, Ozen M, Aydin N (2017) Splice site identification in human genome using random forest. Heal Technol 7:141–152

    Article  Google Scholar 

  55. Pertea M, Lin X, Salzberg SL (2001) GeneSplicer: a new computational method for splice site prediction. Nucleic Acids Res 29:1185–1190

    Article  Google Scholar 

  56. Pollastro P, Rampone S (2002) HS3D, a dataset of homo sapiens splice regions, and its extraction procedure from a major public database. Int J Mod Phys C 13:1105–1117

    Article  Google Scholar 

  57. 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 15:1746–1766

    Article  Google Scholar 

  58. Quang D, Xie X (2019) FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data. Methods 166:40–47

    Article  Google Scholar 

  59. Reese MG, Eeckman FH, Kulp D, Haussler D (1997) Improved splice site detection in genie. J Comput Biol 4:311–323

    Article  Google Scholar 

  60. Rhine CL, Cygan KJ, Soemedi R, Maguire S, Murray MF, Monaghan SF, Fairbrother WG (2018) Hereditary cancer genes are highly susceptible to splicing mutations. PLoS Genet 14:e1007231

    Article  Google Scholar 

  61. Richhariya B, Tanveer M (2019) A fuzzy universum support vector machine based on information entropy. In: Machine Intelligence and Signal Analysis (pp. 569–582), ed: Springer

  62. Schäffer AA, Aravind L, Madden TL, Shavirin S, Spouge JL, Wolf YI, Koonin EV, Altschul SF (2001) Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. Nucleic Acids Res 29:2994–3005

    Article  Google Scholar 

  63. 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 12:2587–2593

    Article  Google Scholar 

  64. Tahir M, Hayat M, Kabir M (2017) Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition. Comput Methods Prog Biomed 146:69–75

    Article  Google Scholar 

  65. Tanveer M, Shubham K, Aldhaifallah M, Ho SS (2016) An efficient regularized K-nearest neighbor based weighted twin support vector regression. Knowl-Based Syst 94:70–87

    Article  Google Scholar 

  66. Tanveer M, Sharma A, Suganthan PN (2019) General twin support vector machine with pinball loss function. Inf Sci 494:311–327

    Article  MathSciNet  MATH  Google Scholar 

  67. Tayara H, Tahir M, Chong KT (2019) iSS-CNN: identifying splicing sites using convolution neural network. Chemom Intell Lab Syst 188:63–69

    Article  Google Scholar 

  68. Thompson TB, Chou K-C, Zheng C (1995) Neural network prediction of the HIV-1 protease cleavage sites. J Theor Biol 177:369–379

    Article  Google Scholar 

  69. Touati R, Messaoudi I, Oueslati AE, Lachiri Z (2019) A combined support vector machine-FCGS classification based on the wavelet transform for Helitrons recognition in C. elegans. Multimed Tools Appl 78:13047–13066

    Article  Google Scholar 

  70. Vaz-Drago R, Custódio N, Carmo-Fonseca M (2017) Deep intronic mutations and human disease. Hum Genet 136:1093–1111

    Article  Google Scholar 

  71. Waris M, Ahmad K, Kabir M, Hayat M (2016) Identification of DNA binding proteins using evolutionary profiles position specific scoring matrix. Neurocomputing 199:154–162

    Article  Google Scholar 

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

    Article  Google Scholar 

  73. Xu Q, Li M (2019) A new cluster computing technique for social media data analysis. Clust Comput 22:2731–2738

    Article  Google Scholar 

  74. Xu Z-C, Wang P, Qiu W-R, Xiao X (2017) iSS-PC: identifying splicing sites via physical-chemical properties using deep sparse auto-encoder. Sci Rep 7:8222

    Article  Google Scholar 

  75. Zhang MQ (1997) Identification of protein coding regions in the human genome by quadratic discriminant analysis. Proc Natl Acad Sci 94:565–568

    Article  Google Scholar 

  76. Zhang XH, Heller KA, Hefter I, Leslie CS, Chasin LA (2003) Sequence information for the splicing of human pre-mRNA identified by support vector machine classification. Genome Res 13:2637–2650

    Article  Google Scholar 

  77. Zhang Y, Liu X, MacLeod J, Liu J (2018) Discerning novel splice junctions derived from RNA-seq alignment: a deep learning approach. BMC Genomics 19:971

    Article  Google Scholar 

  78. Zhang Z, Zhao Y, Liao X, Shi W, Li K, Zou Q, Peng S (2019) Deep learning in omics: a survey and guideline. Brief Funct Genom 18:41–57

    Article  Google Scholar 

  79. Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A (2019) A primer on deep learning in genomics. Nat Genet 51:12–18

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Intelligent Media Laboratory, Sejong University, Seoul, 143-747, Republic of Korea

    Waseem Ullah, Ijaz Ul Haq & Amin Ullah

  2. Visual Analytics for Knowledge Laboratory, Department of Software, Sejong University, Seoul, 143-747, Republic of Korea

    Khan Muhammad

  3. Centre of Biotechnology and Microbiology, University of Peshawar, Peshawar, Pakistan

    Saeed Ullah Khattak

  4. Department of Computer Science, Islamia College Peshawar, Peshawar, Pakistan

    Muhammad Sajjad

Authors
  1. Waseem Ullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khan Muhammad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ijaz Ul Haq
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amin Ullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Saeed Ullah Khattak
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Muhammad Sajjad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Khan Muhammad or Muhammad Sajjad.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ullah, W., Muhammad, K., Ul Haq, I. et al. Splicing sites prediction of human genome using machine learning techniques. Multimed Tools Appl 80, 30439–30460 (2021). https://doi.org/10.1007/s11042-021-10619-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-10619-3

Keywords

Search

Navigation