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Two new methods for DNA splice site prediction based on neuro-fuzzy network and clustering

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An Erratum to this article was published on 30 January 2013

Abstract

Nowadays, genetic disorders, like cancer and birth defects, are a great threat to human life. Since the first noticing of these types of diseases, many efforts have been made and researches performed in order to recognize them and find a cure for them. These disorders affect genes and they appear as abnormal traits in a genetic organism. In order to recognize abnormal genes, we need to predict splice sites in a DNA signal; then, we can process the genetic codes between two continuous splice sites and analyze the trait that it represents. In addition to abnormal genes and their consequent disorders, we can also identify other normal human traits like physical and mental features. So the primary issue here is to estimate splice sites precisely. In this paper, we have introduced two new methods in using neuro-fuzzy network and clustering for DNA splice site prediction. In this method, instead of using raw data and nucleotide sequence as an input to neural network, a survey on the first bunch of the nucleotide sequence of true and false categories of the input is carried out and training of the neuro-fuzzy network is achieved based on the similarities and dissimilarities of the selected sequences. In addition, sequences of the large input data are clustered into smaller categories to improve the prediction as they are really spliced based on different mechanisms. Experimental results show that these improvements have increased the recognition rate of the splice sites.

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References

  1. Churbanov A, Ali H (2005) Combinatorial method of splice site prediction. Proceedings of the 2005 IEEE computational systems bioinformatics conference, pp 189–190

  2. Awadalla S, Ortiz JE, Gopal S (2005) Prediction of trans-splicing sites using a genetic algorithm. Res Comput Mol Biol pp 1–2

  3. Watanabe T, Kudo Y, Shimizu T (2002) Positional correlations in splicing patterns and its application to prediction of splice sites. Genome Inform 13:426–427

    Google Scholar 

  4. Kashiwabara AY, Vieira DCG, Machado-Lima A, Durham AM (2007) Splice site prediction using stochastic regular grammars. Genetic Mol Res 6:105–115

    Google Scholar 

  5. Hebsgaard SM, Korning PG, Tolstrup N, Engelbrecht J, Rouzél P, Brunak S (1996) Splice site prediction in Arabidopsis thaliana pre-mRNA by combining local and global sequence information. Nucleic Acids Res 24:3439–3452

    Article  Google Scholar 

  6. Islamaj R, Getoor L, Wilbur WJ (2007) A feature generation algorithm for sequences with application to splice-site prediction. BMC Bioinform 8:410–425

    Article  Google Scholar 

  7. Brent MR, Guigo R (2004) Recent advances in gene structure prediction. Curr Opin Struct Biol 14:264–272

    Article  Google Scholar 

  8. Culotta A, Kulp D, McCallum A (2007) Gene prediction with conditional random fields. M.Sc. thesis, Department of Computer Science, University of Massachusetts Amherst, pp 75–77

  9. Perti M, Lin X, Salzberg SL (2001) Gene Splicer: a new computational method for splice site prediction. Nucleic Acid Res 29:1185–1190

    Article  Google Scholar 

  10. Xu S, Ma F, Tao L (2007) Learn from the information contained in the false splice sites as well as in the true splice sites using SVM. ISKE-2007 proceedings

  11. Sonnenburg S, Schweikert G, Philips P, Ratsch G (2007) Accurate splice site prediction using SVMs. 15th annual international conference on intelligent systems for molecular biology (ISMB) proceedings

  12. Tchourbanov A, Ali H (2004) Using enhancing signals to improve specificity of ab initio splice site sensors. Proceedings of the 2004 IEEE computational systems bioinformatics conference, pp 672–673

  13. Raponi M, Baralle D (2008) Can donor splice site recognition occur without the involvement of U1 snRNP? Biochem Soc Trans 36:548–550

    Article  Google Scholar 

  14. Bayer R, Davydov K (2005) Splice site prediction using multiple sequence alignment. Stanford University, Stanford

    Google Scholar 

  15. Zhang Y, Chu CH, Zha H, Chen Y, Ji X (2005) A probabilistic kernel for splice site prediction. Proceedings of the 8th joint conference on information sciences, pp 1278–1281

  16. Ionescu M (2006) Prediction of alternatively spliced exons in C. Elegans using LSTM. Institute of Bioinformatics, Johannes Kepler University, Linz

    Google Scholar 

  17. Yamanaka T, Yada T, Nakai K (1999) An aberrant splicing database for finding rules of splice-site selection. Genome Inform pp 286–287

  18. Patterson DJ, Yasuhara K, Ruzzo WL (2002) PRE-mRNA secondary structure prediction aids splice site prediction. Pacific Symposium of Biocomputing, pp 223–234

  19. Segovia-Juareza JL, Colombanob S, Kirschnera D (2007) Identifying DNA splice sites using hyper networks with artificial molecular evolution. Biosystems 87:117–124

    Article  Google Scholar 

  20. Sonnenburg S, Ratsch G, Jagota A, Muller KR (2002) New methods for splice site recognition. Master’s thesis

  21. Reese MG, Eeckman FH (1998) Novel neural network prediction systems for human promoters and sites. Comput Gene Finding pp 4

  22. Johansen O, Ryen T, Eftesol T, Kjosmoen T, Ruoff P (2008) Splice site prediction using artificial neural networks. Computational intelligence methods for bioinformatics and biostatistics, pp 102–113

  23. Seiffert U, Hammer B, Kaski S, Villmann T (2006) Neural networks and machine learning in bioinformatics—theory and applications. 14th European symposium on artificial neural networks, pp 521–532

  24. Matis S, Xu Y, Shah MB, Mural RJ, Uberbacher EC (1996) Gene identification and analysis: an application of neural network based information fusion. Foundations of decision/information fusion workshop on applications to engineering problems

  25. Furutani H (1993) Analysis of abnormal splicing by neural network. Genome Inform 4:306–314

    Google Scholar 

  26. Ho LS, Rajapakse JC (2003) Splice site detection with a higher order Markov model implemented on a neural network. Genome Inform 14:64–72

    Google Scholar 

  27. Taher L, Morgenstern B, Meinicke P (2003) The importance of window length in splice site prediction. Proceedings of the European conference on computational biology, pp 469–470

  28. http://hsc.utoledo.edu/bioinfo/eid. Accessed on 12 June 2012

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Authors and Affiliations

  1. Engineering Department, Science and Research Branch, Islamic Azad University, Hessarak, Tehran, Iran

    Fahimeh Moghimi

  2. Computer Engineering Department, Sharif University of Technology, Azadi, Tehran, Iran

    Mohammad Taghi Manzuri Shalmani

  3. Electrical and Computer Engineering Department, Khajeh Nasir Toosi University of Technology, Seyedkhandan, Tehran, Iran

    Ali Khaki Sedigh

  4. Electrical Engineering Department, Iran University of Science and Technology, Narmak, Tehran, Iran

    Mohammad Kia

Authors
  1. Fahimeh Moghimi
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  2. Mohammad Taghi Manzuri Shalmani
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  3. Ali Khaki Sedigh
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  4. Mohammad Kia
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Correspondence to Mohammad Kia.

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Moghimi, F., Manzuri Shalmani, M.T., Khaki Sedigh, A. et al. Two new methods for DNA splice site prediction based on neuro-fuzzy network and clustering. Neural Comput & Applic 23 (Suppl 1), 407–414 (2013). https://doi.org/10.1007/s00521-012-1257-y

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  • DOI: https://doi.org/10.1007/s00521-012-1257-y

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