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Journal of Plant Biochemistry and Biotechnology

, Volume 24, Issue 4, pp 385–392 | Cite as

Determination of window size and identification of suitable method for prediction of donor splice sites in rice (Oryza sativa) genome

  • Prabina Kumar Meher
  • Tanmaya Kumar Sahu
  • A. R. RaoEmail author
  • S. D. Wahi
Original Article

Abstract

Accurate prediction of the gene structure depends upon the accurate prediction of splice sites. The conserved feature in splicing junction has been successfully used for the prediction of eukaryotic splice sites. In eukaryotes, though the di-nucleotide GT is conserved at 5′ splice sites, the pattern surrounding the conserved di-nucleotide varies from species to species. Most of the work related to splice site analysis has been extensively done in Homo sapiens and Arabidopsis thaliana. However, such works are yet to be fully explored in Oryza sativa and other species of grass family. In this study, statistical techniques have been applied to discriminate the real splice sites from pseudo splice sites in rice, maize and barley genomes and based on this a suitable window size is determined for the prediction of donor splice sites. Depending upon the determined window size, appropriate methods for predicting donor splice sites in rice have been considered and compared in terms of prediction accuracy. The results revealed that a window size of 9 base pair (3 bp at the exon end and 6 bp at the intron start including the conserved di-nucleotide GT at the beginning of intron) is an effective window size in all the three species of grass family for the prediction of donor splice sites. Further, the Maximum Entropy Model based method is found as best among the short sequence based prediction methods for donor splice sites with the 9 base pair window size.

Keywords

Splice sites Prediction accuracy Window size Short sequence motif 

Abbreviations

MLAs

Machine Learning Approaches

MEM

Maximum Entropy Modeling

MDD

Maximal Dependency Decomposition

MM1

Markov Model of 1st order

WMM

Weighted Matrix Method

Notes

Acknowledgements

This study is a part of Ph. D. thesis of P. K. Meher, PG School, IARI, New Delhi. Authors acknowledge the INSPIRE fellowship of Department of Science and Technology, New Delhi and IARI Fellowship. The authors also acknowledge the computational facilities of SCGL, developed under NAIP grant NAIP/Comp-4/C4/C-30033/2008-09.

Supplementary material

13562_2014_286_MOESM1_ESM.tif (252 kb)
Supplementary Fig. 1 Confusion Matrix. TP is the number of TSS being predicted as TSS, TN is the number of FSS being predicted as FSS, FN is the number of TSS being incorrectly predicted as FSS and FP is the number of FSS being incorrectly predicted as TSS. (TIFF 252 kb)
13562_2014_286_MOESM2_ESM.tif (749 kb)
Supplementary Fig. 2 Bar diagram of calculated value of Pearson chi-square obtained from the sequence data of TSS and FSS for the three species. X-axis represents positions of the motif and the height of each bar corresponds to the value of chi-square of each positions. (TIFF 748 kb)
13562_2014_286_MOESM3_ESM.tif (188 kb)
Supplementary Fig. 3 Graphical representation of the Kull-back Leibler Divergence for different positions of the splice site motifs. The height of each bar represents the distance between the true and false splice site for the corresponding position. (TIFF 187 kb)

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Copyright information

© Society for Plant Biochemistry and Biotechnology 2014

Authors and Affiliations

  • Prabina Kumar Meher
    • 1
  • Tanmaya Kumar Sahu
    • 2
  • A. R. Rao
    • 2
    Email author
  • S. D. Wahi
    • 1
  1. 1.Division of Statistical GeneticsIndian Agricultural Statistics Research InstituteNew DelhiIndia
  2. 2.Centre for Agricultural BioinformaticsIndian Agricultural Statistics Research InstituteNew DelhiIndia

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