Issue No. 05 - Sept.-Oct. (2013 vol. 10)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.76
Devendra Kumar Shakya , Dept. of Biomed. Eng., Samrat Ashok Technol. Inst., Vidisha, India
Rajiv Saxena , Jaypee Univ. of Eng. & Technol., Guna, India
Sanjeev Narayan Sharma , Dept. of Biomed. Eng., Samrat Ashok Technol. Inst., Vidisha, India
Signal processing-based algorithms for identification of coding sequences (CDS) in eukaryotes are non-data driven and exploit the presence of three-base periodicity in these regions for their detection. Three-base periodicity is commonly detected using short time Fourier transform (STFT) that uses a window function of fixed length. As the length of the protein coding and noncoding regions varies widely, the identification accuracy of STFT-based algorithms is poor. In this paper, a novel signal processing-based algorithm is developed by enabling the window length adaptation in STFT of DNA sequences for improving the identification of three-base periodicity. The length of the window function has been made adaptive in coding regions to maximize the magnitude of period-3 measure, whereas in the noncoding regions, the window length is tailored to minimize this measure. Simulation results on bench mark data sets demonstrate the advantage of this algorithm when compared with other non-data-driven methods for CDS prediction.
Encoding, DNA, Bioinformatics, Genomics, Signal processing algorithms, Prediction algorithms
D. K. Shakya, R. Saxena and S. N. Sharma, "An Adaptive Window Length Strategy for Eukaryotic CDS Prediction," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 5, pp. 1241-1252, 2014.