Understanding long-range correlations in DNA sequences
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Shape Analysis and Classification: Theory and Practice
Shape Analysis and Classification: Theory and Practice
Preliminary Wavelet Analysis of Genomic Sequences
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Autoregressive modeling and feature analysis of DNA sequences
EURASIP Journal on Applied Signal Processing
Fourier analysis of symbolic data: A brief review
Digital Signal Processing
Computing linear transforms of symbolic signals
IEEE Transactions on Signal Processing
International Journal of Data Mining and Bioinformatics
Identification of protein coding regions using antinotch filters
Digital Signal Processing
A brief comparison of DSP and HMM methods for gene finding
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Improved exon prediction with transforms by de-noising period-3 measure
Digital Signal Processing
An Adaptive Window Length Strategy for Eukaryotic CDS Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Wavelet Analysis in Current Cancer Genome Research: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hi-index | 0.03 |
An important topic in genomic sequence analysis is the identification of protein coding regions. In this context, several coding DNA model-independent methods, based on the occurrence of specific patterns of nucleotides at coding regions, have been proposed. Nonetheless, these methods have not been completely suitable due to their dependence on an empirically pre-defined window length required for a local analysis of a DNA region. We introduce a method, based on a modified Gabor-wavelet transform (MGWT), for the identification of protein coding regions. This novel transform is tuned to analyze periodic signal components and presents the advantage of being independent of the window length. We compared the performance of the MGWT with other methods using eukaryote datasets. The results show that the MGWT outperforms all assessed model-independent methods with respect to identification accuracy. These results indicate that the source of at least part of the identification errors produced by the previous methods is the fixed working scale. The new method not only avoids this source of errors, but also makes available a tool for detailed exploration of the nucleotide occurrence.