Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
Preliminary Wavelet Analysis of Genomic Sequences
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Spectrogram analysis of genomes
EURASIP Journal on Applied Signal Processing
Computing linear transforms of symbolic signals
IEEE Transactions on Signal Processing
Spatially adaptive wavelet thresholding with context modeling for image denoising
IEEE Transactions on Image Processing
Wavelet Analysis in Current Cancer Genome Research: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Recent past has seen an exponential growth in DNA sequence data, which is being made publicly accessible. This has contributed towards enormous effort in understanding the concealed information within DNA sequences. Various heuristic techniques of sequence analysis have given significant results, but as the sequence length increases, these techniques are found to be inefficient, leaving scope for intelligent techniques that can adapt to the variable length of the coding and non-coding sequences. In this paper, we introduce an intrinsic and an intelligent technique of wavelet analysis that has the ability to adapt itself according to the variable length of the coding and non-coding sequences while giving a comprehensive picture of the patterns present within the DNA sequences. These patterns can be compared between the similar genes in different species and can be used for understanding and mapping the process of evolution. We perform the wavelet analysis of nucleotide sequences from different species and report some significant facts about the phylogenetic relationships between the species that are considered to be unrelated. Application of the developed approach provides evidence towards the theory of conservation of genes during the process of evolution.