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DNA CLASSIFICATION USING WAVELET
CHAPTER ONE
INTRODUCTION
1.1 Background of the study
The possible relevance of scale invariance and fractal concepts to the structural complexity of genomic sequences is the subject of considerable increasing interest [1]. During the past few years, there has been intense discussion about the existence, the nature and the origin of long-range correlations in DNA sequences. Dierent techniques including mutual information functions [2], autocorrelation functions [3,4], power spectra [5,6], “DNA walk” representation [1,7], Zipf analysis [8] were used for statistical analysis of DNA sequences. But despite the eort spent, there is still some continuing debate on rather struggling questions. In that respect, it is of fundamental importance to corroborate the fact that the reported long-range correlations are not just an artefact of the compositional heterogeneity of the genome organization [3,4,9–12]. Furthermore, it is still an open question whether the long-range correlation properties are dierent for protein-coding (exonic) and noncoding (intronic, intergenetic) sequences [1–8,13]. One of the main obstacles to long-range correlation analysis is the mosaic structure of DNA sequences which are well known to be formed of “patches” (“strand bias”) of dierent underlying compositions [14–16]. These patches appear as trends in the DNA walk landscapes and are likely to introduce some breaking of scale invariance [9–12].
1.2 Statement of the problem
Most of the technique used so far for characterizing the presence of long-range correlations are not well adapted to study patchy sequences. In a preliminary work [17,18], we have emphasized the wavelet transform (WT) as a very powerful technique for fractal analysis of DNA sequences. By considering analyzing wavelets that make the WT microscope blind to low-frequency trends, one can reveal and quantify the scaling properties of DNA walks. Here we report on recent results obtained by applying the so-called wavelet transform modulus maxima (WTMM) method [19,20] to various genomic sequences mainly selected in the human genome.
1.3 Objectives of the study
1. To understand the importance of dna classification using wavelet
2. To understand the relationship between use of wavelet and classification of dna
1.4 Research Questions
1. What is the importance of dna classification using wavelet
2. What is the relationship between use of wavelet and classification of dna
1.5 Research Hypothesis
H0: There is no relationship between use of wavelet and classification of dna
H1: There is a relationship between use of wavelet and classification of dna
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