Discriminant Analysis for the Eigenvalues of Variance Covariance Matrix of FFT Scaling of DNA Sequences: An Empirical Study of Some Organisms

Abstract

Many studies discussed different numerical representations of DNA sequences. One naive approach for exploring the nature of a DNA sequence is to assign numerical values (or scales) to the nucleotides and then proceed with standard time series methods. The analysis will depend actually on the particular assignment of numerical values.Discriminant analysis aims to examine the dependence of one qualitative (classification) variable from several quantitative variables according to number of variations of qualitative variable we can distinction. Actually, there is a discriminant analysis for two or more groups. The essential work of discriminant analysis is to get the optimal assigning rules that will minimize the likelihood of incorrect classification of elements. In this paper, we discussed the discriminant analysis of the first, second, third and fourth eigenvalues of variance covariance matrix of Fast Fourier Transform (FFT) for numerical values representation of DNA sequences of five organisms, Human, E. coli, Rat, Wheat and Grasshopper. The analysis is based on three methods (All Variables, Forward Selection and Backward Selection) of discrimination. Functions have been reached whereby discrimination is made among organisms under consideration. Empirical studies are conducted to show the value of our point of view and the applications based on. Therefore, we recommended that, other empirical studies should be done for other organisms and statistical methods by using the point of view adopted here. Also, aspects stated here must be used in an applied manner for DNA sequences discrimination.

Sequence Fusion Algorithm of Tumor Gene Sequencing and Alignment Based on Machine Learning

“…e data processing flow begins with reading sequence alignment (BWA [16]), followed by raw data cleansing (Picard [17]), sequence recalibration, filtering, variable invocation, recalibration (GATK [18]), coverage analysis (BedTools [19]), and annotation (Annovar [20] and internal annotation tools).…”