title: An improved model for whole genome phylogenetic analysis by Fourier transform.
abstract: DNA sequence similarity comparison is one of the major steps in computational phylogenetic studies. The sequence comparison of closely related DNA sequences and genomes is usually performed by multiple sequence alignments (MSA). While the MSA method is accurate for some types of sequences, it may produce incorrect results when DNA sequences undergone rearrangements as in many bacterial and viral genomes. It is also limited by its computational complexity for comparing large volumes of data. Previously, we proposed an alignment-free method that exploits the full information contents of DNA sequences by Discrete Fourier Transform (DFT), but still with some limitations. Here, we present a significantly improved method for the similarity comparison of DNA sequences by DFT. In this method, we map DNA sequences into 2-dimensional (2D) numerical sequences and then apply DFT to transform the 2D numerical sequences into frequency domain. In the 2D mapping, the nucleotide composition of a DNA sequence is a determinant factor and the 2D mapping reduces the nucleotide composition bias in distance measure, and thus improving the similarity measure of DNA sequences. To compare the DFT power spectra of DNA sequences with different lengths, we propose an improved even scaling algorithm to extend shorter DFT power spectra to the longest length of the underlying sequences. After the DFT power spectra are evenly scaled, the spectra are in the same dimensionality of the Fourier frequency space, then the Euclidean distances of full Fourier power spectra of the DNA sequences are used as the dissimilarity metrics. The improved DFT method, with increased computational performance by 2D numerical representation, can be applicable to any DNA sequences of different length ranges. We assess the accuracy of the improved DFT similarity measure in hierarchical clustering of different DNA sequences including simulated and real datasets. The method yields accurate and reliable phylogenetic trees and demonstrates that the improved DFT dissimilarity measure is an efficient and effective similarity measure of DNA sequences. Due to its high efficiency and accuracy, the proposed DFT similarity measure is successfully applied on phylogenetic analysis for individual genes and large whole bacterial genomes.
authors.affiliation: Department of Mathematics, Statistics and Computer Science, The University of Illinois at Chicago, Chicago, IL 60607-7045, USA.
authors.forename: Changchuan
authors.initials: C
authors.lastname: Yin
authors.affiliation: Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China. Electronic address: yau@uic.edu.
authors.forename: Stephen S-T
authors.initials: SS
authors.lastname: Yau
mesh_terms: D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D016000:Cluster Analysis; D003198:Computer Simulation; D005583:Fourier Analysis; D016678:Genome; D054629:Genome, Mitochondrial; D006801:Humans; D008322:Mammals / Q000235:genetics; D008957:Models, Genetic; D009154:Mutation / Q000235:genetics; D009245:NADH Dehydrogenase / Q000235:genetics; D009711:Nucleotides / Q000235:genetics; D010802:Phylogeny
chemical_list: D009711:Nucleotides; D009245:NADH Dehydrogenase
keywords: Even scaling; Fourier transform; Genome; Phylogenetic analysis; Similarity distance