Literature DB >> 26004007

Use of whole genome DNA spectrograms in bacterial classification.

Vladimira Kubicova1, Ivo Provaznik2.   

Abstract

A spectrogram reflects the arrangement of nucleotides through the whole chromosome or genome. Our previous study suggested that the spectrogram of whole genome DNA sequences is a suitable tool for the determination of relationships among bacteria. Related bacteria have similar spectrograms, and similarity in spectrograms was measured using a color layout descriptor. Several parameters, such as the mapping of four bases into a spectrogram, the number of considered elements in the color layout descriptor, the color model of the image and the building tree method, can be changed. This study addresses the use of parameter selection to ensure the best classification results. The quality of the classification was measured by Matthew's correlation coefficient (MCC). The proposed method with optimal parameters (called SpectCMP-Spectrogram CoMParison method) achieved an average MCC of 0.73 at the phylum level. The SpectCMP method was also tested at the order level; the average MCC in the classification of class Gammaproteobacteria was 0.76. The success of a classification with respect to the correct phyla was compared to three methods that are used in bacterial phylogeny: the CVTree method, OGTree method and moment vector method. The results show that the SpectCMP method can be used in bacterial classification at various taxonomic levels.
Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Keywords:  DNA spectrogram; Relationships among bacteria; SpectCMP method; Spectrogram comparison; Whole genome comparison

Mesh:

Substances:

Year:  2015        PMID: 26004007     DOI: 10.1016/j.compbiomed.2015.04.038

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  1 in total

1.  Alignment-free genomic sequence comparison using FCGR and signal processing.

Authors:  Daniel Lichtblau
Journal:  BMC Bioinformatics       Date:  2019-12-30       Impact factor: 3.169

  1 in total

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