RESEARCH ARTICLE


Strong Strand Composition Bias in the Genome of Ehrlichia canis Revealed by Multiple Methods



Wen Wei, Feng-Biao Guo*
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China


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© Wei and Guo; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China; Fax: +86 28 83201018; E-mail: fbguo@uestc.edu.cn


Abstract

Genes located on the two replicating strands are found to have two separate base/codon usages in Ehrlichia canis genome. Although strand-specific codon usage is not the first observation, for the first time we have applied multiple methods to the analysis of strand composition bias. By combining multiple methods, comprehensive and interesting results are obtained. Among three types of correspondence analysis (COA), absolute codon usages between genes on the two replicating strands are more distinct than relative synonymous codon usage (RSCU) and base usages are more sensitive than both types of codon usages. PR2-plots show that replication-induced bias is much higher than transcription/translation associated bias in the genome. By using the Z curve method, two common genomic characters, i.e., stronger strand composition bias and lower rearrangement frequency are found to exist in 11 obligate intracellular bacteria with separate base/codon usages. We hope more and more researchers will use the multiple methods to analyze strand composition bias in sequenced microbes, particularly obligate intracellular bacteria.

Keywords: Strand composition bias, separate base/codon usages, obligate intracellular bacteria, correspondence analysis, Z curve, PR2-plot.