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ISSN: 2153-0602

Journal of Data Mining in Genomics & Proteomics
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Research Article

Ranking of Prokaryotic Genomes Based on Maximization of Sortedness of Gene Lengths

Bolshoy A1*, Salih B1,2, Cohen I1,3 and Tatarinova T4

1Department of Evolutionary and Environmental Biology and Institute of Evolution, University of Haifa, Israel

2Department of Computer Science, University of Haifa, Israel, Israel

3The Tauber Bioinformatics Research Center at University of Haifa

4Children’s Hospital Los Angeles, University of Southern California, Los Angeles, California, USA

*Corresponding Author:
Bolshoy A
Department of Evolutionary and Environmental Biology and Institute of Evolution
University of Haifa, Israel
Tel: +97258728025 or 16043538414
E-mail: [email protected]

Received date: January 17, 2014; Accepted date: February 10, 2014; Published date: February 13, 2014

Citation: Bolshoy A, Salih B, Cohen I, Tatarinova T (2014) Ranking of Prokaryotic Genomes Based on Maximization of Sortedness of Gene Lengths. J Data Mining Genomics Proteomics 5:151. doi: 10.4172/2153-0602.1000151

Copyright: © 2014 Bolshoy A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

How variations of gene lengths (some genes become longer than their predecessors, while other genes become shorter and the sizes of these factions are randomly different from organism to organism) depend on organismal evolution and adaptation is still an open question. We propose to rank the genomes according to lengths of their genes, and then find association between the genome rank and variousproperties, such as growth temperature, nucleotide composition, and pathogenicity. This approach reveals evolutionary driving factors. The main purpose of this study is to test effectiveness and robustness of several ranking methods. The selected method of evaluation is measuring of overall sortedness of the data. We have demonstrated that all considered methods give consistent results and Bubble Sort and Simulated Annealing achieve the highest sortedness. Also, Bubble Sort is considerably faster than the Simulated Annealing method.

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