alexa An Accurate Genomic Island Prediction Method for Sequen
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Research Article

An Accurate Genomic Island Prediction Method for Sequenced Bacterial and Archaeal Genomes

Dongsheng Che1*, Han Wang1, John Fazekas1 and Bernard Chen2

1Department of Computer Science, East Stroudsburg University, East Stroudsburg, PA 18301, USA

2Computer Science Department, University of Central Arkansas, Conway AR, 72034, USA

*Corresponding Author:
Dongsheng Che
Department of Computer Science
East Stroudsburg University
East Stroudsburg, PA 18301, USA
Tel: (570)422-2731
E-mail: [email protected]

Received Date: May 27, 2014; Accepted Date: July 07, 2014; Published Date: July 11, 2014

Citation: Che D, Wang H, Fazekas J, Chen B (2014) An Accurate Genomic Island Prediction Method for Sequenced Bacterial and Archaeal Genomes. J Proteomics Bioinform 7:214-221. doi: 10.4172/jpb.1000322

Copyright: © 2014 Che D, 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

A genomic island (GI) is a genomic segment in a host genome, and it was transferred from donor genomes. Since genomic islands (GIs) usually contain important genes such as pathogenicity genes, the detection of GIs becomes extremely critical to medical research and industrial applications. Previous computational GI detection tools used one or a few GI-associate features, and thus they suffered the problem of low prediction accuracy. A systematic approach that uses multiple sources to improve GI prediction accuracy, therefore, is in great demand. In this paper, we report the development of Genomic Island Hunter (GIHunter), an accurate software tool for GI detection. GIHunter is a decision tree based bagging model that uses eight GI-associated features such as sequence composition, mobile gene information, and integrase. The performance metric comparison between our approach and other current existing GI prediction methods has shown that our approach is more accurate than other approaches. We have used GIHunter to predict GIs in more than 2000 prokaryotic genomes. We have also visualized our predicted GIs so that our predicted results become useful and meaningful for biomedical studies. Our GI program GIHunter can be obtained at: https://www.esu.edu/cpsc/che_lab/software/GIHunter. Our GI prediction results are available on our genomic islands database, which is: https://www5.esu.edu/cpsc/bioinfo/dgi.

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