Pathogens 2014, 3(1), 36-56; doi:10.3390/pathogens3010036
Review

Identifying Pathogenicity Islands in Bacterial Pathogenomics Using Computational Approaches

1 Department of Computer Science, East Stroudsburg University of Pennsylvania, East Stroudsburg, PA 18301, USA 2 Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA 3 Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA
* Author to whom correspondence should be addressed.
Received: 30 November 2013; in revised form: 30 December 2013 / Accepted: 7 January 2014 / Published: 13 January 2014
(This article belongs to the Special Issue Bacterial Pathogenomics: From Technology to Application)
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Abstract: High-throughput sequencing technologies have made it possible to study bacteria through analyzing their genome sequences. For instance, comparative genome sequence analyses can reveal the phenomenon such as gene loss, gene gain, or gene exchange in a genome. By analyzing pathogenic bacterial genomes, we can discover that pathogenic genomic regions in many pathogenic bacteria are horizontally transferred from other bacteria, and these regions are also known as pathogenicity islands (PAIs). PAIs have some detectable properties, such as having different genomic signatures than the rest of the host genomes, and containing mobility genes so that they can be integrated into the host genome. In this review, we will discuss various pathogenicity island-associated features and current computational approaches for the identification of PAIs. Existing pathogenicity island databases and related computational resources will also be discussed, so that researchers may find it to be useful for the studies of bacterial evolution and pathogenicity mechanisms.
Keywords: genomic islands; pathogenicity islands; computational methods; genomic signature; mobility gene; virulence factors; pathogenicity island database

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MDPI and ACS Style

Che, D.; Hasan, M.S.; Chen, B. Identifying Pathogenicity Islands in Bacterial Pathogenomics Using Computational Approaches. Pathogens 2014, 3, 36-56.

AMA Style

Che D, Hasan MS, Chen B. Identifying Pathogenicity Islands in Bacterial Pathogenomics Using Computational Approaches. Pathogens. 2014; 3(1):36-56.

Chicago/Turabian Style

Che, Dongsheng; Hasan, Mohammad S.; Chen, Bernard. 2014. "Identifying Pathogenicity Islands in Bacterial Pathogenomics Using Computational Approaches." Pathogens 3, no. 1: 36-56.

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