PIPS: pathogenicity island prediction software.

The adaptability of pathogenic bacteria to hosts is influenced by the genomic plasticity of the bacteria, which can be increased by such mechanisms as horizontal gene transfer. Pathogenicity islands play a major role in this type of gene transfer because they are large, horizontally acquired regions...

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Autores principales:	Siomar C Soares, Vinícius A C Abreu, Rommel T J Ramos, Louise Cerdeira, Artur Silva, Jan Baumbach, Eva Trost, Andreas Tauch, Raphael Hirata, Ana L Mattos-Guaraldi, Anderson Miyoshi, Vasco Azevedo
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1cb15949505e40c8b45064f6c265f7c0
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spelling	oai:doaj.org-article:1cb15949505e40c8b45064f6c265f7c02021-11-18T07:28:14ZPIPS: pathogenicity island prediction software.1932-620310.1371/journal.pone.0030848https://doaj.org/article/1cb15949505e40c8b45064f6c265f7c02012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22355329/?tool=EBIhttps://doaj.org/toc/1932-6203The adaptability of pathogenic bacteria to hosts is influenced by the genomic plasticity of the bacteria, which can be increased by such mechanisms as horizontal gene transfer. Pathogenicity islands play a major role in this type of gene transfer because they are large, horizontally acquired regions that harbor clusters of virulence genes that mediate the adhesion, colonization, invasion, immune system evasion, and toxigenic properties of the acceptor organism. Currently, pathogenicity islands are mainly identified in silico based on various characteristic features: (1) deviations in codon usage, G+C content or dinucleotide frequency and (2) insertion sequences and/or tRNA genetic flanking regions together with transposase coding genes. Several computational techniques for identifying pathogenicity islands exist. However, most of these techniques are only directed at the detection of horizontally transferred genes and/or the absence of certain genomic regions of the pathogenic bacterium in closely related non-pathogenic species. Here, we present a novel software suite designed for the prediction of pathogenicity islands (pathogenicity island prediction software, or PIPS). In contrast to other existing tools, our approach is capable of utilizing multiple features for pathogenicity island detection in an integrative manner. We show that PIPS provides better accuracy than other available software packages. As an example, we used PIPS to study the veterinary pathogen Corynebacterium pseudotuberculosis, in which we identified seven putative pathogenicity islands.Siomar C SoaresVinícius A C AbreuRommel T J RamosLouise CerdeiraArtur SilvaJan BaumbachEva TrostAndreas TauchRaphael HirataAna L Mattos-GuaraldiAnderson MiyoshiVasco AzevedoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 2, p e30848 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Siomar C Soares Vinícius A C Abreu Rommel T J Ramos Louise Cerdeira Artur Silva Jan Baumbach Eva Trost Andreas Tauch Raphael Hirata Ana L Mattos-Guaraldi Anderson Miyoshi Vasco Azevedo PIPS: pathogenicity island prediction software.
description	The adaptability of pathogenic bacteria to hosts is influenced by the genomic plasticity of the bacteria, which can be increased by such mechanisms as horizontal gene transfer. Pathogenicity islands play a major role in this type of gene transfer because they are large, horizontally acquired regions that harbor clusters of virulence genes that mediate the adhesion, colonization, invasion, immune system evasion, and toxigenic properties of the acceptor organism. Currently, pathogenicity islands are mainly identified in silico based on various characteristic features: (1) deviations in codon usage, G+C content or dinucleotide frequency and (2) insertion sequences and/or tRNA genetic flanking regions together with transposase coding genes. Several computational techniques for identifying pathogenicity islands exist. However, most of these techniques are only directed at the detection of horizontally transferred genes and/or the absence of certain genomic regions of the pathogenic bacterium in closely related non-pathogenic species. Here, we present a novel software suite designed for the prediction of pathogenicity islands (pathogenicity island prediction software, or PIPS). In contrast to other existing tools, our approach is capable of utilizing multiple features for pathogenicity island detection in an integrative manner. We show that PIPS provides better accuracy than other available software packages. As an example, we used PIPS to study the veterinary pathogen Corynebacterium pseudotuberculosis, in which we identified seven putative pathogenicity islands.
format	article
author	Siomar C Soares Vinícius A C Abreu Rommel T J Ramos Louise Cerdeira Artur Silva Jan Baumbach Eva Trost Andreas Tauch Raphael Hirata Ana L Mattos-Guaraldi Anderson Miyoshi Vasco Azevedo
author_facet	Siomar C Soares Vinícius A C Abreu Rommel T J Ramos Louise Cerdeira Artur Silva Jan Baumbach Eva Trost Andreas Tauch Raphael Hirata Ana L Mattos-Guaraldi Anderson Miyoshi Vasco Azevedo
author_sort	Siomar C Soares
title	PIPS: pathogenicity island prediction software.
title_short	PIPS: pathogenicity island prediction software.
title_full	PIPS: pathogenicity island prediction software.
title_fullStr	PIPS: pathogenicity island prediction software.
title_full_unstemmed	PIPS: pathogenicity island prediction software.
title_sort	pips: pathogenicity island prediction software.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/1cb15949505e40c8b45064f6c265f7c0
work_keys_str_mv	AT siomarcsoares pipspathogenicityislandpredictionsoftware AT viniciusacabreu pipspathogenicityislandpredictionsoftware AT rommeltjramos pipspathogenicityislandpredictionsoftware AT louisecerdeira pipspathogenicityislandpredictionsoftware AT artursilva pipspathogenicityislandpredictionsoftware AT janbaumbach pipspathogenicityislandpredictionsoftware AT evatrost pipspathogenicityislandpredictionsoftware AT andreastauch pipspathogenicityislandpredictionsoftware AT raphaelhirata pipspathogenicityislandpredictionsoftware AT analmattosguaraldi pipspathogenicityislandpredictionsoftware AT andersonmiyoshi pipspathogenicityislandpredictionsoftware AT vascoazevedo pipspathogenicityislandpredictionsoftware
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PIPS: pathogenicity island prediction software.

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