Use este identificador para citar ou linkar para este item: https://repositorio.ufpa.br/jspui/handle/2011/15625
Tipo: Artigo de Periódico
Data do documento: Fev-2012
Autor(es): SOARES, Siomar de Castro
ABREU, Vinicius Augusto Carvalho de
RAMOS, Rommel Thiago Juca
CERDEIRA, Louise Teixeira
SILVA, Artur Luiz da Costa da
BAUMBACH, Jan
TROST, Eva
TAUCH, Andreas
HIRATA JÚNIOR, Raphael
GUARALDI, Ana Luiza de Mattos
MIYOSHI, Anderson
AZEVEDO, Vasco Ariston de Carvalho
Afiliação do(s) Autor(es): RAMOS, R. T. J.; CERDEIRA, L. T.; SILVA, A. L. C. Universidade Federal do Pará
Título: PIPS: Pathogenicity Island Prediction Software
Agência de fomento: 
Citar como: SOARES, Siomar C. et al. PIPS: Pathogenicity Island Prediction Software. PLoS ONE, online, v. 7, n. 2, e30848, Feb. 2012. DOI: https://doi.org/10.1371/journal.pone.0030848. Disponível em: http://repositorio.ufpa.br/jspui/handle/2011/15625. Acesso em:.
Abstract: 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.
Palavras-chave: Corynebacterium pseudotuberculosis
Título do Periódico: PLoS ONE
ISSN: 1932-6203
País: Estados unidos
Instituição: Public Library of Science
Sigla da Instituição: PLOS
Tipo de Acesso: Acesso Aberto
Fonte URI: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0030848#ack
Identificador DOI: 10.1371/journal.pone.0030848
Aparece nas coleções:Artigos Científicos - ICB

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