PIPS: Pathogenicity Island Prediction Software

Soares SC, Abreu VAC, Ramos RTJ, Cerdeira L, Silva A, Baumbach J, Trost E, Tauch A, Hirata RJ, Mattos-Guaraldi AL, Miyoshi A, et al. (2012)
PLoS ONE 7(2): e30848.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
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.
Erscheinungsjahr
2012
Zeitschriftentitel
PLoS ONE
Band
7
Ausgabe
2
Art.-Nr.
e30848
eISSN
1932-6203
Page URI
https://pub.uni-bielefeld.de/record/2477132

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Soares SC, Abreu VAC, Ramos RTJ, et al. PIPS: Pathogenicity Island Prediction Software. PLoS ONE. 2012;7(2): e30848.
Soares, S. C., Abreu, V. A. C., Ramos, R. T. J., Cerdeira, L., Silva, A., Baumbach, J., Trost, E., et al. (2012). PIPS: Pathogenicity Island Prediction Software. PLoS ONE, 7(2), e30848. doi:10.1371/journal.pone.0030848
Soares, S. C., Abreu, V. A. C., Ramos, R. T. J., Cerdeira, L., Silva, A., Baumbach, J., Trost, E., Tauch, A., Hirata, R. J., Mattos-Guaraldi, A. L., et al. (2012). PIPS: Pathogenicity Island Prediction Software. PLoS ONE 7:e30848.
Soares, S.C., et al., 2012. PIPS: Pathogenicity Island Prediction Software. PLoS ONE, 7(2): e30848.
S.C. Soares, et al., “PIPS: Pathogenicity Island Prediction Software”, PLoS ONE, vol. 7, 2012, : e30848.
Soares, S.C., Abreu, V.A.C., Ramos, R.T.J., Cerdeira, L., Silva, A., Baumbach, J., Trost, E., Tauch, A., Hirata, R.J., Mattos-Guaraldi, A.L., Miyoshi, A., Azevedo, V.: PIPS: Pathogenicity Island Prediction Software. PLoS ONE. 7, : e30848 (2012).
Soares, Siomar C., Abreu, Vinicius A. C., Ramos, Rommel T. J., Cerdeira, Louise, Silva, Artur, Baumbach, Jan, Trost, Eva, Tauch, Andreas, Hirata, Raphael Jr, Mattos-Guaraldi, Ana L., Miyoshi, Anderson, and Azevedo, Vasco. “PIPS: Pathogenicity Island Prediction Software”. PLoS ONE 7.2 (2012): e30848.

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