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Searching algorithm for type IV secretion system effectors 1.0: a tool for predicting type IV effectors and exploring their genomic context

Type IV effectors (T4Es) are proteins produced by pathogenic bacteria to manipulate host cell gene expression and processes, divert the cell machinery for their own profit and circumvent the immune responses. T4Es have been characterized for some bacteria but many remain to be discovered. To help bi...

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Autores principales: Meyer, Damien F., Noroy, Christophe, Moumène, Amal, Raffaele, Sylvain, Albina, Emmanuel, Vachiéry, Nathalie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3814349/
https://www.ncbi.nlm.nih.gov/pubmed/23945940
http://dx.doi.org/10.1093/nar/gkt718
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author Meyer, Damien F.
Noroy, Christophe
Moumène, Amal
Raffaele, Sylvain
Albina, Emmanuel
Vachiéry, Nathalie
author_facet Meyer, Damien F.
Noroy, Christophe
Moumène, Amal
Raffaele, Sylvain
Albina, Emmanuel
Vachiéry, Nathalie
author_sort Meyer, Damien F.
collection PubMed
description Type IV effectors (T4Es) are proteins produced by pathogenic bacteria to manipulate host cell gene expression and processes, divert the cell machinery for their own profit and circumvent the immune responses. T4Es have been characterized for some bacteria but many remain to be discovered. To help biologists identify putative T4Es from the complete genome of α- and γ-proteobacteria, we developed a Perl-based command line bioinformatics tool called S4TE (searching algorithm for type-IV secretion system effectors). The tool predicts and ranks T4E candidates by using a combination of 13 sequence characteristics, including homology to known effectors, homology to eukaryotic domains, presence of subcellular localization signals or secretion signals, etc. S4TE software is modular, and specific motif searches are run independently before ultimate combination of the outputs to generate a score and sort the strongest T4Es candidates. The user keeps the possibility to adjust various searching parameters such as the weight of each module, the selection threshold or the input databases. The algorithm also provides a GC% and local gene density analysis, which strengthen the selection of T4E candidates. S4TE is a unique predicting tool for T4Es, finding its utility upstream from experimental biology.
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spelling pubmed-38143492013-12-27 Searching algorithm for type IV secretion system effectors 1.0: a tool for predicting type IV effectors and exploring their genomic context Meyer, Damien F. Noroy, Christophe Moumène, Amal Raffaele, Sylvain Albina, Emmanuel Vachiéry, Nathalie Nucleic Acids Res Computational Biology Type IV effectors (T4Es) are proteins produced by pathogenic bacteria to manipulate host cell gene expression and processes, divert the cell machinery for their own profit and circumvent the immune responses. T4Es have been characterized for some bacteria but many remain to be discovered. To help biologists identify putative T4Es from the complete genome of α- and γ-proteobacteria, we developed a Perl-based command line bioinformatics tool called S4TE (searching algorithm for type-IV secretion system effectors). The tool predicts and ranks T4E candidates by using a combination of 13 sequence characteristics, including homology to known effectors, homology to eukaryotic domains, presence of subcellular localization signals or secretion signals, etc. S4TE software is modular, and specific motif searches are run independently before ultimate combination of the outputs to generate a score and sort the strongest T4Es candidates. The user keeps the possibility to adjust various searching parameters such as the weight of each module, the selection threshold or the input databases. The algorithm also provides a GC% and local gene density analysis, which strengthen the selection of T4E candidates. S4TE is a unique predicting tool for T4Es, finding its utility upstream from experimental biology. Oxford University Press 2013-11 2013-08-13 /pmc/articles/PMC3814349/ /pubmed/23945940 http://dx.doi.org/10.1093/nar/gkt718 Text en © The Author(s) 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Computational Biology
Meyer, Damien F.
Noroy, Christophe
Moumène, Amal
Raffaele, Sylvain
Albina, Emmanuel
Vachiéry, Nathalie
Searching algorithm for type IV secretion system effectors 1.0: a tool for predicting type IV effectors and exploring their genomic context
title Searching algorithm for type IV secretion system effectors 1.0: a tool for predicting type IV effectors and exploring their genomic context
title_full Searching algorithm for type IV secretion system effectors 1.0: a tool for predicting type IV effectors and exploring their genomic context
title_fullStr Searching algorithm for type IV secretion system effectors 1.0: a tool for predicting type IV effectors and exploring their genomic context
title_full_unstemmed Searching algorithm for type IV secretion system effectors 1.0: a tool for predicting type IV effectors and exploring their genomic context
title_short Searching algorithm for type IV secretion system effectors 1.0: a tool for predicting type IV effectors and exploring their genomic context
title_sort searching algorithm for type iv secretion system effectors 1.0: a tool for predicting type iv effectors and exploring their genomic context
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3814349/
https://www.ncbi.nlm.nih.gov/pubmed/23945940
http://dx.doi.org/10.1093/nar/gkt718
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