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SeqFeatR for the Discovery of Feature-Sequence Associations
Specific selection pressures often lead to specifically mutated genomes. The open source software SeqFeatR has been developed to identify associations between mutation patterns in biological sequences and specific selection pressures (“features”). For instance, SeqFeatR has been used to discover in...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701496/ https://www.ncbi.nlm.nih.gov/pubmed/26731669 http://dx.doi.org/10.1371/journal.pone.0146409 |
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author | Budeus, Bettina Timm, Jörg Hoffmann, Daniel |
author_facet | Budeus, Bettina Timm, Jörg Hoffmann, Daniel |
author_sort | Budeus, Bettina |
collection | PubMed |
description | Specific selection pressures often lead to specifically mutated genomes. The open source software SeqFeatR has been developed to identify associations between mutation patterns in biological sequences and specific selection pressures (“features”). For instance, SeqFeatR has been used to discover in viral protein sequences new T cell epitopes for hosts of given HLA types. SeqFeatR supports frequentist and Bayesian methods for the discovery of statistical sequence-feature associations. Moreover, it offers novel ways to visualize results of the statistical analyses and to relate them to further properties. In this article we demonstrate various functions of SeqFeatR with real data. The most frequently used set of functions is also provided by a web server. SeqFeatR is implemented as R package and freely available from the R archive CRAN (http://cran.r-project.org/web/packages/SeqFeatR/index.html). The package includes a tutorial vignette. The software is distributed under the GNU General Public License (version 3 or later). The web server URL is https://seqfeatr.zmb.uni-due.de. |
format | Online Article Text |
id | pubmed-4701496 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-47014962016-01-15 SeqFeatR for the Discovery of Feature-Sequence Associations Budeus, Bettina Timm, Jörg Hoffmann, Daniel PLoS One Research Article Specific selection pressures often lead to specifically mutated genomes. The open source software SeqFeatR has been developed to identify associations between mutation patterns in biological sequences and specific selection pressures (“features”). For instance, SeqFeatR has been used to discover in viral protein sequences new T cell epitopes for hosts of given HLA types. SeqFeatR supports frequentist and Bayesian methods for the discovery of statistical sequence-feature associations. Moreover, it offers novel ways to visualize results of the statistical analyses and to relate them to further properties. In this article we demonstrate various functions of SeqFeatR with real data. The most frequently used set of functions is also provided by a web server. SeqFeatR is implemented as R package and freely available from the R archive CRAN (http://cran.r-project.org/web/packages/SeqFeatR/index.html). The package includes a tutorial vignette. The software is distributed under the GNU General Public License (version 3 or later). The web server URL is https://seqfeatr.zmb.uni-due.de. Public Library of Science 2016-01-05 /pmc/articles/PMC4701496/ /pubmed/26731669 http://dx.doi.org/10.1371/journal.pone.0146409 Text en © 2016 Budeus et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited |
spellingShingle | Research Article Budeus, Bettina Timm, Jörg Hoffmann, Daniel SeqFeatR for the Discovery of Feature-Sequence Associations |
title | SeqFeatR for the Discovery of Feature-Sequence Associations |
title_full | SeqFeatR for the Discovery of Feature-Sequence Associations |
title_fullStr | SeqFeatR for the Discovery of Feature-Sequence Associations |
title_full_unstemmed | SeqFeatR for the Discovery of Feature-Sequence Associations |
title_short | SeqFeatR for the Discovery of Feature-Sequence Associations |
title_sort | seqfeatr for the discovery of feature-sequence associations |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701496/ https://www.ncbi.nlm.nih.gov/pubmed/26731669 http://dx.doi.org/10.1371/journal.pone.0146409 |
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