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DeSignate: detecting signature characters in gene sequence alignments for taxon diagnoses
BACKGROUND: Molecular characters have been added in integrative taxonomic approaches in recent years. Nevertheless, taxon diagnoses are still widely restricted to morphological characters. The inclusion of molecular characters into taxon diagnoses is not only hampered by problems, such as their defi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7171811/ https://www.ncbi.nlm.nih.gov/pubmed/32312224 http://dx.doi.org/10.1186/s12859-020-3498-6 |
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author | Hütter, Thomas Ganser, Maximilian H Kocher, Manuel Halkic, Merima Agatha, Sabine Augsten, Nikolaus |
author_facet | Hütter, Thomas Ganser, Maximilian H Kocher, Manuel Halkic, Merima Agatha, Sabine Augsten, Nikolaus |
author_sort | Hütter, Thomas |
collection | PubMed |
description | BACKGROUND: Molecular characters have been added in integrative taxonomic approaches in recent years. Nevertheless, taxon diagnoses are still widely restricted to morphological characters. The inclusion of molecular characters into taxon diagnoses is not only hampered by problems, such as their definition and the designation of their positions in a reference alignment, but also by the technical effort. RESULTS: DeSignate is a tool for character-based taxon diagnoses that includes a novel ranking scheme. It detects and classifies individual and combined signature characters (diagnostic molecular characters) based on so-called character state vectors. An intuitive web application guides the user through the analysis process and provides the results at a glance. Further, formal definitions and a uniform terminology of characters are introduced. CONCLUSIONS: DeSignate facilitates the inclusion of diagnostic molecular characters and their positions to complement taxon diagnoses. Compared to previous solutions, the tool simplifies the workflow and improves reproducibility and traceability of the results. The tool is freely available as a web application at (https://designate.dbresearch.uni-salzburg.at/) and is open source (https://github.com/DatabaseGroup/DeSignate/). |
format | Online Article Text |
id | pubmed-7171811 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-71718112020-04-24 DeSignate: detecting signature characters in gene sequence alignments for taxon diagnoses Hütter, Thomas Ganser, Maximilian H Kocher, Manuel Halkic, Merima Agatha, Sabine Augsten, Nikolaus BMC Bioinformatics Software BACKGROUND: Molecular characters have been added in integrative taxonomic approaches in recent years. Nevertheless, taxon diagnoses are still widely restricted to morphological characters. The inclusion of molecular characters into taxon diagnoses is not only hampered by problems, such as their definition and the designation of their positions in a reference alignment, but also by the technical effort. RESULTS: DeSignate is a tool for character-based taxon diagnoses that includes a novel ranking scheme. It detects and classifies individual and combined signature characters (diagnostic molecular characters) based on so-called character state vectors. An intuitive web application guides the user through the analysis process and provides the results at a glance. Further, formal definitions and a uniform terminology of characters are introduced. CONCLUSIONS: DeSignate facilitates the inclusion of diagnostic molecular characters and their positions to complement taxon diagnoses. Compared to previous solutions, the tool simplifies the workflow and improves reproducibility and traceability of the results. The tool is freely available as a web application at (https://designate.dbresearch.uni-salzburg.at/) and is open source (https://github.com/DatabaseGroup/DeSignate/). BioMed Central 2020-04-20 /pmc/articles/PMC7171811/ /pubmed/32312224 http://dx.doi.org/10.1186/s12859-020-3498-6 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Hütter, Thomas Ganser, Maximilian H Kocher, Manuel Halkic, Merima Agatha, Sabine Augsten, Nikolaus DeSignate: detecting signature characters in gene sequence alignments for taxon diagnoses |
title | DeSignate: detecting signature characters in gene sequence alignments for taxon diagnoses |
title_full | DeSignate: detecting signature characters in gene sequence alignments for taxon diagnoses |
title_fullStr | DeSignate: detecting signature characters in gene sequence alignments for taxon diagnoses |
title_full_unstemmed | DeSignate: detecting signature characters in gene sequence alignments for taxon diagnoses |
title_short | DeSignate: detecting signature characters in gene sequence alignments for taxon diagnoses |
title_sort | designate: detecting signature characters in gene sequence alignments for taxon diagnoses |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7171811/ https://www.ncbi.nlm.nih.gov/pubmed/32312224 http://dx.doi.org/10.1186/s12859-020-3498-6 |
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