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ReadXplorer 2—detailed read mapping analysis and visualization from one single source
Motivation: The vast amount of already available and currently generated read mapping data requires comprehensive visualization, and should benefit from bioinformatics tools offering a wide spectrum of analysis functionality from just one source. Appropriate handling of multiple mapped reads during...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5167064/ https://www.ncbi.nlm.nih.gov/pubmed/27540267 http://dx.doi.org/10.1093/bioinformatics/btw541 |
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author | Hilker, Rolf Stadermann, Kai Bernd Schwengers, Oliver Anisiforov, Evgeny Jaenicke, Sebastian Weisshaar, Bernd Zimmermann, Tobias Goesmann, Alexander |
author_facet | Hilker, Rolf Stadermann, Kai Bernd Schwengers, Oliver Anisiforov, Evgeny Jaenicke, Sebastian Weisshaar, Bernd Zimmermann, Tobias Goesmann, Alexander |
author_sort | Hilker, Rolf |
collection | PubMed |
description | Motivation: The vast amount of already available and currently generated read mapping data requires comprehensive visualization, and should benefit from bioinformatics tools offering a wide spectrum of analysis functionality from just one source. Appropriate handling of multiple mapped reads during mapping analyses remains an issue that demands improvement. Results: The capabilities of the read mapping analysis and visualization tool ReadXplorer were vastly enhanced. Here, we present an even finer granulated read mapping classification, improving the level of detail for analyses and visualizations. The spectrum of automatic analysis functions has been broadened to include genome rearrangement detection as well as correlation analysis between two mapping data sets. Existing functions were refined and enhanced, namely the computation of differentially expressed genes, the read count and normalization analysis and the transcription start site detection. Additionally, ReadXplorer 2 features a highly improved support for large eukaryotic data sets and a command line version, enabling its integration into workflows. Finally, the new version is now able to display any kind of tabular results from other bioinformatics tools. Availability and Implementation: http://www.readxplorer.org Contact: readxplorer@computational.bio.uni-giessen.de Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-5167064 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-51670642016-12-20 ReadXplorer 2—detailed read mapping analysis and visualization from one single source Hilker, Rolf Stadermann, Kai Bernd Schwengers, Oliver Anisiforov, Evgeny Jaenicke, Sebastian Weisshaar, Bernd Zimmermann, Tobias Goesmann, Alexander Bioinformatics Original Papers Motivation: The vast amount of already available and currently generated read mapping data requires comprehensive visualization, and should benefit from bioinformatics tools offering a wide spectrum of analysis functionality from just one source. Appropriate handling of multiple mapped reads during mapping analyses remains an issue that demands improvement. Results: The capabilities of the read mapping analysis and visualization tool ReadXplorer were vastly enhanced. Here, we present an even finer granulated read mapping classification, improving the level of detail for analyses and visualizations. The spectrum of automatic analysis functions has been broadened to include genome rearrangement detection as well as correlation analysis between two mapping data sets. Existing functions were refined and enhanced, namely the computation of differentially expressed genes, the read count and normalization analysis and the transcription start site detection. Additionally, ReadXplorer 2 features a highly improved support for large eukaryotic data sets and a command line version, enabling its integration into workflows. Finally, the new version is now able to display any kind of tabular results from other bioinformatics tools. Availability and Implementation: http://www.readxplorer.org Contact: readxplorer@computational.bio.uni-giessen.de Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2016-12-15 2016-08-18 /pmc/articles/PMC5167064/ /pubmed/27540267 http://dx.doi.org/10.1093/bioinformatics/btw541 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), 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 | Original Papers Hilker, Rolf Stadermann, Kai Bernd Schwengers, Oliver Anisiforov, Evgeny Jaenicke, Sebastian Weisshaar, Bernd Zimmermann, Tobias Goesmann, Alexander ReadXplorer 2—detailed read mapping analysis and visualization from one single source |
title | ReadXplorer 2—detailed read mapping analysis and visualization from one single source |
title_full | ReadXplorer 2—detailed read mapping analysis and visualization from one single source |
title_fullStr | ReadXplorer 2—detailed read mapping analysis and visualization from one single source |
title_full_unstemmed | ReadXplorer 2—detailed read mapping analysis and visualization from one single source |
title_short | ReadXplorer 2—detailed read mapping analysis and visualization from one single source |
title_sort | readxplorer 2—detailed read mapping analysis and visualization from one single source |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5167064/ https://www.ncbi.nlm.nih.gov/pubmed/27540267 http://dx.doi.org/10.1093/bioinformatics/btw541 |
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