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The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again

BACKGROUND: The ISA-Tab format and software suite have been developed to break the silo effect induced by technology-specific formats for a variety of data types and to better support experimental metadata tracking. Experimentalists seldom use a single technique to monitor biological signals. Provid...

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Autores principales: González-Beltrán, Alejandra, Neumann, Steffen, Maguire, Eamonn, Sansone, Susanna-Assunta, Rocca-Serra, Philippe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4015122/
https://www.ncbi.nlm.nih.gov/pubmed/24564732
http://dx.doi.org/10.1186/1471-2105-15-S1-S11
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author González-Beltrán, Alejandra
Neumann, Steffen
Maguire, Eamonn
Sansone, Susanna-Assunta
Rocca-Serra, Philippe
author_facet González-Beltrán, Alejandra
Neumann, Steffen
Maguire, Eamonn
Sansone, Susanna-Assunta
Rocca-Serra, Philippe
author_sort González-Beltrán, Alejandra
collection PubMed
description BACKGROUND: The ISA-Tab format and software suite have been developed to break the silo effect induced by technology-specific formats for a variety of data types and to better support experimental metadata tracking. Experimentalists seldom use a single technique to monitor biological signals. Providing a multi-purpose, pragmatic and accessible format that abstracts away common constructs for describing Investigations, Studies and Assays, ISA is increasingly popular. To attract further interest towards the format and extend support to ensure reproducible research and reusable data, we present the Risa package, which delivers a central component to support the ISA format by enabling effortless integration with R, the popular, open source data crunching environment. RESULTS: The Risa package bridges the gap between the metadata collection and curation in an ISA-compliant way and the data analysis using the widely used statistical computing environment R. The package offers functionality for: i) parsing ISA-Tab datasets into R objects, ii) augmenting annotation with extra metadata not explicitly stated in the ISA syntax; iii) interfacing with domain specific R packages iv) suggesting potentially useful R packages available in Bioconductor for subsequent processing of the experimental data described in the ISA format; and finally v) saving back to ISA-Tab files augmented with analysis specific metadata from R. We demonstrate these features by presenting use cases for mass spectrometry data and DNA microarray data. CONCLUSIONS: The Risa package is open source (with LGPL license) and freely available through Bioconductor. By making Risa available, we aim to facilitate the task of processing experimental data, encouraging a uniform representation of experimental information and results while delivering tools for ensuring traceability and provenance tracking. SOFTWARE AVAILABILITY: The Risa package is available since Bioconductor 2.11 (version 1.0.0) and version 1.2.1 appeared in Bioconductor 2.12, both along with documentation and examples. The latest version of the code is at the development branch in Bioconductor and can also be accessed from GitHub https://github.com/ISA-tools/Risa, where the issue tracker allows users to report bugs or feature requests.
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spelling pubmed-40151222014-05-23 The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again González-Beltrán, Alejandra Neumann, Steffen Maguire, Eamonn Sansone, Susanna-Assunta Rocca-Serra, Philippe BMC Bioinformatics Research BACKGROUND: The ISA-Tab format and software suite have been developed to break the silo effect induced by technology-specific formats for a variety of data types and to better support experimental metadata tracking. Experimentalists seldom use a single technique to monitor biological signals. Providing a multi-purpose, pragmatic and accessible format that abstracts away common constructs for describing Investigations, Studies and Assays, ISA is increasingly popular. To attract further interest towards the format and extend support to ensure reproducible research and reusable data, we present the Risa package, which delivers a central component to support the ISA format by enabling effortless integration with R, the popular, open source data crunching environment. RESULTS: The Risa package bridges the gap between the metadata collection and curation in an ISA-compliant way and the data analysis using the widely used statistical computing environment R. The package offers functionality for: i) parsing ISA-Tab datasets into R objects, ii) augmenting annotation with extra metadata not explicitly stated in the ISA syntax; iii) interfacing with domain specific R packages iv) suggesting potentially useful R packages available in Bioconductor for subsequent processing of the experimental data described in the ISA format; and finally v) saving back to ISA-Tab files augmented with analysis specific metadata from R. We demonstrate these features by presenting use cases for mass spectrometry data and DNA microarray data. CONCLUSIONS: The Risa package is open source (with LGPL license) and freely available through Bioconductor. By making Risa available, we aim to facilitate the task of processing experimental data, encouraging a uniform representation of experimental information and results while delivering tools for ensuring traceability and provenance tracking. SOFTWARE AVAILABILITY: The Risa package is available since Bioconductor 2.11 (version 1.0.0) and version 1.2.1 appeared in Bioconductor 2.12, both along with documentation and examples. The latest version of the code is at the development branch in Bioconductor and can also be accessed from GitHub https://github.com/ISA-tools/Risa, where the issue tracker allows users to report bugs or feature requests. BioMed Central 2014-01-10 /pmc/articles/PMC4015122/ /pubmed/24564732 http://dx.doi.org/10.1186/1471-2105-15-S1-S11 Text en Copyright © 2014 González-Beltrán et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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.
spellingShingle Research
González-Beltrán, Alejandra
Neumann, Steffen
Maguire, Eamonn
Sansone, Susanna-Assunta
Rocca-Serra, Philippe
The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again
title The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again
title_full The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again
title_fullStr The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again
title_full_unstemmed The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again
title_short The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again
title_sort risa r/bioconductor package: integrative data analysis from experimental metadata and back again
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4015122/
https://www.ncbi.nlm.nih.gov/pubmed/24564732
http://dx.doi.org/10.1186/1471-2105-15-S1-S11
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