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tRanslatome: an R/Bioconductor package to portray translational control
Summary: High-throughput technologies have led to an explosion of genomic data available for automated analysis. The consequent possibility to simultaneously sample multiple layers of variation along the gene expression flow requires computational methods integrating raw information from different ‘...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3892686/ https://www.ncbi.nlm.nih.gov/pubmed/24222209 http://dx.doi.org/10.1093/bioinformatics/btt634 |
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author | Tebaldi, Toma Dassi, Erik Kostoska, Galena Viero, Gabriella Quattrone, Alessandro |
author_facet | Tebaldi, Toma Dassi, Erik Kostoska, Galena Viero, Gabriella Quattrone, Alessandro |
author_sort | Tebaldi, Toma |
collection | PubMed |
description | Summary: High-throughput technologies have led to an explosion of genomic data available for automated analysis. The consequent possibility to simultaneously sample multiple layers of variation along the gene expression flow requires computational methods integrating raw information from different ‘-omics’. It has been recently demonstrated that translational control is a widespread phenomenon, with profound and still underestimated regulation capabilities. Although detecting changes in the levels of total messenger RNAs (mRNAs; the transcriptome), of polysomally loaded mRNAs (the translatome) and of proteins (the proteome) is experimentally feasible in a high-throughput way, the integration of these levels is still far from being robustly approached. Here we introduce tRanslatome, a new R/Bioconductor package, which is a complete platform for the simultaneous pairwise analysis of transcriptome, translatome and proteome data. The package includes most of the available statistical methods developed for the analysis of high-throughput data, allowing the parallel comparison of differentially expressed genes and the corresponding differentially enriched biological themes. Notably, it also enables the prediction of translational regulatory elements on mRNA sequences. The utility of this tool is demonstrated with two case studies. Availability and implementation: tRanslatome is available in Bioconductor. Contact: t.tebaldi@unitn.it Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-3892686 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-38926862014-01-15 tRanslatome: an R/Bioconductor package to portray translational control Tebaldi, Toma Dassi, Erik Kostoska, Galena Viero, Gabriella Quattrone, Alessandro Bioinformatics Applications Notes Summary: High-throughput technologies have led to an explosion of genomic data available for automated analysis. The consequent possibility to simultaneously sample multiple layers of variation along the gene expression flow requires computational methods integrating raw information from different ‘-omics’. It has been recently demonstrated that translational control is a widespread phenomenon, with profound and still underestimated regulation capabilities. Although detecting changes in the levels of total messenger RNAs (mRNAs; the transcriptome), of polysomally loaded mRNAs (the translatome) and of proteins (the proteome) is experimentally feasible in a high-throughput way, the integration of these levels is still far from being robustly approached. Here we introduce tRanslatome, a new R/Bioconductor package, which is a complete platform for the simultaneous pairwise analysis of transcriptome, translatome and proteome data. The package includes most of the available statistical methods developed for the analysis of high-throughput data, allowing the parallel comparison of differentially expressed genes and the corresponding differentially enriched biological themes. Notably, it also enables the prediction of translational regulatory elements on mRNA sequences. The utility of this tool is demonstrated with two case studies. Availability and implementation: tRanslatome is available in Bioconductor. Contact: t.tebaldi@unitn.it Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2014-01-15 2013-11-12 /pmc/articles/PMC3892686/ /pubmed/24222209 http://dx.doi.org/10.1093/bioinformatics/btt634 Text en © The Author 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 | Applications Notes Tebaldi, Toma Dassi, Erik Kostoska, Galena Viero, Gabriella Quattrone, Alessandro tRanslatome: an R/Bioconductor package to portray translational control |
title | tRanslatome: an R/Bioconductor package to portray translational control |
title_full | tRanslatome: an R/Bioconductor package to portray translational control |
title_fullStr | tRanslatome: an R/Bioconductor package to portray translational control |
title_full_unstemmed | tRanslatome: an R/Bioconductor package to portray translational control |
title_short | tRanslatome: an R/Bioconductor package to portray translational control |
title_sort | translatome: an r/bioconductor package to portray translational control |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3892686/ https://www.ncbi.nlm.nih.gov/pubmed/24222209 http://dx.doi.org/10.1093/bioinformatics/btt634 |
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