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An improved analysis methodology for translational profiling by microarray
Translational regulation plays a central role in the global gene expression of a cell, and detection of such regulation has allowed deciphering of critical biological mechanisms. Genome-wide studies of the regulation of translation (translatome) performed on microarrays represent a substantial propo...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5648029/ https://www.ncbi.nlm.nih.gov/pubmed/28842509 http://dx.doi.org/10.1261/rna.060525.116 |
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author | Sbarrato, Thomas Spriggs, Ruth V. Wilson, Lindsay Jones, Carolyn Dudek, Kate Bastide, Amandine Pichon, Xavier Pöyry, Tuija Willis, Anne E. |
author_facet | Sbarrato, Thomas Spriggs, Ruth V. Wilson, Lindsay Jones, Carolyn Dudek, Kate Bastide, Amandine Pichon, Xavier Pöyry, Tuija Willis, Anne E. |
author_sort | Sbarrato, Thomas |
collection | PubMed |
description | Translational regulation plays a central role in the global gene expression of a cell, and detection of such regulation has allowed deciphering of critical biological mechanisms. Genome-wide studies of the regulation of translation (translatome) performed on microarrays represent a substantial proportion of studies, alongside with recent advances in deep-sequencing methods. However, there has been a lack of development in specific processing methodologies that deal with the distinct nature of translatome array data. In this study, we confirm that polysome profiling yields skewed data and thus violates the conventional transcriptome analysis assumptions. Using a comprehensive simulation of translatome array data varying the percentage and symmetry of deregulation, we show that conventional analysis methods (Quantile and LOESS normalizations) and statistical tests failed, respectively, to correctly normalize the data and to identify correctly deregulated genes (DEGs). We thus propose a novel analysis methodology available as a CRAN package; Internal Control Analysis of Translatome (INCATome) based on a normalization tied to a group of invariant controls. We confirm that INCATome outperforms the other normalization methods and allows a stringent identification of DEGs. More importantly, INCATome implementation on a biological translatome data set (cells silenced for splicing factor PSF) resulted in the best normalization performance and an improved validation concordance for identification of true positive DEGs. Finally, we provide evidence that INCATome is able to infer novel biological pathways with superior discovery potential, thus confirming the benefits for researchers of implementing INCATome for future translatome studies as well as for existing data sets to generate novel avenues for research. |
format | Online Article Text |
id | pubmed-5648029 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Cold Spring Harbor Laboratory Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-56480292017-11-01 An improved analysis methodology for translational profiling by microarray Sbarrato, Thomas Spriggs, Ruth V. Wilson, Lindsay Jones, Carolyn Dudek, Kate Bastide, Amandine Pichon, Xavier Pöyry, Tuija Willis, Anne E. RNA Bioinformatics Translational regulation plays a central role in the global gene expression of a cell, and detection of such regulation has allowed deciphering of critical biological mechanisms. Genome-wide studies of the regulation of translation (translatome) performed on microarrays represent a substantial proportion of studies, alongside with recent advances in deep-sequencing methods. However, there has been a lack of development in specific processing methodologies that deal with the distinct nature of translatome array data. In this study, we confirm that polysome profiling yields skewed data and thus violates the conventional transcriptome analysis assumptions. Using a comprehensive simulation of translatome array data varying the percentage and symmetry of deregulation, we show that conventional analysis methods (Quantile and LOESS normalizations) and statistical tests failed, respectively, to correctly normalize the data and to identify correctly deregulated genes (DEGs). We thus propose a novel analysis methodology available as a CRAN package; Internal Control Analysis of Translatome (INCATome) based on a normalization tied to a group of invariant controls. We confirm that INCATome outperforms the other normalization methods and allows a stringent identification of DEGs. More importantly, INCATome implementation on a biological translatome data set (cells silenced for splicing factor PSF) resulted in the best normalization performance and an improved validation concordance for identification of true positive DEGs. Finally, we provide evidence that INCATome is able to infer novel biological pathways with superior discovery potential, thus confirming the benefits for researchers of implementing INCATome for future translatome studies as well as for existing data sets to generate novel avenues for research. Cold Spring Harbor Laboratory Press 2017-11 /pmc/articles/PMC5648029/ /pubmed/28842509 http://dx.doi.org/10.1261/rna.060525.116 Text en © 2017 Sbarrato et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society http://creativecommons.org/licenses/by-nc/4.0/ This article, published in RNA, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Bioinformatics Sbarrato, Thomas Spriggs, Ruth V. Wilson, Lindsay Jones, Carolyn Dudek, Kate Bastide, Amandine Pichon, Xavier Pöyry, Tuija Willis, Anne E. An improved analysis methodology for translational profiling by microarray |
title | An improved analysis methodology for translational profiling by microarray |
title_full | An improved analysis methodology for translational profiling by microarray |
title_fullStr | An improved analysis methodology for translational profiling by microarray |
title_full_unstemmed | An improved analysis methodology for translational profiling by microarray |
title_short | An improved analysis methodology for translational profiling by microarray |
title_sort | improved analysis methodology for translational profiling by microarray |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5648029/ https://www.ncbi.nlm.nih.gov/pubmed/28842509 http://dx.doi.org/10.1261/rna.060525.116 |
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