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Epitranscriptomics & Cancer Adaptation : A.David

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Our research work focuses on the contribution of post-transcriptional mechanisms on cancer cell adaptation, in particular RNA epigenetic & translational control.

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Added by chplanque
Group name EquipeADV
Item Type Journal Article
Title An improved analysis methodology for translational profiling by microarray
Creator Sbarrato et al.
Author Thomas Sbarrato
Author Ruth V. Spriggs
Author Lindsay Wilson
Author Carolyn Jones
Author Kate Dudek
Author Amandine Bastide
Author Xavier Pichon
Author Tuija Pöyry
Author Anne E. Willis
Abstract

RNA. 2017 Nov;23(11):1601-1613. doi: 10.1261/rna.060525.116. Epub 2017 Aug 25.

ABSTRACT

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.

PMID:28842509 | PMC:PMC5648029 | DOI:10.1261/rna.060525.116

Publication RNA (New York, N.Y.)
Date 2017-08-27 00:00:00
Language en
DOI 10.1261/rna.060525.116
URL https://pubmed.ncbi.nlm.nih.gov/28842509/?utm_source=Firefox&utm_medium=rss&utm_campaign=pubmed-2&utm_content=16WqI68KDOBEDOCNE1P3RxYQYPTwTutpbvUaDhoUuYPuJd459j&fc=20250120064306&ff=20250120064533&v=2.18.0.post9+e462414
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Date Added 2025/01/20 - 14:26:35
Date Modified 2025/01/20 - 15:24:14
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