An improved analysis methodology for translational profiling by microarray

• DOI: 10.1261/rna.060525.116
• Publication Date: 2017-08-26T00:25:32Z
• Authors: Thomas Sbarrato; Ruth V. Spriggs; Lindsay Wilson; Carolyn Jones; Kate Dudek; Amandine Bastide; Xavier Pichon; Tuija Pöyry; Anne E. Willis
• Keywords: Normalization; Ribosome profiling

Translational regulation plays a central role in the global gene expression of cell, and detection such has allowed deciphering critical biological mechanisms. Genome-wide studies translation (translatome) performed on microarrays represent substantial proportion studies, alongside with recent advances deep-sequencing methods. However, there been lack development specific processing methodologies that deal distinct nature translatome array data. In this study, we confirm polysome profiling yields skewed data thus violates conventional transcriptome analysis assumptions. Using comprehensive simulation varying percentage symmetry deregulation, show methods (Quantile LOESS normalizations) statistical tests failed, respectively, to correctly normalize identify deregulated genes (DEGs). We propose novel methodology available as CRAN package; Internal Control Analysis Translatome (INCATome) based normalization tied group invariant controls. INCATome outperforms other allows stringent identification DEGs. More importantly, implementation set (cells silenced for splicing factor PSF) resulted best performance an improved validation concordance true positive Finally, provide evidence is able infer pathways superior discovery potential, confirming benefits researchers implementing future well existing sets generate avenues research.