Genome-wide assessment of differential translations with ribosome profiling data

Zhengtao Xiao, Qin Zou, Yu Liu and Xuerui Yang ()
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Zhengtao Xiao: MOE Key Laboratory of Bioinformatics, Tsinghua University
Qin Zou: MOE Key Laboratory of Bioinformatics, Tsinghua University
Yu Liu: MOE Key Laboratory of Bioinformatics, Tsinghua University
Xuerui Yang: MOE Key Laboratory of Bioinformatics, Tsinghua University

Nature Communications, 2016, vol. 7, issue 1, 1-11

Abstract: Abstract The closely regulated process of mRNA translation is crucial for precise control of protein abundance and quality. Ribosome profiling, a combination of ribosome foot-printing and RNA deep sequencing, has been used in a large variety of studies to quantify genome-wide mRNA translation. Here, we developed Xtail, an analysis pipeline tailored for ribosome profiling data that comprehensively and accurately identifies differentially translated genes in pairwise comparisons. Applied on simulated and real datasets, Xtail exhibits high sensitivity with minimal false-positive rates, outperforming existing methods in the accuracy of quantifying differential translations. With published ribosome profiling datasets, Xtail does not only reveal differentially translated genes that make biological sense, but also uncovers new events of differential translation in human cancer cells on mTOR signalling perturbation and in human primary macrophages on interferon gamma (IFN-γ) treatment. This demonstrates the value of Xtail in providing novel insights into the molecular mechanisms that involve translational dysregulations.

Date: 2016
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DOI: 10.1038/ncomms11194

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