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Bioinformatics. 2013 Dec 1;29(23):2995-3002. doi: 10.1093/bioinformatics/btt533. Epub 2013 Sep 18.

Assessing gene-level translational control from ribosome profiling.

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Department of Epidemiology and Biostatistics, Helen Diller Family Comprehensive Cancer Center, Department of Medicine and Department of Urology, University of California, San Francisco, CA 94158, USA and Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA.



The translational landscape of diverse cellular systems remains largely uncharacterized. A detailed understanding of the control of gene expression at the level of messenger RNA translation is vital to elucidating a systems-level view of complex molecular programs in the cell. Establishing the degree to which such post-transcriptional regulation can mediate specific phenotypes is similarly critical to elucidating the molecular pathogenesis of diseases such as cancer. Recently, methods for massively parallel sequencing of ribosome-bound fragments of messenger RNA have begun to uncover genome-wide translational control at codon resolution. Despite its promise for deeply characterizing mammalian proteomes, few analytical methods exist for the comprehensive analysis of this paired RNA and ribosome data.


We describe the Babel framework, an analytical methodology for assessing the significance of changes in translational regulation within cells and between conditions. This approach facilitates the analysis of translation genome-wide while allowing statistically principled gene-level inference. Babel is based on an errors-in-variables regression model that uses the negative binomial distribution and draws inference using a parametric bootstrap approach. We demonstrate the operating characteristics of Babel on simulated data and use its gene-level inference to extend prior analyses significantly, discovering new translationally regulated modules under mammalian target of rapamycin (mTOR) pathway signaling control.

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