Unsupervised Bayesian Prediction of RNA Translation from Ribosome Profiling Data.

Ribosome profiling has been instrumental in leading to important discoveries in several fields of life sciences. Here we describe a computational approach that enables identification of translation events on a genome-wide scale from ribosome profiling data. Periodic fragment sizes indicative of active translation are selected without supervision for each library. Our workflow allows to map the whole translational landscape of a given cell, tissue, or organism, under varying conditions, and can be used to expand the search for novel, uncharacterized open reading frames, such as regulatory upstream translation events. Through a detailed workflow example, we show how to perform qualitative and quantitative analysis of translatomes.