Unsupervised Bayesian Prediction of RNA Translation from Ribosome Profiling Data

Methods Mol Biol. 2021:2252:295-312. doi: 10.1007/978-1-0716-1150-0_14.

Abstract

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.

Keywords: Bayesian; Open reading frame; Ribosome profiling; Translation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Computational Biology / methods*
  • Humans
  • Open Reading Frames
  • Protein Biosynthesis
  • RNA, Messenger / genetics*
  • Ribosomes / metabolism*
  • Sequence Analysis, RNA
  • Software
  • Unsupervised Machine Learning
  • Workflow

Substances

  • RNA, Messenger