Bayesian prediction of RNA translation from ribosome profiling

Nucleic Acids Res. 2017 Apr 7;45(6):2960-2972. doi: 10.1093/nar/gkw1350.


Ribosome profiling via high-throughput sequencing (ribo-seq) is a promising new technique for characterizing the occupancy of ribosomes on messenger RNA (mRNA) at base-pair resolution. The ribosome is responsible for translating mRNA into proteins, so information about its occupancy offers a detailed view of ribosome density and position which could be used to discover new translated open reading frames (ORFs), among other things. In this work, we propose Rp-Bp, an unsupervised Bayesian approach to predict translated ORFs from ribosome profiles. We use state-of-the-art Markov chain Monte Carlo techniques to estimate posterior distributions of the likelihood of translation of each ORF. Hence, an important feature of Rp-Bp is its ability to incorporate and propagate uncertainty in the prediction process. A second novel contribution is automatic Bayesian selection of read lengths and ribosome P-site offsets (BPPS). We empirically demonstrate that our read length selection technique modestly improves sensitivity by identifying more canonical and non-canonical ORFs. Proteomics- and quantitative translation initiation sequencing-based validation verifies the high quality of all of the predictions. Experimental comparison shows that Rp-Bp results in more peptide identifications and proteomics-validated ORF predictions compared to another recent tool for translation prediction.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Bayes Theorem
  • Caenorhabditis elegans / genetics
  • Computer Simulation
  • HEK293 Cells
  • High-Throughput Nucleotide Sequencing / methods*
  • Humans
  • Mice
  • Open Reading Frames
  • Peptides / chemistry
  • Protein Biosynthesis*
  • Proteomics
  • Ribosomes / chemistry*
  • Ribosomes / metabolism
  • Sequence Analysis, RNA / methods*


  • Peptides