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

Bayesian prediction of RNA translation from ribosome profiling.

Author information

1
Section of Bioinformatics and Systems Cardiology, Department of Internal Medicine III and Klaus Tschira Institute for Integrative Computational Cardiology, University of Heidelberg, 69120 Heidelberg, Germany.
2
DZHK (German Centre for Cardiovascular Research), Partner site Heidelberg/Mannheim, 69120 Heidelberg, Germany.
3
Max Plank Institute for the Biology of Ageing, 50931 Köln, Germany.
4
Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland.
5
Faculty of Science, University of Basel, 4056 Basel, Switzerland.

Abstract

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.

PMID:
28126919
PMCID:
PMC5389577
DOI:
10.1093/nar/gkw1350
[Indexed for MEDLINE]
Free PMC Article

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