Bayesian prediction of RNA translation from ribosome profiling

• Published in: Nucleic acids research Vol. 45; no. 6; pp. 2960 - 2972
• Main Authors: Malone, Brandon, Atanassov, Ilian, Aeschimann, Florian, Li, Xinping, Großhans, Helge, Dieterich, Christoph
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 07.04.2017
• Subjects: Animals; Bayes Theorem; Caenorhabditis elegans - genetics; Computational Biology; 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
• ISSN: 0305-1048; 1362-4962
• 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.