التفاصيل البيبلوغرافية
| العنوان: |
Bayesian prediction of RNA translation from ribosome profiling |
| المؤلفون: |
Malone, B., Atanassov, I., Aeschimann, F., Li, X., Grosshans, H., Dieterich, C. |
| المصدر: |
Nucleic Acids Res |
| بيانات النشر: |
Oxford University Press |
| سنة النشر: |
2017 |
| مصطلحات موضوعية: |
stat, psy |
| الوصف: |
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. |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
unknown |
| Relation: |
http://hdl.handle.net/21.11116/0000-0001-5913-6 |
| الاتاحة: |
http://hdl.handle.net/21.11116/0000-0001-5913-6 |
| Rights: |
undefined |
| رقم الانضمام: |
edsbas.3D73AB34 |
| قاعدة البيانات: |
BASE |