Computational Biology Department, Carnegie Mellon University, United States.
Department of Biological Sciences, Carnegie Mellon University, United States; Computational Biology Department, Carnegie Mellon University, United States.
Methods. 2018 Mar 15;137:67-70. doi: 10.1016/j.ymeth.2018.01.002. Epub 2018 Jan 9.
Ribosome profiling has emerged as a powerful technique to study mRNA translation. Ribosome profiling has the potential to determine the relative quantities and locations of ribosomes on mRNA genome wide. Taking full advantage of this approach requires accurate measurement of ribosome locations. However, experimental inconsistencies often obscure the positional information encoded in ribosome profiling data. Here, we describe the Ribodeblur pipeline, a computational analysis tool that uses a maximum likelihood framework to infer ribosome positions from heterogeneous datasets. Ribodeblur is simple to install, and can be run on an average modern Mac or Linux-based laptop. We detail the process of applying the pipeline to high-coverage ribosome profiling data in yeast, and discuss important considerations for potential extension to other organisms.
核糖体图谱分析已成为研究 mRNA 翻译的有力技术。核糖体图谱分析有可能确定核糖体在 mRNA 基因组上的相对数量和位置。要充分利用这种方法，需要准确测量核糖体的位置。然而，实验的不一致性常常掩盖了核糖体图谱分析数据中编码的位置信息。在这里，我们描述了 Ribodeblur 管道，这是一种计算分析工具，它使用最大似然框架从异构数据集推断核糖体的位置。Ribodeblur 易于安装，并且可以在普通的现代 Mac 或基于 Linux 的笔记本电脑上运行。我们详细介绍了将该管道应用于酵母中高覆盖率核糖体图谱分析数据的过程，并讨论了将其扩展到其他生物体的重要注意事项。
