Li Wenzheng, Wang Weili, Uren Philip J, Penalva Luiz O F, Smith Andrew D
Molecular and Computational Biology, Division of Biological Sciences, University of Southern California, Los Angeles, CA, USA.
Department of Cellular and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
Bioinformatics. 2017 Jun 1;33(11):1735-1737. doi: 10.1093/bioinformatics/btx047.
Global analysis of translation regulation has recently been enabled by the development of Ribosome Profiling, or Ribo-seq, technology. This approach provides maps of ribosome activity for each expressed gene in a given biological sample. Measurements of translation efficiency are generated when Ribo-seq data is analyzed in combination with matched RNA-seq gene expression profiles. Existing computational methods for identifying genes with differential translation across samples are based on sound principles, but require users to choose between accuracy and speed.
We present Riborex, a computational tool for mapping genome-wide differences in translation efficiency. Riborex shares a similar mathematical structure with existing methods, but has a simplified implementation. Riborex directly leverages established RNA-seq analysis frameworks for all parameter estimation, providing users with a choice among robust engines for these computations. The result is a method that is dramatically faster than available methods without sacrificing accuracy.
https://github.com/smithlabcode/riborex.
Supplementary data are available at Bioinformatics online.
核糖体分析技术(Ribo-seq)的发展使得对翻译调控进行全局分析成为可能。这种方法能够提供给定生物样本中每个表达基因的核糖体活性图谱。当将Ribo-seq数据与匹配的RNA-seq基因表达谱结合分析时,就能得出翻译效率的测量结果。现有的用于识别不同样本间翻译差异基因的计算方法基于合理的原理,但要求用户在准确性和速度之间做出选择。
我们提出了Riborex,一种用于绘制全基因组翻译效率差异图谱的计算工具。Riborex与现有方法具有相似的数学结构,但实现方式更为简化。Riborex直接利用已建立的RNA-seq分析框架进行所有参数估计,为用户提供了多种用于这些计算的强大引擎供其选择。结果是一种在不牺牲准确性的情况下比现有方法快得多的方法。
https://github.com/smithlabcode/riborex。
补充数据可在《生物信息学》在线获取。