Division of Epigenetics, DKFZ-ZMBH Alliance, German Cancer Research Center, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany.
Independent researcher, Kreuzstr. 5, 68259 Mannheim, Germany.
Nucleic Acids Res. 2020 Jan 24;48(2):e7. doi: 10.1093/nar/gkz1074.
Recently, newly developed ribosome profiling methods based on high-throughput sequencing of ribosome-protected mRNA footprints allow to study genome-wide translational changes in detail. However, computational analysis of the sequencing data still represents a bottleneck for many laboratories. Further, specific pipelines for quality control and statistical analysis of ribosome profiling data, providing high levels of both accuracy and confidence, are currently lacking. In this study, we describe automated bioinformatic and statistical diagnoses to perform robust quality control of ribosome profiling data (RiboQC), to efficiently visualize ribosome positions and to estimate ribosome speed (RiboMine) in an unbiased way. We present an R pipeline to setup and undertake the analyses that offers the user an HTML page to scan own data regarding the following aspects: periodicity, ligation and digestion of footprints; reproducibility and batch effects of replicates; drug-related artifacts; unbiased codon enrichment including variability between mRNAs, for A, P and E sites; mining of some causal or confounding factors. We expect our pipeline to allow an optimal use of the wealth of information provided by ribosome profiling experiments.
最近，基于核糖体保护的 mRNA 足迹高通量测序的新开发的核糖体谱分析方法允许详细研究全基因组的翻译变化。然而，测序数据的计算分析仍然是许多实验室的瓶颈。此外，目前还缺乏用于核糖体谱数据分析的质量控制和统计分析的特定管道，以提供高水平的准确性和可信度。在这项研究中，我们描述了自动化的生物信息学和统计诊断方法，以进行核糖体谱数据的稳健质量控制（RiboQC），以高效地可视化核糖体位置并以无偏的方式估计核糖体速度（RiboMine）。我们提供了一个 R 管道来设置和进行分析，为用户提供一个 HTML 页面来扫描自己的数据，以检查以下方面：足迹的周期性、连接和消化；重复的可重复性和批次效应；与药物相关的伪影；包括 A、P 和 E 位之间的 mRNA 变异在内的无偏密码子富集；挖掘一些因果或混杂因素。我们希望我们的管道能够优化利用核糖体谱分析实验提供的大量信息。
