Chair for Microbial Ecology, Technical University of Munich, Freising, Germany.
Chair for Microbial Ecology, Technical University of Munich, Freising, Germany; Core Facility Microbiome, ZIEL Institute for Food and Health, Technical University of Munich, Freising, Germany.
J Biol Chem. 2020 Jul 3;295(27):8999-9011. doi: 10.1074/jbc.RA119.012161. Epub 2020 May 8.
Ribosome profiling (RIBO-Seq) has improved our understanding of bacterial translation, including finding many unannotated genes. However, protocols for RIBO-Seq and corresponding data analysis are not yet standardized. Here, we analyzed 48 RIBO-Seq samples from nine studies of K12 grown in lysogeny broth medium and particularly focused on the size-selection step. We show that for conventional expression analysis, a size range between 22 and 30 nucleotides is sufficient to obtain protein-coding fragments, which has the advantage of removing many unwanted rRNA and tRNA reads. More specific analyses may require longer reads and a corresponding improvement in rRNA/tRNA depletion. There is no consensus about the appropriate sequencing depth for RIBO-Seq experiments in prokaryotes, and studies vary significantly in total read number. Our analysis suggests that 20 million reads that are not mapping to rRNA/tRNA are required for global detection of translated annotated genes. We also highlight the influence of drug-induced ribosome stalling, which causes bias at translation start sites. The resulting accumulation of reads at the start site may be especially useful for detecting weakly expressed genes. As different methods suit different questions, it may not be possible to produce a "one-size-fits-all" ribosome profiling data set. Therefore, experiments should be carefully designed in light of the scientific questions of interest. We propose some basic characteristics that should be reported with any new RIBO-Seq data sets. Careful attention to the factors discussed should improve prokaryotic gene detection and the comparability of ribosome profiling data sets.
核糖体图谱(RIBO-Seq)提高了我们对细菌翻译的理解,包括发现许多未注释的基因。然而,RIBO-Seq 的方案和相应的数据分析尚未标准化。在这里,我们分析了 9 项研究中来自 K12 的 48 个 RIBO-Seq 样本,这些样本在溶菌肉汤培养基中生长,特别关注大小选择步骤。我们表明,对于常规表达分析,22 到 30 个核苷酸的大小范围足以获得编码蛋白的片段,这具有去除许多不需要的 rRNA 和 tRNA 读取的优点。更具体的分析可能需要更长的读取和相应提高 rRNA/tRNA 的去除效率。在原核生物中,对于 RIBO-Seq 实验的合适测序深度没有共识,并且研究在总读取数上差异很大。我们的分析表明,需要 2000 万条不映射到 rRNA/tRNA 的读取来全局检测翻译注释基因。我们还强调了药物诱导的核糖体停滞的影响,这会导致翻译起始位点的偏差。在起始位点积累的读取可能特别有助于检测弱表达的基因。由于不同的方法适用于不同的问题,因此可能无法生成“一刀切”的核糖体图谱数据集。因此,应根据感兴趣的科学问题仔细设计实验。我们提出了一些新的 RIBO-Seq 数据集应报告的基本特征。对所讨论因素的仔细关注将提高原核基因的检测和核糖体图谱数据集的可比性。