Alkan Ferhat, Silva Joana, Pintó Barberà Eric, Faller William J
Division of Oncogenomics, Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands.
Bioinformatics. 2021 Sep 9;37(17):2659-2667. doi: 10.1093/bioinformatics/btab171.
Ribosome Profiling (Ribo-seq) has revolutionized the study of RNA translation by providing information on ribosome positions across all translated RNAs with nucleotide-resolution. Yet several technical limitations restrict the sequencing depth of such experiments, the most common of which is the overabundance of rRNA fragments. Various strategies can be employed to tackle this issue, including the use of commercial rRNA depletion kits. However, as they are designed for more standardized RNAseq experiments, they may perform suboptimally in Ribo-seq. In order to overcome this, it is possible to use custom biotinylated oligos complementary to the most abundant rRNA fragments, however currently no computational framework exists to aid the design of optimal oligos.
Here, we first show that a major confounding issue is that the rRNA fragments generated via Ribo-seq vary significantly with differing experimental conditions, suggesting that a 'one-size-fits-all' approach may be inefficient. Therefore we developed Ribo-ODDR, an oligo design pipeline integrated with a user-friendly interface that assists in oligo selection for efficient experiment-specific rRNA depletion. Ribo-ODDR uses preliminary data to identify the most abundant rRNA fragments, and calculates the rRNA depletion efficiency of potential oligos. We experimentally show that Ribo-ODDR designed oligos outperform commercially available kits and lead to a significant increase in rRNA depletion in Ribo-seq.
Ribo-ODDR is freely accessible at https://github.com/fallerlab/Ribo-ODDR.
Supplementary data are available at Bioinformatics online.
核糖体谱分析（Ribo-seq）通过提供全转录RNA上核糖体位置的核苷酸分辨率信息，彻底改变了RNA翻译的研究。然而，一些技术限制制约了此类实验的测序深度，其中最常见的是rRNA片段过多。可以采用多种策略来解决这个问题，包括使用商业rRNA去除试剂盒。然而，由于它们是为更标准化的RNA测序实验设计的，在Ribo-seq中可能表现欠佳。为了克服这一问题，可以使用与最丰富的rRNA片段互补的定制生物素化寡核苷酸，但目前尚无计算框架来辅助设计最佳寡核苷酸。
在这里，我们首先表明一个主要的混杂问题是，通过Ribo-seq产生的rRNA片段在不同实验条件下差异很大，这表明“一刀切”的方法可能效率低下。因此，我们开发了Ribo-ODDR，这是一个与用户友好界面集成的寡核苷酸设计流程，有助于为高效的实验特异性rRNA去除选择寡核苷酸。Ribo-ODDR使用初步数据识别最丰富的rRNA片段，并计算潜在寡核苷酸的rRNA去除效率。我们通过实验表明，Ribo-ODDR设计的寡核苷酸优于市售试剂盒，并导致Ribo-seq中rRNA去除率显著提高。
Ribo-ODDR可在https://github.com/fallerlab/Ribo-ODDR上免费获取。
补充数据可在《生物信息学》在线获取。
