Ribo-seq rRNA reads 揭示核糖体异质性与碰撞的共预测特征
Ribo-seq rRNA reads reveal co-predicted signatures of ribosome heterogeneity and collisions
利用 Ribo-seq 数据中的 rRNA reads，dripARF 与 dricARF 可在不同条件间检测核糖体蛋白组成变化和核糖体碰撞。研究将该方法应用于公开 Ribo-seq 数据集，发现预测集对应的 rRNA 区域在 rRNA reads 中持续富集，支持方法的核心假设；同时分析实验流程差异对 rRNA read 分布的影响，并引入基于单样本 rRNA 位置集富集分析的权重以限制假阳性。跨研究一致的核糖体变化包括核糖体碰撞、eS25 和 uS19 等显著共预测因子。
来源：Briefings in Bioinformatics · doi.org
Ribosome heterogeneity is increasingly recognized as a key factor in various biological contexts, including development, cancer, and immune responses. To enable systematic exploration of this diversity, we previously developed a bioinformatics method that utilized rRNA reads from Ribo-seq data, detecting changes in ribosomal protein composition (dripARF) and ribosome collisions (dricARF) between conditions simply and efficiently. The method works by projecting differences in rRNA read abundance onto the 3D-structure of the ribosome, following which we perform enrichment tests on predefined heterogeneity-specific rRNA position sets. In this study, we apply this tool to publicly available Ribo-seq datasets to understand the impact of experimental protocol variations and to identify shared biological signals across studies. Our analyses show that the rRNA regions assigned to our prediction sets are consistently enriched in rRNA reads, supporting the core assumptions of our approach. We further examine how various protocol factors influence rRNA read distribution and implement a single-sample rRNA position set enrichment analysis-based weight to limit false positives. Finally, we highlight consistent ribosomal changes across these studies, including prominent co-predictors such as ribosome collisions, eS25, and uS19.