Ribo-seq rRNA reads reveal co-predicted signatures of ribosome heterogeneity and collisions.

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.