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The quickly accumulating ribosome profiling data is an insightful resource for studying the critical details of translation regulation under various biological contexts. Rocaglamide A (RocA), an antitumor heterotricyclic natural compound, has been shown to inhibit translation initiation of a large group of mRNA species by clamping eIF4A onto poly-purine motifs in the 5' UTRs. However, reanalysis of previous ribosome profiling datasets reveals an unexpected shift of the ribosome occupancy pattern, upon RocA treatment in various types of cells, during early translation elongation for a specific group of mRNA transcripts without poly-purine motifs over-represented in their 5' UTRs. Such perturbation of translation elongation dynamics can be attributed to the blockage of translating ribosomes due to the binding of eIF4A to the poly-purine sequence in coding regions. In summary, our study presents the complete dual modes of RocA in blocking translation initiation and elongation, which underlie the potent antitumor effect of RocA., (© 2023. The Author(s).)

Identification of open reading frames (ORFs), especially those encoding small peptides and being actively translated under specific physiological contexts, is critical for comprehensive annotations of context-dependent translatomes. Ribosome profiling, a technique for detecting the binding locations and densities of translating ribosomes on RNA, offers an avenue to rapidly discover where translation is occurring at the genome-wide scale. However, it is not a trivial task in bioinformatics to efficiently and comprehensively identify the translating ORFs for ribosome profiling. Described here is an easy-to-use package, named RiboCode, designed to search for actively translating ORFs of any size from distorted and ambiguous signals in ribosome profiling data. Taking our previously published dataset as an example, this article provides step-by-step instructions for the entire RiboCode pipeline, from preprocessing of the raw data to interpretation of the final output result files. Furthermore, for evaluating the translation rates of the annotated ORFs, procedures for visualization and quantification of ribosome densities on each ORF are also described in detail. In summary, the present article is a useful and timely instruction for the research fields related to translation, small ORFs, and peptides.

Background: Ribosome profiling has been widely used for studies of translation under a large variety of cellular and physiological contexts. Many of these studies have greatly benefitted from a series of data-mining tools designed for dissection of the translatome from different aspects. However, as the studies of translation advance quickly, the current toolbox still falls in short, and more specialized tools are in urgent need for deeper and more efficient mining of the important and new features of the translation landscapes., Results: Here, we present RiboMiner, a bioinformatics toolset for mining of multi-dimensional features of the translatome with ribosome profiling data. RiboMiner performs extensive quality assessment of the data and integrates a spectrum of tools for various metagene analyses of the ribosome footprints and for detailed analyses of multiple features related to translation regulation. Visualizations of all the results are available. Many of these analyses have not been provided by previous methods. RiboMiner is highly flexible, as the pipeline could be easily adapted and customized for different scopes and targets of the studies., Conclusions: Applications of RiboMiner on two published datasets did not only reproduced the main results reported before, but also generated novel insights into the translation regulation processes. Therefore, being complementary to the current tools, RiboMiner could be a valuable resource for dissections of the translation landscapes and the translation regulations by mining the ribosome profiling data more comprehensively and with higher resolution. RiboMiner is freely available at https://github.com/xryanglab/RiboMiner and https://pypi.org/project/RiboMiner .