The ggRibo single-gene viewer reveals insights into translatome and other nucleotide-resolution omics data
Abstract
Visualizing Ribo-seq and other sequencing data within genes of interest is a powerful approach to studying gene expression, but its application is limited by a lack of robust tools. Here, we introduce ggRibo, a user-friendly R package for visualizing individual gene expression, integrating Ribo-seq, RNA-seq, and other genome-wide data sets with flexible scaling options. ggRibo visualizes 3 nt periodicity, a hallmark of translating ribosomes, within a gene-structure context, including introns and untranslated regions, enabling the study of novel open reading frames (ORFs), translation of different isoforms, and mechanisms of translational regulation. ggRibo can plot multiple Ribo-seq/RNA-seq data sets from different conditions for comparison. It also contains functions for plotting single-transcript views, reading-frame decomposition, and RNA-seq coverage alone. Importantly, ggRibo supports the visualization of other omics data sets that could also be presented with single-nucleotide resolution, such as RNA degradome, transcription start sites, translation initiation sites, and epitranscriptomic modifications. We demonstrate its utility with examples of upstream ORFs, downstream ORFs, nested ORFs, and differential isoform translation in humans, Arabidopsis, tomatos, and rice. We also provide examples of multiomic comparisons that reveal insights that connect the transcriptome, translatome, and degradome. In summary, ggRibo is an advanced single-gene viewer that offers a valuable resource for studying gene expression regulation through its intuitive and flexible platform.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.280480.125.
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Freely available online through the Genome Research Open Access option.
- Received February 10, 2025.
- Accepted July 17, 2025.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.











