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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 datasets with flexible scaling options. ggRibo visualizes 3-nucleotide periodicity, a hallmark of translating ribosomes, within a gene-structure context, including introns and untranslated regions, enabling the study of novel ORFs, translation of different isoforms, and mechanisms of translational regulation. ggRibo can plot multiple Ribo-seq/RNA-seq datasets from different conditions for comparison. It also contains functions for plotting single-transcript view, reading-frame decomposition, and RNA-seq coverage alone. Importantly, ggRibo supports the visualization of other omics datasets 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, tomato, 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. 

Abstract Translation is a crucial step in gene expression and plays a vital role in regulating various aspects of plant development and environmental responses. It is a dynamic and complex program that involves interactions between mRNAs, tRNAs, and the ribosome machinery, through both cis- and trans-regulation, while integrating internal and external signals. Translational control can act in a global (transcriptome-wide) or mRNA-specific manner. Recent advances in genome-wide techniques, particularly ribosome profiling and proteomics, have led to numerous exciting discoveries in both global and mRNA-specific translation. In this review, we aim to provide a ‘primer’ that introduces readers to this fascinating yet complex cellular process and provide a big picture of how essential components connect within the network. We begin with an overview of mRNA translation, followed by a discussion of the experimental approaches and recent findings in the field, focusing on unannotated translation events and translational control through cis-regulatory elements on mRNAs and trans-acting factors, as well as signaling networks through three conserved translational regulators TOR, SnRK1, and GCN2. Finally, we briefly touch on the spatial regulation of mRNAs in translational control. Here, we focus on cytosolic mRNAs, and translation in organelles and viruses is not covered in this review. 

Abstract A crucial step in functional genomics is identifying actively translated open reading frames (ORFs) and linking them to biological functions. The challenge lies in identifying short ORFs, as their identification is greatly influenced by data quality and depth. Here, we improved the coverage of super-resolution Ribo-seq in Arabidopsis (Arabidopsis thaliana), revealing uncharacterized translation events for nuclear, chloroplastic, and mitochondrial genes. Assisted by a transcriptome assembly, we identified 7,751 unconventional translation events, comprising 6,996 upstream ORFs (uORFs) and 209 downstream ORFs on annotated protein-coding genes, as well as 546 ORFs in presumed non-coding RNAs. Proteomics data confirmed the production of stable proteins from some of these unannotated translation events. We present evidence of active translation from primary transcripts of tasiRNAs (TAS1–4) and microRNAs (pri-MIR163, pri-MIR169), and periodic ribosome stalling supporting co-translational decay. Additionally, we developed a method for identifying extremely short uORFs, including 370 minimum uORFs (AUG-stop), and 2,921 tiny uORFs (2–10 amino acids), and 681 uORFs that overlap with each other. Remarkably, these short uORFs exhibit strong translational repression as do longer uORFs. We also systematically discovered 594 uORFs regulated by alternative splicing, suggesting widespread isoform-specific translational control. Finally, these prevalent uORFs are associated with numerous important pathways. In summary, our improved Arabidopsis translational landscape provides valuable resources to study gene expression regulation. 

Abstract BackgroundRibosome profiling, also known as Ribo-seq, is a powerful technique to study genome-wide mRNA translation. It reveals the precise positions and quantification of ribosomes on mRNAs through deep sequencing of ribosome footprints. We previously optimized the resolution of this technique in plants. However, several key reagents in our original method have been discontinued, and thus, there is an urgent need to establish an alternative protocol. ResultsHere we describe a step-by-step protocol that combines our optimized ribosome footprinting in plants with available custom library construction methods established in yeast and bacteria. We tested this protocol in 7-day-old Arabidopsis seedlings and evaluated the quality of the sequencing data regarding ribosome footprint length, mapped genomic features, and the periodic properties corresponding to actively translating ribosomes through open resource bioinformatic tools. We successfully generated high-quality Ribo-seq data comparable with our original method. ConclusionsWe established a custom library construction method for super-resolution Ribo-seq in Arabidopsis. The experimental protocol and bioinformatic pipeline should be readily applicable to other plant tissues and species. 

Abstract Background Ribo-seq has revolutionized the study of genome-wide mRNA translation. High-quality Ribo-seq data display strong 3-nucleotide (nt) periodicity, which corresponds to translating ribosomes deciphering three nts at a time. While 3-nt periodicity has been widely used to study novel translation events such as upstream ORFs in 5′ untranslated regions and small ORFs in presumed non-coding RNAs, tools that allow the visualization of these events remain underdeveloped. Results RiboPlotR is a visualization package written in R that presents both RNA-seq coverage and Ribo-seq reads in genomic coordinates for all annotated transcript isoforms of a gene. Specifically, for individual isoform models, RiboPlotR plots Ribo-seq data in the context of gene structures, including 5′ and 3′ untranslated regions and introns, and it presents the reads for all three reading frames in three different colors. The inclusion of gene structures and color-coding the reading frames facilitate observing new translation events and identifying potential regulatory mechanisms. Conclusions RiboPlotR is freely available ( https://github.com/hsinyenwu/RiboPlotR and https://sourceforge.net/projects/riboplotr/ ) and allows the visualization of translated features identified in Ribo-seq data. 

Abstract Long intergenic noncoding RNAs (lincRNAs) are a large yet enigmatic class of eukaryotic transcripts that can have critical biological functions. The wealth of RNA-sequencing (RNA-seq) data available for plants provides the opportunity to implement a harmonized identification and annotation effort for lincRNAs that enables cross-species functional and genomic comparisons as well as prioritization of functional candidates. In this study, we processed >24 Tera base pairs of RNA-seq data from >16,000 experiments to identify ∼130,000 lincRNAs in four Brassicaceae: Arabidopsis thaliana, Camelina sativa, Brassica rapa, and Eutrema salsugineum. We used nanopore RNA-seq, transcriptome-wide structural information, peptide data, and epigenomic data to characterize these lincRNAs and identify conserved motifs. We then used comparative genomic and transcriptomic approaches to highlight lincRNAs in our data set with sequence or transcriptional conservation. Finally, we used guilt-by-association analyses to assign putative functions to lincRNAs within our data set. We tested this approach on a subset of lincRNAs associated with germination and seed development, observing germination defects for Arabidopsis lines harboring T-DNA insertions at these loci. LincRNAs with Brassicaceae-conserved putative miRNA binding motifs, small open reading frames, or abiotic-stress modulated expression are a few of the annotations that will guide functional analyses into this cryptic portion of the transcriptome.