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Motivation Ribosome profiling is a widely-used technique for measuring ribosome occupancy at nucleotide resolution. However, the need to analyze this data at nucleotide resolution introduces unique challenges in data visualization and analyses. Results In this study, we introduce RiboGraph, a dedicated visualization tool designed to work with.ribo files, a specialized and efficient format for ribosome occupancy data. Unlike existing solutions that rely on large alignment files and time-consuming preprocessing steps, RiboGraph operates on a purpose designed compact file type. This efficiency allows for interactive, real-time visualization at ribosome-protected fragment length resolution. By providing an integrated toolset, RiboGraph empowers researchers to conduct comprehensive visual analysis of ribosome occupancy data. Availability and implementation Source code, step-by-step installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling/ribograph. On the same page, we provide test files and a step-by-step tutorial highlighting the key features of RiboGraph. [ABSTRACT FROM AUTHOR]

Summary Ribosome occupancy measurements enable protein abundance estimation and infer mechanisms of translation. Recent studies have revealed that sequence read lengths in ribosome profiling data are highly variable and carry critical information. Consequently, data analyses require the computation and storage of multiple metrics for a wide range of ribosome footprint lengths. We developed a software ecosystem including a new efficient binary file format named 'ribo'. Ribo files store all essential data grouped by ribosome footprint lengths. Users can assemble ribo files using our RiboFlow pipeline that processes raw ribosomal profiling sequencing data. RiboFlow is highly portable and customizable across a large number of computational environments with built-in capabilities for parallelization. We also developed interfaces for writing and reading ribo files in the R (RiboR) and Python (RiboPy) environments. Using RiboR and RiboPy, users can efficiently access ribosome profiling quality control metrics, generate essential plots and carry out analyses. Altogether, these components create a software ecosystem for researchers to study translation through ribosome profiling. Availability and implementation For a quickstart, please see https://ribosomeprofiling.github.io. Source code, installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary Compared to noncoding RNAs (ncRNAs), such as rRNAs and ribozymes, for which high-resolution structures abound, little is known about the tertiary structures of mRNAs. In eukaryotic cells, newly made mRNAs are packaged with proteins in highly compacted mRNA particles (mRNPs), but the manner of this mRNA compaction is unknown. Here, we developed and implemented RIPPLiT (RNA immunoprecipitation and proximity ligation in tandem), a transcriptome-wide method for probing the 3D conformations of RNAs stably associated with defined proteins, in this case, exon junction complex (EJC) core factors. EJCs multimerize with other mRNP components to form megadalton-sized complexes that protect large swaths of newly synthesized mRNAs from endonuclease digestion. Unlike ncRNPs, wherein strong locus-specific structures predominate, mRNPs behave more like flexible polymers. Polymer analysis of proximity ligation data for hundreds of mRNA species demonstrates that nascent and pre-translational mammalian mRNAs are compacted by their associated proteins into linear rod-like structures. Graphical Abstract Highlights • RIPPLiT captures 3D structure information in diverse RNA protein complexes (RNPs) • RIPPLiT provides RNA-RNA proximity information independent of secondary structure • Spliced mRNAs behave like flexible polymers without strong locus-specific structures • Polymer analysis reveals that pre-translational mRNPs form linear rods Metkar et al. developed a new toolkit, a biochemical approach, RIPPLiT and a bioinformatics suite to capture and analyze higher-order organization of RNPs. They were able to enrich for interactions within mRNAs and further, using polymer analysis on 100 s of mRNAs, identified a unifying principle for mRNP packaging. [ABSTRACT FROM AUTHOR]

A major challenge in biology is to understand how complex gene expression patterns are encoded in the genome. While transcriptional enhancers have been studied extensively, few transcriptional silencers have been identified, and they remain poorly understood. Here, we used a novel strategy to screen hundreds of sequences for tissue-specific silencer activity in whole Drosophila embryos. Almost all of the transcriptional silencers that we identified were also active enhancers in other cellular contexts. These elements are bound by more transcription factors than non-silencers. A subset of these silencers forms long-range contacts with promoters. Deletion of a silencer caused derepression of its target gene. Our results challenge the common practice of treating enhancers and silencers as separate classes of regulatory elements and suggest the possibility that thousands or more bifunctional CRMs remain to be discovered in Drosophila and 104–105 in humans. • Silencers are bifunctional and can act as enhancers in other cellular contexts • A subset of silencers forms long-range contacts to promoters • Deletion of a silencer by genome editing caused derepression of its target gene • Results suggest that thousands of bifunctional elements in flies remain to be discovered Gisselbrecht et al. performed a screen in developing Drosophila embryos for genomic sequences that can act as transcriptional silencers. They report that nearly all silencers are enhancers in other tissues or at other developmental stages. Their silencers fall into two classes, one of which forms physical chromosomal contacts with promoters. [ABSTRACT FROM AUTHOR]