The CRISPR–Cas system is a powerful genome editing tool that functions in a diverse array of organisms and cell types. The technology was initially developed to induce targeted mutations in DNA, but CRISPR–Cas has now been adapted to target nucleic acids for a range of purposes. CHOPCHOP is a web tool for identifying CRISPR–Cas single guide RNA (sgRNA) targets. In this major update of CHOPCHOP, we expand our toolbox beyond knockouts. We introduce functionality for targeting RNA with Cas13, which includes support for alternative transcript isoforms and RNA accessibility predictions. We incorporate new DNA targeting modes, including CRISPR activation/repression, targeted enrichment of loci for long-read sequencing, and prediction of Cas9 repair outcomes. Finally, we expand our results page visualization to reveal alternative isoforms and downstream ATG sites, which will aid users in avoiding the expression of truncated proteins. The CHOPCHOP web tool now supports over 200 genomes and we have released a command-line script for running larger jobs and handling unsupported genomes. CHOPCHOP v3 can be found at https://chopchop.cbu.uib.no

Highlights d RCP-seq allows the study of both scanning and translating ribosomal complexes d Threading of mRNA through the small ribosomal subunit loads the subunits onto the 5 0 UTR d Processivity of small ribosomal subunits is high while scanning 5 0 UTRs d Direct measurements of initiation redefine optimal start codon context

Background With the rapid growth in the use of high-throughput methods for characterizing translation and the continued expansion of multi-omics, there is a need for back-end functions and streamlined tools for processing, analyzing, and characterizing data produced by these assays. Results Here, we introduce ORFik, a user-friendly R/Bioconductor API and toolbox for studying translation and its regulation. It extends GenomicRanges from the genome to the transcriptome and implements a framework that integrates data from several sources. ORFik streamlines the steps to process, analyze, and visualize the different steps of translation with a particular focus on initiation and elongation. It accepts high-throughput sequencing data from ribosome profiling to quantify ribosome elongation or RCP-seq/TCP-seq to also quantify ribosome scanning. In addition, ORFik can use CAGE data to accurately determine 5′UTRs and RNA-seq for determining translation relative to RNA abundance. ORFik supports and calculates over 30 different translation-related features and metrics from the literature and can annotate translated regions such as proteins or upstream open reading frames (uORFs). As a use-case, we demonstrate using ORFik to rapidly annotate the dynamics of 5′ UTRs across different tissues, detect their uORFs, and characterize their scanning and translation in the downstream protein-coding regions. Conclusion In summary, ORFik introduces hundreds of tested, documented and optimized methods. ORFik is designed to be easily customizable, enabling users to create complete workflows from raw data to publication-ready figures for several types of sequencing data. Finally, by improving speed and scope of many core Bioconductor functions, ORFik offers enhancement benefiting the entire Bioconductor environment. Availability http://bioconductor.org/packages/ORFik.

Advances in genomics and molecular biology have revealed an abundance of small open reading frames (sORFs) across all types of transcripts. While these sORFs are often assumed to be non-functional, many have been implicated in physiological functions and a significant number of sORFs have been described in human diseases. Thus, sORFs may represent a hidden repository of functional elements that could serve as therapeutic targets. Unlike protein-coding genes, it is not necessarily the encoded peptide of an sORF that enacts its function, sometimes simply the act of translating an sORF might have a regulatory role. Indeed, the most studied sORFs are located in the 5′UTRs of coding transcripts and can have a regulatory impact on the translation of the downstream protein-coding sequence. However, sORFs have also been abundantly identified in non-coding RNAs including lncRNAs, circular RNAs and ribosomal RNAs suggesting that sORFs may be diverse in function. Of the many different experimental methods used to discover sORFs, the most commonly used are ribosome profiling and mass spectrometry. These can confirm interactions between transcripts and ribosomes and the production of a peptide, respectively. Extensions to ribosome profiling, which also capture scanning ribosomes, have further made it possible to see how sORFs impact the translation initiation of mRNAs. While high-throughput techniques have made the identification of sORFs less difficult, defining their function, if any, is typically more challenging. Together, the abundance and potential function of many of these sORFs argues for the necessity of including sORFs in gene annotations and systematically characterizing these to understand their potential functional roles. In this review, we will focus on the high-throughput methods used in the detection and characterization of sORFs and discuss techniques for validation and functional characterization.

ABSTRACT•BackgroundWith the rapid growth in the use of high-throughput methods for characterizing translation and the continued expansion of multi-omics, there is a need for back-end functions and streamlined tools for processing, analyzing, and characterizing data produced by these assays.•ResultsHere, we introduce ORFik, a user-friendly R/Bioconductor toolbox for studying translation and its regulation. It extends GenomicRanges from the genome to the transcriptome and implements a framework that integrates data from several sources. ORFik streamlines the steps to process, analyze, and visualize the different steps of translation with a particular focus on initiation and elongation. It accepts high-throughput sequencing data from ribosome profiling to quantify ribosome elongation or RCP-seq/TCP-seq to also quantify ribosome scanning. In addition, ORFik can use CAGE data to accurately determine 5’UTRs and RNA-seq for determining translation relative to RNA abundance. ORFik supports and calculates over 30 different translation-related features and metrics from the literature and can annotate translated regions such as proteins or upstream open reading frames. As a use-case, we demonstrate using ORFik to rapidly annotate the dynamics of 5’ UTRs across different tissues, detect their uORFs, and characterize their scanning and translation in the downstream protein-coding regions.•Availabilityhttp://bioconductor.org/packages/ORFik