ORFik: a comprehensive R toolkit for the analysis of translation

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 .

CRISPR Genome Editing Made Easy Through the CHOPCHOP Website

The design of optimal guide RNA (gRNA) sequences for CRISPR systems is challenged by the need to achieve highly efficient editing at the desired location (on-target editing) with minimal editing at unintended locations (off-target editing). Although laboratory validation should ideally be used to detect off-target activity, computational predictions are almost always preferred in practice due to their speed and low cost. Several studies have therefore explored gRNA-DNA interactions in order to understand how CRISPR complexes select their genomic targets. CHOPCHOP (https://chopchop.cbu.uib.no/) leverages these developments to build a user-friendly web interface that helps users design optimal gRNAs. CHOPCHOP supports a wide range of CRISPR applications, including gene knock-out, sequence knock-in, and RNA knock-down. Furthermore, CHOPCHOP offers visualization that enables an informed choice of gRNAs and supports experimental validation. In these protocols, we describe the best practices for gRNA design using CHOPCHOP. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Design of gRNAs for gene knock-out Alternate Protocol 1: Design of gRNAs for dCas9 fusion/effector targeting Support Protocol: Design of gRNAs for targeting transgenic or plasmid sequences Basic Protocol 2: Design of gRNAs for RNA targeting Basic Protocol 3: Design of gRNAs for sequence knock-in Alternate Protocol 2: Design of gRNAs for knock-in using non-homologous end joining Basic Protocol 4: Design of gRNAs for knock-in using Cas9 nickases.