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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 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.ConclusionIn 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.Availabilityhttp://bioconductor.org/packages/ORFik.
Highlights
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
To illustrate the functionality of ORFik we show two use cases, where we (1) study translation initiation with profiling of scanning small subunit (SSU), and (2) make a simple pipeline to predict and characterize translated Upstream open reading frame (uORF)
ORFik was here applied to a TCP-seq data set from HeLa cells [8], and produces metaprofiles over transcripts, separated into scanning SSU and 80S elongating ribosomes (Fig. 4a)
Summary
To illustrate the functionality of ORFik we show two use cases, where we (1) study translation initiation with profiling of scanning SSUs, and (2) make a simple pipeline to predict and characterize translated uORFs. TOP motifs are known to be involved in the regulation of specific genes and ORFik can correlate such features to other observations of the transcript An example of such an analysis is associating motifs with the scanning efficiency (SE) — the number of scanning ribosomes on the 5′UTR relative to the RNA abundance [5].
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