Utilization of non-AUG alternative translation start sites is most common in bacteria and viruses, but it has been also reported in other organisms. This phenomenon increases proteome complexity by allowing expression of multiple protein isoforms from a single gene. In Saccharomyces cerevisiae, a few described cases concern proteins that are translated from upstream near-cognate start codons as N-terminally extended variants that localize to mitochondria. Using bioinformatics tools, we provide compelling evidence that in yeast the potential for producing alternative protein isoforms by non-AUG translation initiation is much more prevalent than previously anticipated and may apply to as many as a few thousand proteins. Several hundreds of candidates are predicted to gain a mitochondrial targeting signal (MTS), generating an unrecognized pool of mitochondrial proteins. We confirmed mitochondrial localization of a subset of proteins previously not identified as mitochondrial, whose standard forms do not carry an MTS. Our data highlight the potential of non-canonical translation initiation in expanding the capacity of the mitochondrial proteome and possibly also other cellular features.

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.

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

Many new biotechnology applications make use of non-traditional yeast species that possess characteristics and traits that are advantageous in specific settings. To a large extent, this is possible because of advances in molecular and genomic technologies that provide opportunities to study and manipulate the genomes of diverse yeasts. There remains, however, a substantial gap between what we know about well-studied models versus emerging industrial yeast species. In this study, we applied a multi-faceted omics analysis to better understand organisation of protein coding gene expression in Kluyveromyces marxianus, a yeast widely used in food and industrial biotechnology. We combined advanced transcriptomic techniques for mapping 5' and 3' ends of RNA transcripts with ribosome profiling to explore the transcriptional and translational landscapes of this yeast. This allowed us to improve the genome annotation and identify over 300 un-annotated or mis-annotated genes. We discovered numerous examples of novel proteoforms due to use of alternative transcription or translation start sites, many genes with translated upstream open reading frames (uORFs), and a novel case of programmed ribosomal frameshifting. Only some of newly discovered features are shared with distant species, for example, being present in Saccharomyces cerevisiae orthologs. The exploration of shared and unique features may provide an insight into the evolution of gene regulation in pre- and post- WGD yeasts. The processed data has been made available on the GWIPS-viz and Trips-Viz browsers, thus providing an accurate data-driven annotation of transcripts and their protein coding regions along with quantitative information on their transcription and translation.