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ORFik: a comprehensive R toolkit for the analysis of translation.
Tjeldnes, Håkon; Labun, Kornel; Torres Cleuren, Yamila; Chyzynska, Katarzyna; Swirski, Michal; Valen, Eivind.
Afiliação
  • Tjeldnes H; Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
  • Labun K; Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
  • Torres Cleuren Y; Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
  • Chyzynska K; Sars International Centre for Marine Molecular Biology, University of Bergen, Bergen, Norway.
  • Swirski M; Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
  • Valen E; Institute of Genetics and Biotechnology, Faculty of Biology, University of Warsaw, Warsaw, Poland.
BMC Bioinformatics ; 22(1): 336, 2021 Jun 19.
Article em En | MEDLINE | ID: mdl-34147079
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 .
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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Ribossomos / Biossíntese de Proteínas Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Noruega

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Ribossomos / Biossíntese de Proteínas Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Noruega