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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,...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8214792/ https://www.ncbi.nlm.nih.gov/pubmed/34147079 http://dx.doi.org/10.1186/s12859-021-04254-w |
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author | Tjeldnes, Håkon Labun, Kornel Torres Cleuren, Yamila Chyżyńska, Katarzyna Świrski, Michał Valen, Eivind |
author_facet | Tjeldnes, Håkon Labun, Kornel Torres Cleuren, Yamila Chyżyńska, Katarzyna Świrski, Michał Valen, Eivind |
author_sort | Tjeldnes, Håkon |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04254-w. |
format | Online Article Text |
id | pubmed-8214792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82147922021-06-23 ORFik: a comprehensive R toolkit for the analysis of translation Tjeldnes, Håkon Labun, Kornel Torres Cleuren, Yamila Chyżyńska, Katarzyna Świrski, Michał Valen, Eivind BMC Bioinformatics Software 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04254-w. BioMed Central 2021-06-19 /pmc/articles/PMC8214792/ /pubmed/34147079 http://dx.doi.org/10.1186/s12859-021-04254-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Tjeldnes, Håkon Labun, Kornel Torres Cleuren, Yamila Chyżyńska, Katarzyna Świrski, Michał Valen, Eivind ORFik: a comprehensive R toolkit for the analysis of translation |
title | ORFik: a comprehensive R toolkit for the analysis of translation |
title_full | ORFik: a comprehensive R toolkit for the analysis of translation |
title_fullStr | ORFik: a comprehensive R toolkit for the analysis of translation |
title_full_unstemmed | ORFik: a comprehensive R toolkit for the analysis of translation |
title_short | ORFik: a comprehensive R toolkit for the analysis of translation |
title_sort | orfik: a comprehensive r toolkit for the analysis of translation |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8214792/ https://www.ncbi.nlm.nih.gov/pubmed/34147079 http://dx.doi.org/10.1186/s12859-021-04254-w |
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