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XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data
Ribosome profiling, an application of nucleic acid sequencing for monitoring ribosome activity, has revolutionized our understanding of protein translation dynamics. This technique has been available for a decade, yet the current state and standardization of publicly available computational tools fo...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7015430/ https://www.ncbi.nlm.nih.gov/pubmed/32004313 http://dx.doi.org/10.1371/journal.pcbi.1007625 |
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author | Berg, Jordan A. Belyeu, Jonathan R. Morgan, Jeffrey T. Ouyang, Yeyun Bott, Alex J. Quinlan, Aaron R. Gertz, Jason Rutter, Jared |
author_facet | Berg, Jordan A. Belyeu, Jonathan R. Morgan, Jeffrey T. Ouyang, Yeyun Bott, Alex J. Quinlan, Aaron R. Gertz, Jason Rutter, Jared |
author_sort | Berg, Jordan A. |
collection | PubMed |
description | Ribosome profiling, an application of nucleic acid sequencing for monitoring ribosome activity, has revolutionized our understanding of protein translation dynamics. This technique has been available for a decade, yet the current state and standardization of publicly available computational tools for these data is bleak. We introduce XPRESSyourself, an analytical toolkit that eliminates barriers and bottlenecks associated with this specialized data type by filling gaps in the computational toolset for both experts and non-experts of ribosome profiling. XPRESSyourself automates and standardizes analysis procedures, decreasing time-to-discovery and increasing reproducibility. This toolkit acts as a reference implementation of current best practices in ribosome profiling analysis. We demonstrate this toolkit’s performance on publicly available ribosome profiling data by rapidly identifying hypothetical mechanisms related to neurodegenerative phenotypes and neuroprotective mechanisms of the small-molecule ISRIB during acute cellular stress. XPRESSyourself brings robust, rapid analysis of ribosome-profiling data to a broad and ever-expanding audience and will lead to more reproducible and accessible measurements of translation regulation. XPRESSyourself software is perpetually open-source under the GPL-3.0 license and is hosted at https://github.com/XPRESSyourself, where users can access additional documentation and report software issues. |
format | Online Article Text |
id | pubmed-7015430 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-70154302020-02-26 XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data Berg, Jordan A. Belyeu, Jonathan R. Morgan, Jeffrey T. Ouyang, Yeyun Bott, Alex J. Quinlan, Aaron R. Gertz, Jason Rutter, Jared PLoS Comput Biol Research Article Ribosome profiling, an application of nucleic acid sequencing for monitoring ribosome activity, has revolutionized our understanding of protein translation dynamics. This technique has been available for a decade, yet the current state and standardization of publicly available computational tools for these data is bleak. We introduce XPRESSyourself, an analytical toolkit that eliminates barriers and bottlenecks associated with this specialized data type by filling gaps in the computational toolset for both experts and non-experts of ribosome profiling. XPRESSyourself automates and standardizes analysis procedures, decreasing time-to-discovery and increasing reproducibility. This toolkit acts as a reference implementation of current best practices in ribosome profiling analysis. We demonstrate this toolkit’s performance on publicly available ribosome profiling data by rapidly identifying hypothetical mechanisms related to neurodegenerative phenotypes and neuroprotective mechanisms of the small-molecule ISRIB during acute cellular stress. XPRESSyourself brings robust, rapid analysis of ribosome-profiling data to a broad and ever-expanding audience and will lead to more reproducible and accessible measurements of translation regulation. XPRESSyourself software is perpetually open-source under the GPL-3.0 license and is hosted at https://github.com/XPRESSyourself, where users can access additional documentation and report software issues. Public Library of Science 2020-01-31 /pmc/articles/PMC7015430/ /pubmed/32004313 http://dx.doi.org/10.1371/journal.pcbi.1007625 Text en © 2020 Berg et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Berg, Jordan A. Belyeu, Jonathan R. Morgan, Jeffrey T. Ouyang, Yeyun Bott, Alex J. Quinlan, Aaron R. Gertz, Jason Rutter, Jared XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data |
title | XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data |
title_full | XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data |
title_fullStr | XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data |
title_full_unstemmed | XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data |
title_short | XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data |
title_sort | xpressyourself: enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7015430/ https://www.ncbi.nlm.nih.gov/pubmed/32004313 http://dx.doi.org/10.1371/journal.pcbi.1007625 |
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