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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...

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Autores principales: Berg, Jordan A., Belyeu, Jonathan R., Morgan, Jeffrey T., Ouyang, Yeyun, Bott, Alex J., Quinlan, Aaron R., Gertz, Jason, Rutter, Jared
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
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.
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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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