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RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution

SUMMARY: Ribosome occupancy measurements enable protein abundance estimation and infer mechanisms of translation. Recent studies have revealed that sequence read lengths in ribosome profiling data are highly variable and carry critical information. Consequently, data analyses require the computation...

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Detalles Bibliográficos
Autores principales: Ozadam, Hakan, Geng, Michael, Cenik, Can
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7203755/
https://www.ncbi.nlm.nih.gov/pubmed/31930375
http://dx.doi.org/10.1093/bioinformatics/btaa028
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author Ozadam, Hakan
Geng, Michael
Cenik, Can
author_facet Ozadam, Hakan
Geng, Michael
Cenik, Can
author_sort Ozadam, Hakan
collection PubMed
description SUMMARY: Ribosome occupancy measurements enable protein abundance estimation and infer mechanisms of translation. Recent studies have revealed that sequence read lengths in ribosome profiling data are highly variable and carry critical information. Consequently, data analyses require the computation and storage of multiple metrics for a wide range of ribosome footprint lengths. We developed a software ecosystem including a new efficient binary file format named ‘ribo’. Ribo files store all essential data grouped by ribosome footprint lengths. Users can assemble ribo files using our RiboFlow pipeline that processes raw ribosomal profiling sequencing data. RiboFlow is highly portable and customizable across a large number of computational environments with built-in capabilities for parallelization. We also developed interfaces for writing and reading ribo files in the R (RiboR) and Python (RiboPy) environments. Using RiboR and RiboPy, users can efficiently access ribosome profiling quality control metrics, generate essential plots and carry out analyses. Altogether, these components create a software ecosystem for researchers to study translation through ribosome profiling. AVAILABILITY AND IMPLEMENTATION: For a quickstart, please see https://ribosomeprofiling.github.io. Source code, installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-72037552020-05-11 RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution Ozadam, Hakan Geng, Michael Cenik, Can Bioinformatics Applications Notes SUMMARY: Ribosome occupancy measurements enable protein abundance estimation and infer mechanisms of translation. Recent studies have revealed that sequence read lengths in ribosome profiling data are highly variable and carry critical information. Consequently, data analyses require the computation and storage of multiple metrics for a wide range of ribosome footprint lengths. We developed a software ecosystem including a new efficient binary file format named ‘ribo’. Ribo files store all essential data grouped by ribosome footprint lengths. Users can assemble ribo files using our RiboFlow pipeline that processes raw ribosomal profiling sequencing data. RiboFlow is highly portable and customizable across a large number of computational environments with built-in capabilities for parallelization. We also developed interfaces for writing and reading ribo files in the R (RiboR) and Python (RiboPy) environments. Using RiboR and RiboPy, users can efficiently access ribosome profiling quality control metrics, generate essential plots and carry out analyses. Altogether, these components create a software ecosystem for researchers to study translation through ribosome profiling. AVAILABILITY AND IMPLEMENTATION: For a quickstart, please see https://ribosomeprofiling.github.io. Source code, installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-05-01 2020-01-13 /pmc/articles/PMC7203755/ /pubmed/31930375 http://dx.doi.org/10.1093/bioinformatics/btaa028 Text en © The Author(s) 2020. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Ozadam, Hakan
Geng, Michael
Cenik, Can
RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution
title RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution
title_full RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution
title_fullStr RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution
title_full_unstemmed RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution
title_short RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution
title_sort riboflow, ribor and ribopy: an ecosystem for analyzing ribosome profiling data at read length resolution
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7203755/
https://www.ncbi.nlm.nih.gov/pubmed/31930375
http://dx.doi.org/10.1093/bioinformatics/btaa028
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