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HRIBO: high-throughput analysis of bacterial ribosome profiling data

MOTIVATION: Ribosome profiling (Ribo-seq) is a powerful approach based on deep sequencing of cDNA libraries generated from ribosome-protected RNA fragments to explore the translatome of a cell, and is especially useful for the detection of small proteins (50–100 amino acids) that are recalcitrant to...

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Autores principales: Gelhausen, Rick, Svensson, Sarah L, Froschauer, Kathrin, Heyl, Florian, Hadjeras, Lydia, Sharma, Cynthia M, Eggenhofer, Florian, Backofen, Rolf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8337001/
https://www.ncbi.nlm.nih.gov/pubmed/33175953
http://dx.doi.org/10.1093/bioinformatics/btaa959
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author Gelhausen, Rick
Svensson, Sarah L
Froschauer, Kathrin
Heyl, Florian
Hadjeras, Lydia
Sharma, Cynthia M
Eggenhofer, Florian
Backofen, Rolf
author_facet Gelhausen, Rick
Svensson, Sarah L
Froschauer, Kathrin
Heyl, Florian
Hadjeras, Lydia
Sharma, Cynthia M
Eggenhofer, Florian
Backofen, Rolf
author_sort Gelhausen, Rick
collection PubMed
description MOTIVATION: Ribosome profiling (Ribo-seq) is a powerful approach based on deep sequencing of cDNA libraries generated from ribosome-protected RNA fragments to explore the translatome of a cell, and is especially useful for the detection of small proteins (50–100 amino acids) that are recalcitrant to many standard biochemical and in silico approaches. While pipelines are available to analyze Ribo-seq data, none are designed explicitly for the automatic processing and analysis of data from bacteria, nor are they focused on the discovery of unannotated open reading frames (ORFs). RESULTS: We present HRIBO (High-throughput annotation by Ribo-seq), a workflow to enable reproducible and high-throughput analysis of bacterial Ribo-seq data. The workflow performs all required pre-processing and quality control steps. Importantly, HRIBO outputs annotation-independent ORF predictions based on two complementary bacteria-focused tools, and integrates them with additional feature information and expression values. This facilitates the rapid and high-confidence discovery of novel ORFs and their prioritization for functional characterization. AVAILABILITY AND IMPLEMENTATION: HRIBO is a free and open source project available under the GPL-3 license at: https://github.com/RickGelhausen/HRIBO.
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spelling pubmed-83370012021-08-09 HRIBO: high-throughput analysis of bacterial ribosome profiling data Gelhausen, Rick Svensson, Sarah L Froschauer, Kathrin Heyl, Florian Hadjeras, Lydia Sharma, Cynthia M Eggenhofer, Florian Backofen, Rolf Bioinformatics Applications Notes MOTIVATION: Ribosome profiling (Ribo-seq) is a powerful approach based on deep sequencing of cDNA libraries generated from ribosome-protected RNA fragments to explore the translatome of a cell, and is especially useful for the detection of small proteins (50–100 amino acids) that are recalcitrant to many standard biochemical and in silico approaches. While pipelines are available to analyze Ribo-seq data, none are designed explicitly for the automatic processing and analysis of data from bacteria, nor are they focused on the discovery of unannotated open reading frames (ORFs). RESULTS: We present HRIBO (High-throughput annotation by Ribo-seq), a workflow to enable reproducible and high-throughput analysis of bacterial Ribo-seq data. The workflow performs all required pre-processing and quality control steps. Importantly, HRIBO outputs annotation-independent ORF predictions based on two complementary bacteria-focused tools, and integrates them with additional feature information and expression values. This facilitates the rapid and high-confidence discovery of novel ORFs and their prioritization for functional characterization. AVAILABILITY AND IMPLEMENTATION: HRIBO is a free and open source project available under the GPL-3 license at: https://github.com/RickGelhausen/HRIBO. Oxford University Press 2020-11-11 /pmc/articles/PMC8337001/ /pubmed/33175953 http://dx.doi.org/10.1093/bioinformatics/btaa959 Text en © The Author(s) 2020. Published by Oxford University Press. https://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/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Gelhausen, Rick
Svensson, Sarah L
Froschauer, Kathrin
Heyl, Florian
Hadjeras, Lydia
Sharma, Cynthia M
Eggenhofer, Florian
Backofen, Rolf
HRIBO: high-throughput analysis of bacterial ribosome profiling data
title HRIBO: high-throughput analysis of bacterial ribosome profiling data
title_full HRIBO: high-throughput analysis of bacterial ribosome profiling data
title_fullStr HRIBO: high-throughput analysis of bacterial ribosome profiling data
title_full_unstemmed HRIBO: high-throughput analysis of bacterial ribosome profiling data
title_short HRIBO: high-throughput analysis of bacterial ribosome profiling data
title_sort hribo: high-throughput analysis of bacterial ribosome profiling data
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8337001/
https://www.ncbi.nlm.nih.gov/pubmed/33175953
http://dx.doi.org/10.1093/bioinformatics/btaa959
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