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ggsashimi: Sashimi plot revised for browser- and annotation-independent splicing visualization

We present ggsashimi, a command-line tool for the visualization of splicing events across multiple samples. Given a specified genomic region, ggsashimi creates sashimi plots for individual RNA-seq experiments as well as aggregated plots for groups of experiments, a feature unique to this software. C...

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Detalles Bibliográficos
Autores principales: Garrido-Martín, Diego, Palumbo, Emilio, Guigó, Roderic, Breschi, Alessandra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6114895/
https://www.ncbi.nlm.nih.gov/pubmed/30118475
http://dx.doi.org/10.1371/journal.pcbi.1006360
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author Garrido-Martín, Diego
Palumbo, Emilio
Guigó, Roderic
Breschi, Alessandra
author_facet Garrido-Martín, Diego
Palumbo, Emilio
Guigó, Roderic
Breschi, Alessandra
author_sort Garrido-Martín, Diego
collection PubMed
description We present ggsashimi, a command-line tool for the visualization of splicing events across multiple samples. Given a specified genomic region, ggsashimi creates sashimi plots for individual RNA-seq experiments as well as aggregated plots for groups of experiments, a feature unique to this software. Compared to the existing versions of programs generating sashimi plots, it uses popular bioinformatics file formats, it is annotation-independent, and allows the visualization of splicing events even for large genomic regions by scaling down the genomic segments between splice sites. ggsashimi is freely available at https://github.com/guigolab/ggsashimi. It is implemented in python, and internally generates R code for plotting.
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spelling pubmed-61148952018-09-15 ggsashimi: Sashimi plot revised for browser- and annotation-independent splicing visualization Garrido-Martín, Diego Palumbo, Emilio Guigó, Roderic Breschi, Alessandra PLoS Comput Biol Research Article We present ggsashimi, a command-line tool for the visualization of splicing events across multiple samples. Given a specified genomic region, ggsashimi creates sashimi plots for individual RNA-seq experiments as well as aggregated plots for groups of experiments, a feature unique to this software. Compared to the existing versions of programs generating sashimi plots, it uses popular bioinformatics file formats, it is annotation-independent, and allows the visualization of splicing events even for large genomic regions by scaling down the genomic segments between splice sites. ggsashimi is freely available at https://github.com/guigolab/ggsashimi. It is implemented in python, and internally generates R code for plotting. Public Library of Science 2018-08-17 /pmc/articles/PMC6114895/ /pubmed/30118475 http://dx.doi.org/10.1371/journal.pcbi.1006360 Text en © 2018 Garrido-Martín 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
Garrido-Martín, Diego
Palumbo, Emilio
Guigó, Roderic
Breschi, Alessandra
ggsashimi: Sashimi plot revised for browser- and annotation-independent splicing visualization
title ggsashimi: Sashimi plot revised for browser- and annotation-independent splicing visualization
title_full ggsashimi: Sashimi plot revised for browser- and annotation-independent splicing visualization
title_fullStr ggsashimi: Sashimi plot revised for browser- and annotation-independent splicing visualization
title_full_unstemmed ggsashimi: Sashimi plot revised for browser- and annotation-independent splicing visualization
title_short ggsashimi: Sashimi plot revised for browser- and annotation-independent splicing visualization
title_sort ggsashimi: sashimi plot revised for browser- and annotation-independent splicing visualization
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6114895/
https://www.ncbi.nlm.nih.gov/pubmed/30118475
http://dx.doi.org/10.1371/journal.pcbi.1006360
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