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Visualization and analysis of RNA-Seq assembly graphs
RNA-Seq is a powerful transcriptome profiling technology enabling transcript discovery and quantification. Whilst most commonly used for gene-level quantification, the data can be used for the analysis of transcript isoforms. However, when the underlying transcript assemblies are complex, current vi...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6698738/ https://www.ncbi.nlm.nih.gov/pubmed/31305886 http://dx.doi.org/10.1093/nar/gkz599 |
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author | Nazarie, Fahmi W Shih, Barbara Angus, Tim Barnett, Mark W Chen, Sz-Hau Summers, Kim M Klein, Karsten Faulkner, Geoffrey J Saini, Harpreet K Watson, Mick van Dongen, Stijn Enright, Anton J Freeman, Tom C |
author_facet | Nazarie, Fahmi W Shih, Barbara Angus, Tim Barnett, Mark W Chen, Sz-Hau Summers, Kim M Klein, Karsten Faulkner, Geoffrey J Saini, Harpreet K Watson, Mick van Dongen, Stijn Enright, Anton J Freeman, Tom C |
author_sort | Nazarie, Fahmi W |
collection | PubMed |
description | RNA-Seq is a powerful transcriptome profiling technology enabling transcript discovery and quantification. Whilst most commonly used for gene-level quantification, the data can be used for the analysis of transcript isoforms. However, when the underlying transcript assemblies are complex, current visualization approaches can be limiting, with splicing events a challenge to interpret. Here, we report on the development of a graph-based visualization method as a complementary approach to understanding transcript diversity from short-read RNA-Seq data. Following the mapping of reads to a reference genome, a read-to-read comparison is performed on all reads mapping to a given gene, producing a weighted similarity matrix between reads. This is used to produce an RNA assembly graph, where nodes represent reads and edges similarity scores between them. The resulting graphs are visualized in 3D space to better appreciate their sometimes large and complex topology, with other information being overlaid on to nodes, e.g. transcript models. Here we demonstrate the utility of this approach, including the unusual structure of these graphs and how they can be used to identify issues in assembly, repetitive sequences within transcripts and splice variants. We believe this approach has the potential to significantly improve our understanding of transcript complexity. |
format | Online Article Text |
id | pubmed-6698738 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-66987382019-08-22 Visualization and analysis of RNA-Seq assembly graphs Nazarie, Fahmi W Shih, Barbara Angus, Tim Barnett, Mark W Chen, Sz-Hau Summers, Kim M Klein, Karsten Faulkner, Geoffrey J Saini, Harpreet K Watson, Mick van Dongen, Stijn Enright, Anton J Freeman, Tom C Nucleic Acids Res Computational Biology RNA-Seq is a powerful transcriptome profiling technology enabling transcript discovery and quantification. Whilst most commonly used for gene-level quantification, the data can be used for the analysis of transcript isoforms. However, when the underlying transcript assemblies are complex, current visualization approaches can be limiting, with splicing events a challenge to interpret. Here, we report on the development of a graph-based visualization method as a complementary approach to understanding transcript diversity from short-read RNA-Seq data. Following the mapping of reads to a reference genome, a read-to-read comparison is performed on all reads mapping to a given gene, producing a weighted similarity matrix between reads. This is used to produce an RNA assembly graph, where nodes represent reads and edges similarity scores between them. The resulting graphs are visualized in 3D space to better appreciate their sometimes large and complex topology, with other information being overlaid on to nodes, e.g. transcript models. Here we demonstrate the utility of this approach, including the unusual structure of these graphs and how they can be used to identify issues in assembly, repetitive sequences within transcripts and splice variants. We believe this approach has the potential to significantly improve our understanding of transcript complexity. Oxford University Press 2019-08-22 2019-07-15 /pmc/articles/PMC6698738/ /pubmed/31305886 http://dx.doi.org/10.1093/nar/gkz599 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Computational Biology Nazarie, Fahmi W Shih, Barbara Angus, Tim Barnett, Mark W Chen, Sz-Hau Summers, Kim M Klein, Karsten Faulkner, Geoffrey J Saini, Harpreet K Watson, Mick van Dongen, Stijn Enright, Anton J Freeman, Tom C Visualization and analysis of RNA-Seq assembly graphs |
title | Visualization and analysis of RNA-Seq assembly graphs |
title_full | Visualization and analysis of RNA-Seq assembly graphs |
title_fullStr | Visualization and analysis of RNA-Seq assembly graphs |
title_full_unstemmed | Visualization and analysis of RNA-Seq assembly graphs |
title_short | Visualization and analysis of RNA-Seq assembly graphs |
title_sort | visualization and analysis of rna-seq assembly graphs |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6698738/ https://www.ncbi.nlm.nih.gov/pubmed/31305886 http://dx.doi.org/10.1093/nar/gkz599 |
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