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Swan: a library for the analysis and visualization of long-read transcriptomes

MOTIVATION: Long-read RNA-sequencing technologies such as PacBio and Oxford Nanopore have discovered an explosion of new transcript isoforms that are difficult to visually analyze using currently available tools. We introduce the Swan Python library, which is designed to analyze and visualize transc...

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Detalles Bibliográficos
Autores principales: Reese, Fairlie, Mortazavi, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8189675/
https://www.ncbi.nlm.nih.gov/pubmed/32991665
http://dx.doi.org/10.1093/bioinformatics/btaa836
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author Reese, Fairlie
Mortazavi, Ali
author_facet Reese, Fairlie
Mortazavi, Ali
author_sort Reese, Fairlie
collection PubMed
description MOTIVATION: Long-read RNA-sequencing technologies such as PacBio and Oxford Nanopore have discovered an explosion of new transcript isoforms that are difficult to visually analyze using currently available tools. We introduce the Swan Python library, which is designed to analyze and visualize transcript models. RESULTS: Swan finds 4909 differentially expressed transcripts between cell lines HepG2 and HFFc6, including 279 that are differentially expressed even though the parent gene is not. Additionally, Swan discovers 285 reproducible exon skipping and 47 intron retention events not recorded in the GENCODE v29 annotation. AVAILABILITY AND IMPLEMENTATION: The Swan library for Python 3 is available on PyPi at https://pypi.org/project/swan-vis/ and on GitHub at https://github.com/mortazavilab/swan_vis.
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spelling pubmed-81896752021-06-10 Swan: a library for the analysis and visualization of long-read transcriptomes Reese, Fairlie Mortazavi, Ali Bioinformatics Applications Notes MOTIVATION: Long-read RNA-sequencing technologies such as PacBio and Oxford Nanopore have discovered an explosion of new transcript isoforms that are difficult to visually analyze using currently available tools. We introduce the Swan Python library, which is designed to analyze and visualize transcript models. RESULTS: Swan finds 4909 differentially expressed transcripts between cell lines HepG2 and HFFc6, including 279 that are differentially expressed even though the parent gene is not. Additionally, Swan discovers 285 reproducible exon skipping and 47 intron retention events not recorded in the GENCODE v29 annotation. AVAILABILITY AND IMPLEMENTATION: The Swan library for Python 3 is available on PyPi at https://pypi.org/project/swan-vis/ and on GitHub at https://github.com/mortazavilab/swan_vis. Oxford University Press 2020-11-23 /pmc/articles/PMC8189675/ /pubmed/32991665 http://dx.doi.org/10.1093/bioinformatics/btaa836 Text en © The Author(s) 2020. Published by Oxford University Press. https://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/ (https://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
Reese, Fairlie
Mortazavi, Ali
Swan: a library for the analysis and visualization of long-read transcriptomes
title Swan: a library for the analysis and visualization of long-read transcriptomes
title_full Swan: a library for the analysis and visualization of long-read transcriptomes
title_fullStr Swan: a library for the analysis and visualization of long-read transcriptomes
title_full_unstemmed Swan: a library for the analysis and visualization of long-read transcriptomes
title_short Swan: a library for the analysis and visualization of long-read transcriptomes
title_sort swan: a library for the analysis and visualization of long-read transcriptomes
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8189675/
https://www.ncbi.nlm.nih.gov/pubmed/32991665
http://dx.doi.org/10.1093/bioinformatics/btaa836
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