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Opportunities and challenges in long-read sequencing data analysis
Long-read technologies are overcoming early limitations in accuracy and throughput, broadening their application domains in genomics. Dedicated analysis tools that take into account the characteristics of long-read data are thus required, but the fast pace of development of such tools can be overwhe...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7006217/ https://www.ncbi.nlm.nih.gov/pubmed/32033565 http://dx.doi.org/10.1186/s13059-020-1935-5 |
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author | Amarasinghe, Shanika L. Su, Shian Dong, Xueyi Zappia, Luke Ritchie, Matthew E. Gouil, Quentin |
author_facet | Amarasinghe, Shanika L. Su, Shian Dong, Xueyi Zappia, Luke Ritchie, Matthew E. Gouil, Quentin |
author_sort | Amarasinghe, Shanika L. |
collection | PubMed |
description | Long-read technologies are overcoming early limitations in accuracy and throughput, broadening their application domains in genomics. Dedicated analysis tools that take into account the characteristics of long-read data are thus required, but the fast pace of development of such tools can be overwhelming. To assist in the design and analysis of long-read sequencing projects, we review the current landscape of available tools and present an online interactive database, long-read-tools.org, to facilitate their browsing. We further focus on the principles of error correction, base modification detection, and long-read transcriptomics analysis and highlight the challenges that remain. |
format | Online Article Text |
id | pubmed-7006217 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-70062172020-02-13 Opportunities and challenges in long-read sequencing data analysis Amarasinghe, Shanika L. Su, Shian Dong, Xueyi Zappia, Luke Ritchie, Matthew E. Gouil, Quentin Genome Biol Review Long-read technologies are overcoming early limitations in accuracy and throughput, broadening their application domains in genomics. Dedicated analysis tools that take into account the characteristics of long-read data are thus required, but the fast pace of development of such tools can be overwhelming. To assist in the design and analysis of long-read sequencing projects, we review the current landscape of available tools and present an online interactive database, long-read-tools.org, to facilitate their browsing. We further focus on the principles of error correction, base modification detection, and long-read transcriptomics analysis and highlight the challenges that remain. BioMed Central 2020-02-07 /pmc/articles/PMC7006217/ /pubmed/32033565 http://dx.doi.org/10.1186/s13059-020-1935-5 Text en © The Author(s) 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Review Amarasinghe, Shanika L. Su, Shian Dong, Xueyi Zappia, Luke Ritchie, Matthew E. Gouil, Quentin Opportunities and challenges in long-read sequencing data analysis |
title | Opportunities and challenges in long-read sequencing data analysis |
title_full | Opportunities and challenges in long-read sequencing data analysis |
title_fullStr | Opportunities and challenges in long-read sequencing data analysis |
title_full_unstemmed | Opportunities and challenges in long-read sequencing data analysis |
title_short | Opportunities and challenges in long-read sequencing data analysis |
title_sort | opportunities and challenges in long-read sequencing data analysis |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7006217/ https://www.ncbi.nlm.nih.gov/pubmed/32033565 http://dx.doi.org/10.1186/s13059-020-1935-5 |
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