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Towards population-scale long-read sequencing

Long-read sequencing technologies have now reached a level of accuracy and yield that allows their application to variant detection at a scale of tens to thousands of samples. Concomitant with the development of new computational tools, the first population-scale studies involving long-read sequenci...

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Autores principales: De Coster, Wouter, Weissensteiner, Matthias H., Sedlazeck, Fritz J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8161719/
https://www.ncbi.nlm.nih.gov/pubmed/34050336
http://dx.doi.org/10.1038/s41576-021-00367-3
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author De Coster, Wouter
Weissensteiner, Matthias H.
Sedlazeck, Fritz J.
author_facet De Coster, Wouter
Weissensteiner, Matthias H.
Sedlazeck, Fritz J.
author_sort De Coster, Wouter
collection PubMed
description Long-read sequencing technologies have now reached a level of accuracy and yield that allows their application to variant detection at a scale of tens to thousands of samples. Concomitant with the development of new computational tools, the first population-scale studies involving long-read sequencing have emerged over the past 2 years and, given the continuous advancement of the field, many more are likely to follow. In this Review, we survey recent developments in population-scale long-read sequencing, highlight potential challenges of a scaled-up approach and provide guidance regarding experimental design. We provide an overview of current long-read sequencing platforms, variant calling methodologies and approaches for de novo assemblies and reference-based mapping approaches. Furthermore, we summarize strategies for variant validation, genotyping and predicting functional impact and emphasize challenges remaining in achieving long-read sequencing at a population scale.
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spelling pubmed-81617192021-05-28 Towards population-scale long-read sequencing De Coster, Wouter Weissensteiner, Matthias H. Sedlazeck, Fritz J. Nat Rev Genet Review Article Long-read sequencing technologies have now reached a level of accuracy and yield that allows their application to variant detection at a scale of tens to thousands of samples. Concomitant with the development of new computational tools, the first population-scale studies involving long-read sequencing have emerged over the past 2 years and, given the continuous advancement of the field, many more are likely to follow. In this Review, we survey recent developments in population-scale long-read sequencing, highlight potential challenges of a scaled-up approach and provide guidance regarding experimental design. We provide an overview of current long-read sequencing platforms, variant calling methodologies and approaches for de novo assemblies and reference-based mapping approaches. Furthermore, we summarize strategies for variant validation, genotyping and predicting functional impact and emphasize challenges remaining in achieving long-read sequencing at a population scale. Nature Publishing Group UK 2021-05-28 2021 /pmc/articles/PMC8161719/ /pubmed/34050336 http://dx.doi.org/10.1038/s41576-021-00367-3 Text en © Springer Nature Limited 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Review Article
De Coster, Wouter
Weissensteiner, Matthias H.
Sedlazeck, Fritz J.
Towards population-scale long-read sequencing
title Towards population-scale long-read sequencing
title_full Towards population-scale long-read sequencing
title_fullStr Towards population-scale long-read sequencing
title_full_unstemmed Towards population-scale long-read sequencing
title_short Towards population-scale long-read sequencing
title_sort towards population-scale long-read sequencing
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8161719/
https://www.ncbi.nlm.nih.gov/pubmed/34050336
http://dx.doi.org/10.1038/s41576-021-00367-3
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