Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing

We develop a general computational approach for improving the accuracy of basecalling with Oxford Nanopore’s 1D(2) and related sequencing protocols. Our software PoreOver (https://github.com/jordisr/poreover) finds the consensus of two neural networks by aligning their probability profiles, and is c...

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Detalles Bibliográficos
Autores principales: Silvestre-Ryan, Jordi, Holmes, Ian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Short Report
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7814537/
https://www.ncbi.nlm.nih.gov/pubmed/33468205
http://dx.doi.org/10.1186/s13059-020-02255-1
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author Silvestre-Ryan, Jordi
Holmes, Ian
author_facet Silvestre-Ryan, Jordi
Holmes, Ian
author_sort Silvestre-Ryan, Jordi
collection PubMed
description We develop a general computational approach for improving the accuracy of basecalling with Oxford Nanopore’s 1D(2) and related sequencing protocols. Our software PoreOver (https://github.com/jordisr/poreover) finds the consensus of two neural networks by aligning their probability profiles, and is compatible with multiple nanopore basecallers. When applied to the recently-released Bonito basecaller, our method reduces the median sequencing error by more than half. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-020-02255-1).
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spelling pubmed-78145372021-01-19 Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing Silvestre-Ryan, Jordi Holmes, Ian Genome Biol Short Report We develop a general computational approach for improving the accuracy of basecalling with Oxford Nanopore’s 1D(2) and related sequencing protocols. Our software PoreOver (https://github.com/jordisr/poreover) finds the consensus of two neural networks by aligning their probability profiles, and is compatible with multiple nanopore basecallers. When applied to the recently-released Bonito basecaller, our method reduces the median sequencing error by more than half. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-020-02255-1). BioMed Central 2021-01-19 /pmc/articles/PMC7814537/ /pubmed/33468205 http://dx.doi.org/10.1186/s13059-020-02255-1 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Short Report
Silvestre-Ryan, Jordi
Holmes, Ian
Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing
title Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing
title_full Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing
title_fullStr Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing
title_full_unstemmed Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing
title_short Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing
title_sort pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing
topic Short Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7814537/
https://www.ncbi.nlm.nih.gov/pubmed/33468205
http://dx.doi.org/10.1186/s13059-020-02255-1
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