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

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

Abstract We develop a general computational approach for improving the accuracy of basecalling with Oxford Nanopore’s 1D2 and related sequencing protocols. Our software PoreOver ( https://github.com/jordisr/poreover ) finds the consensus of two neural networks by aligning their probability profiles,...

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Bibliographic Details
Main Authors: Jordi Silvestre-Ryan, Ian Holmes
Format: Article
Language:English
Published: BMC 2021-01-01
Series:Genome Biology
Online Access:https://doi.org/10.1186/s13059-020-02255-1
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spelling doaj-61f72263d4fe493f98a831ce6b7c96372021-01-24T12:44:25ZengBMCGenome Biology1474-760X2021-01-012211610.1186/s13059-020-02255-1Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencingJordi Silvestre-Ryan0Ian Holmes1Department of Bioengineering, University of CaliforniaDepartment of Bioengineering, University of CaliforniaAbstract We develop a general computational approach for improving the accuracy of basecalling with Oxford Nanopore’s 1D2 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.https://doi.org/10.1186/s13059-020-02255-1
collection DOAJ
language English
format Article
sources DOAJ
author Jordi Silvestre-Ryan
Ian Holmes
spellingShingle Jordi Silvestre-Ryan
Ian Holmes
Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing
Genome Biology
author_facet Jordi Silvestre-Ryan
Ian Holmes
author_sort Jordi Silvestre-Ryan
title 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_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_sort pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing
publisher BMC
series Genome Biology
issn 1474-760X
publishDate 2021-01-01
description Abstract We develop a general computational approach for improving the accuracy of basecalling with Oxford Nanopore’s 1D2 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.
url https://doi.org/10.1186/s13059-020-02255-1
work_keys_str_mv AT jordisilvestreryan pairconsensusdecodingimprovesaccuracyofneuralnetworkbasecallersfornanoporesequencing
AT ianholmes pairconsensusdecodingimprovesaccuracyofneuralnetworkbasecallersfornanoporesequencing
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