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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:
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
Descripción
Sumario: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).