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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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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 |
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). |
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