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,...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2021-01-01
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| Series: | Genome Biology |
| Online Access: | https://doi.org/10.1186/s13059-020-02255-1 |
| Summary: | 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. |
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| ISSN: | 1474-760X |