Abstract
Basecalling accuracy has seen significant improvements over the last 2 years. The current version of ONT's Guppy basecaller performs well overall, with good accuracy and fast performance. If higher accuracy is required, users should consider producing a custom model using a larger neural network and/or training data from the same species.
Keywords
BiologyNanopore sequencingGenome BiologyHuman geneticsComputational biologyArtificial neural networkDNA sequencingNanoporeEvolutionary biologyGenomicsArtificial intelligenceGeneticsComputer scienceGenomeNanotechnologyGeneMaterials science
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Publication Info
- Year
- 2019
- Type
- article
- Volume
- 20
- Issue
- 1
- Pages
- 129-129
- Citations
- 3057
- Access
- Closed
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Cite This
Ryan R. Wick,
Louise M. Judd,
Kathryn E. Holt
(2019).
Performance of neural network basecalling tools for Oxford Nanopore sequencing.
Genome biology
, 20
(1)
, 129-129.
https://doi.org/10.1186/s13059-019-1727-y
Identifiers
- DOI
- 10.1186/s13059-019-1727-y