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Performance of neural network basecalling tools for Oxford Nanopore sequencing

Genome biology · 2019 · Vol. 20(1) · pp. 129–129
Ryan R. WickLouise M. JuddKathryn E. Holt

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.

Genomics and Phylogenetic StudiesRNA modifications and cancerMachine Learning in BioinformaticsBiologyNanopore sequencingGenome BiologyHuman geneticsComputational biologyArtificial neural networkDNA sequencingNanoporeEvolutionary biologyGenomics

MeSH terms

Klebsiella pneumoniaeSoftwareNeural Networks, ComputerSequence Analysis, DNANanopores

Funding

  • Bill and Melinda Gates Foundation
  • Sylvia and Charles Viertel Charitable Foundation
  • Australian Government
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