Performance of neural network basecalling tools for Oxford Nanopore sequencing.

Ryan R Wick ORCID logo ; Louise M Judd ; Kathryn E Holt ORCID logo ; (2019) Performance of neural network basecalling tools for Oxford Nanopore sequencing. GENOME BIOLOGY, 20 (1). 129-. ISSN 1474-760X DOI: 10.1186/s13059-019-1727-y
Copy

BACKGROUND: Basecalling, the computational process of translating raw electrical signal to nucleotide sequence, is of critical importance to the sequencing platforms produced by Oxford Nanopore Technologies (ONT). Here, we examine the performance of different basecalling tools, looking at accuracy at the level of bases within individual reads and at majority-rule consensus basecalls in an assembly. We also investigate some additional aspects of basecalling: training using a taxon-specific dataset, using a larger neural network model and improving consensus basecalls in an assembly by additional signal-level analysis with Nanopolish. RESULTS: Training basecallers on taxon-specific data results in a significant boost in consensus accuracy, mostly due to the reduction of errors in methylation motifs. A larger neural network is able to improve both read and consensus accuracy, but at a cost to speed. Improving consensus sequences ('polishing') with Nanopolish somewhat negates the accuracy differences in basecallers, but pre-polish accuracy does have an effect on post-polish accuracy. CONCLUSIONS: 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.


picture_as_pdf
Performance of neural network basecalling tools for Oxford Nanopore sequencing.pdf
subject
Published Version
Available under Creative Commons: Attribution 3.0

View Download

Atom BibTeX OpenURL ContextObject in Span Multiline CSV OpenURL ContextObject Dublin Core Dublin Core MPEG-21 DIDL Data Cite XML EndNote HTML Citation JSON MARC (ASCII) MARC (ISO 2709) METS MODS RDF+N3 RDF+N-Triples RDF+XML RIOXX2 XML Reference Manager Refer Simple Metadata ASCII Citation EP3 XML
Export

Downloads