Journal article

Performance of neural network basecalling tools for Oxford Nanopore sequencing

Ryan R Wick, Louise M Judd, Kathryn E Holt

Genome Biology | BMC | Published : 2019


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 result..

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University of Melbourne Researchers


Funding Acknowledgements

This work was supported by the Bill & Melinda Gates Foundation, Seattle and an Australian Government Research Training Program Scholarship. KEH is supported by a Senior Medical Research Fellowship from the Viertel Foundation of Victoria.