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..View full abstract
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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.