Performance of neural network basecalling tools for Oxford Nanopore sequencing.

ABSTRACT:

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 i

SUBMITTER: Wick RR 

PROVIDER: S-EPMC6591954 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature