Performance of neural network basecalling tools for Oxford Nanopore sequencing

By Ryan R. Wick, Louise M. Judd, Kathryn E. Holt. Genome Biology, 2019

1,539 citations recorded by the source corpus.

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. 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. 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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d75cb46415e07a014365741196dbe7b69d4dfed3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

Nanopore sequencing technology, bioinformatics and applications (2021, methodology): “Overall, method development for base calling went through four stages 32,44,58,67,68 : (1) base calling from the segmented current data by HMM at the early stage and by recurrent neural network in late 2016, (2) base calling from raw current data in 2017, (3) using a flip–flop model for identifying…”
Long-read human genome sequencing and its applications (2020, positive): “ONT raw read accuracy is highly dependent on the base-calling algorithm used 38,57 , and recent improvements to these algorithms have increased raw read accuracy substantially in the past 5 years 38 .”
Sequencing DNA with nanopores: Troubles and biases (2021, methodology): “The software in charge of the translation from signal to nucleic sequences, the basecaller, has proven to be crucial over the years for the accuracy of the resulting raw read sequences [4].”
Artificial intelligence in clinical and genomic diagnostics (2019, background): “…deep learning is poised to revolutionize base calling (and as a result, variant identification) for nanopore-based sequencing technologies, which have historically struggled to compete with established sequencing technology because of the error-prone nature of prior base-calling algorithms [55].”