Dataset Information

Increasing the accuracy of nanopore DNA sequencing using a time-varying cross membrane voltage.

ABSTRACT: Nanopore DNA sequencing is limited by low base-calling accuracy. Improved base-calling accuracy has so far relied on specialized base-calling algorithms, different nanopores and motor enzymes, or biochemical methods to re-read DNA molecules. Two primary error modes hamper sequencing accuracy: enzyme mis-steps and sequences with indistinguishable signals. We vary the driving voltage from 100 to 200 mV, with a frequency of 200 Hz, across a Mycobacterium smegmatis porin A (MspA) nanopore, thus changing how the DNA strand moves through the nanopore. A DNA helicase moves the DNA through the nanopore in discrete steps, and the variable voltage moves the DNA continuously between these steps. The electronic signal produced with variable voltage is used to overcome the primary error modes in base calling. We found that single-passage de novo base-calling accuracy of 62.7 ± 0.5% with a constant driving voltage improves to 79.3 ± 0.3% with a variable driving voltage. The variable-voltage sequencing mode is complementary to other methods to boost the accuracy of nanopore sequencing and could be incorporated into any enzyme-actuated nanopore sequencing device.

SUBMITTER: Noakes MT

PROVIDER: S-EPMC6658736 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Increasing the accuracy of nanopore DNA sequencing using a time-varying cross membrane voltage.

Noakes Matthew T MT Brinkerhoff Henry H Laszlo Andrew H AH Derrington Ian M IM Langford Kyle W KW Mount Jonathan W JW Bowman Jasmine L JL Baker Katherine S KS Doering Kenji M KM Tickman Benjamin I BI Gundlach Jens H JH

Nature biotechnology 20190422 6

Nanopore DNA sequencing is limited by low base-calling accuracy. Improved base-calling accuracy has so far relied on specialized base-calling algorithms, different nanopores and motor enzymes, or biochemical methods to re-read DNA molecules. Two primary error modes hamper sequencing accuracy: enzyme mis-steps and sequences with indistinguishable signals. We vary the driving voltage from 100 to 200 mV, with a frequency of 200 Hz, across a Mycobacterium smegmatis porin A (MspA) nanopore, thus chan ...[more]

PMID: 31011178

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Project description:BackgroundLong-read sequencing platforms offered by Oxford Nanopore Technologies (ONT) allow native DNA containing epigenetic modifications to be directly sequenced, but can be limited by lower per-base accuracies. A key step post-sequencing is basecalling, the process of converting raw electrical signals produced by the sequencing device into nucleotide sequences. This is challenging as current basecallers are primarily based on mixtures of model species for training. Here we utilise both ONT PromethION and higher accuracy PacBio Sequel II HiFi sequencing on two plants, Phebalium stellatum and Xanthorrhoea johnsonii, to train species-specific basecaller models with the aim of improving per-base accuracy. We investigate sequencing accuracies achieved by ONT basecallers and assess accuracy gains by training single-species and species-specific basecaller models. We also evaluate accuracy gains from ONT's improved flowcells (R10.4, FLO-PRO112) and sequencing kits (SQK-LSK112). For the truth dataset for both model training and accuracy assessment, we developed highly accurate, contiguous diploid reference genomes with PacBio Sequel II HiFi reads.ResultsBasecalling with ONT Guppy 5 and 6 super-accurate gave almost identical results, attaining read accuracies of 91.96% and 94.15%. Guppy's plant-specific model gave highly mixed results, attaining read accuracies of 91.47% and 96.18%. Species-specific basecalling models improved read accuracy, attaining 93.24% and 95.16% read accuracies. R10.4 sequencing kits also improve sequencing accuracy, attaining read accuracies of 95.46% (super-accurate) and 96.87% (species-specific).ConclusionsThe use of a single mixed-species basecaller model, such as ONT Guppy super-accurate, may be reducing the accuracy of nanopore sequencing, due to conflicting genome biology within the training dataset and study species. Training of single-species and genome-specific basecaller models improves read accuracy. Studies that aim to do large-scale long-read genotyping would primarily benefit from training their own basecalling models. Such studies could use sequencing accuracy gains and improving bioinformatics tools to improve study outcomes.

Dataset Information

Increasing the accuracy of nanopore DNA sequencing using a time-varying cross membrane voltage.

Publications

Increasing the accuracy of nanopore DNA sequencing using a time-varying cross membrane voltage.

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