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Applications Nanopore sequencing accuracy

Accuracy

Page last updated: May 2021

For many years Oxford Nanopore has continuously iterated our technology to improve its performance. We continue to improve the nanopore sensing system, through updates to analytical methods and new chemistries. This page guides you on what to expect from the nanopore sequencing system, and which tools to choose to achieve these results.

introduction

Introduction

Nanopore DNA and RNA sequencing accuracy can be measured in a number of ways, and the relevant metric for a scientist will depend on the specific experiments being performed.

 

As with all systems, choosing the most up to date analysis tools for the analysis that you are interested in is critical, and the quality of the sample can also influence the outcome. With so many relevant variables, clear guidelines are important, and below we have defined some accuracy measurement types, and included recommendations for best performance.

Introduction 2

Raw read accuracy

Nanopore sequencing provides direct electronic analysis of the target molecule, rather than sequencing a synthetic copy or using surrogate markers such as fluorescence. Basecalling algorithms are then used to provide an interpretable output of the sequencing reads. Nanopore basecalling algorithms are continuously improved to enhance accuracy over time, also allowing new methods to be applied to previously sequenced raw data. 

Direct sequencing avoids sources of bias such as PCR and gives native information about the target molecule. We define raw read accuracy as the accuracy achieved when reading a single DNA or RNA fragment/molecule once. Applications for which raw read sequencing is relevant include those where time-to-result maybe be critical, but at this time most applications are more likely to focus on variant calling, consensus accuracy or other metrics. Improvements in raw read accuracy can drive improvements in other accuracy metrics.

Latest updates to nanopore sequencing achieve:

Chemistry Raw read accuracy (modal) Analytical tools Sample
R9.4.1 98.3%

Production software

MinKNOW 4.3 ("Super-accuracy" basecalling model), Guppy 5

Mixed genomes
R10.3 97.5%

Production software

MinKNOW 4.3 ("Super-accuracy" basecalling model), Guppy 5

Mixed genomes
"Q20+" > 99.3% modal

Research algorithm

Bonito

Various

Note this chemistry is in early access phase

introduction 3

Latest updates to nanopore sequencing achieve:

Chemistry Consensus accuracy Analytical tools Sample
R9.4.1 Q50 at ~100X Guppy basecall, Flye assembly, Medaka polish Zymo mock community (bacterial)
R10.3 Q50 at ~50X Guppy basecall, Flye assembly, Medaka polish Zymo mock community (bacterial)
R10.3

Q47 at ~ 60X

with ~80 Mb N50

Guppy basecall, Flye assembly, Medaka polish (human-specific model) Human, HG002, assessed on chr20

 

Consensus accuracy

Building a consensus sequence involves combining multiple copies of a specific DNA/RNA region, sequenced in separate reads, into a single high-quality sequence. In doing so, the multiple copies combined together to form a single sequence means any random errors are averaged and so 'cancelled' out, producing a more accurate ‘consensus’ sequence to work from.

Find out more about assembly & whole-genome sequencing

Introduction 4

Single molecule consensus

Consensus generation can also be applied to specific regions of interest, by combining multiple exact copies of a single original fragment or molecule into a single high-quality sequence. These exact copies could be sequenced together in a single read, for example generated by circular or linear amplification, or could be associated by use of a unique identifier (UMI). Through combining multiple copies together, a higher confidence in accuracy is achieved.

 

Applications where single molecule consensus could be particularly useful include liquid biopsy low-frequency variant detection, or 16S sequencing.

Latest updates to nanopore sequencing achieve:

Chemistry Single molecule consensus accuracy Analytical tools Sample
R9.4.1 ~99.995%, Q45 UMI rRNA amplicons (25X)
R10.3 ~99.995%, Q45 UMI rRNA amplicons (15X)
Introduction 5

Covering all of the genome

To create an accurate picture of the genome, it is important for a sequencing technology to reach all parts of it, even the parts which are difficult to map. Genomes are littered with repetitive and low-complexity regions, which are difficult to sequence and align using traditional technologies. For example, it is estimated that short-read technology reaches only 92% of the human genome, leaving 8% that contains many disease-relevant genes, excluded from the dataset. Nanopore technology has been shown to reduce these “dark” areas of the genome by 81%, shedding light on parts of the genome not sequenced by any other technology (Ebbert, 2019), and giving a more complete picture.

Introduction 6

Variant calling

Single nucleotide variants (SNVs), small indels and structural variants (SVs) are critical for our understanding of how genomic changes drive phenotypes. The ability of nanopore technology to sequence any length of nucleic acid molecule allows for unprecedented resolution of complex structural variants, as well as identification and haplotype phasing of single nucleotide alterations.

 

The ability to accurately call variants is often expressed as precision and recall values, generated from reads covering the position of interest multiple times. Precision is the proportion of calls in the call set that are correct, whereas recall is the percentage of variants present in the genome that are found in the call set.

 

The latest precision, recall and F1 (a harmonic mean of precision and recall) for nanopore chemistries can be found below, along with a recommended tool chain to achieve similar metrics.

Read more about structural variation and small variant calling & phasing.

Oxford Nanopore Technologies Open Datasets: SV, SNP.

Latest updates to nanopore sequencing achieve:

  Chemistry Coverage Precision Recall F1 Tools Sample
SV R9.4.1 50X 95.5 97.5 96.49​ EPI2ME workflow, github pipeline, EPI2ME Labs tutorial human, HG002
  R10.3 60X 95.6 96.4 96.0 EPI2ME workflow, github pipeline, EPI2ME Labs tutorial human, HG002
SNP R9.4.1 50X 99.92 99.92 99.92 Medaka, EPI2ME Labs tutorial human, HG002
  R10.3 60X 98.92 99.46 99.12 Medaka, EPI2ME Labs tutorial human, HG002
Introduction 7

Base modifications

The four ‘canonical’ bases (A, C, G and T in DNA and A, C, G and U in RNA) can be biologically modified by the presence of additional chemical group, such as methylation. These modifications can significantly alter gene expression and are implicated in a range of diseases including cancer. Scientists are only just beginning to scratch the surface of how newly-recognised epigenetic changes impact function, for example, RNA is known to possess over 170 distinct modifications.

Oxford Nanopore’s technology can sequence the DNA or RNA molecules directly, enabling direct, real-time detection of 5mC, 5hmC, 6mA.

This allows for detection of these base modifications with no additional experiments or sample preparation steps required, and modification information is accessible through onboard software. In contrast, traditional technologies can require a separate process called bisulphite sequencing, which uses aggressive sample treatment and has a number of limitations.

Recent publications have shown excellent correlation between nanopore modification detection and existing techniques, as well as further modification sites detected that had previously been missed by alternative approaches.

Read more about epigenetics & methylation.

Find out more about our methylation analysis benchmarking.

Compared to whole-genome bisulphite sequencing, nanopore demonstrates:
Strong correlation
Higher number of CpG positions called
Less data required
Faster analysis
Simpler workflow with no toxic components
Better reproducibility and consistency run-to-run
Phasing of methylation is possible
More even coverage, less effect of GC bias
Introduction 8

Test accuracy

Sequencing may be used to perform a certain biological test, for example presence or absence of a particular organism, species identification, testing for one or more genetic variants, or to perform multi-omics testing in one assay. Test accuracy can be defined as the ability of the technology to answer that question correctly every time, and this can be quantified by identifying the proportion of true and false positives and negatives among a total number of cases. Test accuracy is an important metric for areas such as food safety, and microbial surveillance. Nanopore sequencing has been shown to be effective at accurately performing many different types of tests. Browse the resource centre for examples.

 

For these examples, the analysis pipeline is specific to the test in question, but tool recommendations can be found in the protocol builder.

 

In 2020, the UK Government published a study of 23,000 samples showing that Oxford Nanopore’s first regulated test has gold-standard accuracy. Read the study.

Introduction 9

Future developments

Our goal is to enable to genetic analysis of anything, by anyone, anywhere, and as such we are pursuing constant iterative performance improvements. For many years Oxford Nanopore has continuously iterated our technology to improve its performance. We continue to improve the nanopore sensing system, boosting accuracy performance through updates to analytical methods and new chemistries. Latest releases can be found in the Nanopore Community, or in the News section.

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