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Enhancing the accuracy of next-generation sequencing for detecting rare and subclonal mutations

Nature Reviews Genetics volume 19, pages 269–285 (2018)Cite this article

Subjects

Key Points

  • The ability to identify low-frequency genetic variants among heterogeneous populations of cells or DNA molecules is important in many fields of basic science, clinical medicine and other applications, yet current high-throughput DNA sequencing technologies have an error rate between 1 per 100 and 1 per 1,000 base pairs sequenced, which obscures their presence below this level.

  • As next-generation sequencing technologies evolved over the decade, throughput has improved markedly, but raw accuracy has remained generally unchanged. Researchers with a need for high accuracy developed data filtering methods and incremental biochemical improvements that modestly improve low-frequency variant detection, but background errors remain limiting in many fields.

  • The most profoundly impactful means for reducing errors, first developed approximately 7 years ago, has been the concept of single-molecule consensus sequencing. This entails redundant sequencing of multiple copies of a given specific DNA molecule and discounting of variants that are not present in all or most of the copies as likely errors.

  • Consensus sequencing can be achieved by labelling each molecule with a unique molecular barcode before generating copies, which allows subsequent comparison of these copies or schemes whereby copies are physically joined and sequenced together. Because of trade-offs in cost, time and accuracy, no single method is optimal for every application, and each method should be considered on a case-by-case basis.

  • Major applications for high-accuracy DNA sequencing include non-invasive cancer diagnostics, cancer screening, early detection of cancer relapse or impending drug resistance, infectious disease applications, prenatal diagnostics, forensics and mutagenesis assessment.

  • Future advances in ultra-high-accuracy sequencing are likely to be driven by an emerging generation of single-molecule sequencers, particularly those that allow independent sequence comparison of both strands of native DNA duplexes.

Abstract

Mutations, the fuel of evolution, are first manifested as rare DNA changes within a population of cells. Although next-generation sequencing (NGS) technologies have revolutionized the study of genomic variation between species and individual organisms, most have limited ability to accurately detect and quantify rare variants among the different genome copies in heterogeneous mixtures of cells or molecules. We describe the technical challenges in characterizing subclonal variants using conventional NGS protocols and the recent development of error correction strategies, both computational and experimental, including consensus sequencing of single DNA molecules. We also highlight major applications for low-frequency mutation detection in science and medicine, describe emerging methodologies and provide our vision for the future of DNA sequencing.

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Figure 1: The signal-to-noise problem.
Figure 2: Methods of consensus-based error correction on short-read platforms.
Figure 3: Methods of single-molecule sequencing consensus-based error correction.
Figure 4: Impact of error correction technology on detection sensitivity.
Figure 5: Applications of rare variant detection.

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Acknowledgements

The authors thank R. Risques, J. Hiatt and A. Boswell for critical review; E. Fox and E. H. Ahn for contributions to early drafts; K. Loubet-Senear, R. Risques and M. Emond for graphics ideas; N. Homer and C. Valentine for software information and members of the Loeb, Kennedy and Risques laboratories at the University of Washington for many lively discussions. This work was supported by National Institutes of Health grants T32CA009515 (J.J.S.) and R01CA193649, P01CA77852, and R33CA181771 (L.A.L.).

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Authors and Affiliations

  1. Department of Pathology, University of Washington School of Medicine, Seattle, WA, USA

    Jesse J. Salk, Michael W. Schmitt & Lawrence A. Loeb

  2. Department of Medicine, Divisions of Hematology and Medical Oncology, University of Washington School of Medicine, Seattle, WA, USA

    Jesse J. Salk & Michael W. Schmitt

  3. Department of Biochemistry, University of Washington School of Medicine, Seattle, WA, USA

    Lawrence A. Loeb

  4. Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Jesse J. Salk & Michael W. Schmitt

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Contributions

J.J.S and L.A.L. contributed to discussion of content and reviewing and editing the manuscript before submission. J.J.S. was primarily responsible for researching data and writing the manuscript. M.W.S. researched the literature and contributed to writing parts of the initial drafts of the article.

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Correspondence to Jesse J. Salk or Lawrence A. Loeb.

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J.J.S., M.W.S. and L.A.L. are equity holders in TwinStrand Biosciences, Inc.

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PowerPoint slides

Glossary

Clonal

When referring to a genetic variant or mutation, it is one that is present in all or most molecules in a population being sequenced. The term typically implies that it arose from a common ancestor, such as a fertilized egg in the case of germline variation, or the earliest founder cell of a tumour.

Subclonal

When referring to a genetic variant or mutation, it is one that is present in only a subset of molecules being sequenced. This may refer to either a variant carried by a subpopulation that arose and expanded within a larger population or through mixing of two or more distinct populations.

Sequencing accuracy

The number of errors made per base pair sequenced. It may be stratified by subtype of error, such as a specific type of base substitution.

Sequencing sensitivity

The ability to detect a variant at a particular variant allele frequency. This depends on both the sequencing accuracy and the number of independent DNA molecules successfully sequenced that include the genomic position (or positions) of interest.

Variant allele frequency

(VAF). The fraction of all molecules being sequenced that carry a specific genetic change or mutation at a particular genomic position.

Digital PCR

DNA amplification carried out in single-molecule reaction chambers. Recently, this has most often entailed microscopic aqueous droplets immersed in oil. When DNA input is sufficiently low, only one molecule will seed each reaction. When allele-specific amplification conditions are used, the number of droplets that successfully amplify can be digitally tabulated to determine the variant allele frequency.

Polony

A population of identical amplification copies that originated from a single founder molecule and are spatially colocalized, such as on the surface of a microbead or as a spot on a surface. It is the biochemical analogue of a bacterial colony on a Petri dish.

Tag-based error correction

Also known as consensus sequencing, an approach for error correction whereby individual DNA molecules are uniquely labelled before amplification and sequencing, and the sequences of the related derivative copies are then compared with each other to exclude errors.

Short-read platforms

Next-generation sequencing systems that generate reads that are dozens to several hundreds of nucleotides in length, for example, the current Illumina and Thermo Fisher Scientific Ion Torrent platforms and previously manufactured Roche 454 and ABI SOLiD platforms. Current versions sequence amplified polonies, not single molecules.

Long-read platforms

Next-generation sequencing systems that generate reads that are thousands to tens of thousands of nucleotides in length. These currently include Pacific Biosciences (PacBio) and Oxford Nanopore Technologies, which sequence single molecules, not polonies, and therefore have a higher error rate than short-read platforms.

Molecular barcode

Also known as a unique molecular identifier (UMI). A set of DNA nucleotide codes where each is affixed to only one or a subset of individual DNA molecules within a sample. The purpose is to uniquely label single molecules for consensus-based error correction or molecular counting. These may be informatically combined with molecule fragmentation points for greater label diversity.

Index sequence

A particular DNA nucleotide code affixed to all molecules within a given DNA sample that is used for multiplexing samples on a single sequencer run.

Sequencing depth

The number of sequencing reads that include a particular genomic position in their sequence. Some may be simply PCR copies of the same molecule.

Molecular depth

The number of collapsed consensus reads derived from an independent DNA molecule that include a particular genomic position.

Tag clashes

The occurrence of two independent molecules being identically labelled by random chance. This may happen if the diversity of the applied molecular barcodes is too low for the number of DNA molecules sequenced. True mutations may erroneously be excluded.

False families

Sets of related molecules where an error has occurred during amplification that mutates the common tag sequence to erroneously make it appear that two independent molecules gave rise to these molecules.

Consensus-making efficiency

The number of raw sequencing reads that are required to form a consensus read. This typically refers to an average: total raw reads divided by total consensus reads.

Molecular conversion efficiency

The fraction of inputted DNA molecules of interest that are recovered as consensus sequences. This is often described in terms of genome-equivalents.

Aneuploidies

Abnormal numbers of chromosomes in a cell. This may be inherited, such as trisomy 21, the basis of Down syndrome, or somatically acquired, such as in cancer.

Metagenomics

The study of complex microbial populations encompassing many co-mingling species that form an ecosystem, for example, an individual's gut microbiota.

Phasing

The proper assignment of two or more variants at spatially distant genomic locations to the derivative nucleic acid molecule, for example, the maternal or paternal allele.

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Salk, J., Schmitt, M. & Loeb, L. Enhancing the accuracy of next-generation sequencing for detecting rare and subclonal mutations. Nat Rev Genet 19, 269–285 (2018). https://doi.org/10.1038/nrg.2017.117

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