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

被引:0
|
作者
Jesse J. Salk
Michael W. Schmitt
Lawrence A. Loeb
机构
[1] University of Washington School of Medicine,Department of Pathology
[2] University of Washington School of Medicine,Department of Medicine, Divisions of Hematology and Medical Oncology
[3] University of Washington School of Medicine,Department of Biochemistry
[4] Fred Hutchinson Cancer Research Center,Clinical Research Division
来源
Nature Reviews Genetics | 2018年 / 19卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:269 / 285
页数:16
相关论文
共 50 条
  • [21] Next-Generation Sequencing Demands Next-Generation Phenotyping
    Hennekam, Raoul C. M.
    Biesecker, Leslie G.
    HUMAN MUTATION, 2012, 33 (05) : 884 - 886
  • [22] Next-Generation Sequencing
    Xiong, Momiao
    Zhao, Zhongming
    Arnold, Jonathan
    Yu, Fuli
    JOURNAL OF BIOMEDICINE AND BIOTECHNOLOGY, 2010,
  • [23] Next-generation sequencing
    Haferlach, T.
    ONCOLOGY RESEARCH AND TREATMENT, 2016, 39 : 40 - 41
  • [24] Next-Generation Sequencing
    Le Gallo, Matthieu
    Lozy, Fred
    Bell, Daphne W.
    MOLECULAR GENETICS OF ENDOMETRIAL CARCINOMA, 2017, 943 : 119 - 148
  • [25] Next-generation sequencing
    Jorge S Reis-Filho
    Breast Cancer Research, 11
  • [26] Next-generation sequencing
    Reis-Filho, Jorge S.
    BREAST CANCER RESEARCH, 2009, 11
  • [28] Detecting novel genetic mutations in Chinese Usher syndrome families using next-generation sequencing technology
    Qu, Ling-Hui
    Jin, Xin
    Xu, Hai-Wei
    Li, Shi-Ying
    Yin, Zheng-Qin
    MOLECULAR GENETICS AND GENOMICS, 2015, 290 (01) : 353 - 363
  • [29] Next-generation sequencing data interpretation: enhancing reproducibility and accessibility
    Nekrutenko, Anton
    Taylor, James
    NATURE REVIEWS GENETICS, 2012, 13 (09) : 667 - U93
  • [30] Next-generation sequencing data interpretation: enhancing reproducibility and accessibility
    Anton Nekrutenko
    James Taylor
    Nature Reviews Genetics, 2012, 13 : 667 - 672