Benchmarking of computational error-correction methods for next-generation sequencing data
Benchmarking of computational error-correction methods for next-generation sequencing data
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DOI：
10.1186/s13059-020-01988-3
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发表时间：
2020-03-17
期刊：
影响因子：
12.3
通讯作者：
Mangul, Serghei
中科院分区：
文献类型：
--
作者：
Mitchell, Keith;Brito, Jaqueline J.;Mangul, Serghei
背景下一代测序的最新进展迅速提高了我们以前所未有的规模研究基因组材料的能力。尽管测序技术有了实质性的改进，但数据中存在的错误仍然有混淆下游分析的风险，并限制了测序技术在临床工具中的适用性。计算误差校正有望消除测序误差，但误差校正算法的相对准确性仍然未知。结果在本文中，我们评估了纠错算法修复不同类型的数据集，包含不同程度的异质性的错误的能力。我们强调了计算误差校正技术在不同生物学领域的优势和局限性，包括免疫基因组学和病毒学。为了证明我们的技术的有效性，我们应用基于UMI的高保真测序方案来消除来自模拟数据和原始读数的测序错误。然后，我们进行了一个现实的误差校正方法的评估。结论在准确性方面，我们发现方法性能在不同类型的数据集之间差异很大，没有一种方法在所有类型的检查数据上表现最好。最后，我们还确定了在精度和灵敏度之间提供良好平衡的技术。
Background Recent advancements in next-generation sequencing have rapidly improved our ability to study genomic material at an unprecedented scale. Despite substantial improvements in sequencing technologies, errors present in the data still risk confounding downstream analysis and limiting the applicability of sequencing technologies in clinical tools. Computational error correction promises to eliminate sequencing errors, but the relative accuracy of error correction algorithms remains unknown. Results In this paper, we evaluate the ability of error correction algorithms to fix errors across different types of datasets that contain various levels of heterogeneity. We highlight the advantages and limitations of computational error correction techniques across different domains of biology, including immunogenomics and virology. To demonstrate the efficacy of our technique, we apply the UMI-based high-fidelity sequencing protocol to eliminate sequencing errors from both simulated data and the raw reads. We then perform a realistic evaluation of error-correction methods. Conclusions In terms of accuracy, we find that method performance varies substantially across different types of datasets with no single method performing best on all types of examined data. Finally, we also identify the techniques that offer a good balance between precision and sensitivity.