Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data.
Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data.
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DOI：
10.1186/s12864-015-1456-x
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发表时间：
2015-03-24
期刊：
影响因子：
4.4
通讯作者：
Haydon DT
中科院分区：
文献类型：
--
作者：
Orton RJ;Wright CF;Morelli MJ;King DJ;Paton DJ;King DP;Haydon DT
RNA病毒突变率高，在宿主体内以庞大、复杂和异质的群体存在，包括一系列相关但不相同的基因组序列。下一代测序技术通过对病毒基因组进行超深度测序，以及随后对病毒种群内全谱变异的识别，正在彻底改变病毒种群的研究。识别低频变异对于我们理解突变动力学、疾病进展、免疫压力以及检测耐药或致病性突变非常重要。然而，目前的挑战是准确地模拟序列数据中的错误，并区分真正的病毒变体，特别是那些存在于低频率的病毒变体，以及在测序和样品处理过程中引入的错误，这两者都可能是实质性的。我们已经创建了一套新的实验室对照样本，这些样本来自一个包含全长病毒基因组的质粒，在起始种群中具有极其有限的多样性。一个样本在没有PCR扩增的情况下测序，而其他样本在超深测序之前进行RT和PCR扩增。这使得RT和PCR过程引入的误差水平得以评估，并为真正的病毒变异鉴定设定最低频率阈值。我们开发了样本处理和NGS调用过程的基因组尺度计算模型，以详细了解每个步骤的错误，预测RT和PCR错误更可能发生在某些基因组位点。该模型还可用于研究在任何NGS数据集中，在给定的兴趣位点观察到的突变数量是否大于单独处理错误所期望的突变数量。在提供了基本的样本处理信息和站点的覆盖率和质量分数之后，该模型利用拟合的RT-PCR误差分布来模拟仅从处理错误中观察到的突变数量。这些数据集和模型提供了一种有效的方法来分离真正的病毒突变与那些在样品处理和测序过程中错误引入的病毒突变。本文的在线版本（doi:10.1186/s12864-015-1456-x）包含补充材料，授权用户可以使用。
RNA viruses have high mutation rates and exist within their hosts as large, complex and heterogeneous populations, comprising a spectrum of related but non-identical genome sequences. Next generation sequencing is revolutionising the study of viral populations by enabling the ultra deep sequencing of their genomes, and the subsequent identification of the full spectrum of variants within the population. Identification of low frequency variants is important for our understanding of mutational dynamics, disease progression, immune pressure, and for the detection of drug resistant or pathogenic mutations. However, the current challenge is to accurately model the errors in the sequence data and distinguish real viral variants, particularly those that exist at low frequency, from errors introduced during sequencing and sample processing, which can both be substantial. We have created a novel set of laboratory control samples that are derived from a plasmid containing a full-length viral genome with extremely limited diversity in the starting population. One sample was sequenced without PCR amplification whilst the other samples were subjected to increasing amounts of RT and PCR amplification prior to ultra-deep sequencing. This enabled the level of error introduced by the RT and PCR processes to be assessed and minimum frequency thresholds to be set for true viral variant identification. We developed a genome-scale computational model of the sample processing and NGS calling process to gain a detailed understanding of the errors at each step, which predicted that RT and PCR errors are more likely to occur at some genomic sites than others. The model can also be used to investigate whether the number of observed mutations at a given site of interest is greater than would be expected from processing errors alone in any NGS data set. After providing basic sample processing information and the site’s coverage and quality scores, the model utilises the fitted RT-PCR error distributions to simulate the number of mutations that would be observed from processing errors alone. These data sets and models provide an effective means of separating true viral mutations from those erroneously introduced during sample processing and sequencing. The online version of this article (doi:10.1186/s12864-015-1456-x) contains supplementary material, which is available to authorized users.
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使用下一代靶向重新取样对稀有突变的超敏感检测。
影响因子：
14.9
作者：
Flaherty P;Natsoulis G;Muralidharan O;Winters M;Buenrostro J;Bell J;Brown S;Holodniy M;Zhang N;Ji HP
通讯作者：
Ji HP
影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
影响因子：
3.8
作者：
Ellard, FM;Drew, J;King, AMQ
通讯作者：
King, AMQ
影响因子：
--
作者：
EIGEN, M;SCHUSTER, P
通讯作者：
SCHUSTER, P
影响因子：
3.1
作者：
Kampmann, Marie-Louise;Fordyce, Sarah L.;Gilbert, M. Thomas P.
通讯作者：
Gilbert, M. Thomas P.