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Indel and Carryforward Correction (ICC): a new analysis approach for processing 454 pyrosequencing data

机译:插入缺失和进位校正(ICC):一种处理454个焦磷酸测序数据的新分析方法

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Motivation: Pyrosequencing technology provides an important new approach to more extensively characterize diverse sequence populations and detect low frequency variants. However, the promise of this technology has been difficult to realize, as careful correction of sequencing errors is crucial to distinguish rare variants (similar to 1%) in an infected host with high sensitivity and specificity. Results: We developed a new approach, referred to as Indel and Carryforward Correction (ICC), to cluster sequences without substitutions and locally correct only indel and carryforward sequencing errors within clusters to ensure that no rare variants are lost. ICC performs sequence clustering in the order of (i) homopolymer indel patterns only, (ii) indel patterns only and (iii) carryforward errors only, without the requirement of a distance cutoff value. Overall, ICC removed 93-95% of sequencing errors found in control datasets. On pyrosequencing data from a PCR fragment derived from 15 HIV-1 plasmid clones mixed at various frequencies as low as 0.1%, ICC achieved the highest sensitivity and similar specificity compared with other commonly used error correction and variant calling algorithms.
机译:动机:焦磷酸测序技术提供了一种重要的新方法,可以更广泛地表征各种序列种群并检测低频变异。但是,这项技术的前景很难实现,因为认真纠正测序错误对于区分感染宿主中的稀有变异体(大约1%)具有高灵敏度和特异性至关重要。结果:我们开发了一种新方法,称为Indel和进位校正(ICC),以对没有取代的序列进行聚类,并在集群中仅局部校正indel和前馈测序错误,以确保不会丢失稀有变体。 ICC仅按以下顺序执行序列聚类:(i)均聚物indel图案,(ii)仅indel图案和(iii)仅结转误差,而无需距离截止值。总体而言,ICC消除了在对照数据集中发现的93-95%的测序错误。根据来自以15%的低频率混合的15个HIV-1质粒克隆的PCR片段的焦磷酸测序数据,与其他常用的纠错和变异调用算法相比,ICC获得了最高的灵敏度和相似的特异性。

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