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Muver, a computational framework for accurately calling accumulated mutations

机译：Muver，一种用于准确调用累积突变的计算框架

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Identification of mutations from next-generation sequencing data typically requires a balance between sensitivity and accuracy. This is particularly true of DNA insertions and deletions (indels), that can impart significant phenotypic consequences on cells but are harder to call than substitution mutations from whole genome mutation accumulation experiments. To overcome these difficulties, we present muver, a computational framework that integrates established bioinformatics tools with novel analytical methods to generate mutation calls with the extremely low false positive rates and high sensitivity required for accurate mutation rate determination and comparison. Muver uses statistical comparison of ancestral and descendant allelic frequencies to identify variant loci and assigns genotypes with models that include per-sample assessments of sequencing errors by mutation type and repeat context. Muver identifies maximally parsimonious mutation pathways that connect these genotypes, differentiating potential allelic conversion events and delineating ambiguities in mutation location, type, and size. Benchmarking with a human gold standard father-son pair demonstrates muver’s sensitivity and low false positive rates. In DNA mismatch repair (MMR) deficient Saccharomyces cerevisiae, muver detects multi-base deletions in homopolymers longer than the replicative polymerase footprint at rates greater than predicted for sequential single-base deletions, implying a novel multi-repeat-unit slippage mechanism. Benchmarking results demonstrate the high accuracy and sensitivity achieved with muver, particularly for indels, relative to available tools. Applied to an MMR-deficient Saccharomyces cerevisiae system, muver mutation calls facilitate mechanistic insights into DNA replication fidelity.
机译：从下一代测序数据识别突变通常需要在灵敏度和精度之间的平衡。对于DNA插入和缺失（吲哚）尤其如此，这可以赋予对细胞的显着表型后果，但比来自全基因组突变累积实验的取代突变更难以呼叫。为了克服这些困难，我们呈现MUVER，这是一种计算框架，其与建立的生物信息学工具集成了具有新的分析方法，以产生具有极低误差率和精确突变率测定和比较所需的极低假阳性率和高灵敏度的突变调用。 Muver使用祖先和后代等位基因的统计比较来识别变体基因座，并通过突变类型和重复上下文将基因型分配包含测序误差的每个样本评估。 MUVER识别最大的突变突变途径，可以连接这些基因型，区分潜在的等位基因转换事件和划定突变位置，类型和尺寸的歧义。与人金标准的父子对的基准展示了Muver的敏感性和低假阳性率。在DNA错配修复（MMR）缺陷酿酒酵母酿酒酵母中，MUVER在均聚物上检测比重复聚合酶足部的均聚物的多基础缺失，比预测的顺序单碱基缺失更大，暗示了一种新的多重复单元滑动机制。基准测试结果展示了Muver实现的高精度和灵敏度，特别是对于可用工具而言，特别是对于Indels。应用于MMR缺乏酿酒酵母酿酒酵母系统，MUVER突变调用促进了机械洞察力的DNA复制保真度。

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