EAGLE: Explicit Alternative Genome Likelihood Evaluator.
EAGLE: Explicit Alternative Genome Likelihood Evaluator.
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DOI：
10.1186/s12920-018-0342-1
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发表时间：
2018-04-20
影响因子：
2.7
通讯作者：
Horton P
中科院分区：
文献类型：
--
作者：
Kuo T;Frith MC;Sese J;Horton P
从单个样品DNA测序数据中可靠地检测基因组变异，特别是插入和缺失（indel）仍然具有挑战性，部分原因是将测序读数与参考基因组比对中涉及的固有不确定性。在实践中，采用各种专门的质量过滤方法来产生更可靠的推定变体列表，但所得列表通常仍包括许多假阳性。因此，希望能够严格评估数据支持每个推定变体的程度。不幸的是，希望这样做的用户，例如为了优先验证实验的目的，面临着有限的选择。在这里，我们提出了EAGLE，用于评估测序数据支持给定候选基因组变异的程度的方法。EAGLE将候选变异纳入关于个体基因组的明确假设中，然后计算每个假设下观察到的数据（测序读数）的概率。与严重依赖读数与参考基因组的特定比对的方法相比，EAGLE容易解释可能由多重映射或局部未对准引起的不确定性，并使用每个读数的整个长度。我们比较了由几个著名的变异调用者分配给EAGLE的分数，用于在模拟数据和基于真实的基因组测序的基准上对真实的推定变异进行排名的任务。对于插入缺失，EAGLE在模拟数据和全基因组测序基准上获得了显著的改善，并且在外显子组测序基准上获得了适度但统计学上显著的改善。EAGLE将真实变体的评分高于调用者报告的评分，并可用于提高变体调用的特异性。EAGLE可在https://github.com/tony-kuo/eagle上免费获得。本文的在线版本（10.1186/s12920-018-0342-1）包含补充材料，可供授权用户使用。
Reliable detection of genome variations, especially insertions and deletions (indels), from single sample DNA sequencing data remains challenging, partially due to the inherent uncertainty involved in aligning sequencing reads to the reference genome. In practice a variety of ad hoc quality filtering methods are employed to produce more reliable lists of putative variants, but the resulting lists typically still include numerous false positives. Thus it would be desirable to be able to rigorously evaluate the degree to which each putative variant is supported by the data. Unfortunately, users who wish to do this, e.g. for the purpose of prioritizing validation experiments, have been faced with limited options. Here we present EAGLE, a method for evaluating the degree to which sequencing data supports a given candidate genome variant. EAGLE incorporates candidate variants into explicit hypotheses about the individual’s genome, and then computes the probability of the observed data (the sequencing reads) under each hypothesis. In comparison with methods which rely heavily on a particular alignment of the reads to the reference genome, EAGLE readily accounts for uncertainties that may arise from multi-mapping or local misalignment and uses the entire length of each read. We compared the scores assigned by several well-known variant callers to EAGLE for the task of ranking true putative variants on both simulated data and real genome sequencing based benchmarks. For indels, EAGLE obtained marked improvement on simulated data and a whole genome sequencing benchmark, and modest but statistically significant improvement on an exome sequencing benchmark. EAGLE ranked true variants higher than the scores reported by the callers and can used to improve specificity in variant calling. EAGLE is freely available at https://github.com/tony-kuo/eagle. The online version of this article (10.1186/s12920-018-0342-1) contains supplementary material, which is available to authorized users.
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