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LUMPY: a probabilistic framework for structural variant discovery

机译:Lumpy:结构变体发现的概率框架

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Comprehensive discovery of structural variation (SV) from whole genome sequencing data requires multiple detection signals including read-pair, split-read, read-depth and prior knowledge. Owing to technical challenges, extant SV discovery algorithms either use one signal in isolation, or at best use two sequentially. We present LUMPY, a novel SV discovery framework that naturally integrates multiple SV signals jointly across multiple samples. We show that LUMPY yields improved sensitivity, especially when SV signal is reduced owing to either low coverage data or low intra-sample variant allele frequency. We also report a set of 4,564 validated breakpoints from the NA12878 human genome. https://github.com/arq5x/lumpy-sv.
机译:来自整个基因组测序数据的结构变化(SV)的综合发现需要多种检测信号,包括读对,分流读取,读取深度和先验知识。由于技术挑战,现存的SV发现算法可以在隔离中使用一个信号,或者最佳使用两个信号。我们提出了一种新的SV发现框架,其自然地整合了多个样本的多个SV信号。我们表明,块状产量提高了灵敏度,尤其是当由于低覆盖数据或低帧内变异等位基因频率而减小了SV信号时。我们还报告了来自NA12878人类基因组的一组4,564个验证的断点。 https://github.com/arq5x/lumpy-sv。

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