一种整合概率模型，用于鉴定测序数据中的结构变异。

Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA.

Genome Biol. 2012;13(3):R22. doi: 10.1186/gb-2012-13-3-r22.

Paired-end sequencing is a common approach for identifying structural variation (SV) in genomes. Discrepancies between the observed and expected alignments indicate potential SVs. Most SV detection algorithms use only one of the possible signals and ignore reads with multiple alignments. This results in reduced sensitivity to detect SVs, especially in repetitive regions. We introduce GASVPro, an algorithm combining both paired read and read depth signals into a probabilistic model which can analyze multiple alignments of reads. GASVPro outperforms existing methods with a 50-90% improvement in specificity on deletions and a 50% improvement on inversions.

双端测序是一种常用的方法，用于鉴定基因组中的结构变异（SV）。观察到的和预期的比对之间的差异表明存在潜在的 SV。大多数 SV 检测算法仅使用可能的信号之一，并忽略具有多个比对的读取。这导致对 SV 的检测灵敏度降低，特别是在重复区域。我们引入了 GASVPro，这是一种将配对读取和读取深度信号组合到概率模型中的算法，该算法可以分析读取的多个比对。GASVPro 在缺失检测的特异性方面优于现有方法，提高了 50-90%，在倒位检测方面提高了 50%。