Inversion detection using PacBio long reads
Structural variations have received considerable attention in the past decade owing to their importance in disease aetiology and ecological adaptation. Many prior efforts have exploited short paired-end reads to detect structural variations and, more recently, improved approaches have combined newer long reads with short ones to better predict variants. In this paper, we propose a new computational framework that uses only long reads to target a specific type of structural variations: large inversions. Our approach is complementary to state-of-the-art methods, but models identifying inversions as a Max-Cut problem. We show that this new approach is effective for predicting large inversions comparing to current structural variation detection tools. This new formulation also uncovers more complex structural variants that are not discovered by alternative frameworks. We conclude that our new approach is potentially powerful for detecting inversions in complex genomes.

使用PacBio长读取反演检测
由于结构变异在疾病病因学和生态适应方面的重要性，结构变异在过去十年中受到了相当大的关注。
许多先前的工作已经利用短配对端读取来检测结构变化，最近，改进的方法结合了新的长读取和短读取来更好地预测变异。
在这篇论文中，我们提出了一个新的计算框架，它只使用长读取来针对一种特定类型的结构变化:大反转。
我们的方法是对最先进的方法的补充，但模型识别逆序是一个最大割问题。
我们表明，与目前的结构变化检测工具相比，这种新方法对预测大的反演是有效的。
这个新的公式还揭示了其他框架所没有发现的更复杂的结构变体。
我们得出的结论是，我们的新方法在检测复杂基因组的反转方面具有强大的潜力。