Identification of de novo indels from whole genome or exome sequencing data of parent-offspring trios is a challenging task in human disease studies and clinical practices. Existing computational approaches usually yield high false positive rate. In this study, we developed a gradient boosting approach for filtering de novo indels obtained by any computational approaches. Through application on the real genome sequencing data, our approach showed it could significantly reduce the false positive rate of de novo indels without a significant compromise on sensitivity. The software DNMFilter_Indel was written in a combination of Java and R and freely available from the website at https://github.com/yongzhuang/DNMFilter_Indel .

中文翻译：

---

过滤父代/后代三重奏中的从头插入缺失

从全基因组或亲本后代三重奏的外显子组测序数据中鉴定从头插入缺失是人类疾病研究和临床实践中的一项艰巨任务。现有的计算方法通常会产生很高的误报率。在这项研究中，我们开发了一种梯度增强方法来过滤通过任何计算方法获得的从头插入缺失。通过在实际基因组测序数据上的应用，我们的方法表明它可以显着降低从头插入缺失的假阳性率，而不会显着影响灵敏度。DNMFilter_Indel软件是用Java和R编写的，可以从https://github.com/yongzhuang/DNMFilter_Indel网站免费获得。