Filtering de novo indels in parent-offspring trios

Abstract

Background: 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. Results: 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. Conclusions: 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.