HyINDEL - A Hybrid approach for Detection of Insertions and Deletions

Abstract

Insertion and deletion (INDELs) mutations, the most common type of structural variation in the human genome, have been implicated in numerous human traits and diseases including rare genetic disorders and cancer. Next generation sequencing (NGS) technologies have drastically reduced the cost of sequencing whole genomes, greatly contributing to genome-wide detection of structural variants. However, due to large variations in INDEL sizes and presence of low complexity and repeat regions, their detection remains a challenge. Here we present a hybrid approach, HyINDEL, which integrates clustering, split-mapping and assembly-based approaches, for the detection of INDELs of all sizes (from small to large) and also identifies the insertion sequences. The method starts with identifying clusters of discordant and soft-clip reads which are validated by depth-of-coverage and alignment of soft-clip reads to identify candidate INDELs, while the assembly -based approach is used in identifying the insertion sequence. Performance of HyINDEL is evaluated on both simulated and real datasets and compared with state-of-the-art tools. A significant improvement in recall and F-score metrics as well as in breakpoint support is observed on using soft-clip alignments. It is freely available at https://github.com/alok123t/HyINDEL.