HyINDEL - A Hybrid approach for Detection of Insertions and Deletions in Next Generation Sequencing Data

Abstract

Insertion and deletion (INDELs) mutations, the most common type of structural variation in the human genome, has been implicated in numerous human traits and diseases including rare genetic dis- orders and cancer. Next generation sequencing (NGS) technologies have drastically reduced the cost of sequencing whole genomes, greatly contributing to the 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, for the detection of INDELs from paired-end NGS data which integrates clustering, split-mapping and assembly-based approaches. 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 the 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. HyINDEL detects INDELs of all sizes (from small to large) and also identifies the insertion sequences. It is freely available at https://github.com/alok123t/HyINDEL.