SVXplorer: Identification of structural variants through overlap of discordant clusters

Motivation: The identification of structural variants using short-read data remains challenging. Most approaches ignore signatures of complex variants such as those generated by transposable elements. This can result in lower precision and sensitivity in identification of the more common structural variants such as deletions and duplications. Results: We present SVXplorer, which uses a streamlined sequential approach to integrate discordant paired-end alignments with split-reads and read depth information. We show that it outperforms several existing approaches in both reproducibility and accuracy on real and simulated datasets. Availability: SVXplorer is available at https://github.com/kunalkathuria/SVXplorer.