SVXplorer: Three-tier approach to identification of structural variants via sequential recombination of discordant cluster signatures

The identification of structural variants using short-read data remains challenging. Most approaches that use discordant paired-end sequences ignore non-trivial signatures presented by variants containing 3 breakpoints, such as those generated by various copy-paste and cut-paste mechanisms. This can result in lower precision and sensitivity in the identification of the more common structural variants such as deletions and duplications. We present SVXplorer, which uses a graph-based clustering approach streamlined by the integration of non-trivial signatures from discordant paired-end alignments, split-reads and read depth information to improve upon existing methods. We show that SVXplorer is more sensitive and precise compared to several existing approaches on multiple real and simulated datasets. SVXplorer is available for download at https://github.com/kunalkathuria/SVXplorer.Author summary: Structural variants (SVs) that include duplications, deletions and inversions of large blocks of DNA sequence account for the greatest share of total nucleotide differences between individuals. Most methods to identify SVs focus on deletions, duplications, and inversions which can be identified by the integration of information from coverage and insert length of aligned reads around the breakpoints. These methods either ignore signatures from other non-trivial rearrangements or represent them as a set of novel adjacencies (breakends) in the output to be processed separately. Here, we show that precise accounting of such signatures and using improved methods for clustering and variant filtering markedly enhance the precision and sensitivity of SV calls such as deletions and duplications, and allow for detection of several other categories of SVs.