Structural variants (SVs), defined as rearrangements of genomic sequences, are both a major source of genetic diversity in human populations and are also directly responsible for the pathogenesis of numerous diseases. Many studies have been conducted in the past decade to discover and analyze SVs, however these have predominantly focused on analyzing short-read sequencing data to infer the presence and structure of these variants. A primary limitation shared by all these approaches is their inability to access more complex regions of the genome, particularly repetitive sequences, which preclude the accurately alignment from the shorter read lengths. Recent studies using long-read sequencing technologies have suggested that the genomics community is thus routinely missing tens of thousands of SVs. Here, I outline several efforts to leverage these longer sequences to identify and assess structural variants in previously interrogated well-characterized genomes. I will first describe a new method, PALMER, that uses a pre-masking strategy to identify nested retrotransposition events that have inserted into existing repetitive sequences. Our early results suggest that as many as 40% of mobile element insertions fall in such regions, and thus their discovery will impact ongoing estimates of mobile element insertion rates. Next, I will present a strategy we recently developed for the high throughput assessment and validation of SVs using recurrence analysis of long-reads. This has enabled the proper interpretation of many false positive or mis-annotated variants called by SV detection algorithms, particularly around complex and repetitive sequences, as well as providing additional support for previously tenuous predictions. We believe these tools will aid the genomics community into better deciphering chromosomal structural rearrangements and furthering our understanding of their mechanistic origins and functional impact.