Unraveling Complex Local Genomic Rearrangements From Long-Read Data

In this paper, we present a graph search approach for identifying arbitrarily complex structural genomic variation. Our method leverages the ability of long reads (e.g. from Pacific Biosciences platforms) to span multiple breakpoints of complicated local rearrangements, allowing us to resolve small-scale complexities that may be overlooked by other tools. We applied our method to a subset of NA12878 germline events using two long read datasets and demonstrate, with a concordance rate of 88.4% between the two sets, an increased ability to denote complex events over baseline calls from short read data. In a majority of the regions analyzed we detected small complexities that flank the breakpoints of larger events, including small insertions, inversions, and duplicated sequences. These patterns of complexity match known mechanisms associated with DNA replication and structural variant formation, and showcase the ability of our approach to efficiently unravel such events. Our method automatically classifies complex structural variant calls as a combination of nested or adjacent reference transformations, allowing users to identify specific structure types of interest. Additionally, an output report is generated for each event with interactive visual representations of the rearrangement.