  Comprehensive discovery of structural variation (SV) in human genomes from
DNA sequencing requires the integration of multiple alignment signals including
read-pair, split-read and read-depth. However, owing to inherent technical
challenges, most existing SV discovery approaches utilize only one signal and
consequently suffer from reduced sensitivity, especially at low sequence
coverage and for smaller SVs. We present a novel and extremely flexible
probabilistic SV discovery framework that is capable of integrating any number
of SV detection signals including those generated from read alignments or prior
evidence. We demonstrate improved sensitivity over extant methods by combining
paired-end and split-read alignments and emphasize the utility of our framework
for comprehensive studies of structural variation in heterogeneous tumor
genomes. We further discuss the broader utility of this approach for
probabilistic integration of diverse genomic interval datasets.
