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We introduce the framework Cue designed to call and genotype structural variants (SV), including complex and subclonal SVs, using data from a range of sequencing platforms. At a high level, Cue first converts sequence alignments into multi-channel images that capture platform-specific read alignment signals and then detects SV breakpoints (categorized by type and genotype) directly in these images using a stacked hourglass network. In this talk we will provide an overview of the framework and present the latest results in the detection of five common SV types (namely: deletions, tandem duplications, inversions, inverted duplications, and inversions flanked by deletions; the latter two are examples of complex SVs, which have been linked to several genomic disorders) from short, linked, and long read data. We will also discuss the challenges of generating training data and benchmarking in the absence of comprehensive ground-truth SV callsets.