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DNA sequencing to identify genetic variants is becoming increasingly valuablein clinical settings. Assessment of variants in such sequencing data iscommonly implemented through Bayesian heuristic algorithms. Machine learninghas shown great promise in improving on these variant calls, but the input forthese is still a standardized "pile-up" image, which is not always best suited.In this paper, we present a novel method for generating images from DNAsequencing data, which interlaces the human reference genome with personalizedsequencing output, to maximize usage of sequencing reads and improve machinelearning algorithm performance. We demonstrate the success of this in improvingstandard germline variant calling. We also furthered this approach to includesomatic variant calling across tumor/normal data with Siamese networks. Theseapproaches can be used in machine learning applications on sequencing data withthe hope of improving clinical outcomes, and are freely available fornoncommercial use at www.ccg.ai.

Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection