MIA: Victoria Popic and Chris Rohlicek, A deep learning approach to structural variant discovery

Models, Inference and Algorithms Broad Institute of MIT and Harvard Primer: A deep learning approach to structural variant discovery Chris Rohlicek Popic Lab, Broad Institute Victoria Popic Broad Institute Structural variants (SV) are the greatest source of genetic diversity in the human genome and play a pivotal role in diseases such as Alzheimers, autism, autoimmune and cardiovascular disorders, and cancer. Breakthroughs in wholegenome sequencing, especially the advent of longread technologies, have enabled significant progress in method development geared toward SV detection. Current stateoftheart approaches extract handcrafted features from the data and employ expertdriven statistical modeling or heuristics to predict different SV classes. However, manual engineering of SVinformative features and models is challenging given the multidimensionality of the sequencing data and the diversity of SV types, sizes, and sequencing platforms. As a result, general SV discov