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 Alzheimer’s, autism, autoimmune and cardiovascular disorders, and cancer. Breakthroughs in whole-genome sequencing, especially the advent of long-read technologies, have enabled significant progress in method development geared toward SV detection. Current state-of-the-art approaches extract hand-crafted features from the data and employ expert-driven statistical modeling or heuristics to predict different SV classes. However, manual engineering of SV-informative features and models is challenging given the multi-dimensionality of the sequencing data and the diversity of SV types, sizes, and sequencing platforms. As a result, general SV discov