For decades, forward and reverse genetic screens have been central in functional studies of genes and beyond. Forward genetic screen starts with phenotypes and aims to determine the genetic basis responsible for a given phenotype, while reverse genetic screen starts from known genes - or more broadly, DNA sequences - and assays the effect of each gene upon perturbation. However, traditional genetic screen typically requires a large-scale experimental setup, and is strongly limited by the available resources and experimental feasibility.
The Xia lab proposed and developed the in silico genetic screen (ISGS) research framework as a next-generation approach to genetic discoveries. The ISGS framework integrates advanced machine learning models and high-throughput in silico genetic perturbation. Similar to the experimental genetic screen, the ISGS framework interrogates the effect of genetic perturbation through accurate computational modeling in an ultra-high-throughput scenario. We recently developed C.Origami, a deep neural network that performs de novo prediction of cell type-specific chromatin organization with optimal performance. Coupling the C.Origami model with the ISGS framework, we systematically analyzed how individual DNA elements affect chromatin organization across the genome. We continue developing novel high-throughput in silico genetic screen frameworks that will enable more widespread applications to drive our discovery in the genome sciences.