Publications

This paper presents a new way to rapidly assess variant function using cloud deep learning. Since most tumor suppressors have well-defined subcellular localizations, our approach could be widely applied to functionalize variants of uncertain significance and help improve the utility of genetic testing.

Chao JT*, Roskelley CD, Loewen CJR

BMC Bioinformatics 22, 202 (2021)

As sequencing becomes more economical, we are identifying sequence variations in the population faster than ever. For disease-associated genes, it is imperative that we differentiate a sequence variant as either benign or pathogenic, such that the appropriate therapeutic interventions or surveillance can be implemented. This assay outperformed computational predictions, was scalable, and had a short run time, serving as an ideal alternative for annotating the clinical significance of cancer-associated PTEN variants.

Chao JT, Hollman R, Meyers WM, Melli F, Matreyek KA, Dean P, Fowler DM, Haas K, Roskelley CD, Loewen CJR

Cancer Res 80, 13 (2020)