Selin Jessa

This paper develops beCasKAS, a method to directly detect off-target base editor activity in primary cells, revealing distinct off-target profiles and enabling risk stratification of edits.

The paper presents a single-cell multi-omics atlas of human development and uses deep learning to analyze regulatory DNA motifs and their impact on chromatin accessibility and gene expression.

The paper predicts the regulatory effects of non-coding variants across brain and heart cellular contexts using deep learning, identifying rare variants with significant developmental impacts.

This study identifies somatic coding and regulatory variants in DLBCL, revealing novel mechanisms of oncogene deregulation and potential therapeutic targets.

The paper introduces Galaxy tools for analyzing cancer genomics data, demonstrating their use in discovering new lymphoma-related genes from a cohort of 96 diffuse large B-cell lymphomas.