Ketrin Gjoni

The paper develops a computational method to predict structural variant impacts on genome folding and identifies ASD-associated variants disrupting neuronal regulatory interactions.

The paper uses a convolutional neural network to predict chromatin organization disruptions caused by somatic structural variants in pediatric tumors, identifying high-impact SVs and regions associated with cancer.

The paper shows human-specific enhancer HAR1984 promotes neurogenesis by altering chromatin interactions and gene regulation in cortical organoids and mice.

This study introduces novel methods for comparing chromatin contact maps and evaluates their performance using Hi-C data and in silico maps, highlighting the need for biologically informed approaches.

SuPreMo is a tool for in silico mutagenesis using sequence-based predictive models to prioritize pathogenic variants and discover functional sequences.

The paper maps lamin-associated chromatin across twelve human cell types, identifying two subtypes with distinct features and developmental dynamics.

The paper introduces ddXR, a method to rapidly and accurately quantify DNA excisions and inversions without sequencing, revealing high rates of large excisions and low dependence on linear distance.

The paper identifies genetic modifiers of cell growth in response to a PCSK9 translational inhibitor and reveals their roles in ribosome quality control.