It is shown that genome-wide enhancer annotations and candidate genetic variants predicted by DNABERT-Enhancer provide valuable resources for genome interpretation in functional and clinical genomics studies and can be explored interactively via the web application.
PGViS (Personal Genome Variant interpretation Score), a statistical framework that quantifies individual non-coding germline regulatory risk in non–small cell lung cancer (NSCLC), is presented, offering a cost-effective route to personal-genome risk assessment from germline variants alone.
Inherited factors account for a large share of breast cancer susceptibility, yet the biological consequences of most risk variants are still poorly understood. To address this gap, we studied 175 breast cancer risk variants confirmed by genome‐wide association studies and gathered the genes that lie near them. Two co...
A machine learning model is trained, DBP-CanPred, to identify driver mutations in DBPs using the sequence-derived evolutionary features, as well as structure-based features such as mutation-perturbed structural descriptors, which contributes to understanding mutation patterns in DNA-binding proteins and supports varian...
A family of classification models, scE2G, is introduced that predict enhancer–gene regulatory interactions from single-cell datasets and enable mapping of these interactions across diverse cell types and tissues and will enable accurate mapping of enhancer–gene regulatory interactions across thousands of human cell typ...
This work identified 709 variants mapping to 140 risk regions, that are associated with significant variation between REF and ALT alleles, that may influence breast cancer risk through expression of CCDC88C, which in turn could impact prognosis.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
