Alden King-Yung Leung

This study compares divergent and unidirectional transcriptional regulatory elements, finding that unidirectional elements are younger and less constrained, with some transcription factors modulating their directionality.

This paper introduces DeepDETAILS, a method to reconstruct cell-type-specific genomic signals from bulk data, outperforming traditional methods and providing insights into disease mechanisms.

The paper introduces CIERA-seq to study transcription regulatory element interactions and finds pdTREs and edTREs have distinct functional and chromatin features.

This study identifies a SWI/SNF-targeting therapy effective against a double-negative prostate cancer subtype, showing it reduces viability in both AR-dependent and WNT-dependent models.

This study evaluates multiple MPRA and STARR-seq datasets to identify enhancers, finding inconsistencies due to technical factors and proposing a unified pipeline to improve cross-assay agreement.

The paper presents a high-resolution atlas of transcriptional regulatory elements in human tissues and disease states using PRO-cap and deep learning models to prioritize noncoding variants.

The paper develops a dual reporter assay to measure promoter and enhancer potential, finding that they share regulatory logic and can function reciprocally.

This paper introduces NetFlow3D, a framework that integrates protein structure and interaction networks to identify significant mutation clusters and modules in cancer, revealing biological processes not found by traditional methods.

The paper introduces COMSV, a method for identifying structural variations in cancer samples, showing high sensitivity and specificity and detecting hundreds of novel SVs per sample.

The paper compares RNA sequencing assays and introduces a tool to identify active enhancers, compiling a compendium of enhancer candidates across 120 cell types.

The paper identifies fundamental enhancer units defined by transcription start sites and shows adjacent units operate non-cooperatively.

MaXLinker improves cross-link identification by reducing false positives and increasing sensitivity, revealing 9,319 cross-links in K562 cells.

OMSV identifies large structural variations from optical maps with high accuracy, outperforming existing methods and detecting many missed by sequencing.