Jin Hua Liang

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

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.

GRAM predicts the molecular effect of non-coding variants in a cell-type specific manner using SELEX and expression profiles, achieving high performance on MPRA and luciferase assays.

The paper introduces Interactome INSIDER, a browser for genomic studies that provides predicted protein interaction interfaces and enables analysis of mutations in these interfaces.