Ling Chen

Single-cell eQTL mapping in 5.4 million PBMCs from 1,925 individuals with matched WGS finds common-variant eQTLs for 83% and rare-variant signals for 47% of 21,404 genes.

The paper identifies immune cell type-specific gene expression regulated by tandem repeats using multi-omics data from large cohorts.

The paper uses single cell RNA sequencing to assess the effects of protein-altering variants in three genes, showing that single cell expression signatures are more effective than bulk assays and computational models for predicting pathogenicity.

This study investigates how deep residual neural networks learn and interpret gene regulatory grammars in enhancer sequences, showing their effectiveness and limitations in different scenarios.

The paper identifies short DNA sequence patterns that predict broadly active human enhancers using machine learning, showing that GC-rich motifs are associated with broad activity while DRMs are not conserved across species.