Donghoon Lee

The paper maps gene expression, chromatin accessibility, and DNA methylation in developing human brain cells to understand regulatory interactions and their role in disease.

The paper introduces dreamlet, an R package for efficient differential expression analysis of large-scale single-cell transcriptomics data, showing improved speed and memory usage compared to existing methods.

This study performs a single-nucleus transcriptome-wide association study to identify gene-trait associations in brain disorders, revealing cell-type-specific genetic regulation and ancestry-specific patterns.

The paper constructs a single-nucleus atlas of genetic regulation in the human brain, identifying cell-type-specific regulatory effects and disease-associated genes.

This study shows that single-cell experiments often lack power to detect small-effect genes, even with 600 donors, and highlights the impact of sequencing depth and cell counts on results.

The paper profiles human myeloid cells across aging and Alzheimer's disease, identifies AD-associated changes, and links GPNMB to neuroprotection via TREM2.

The paper identifies three gene regulatory states associated with cognitive resilience and pathogenesis in Alzheimer's disease using single-cell data from the dorsolateral prefrontal cortex.

The paper constructs a single-cell transcriptomic atlas of the human dorsolateral prefrontal cortex across neurodegenerative and neuropsychiatric diseases, revealing shared and distinct molecular mechanisms.

This study creates a single-nucleus transcriptomic atlas of Parkinson's disease progression, identifying cell-type-specific gene expression changes and linking them to genetic risk factors and disease stages.

This study develops a human brain tissue model (miBrain) to show that APOE4 astrocytes with cholesterol dysregulation promote α-synuclein pathology through endolysosomal dysfunction.

This study creates a single-nucleus transcriptomic atlas of the human DLPFC across the lifespan, revealing three transcriptomic phases and ten age-related trajectories linked to neurodevelopmental and neurodegenerative disease risk.

The paper processes single-nuclei multi-omics data from 388 human brains to identify regulatory elements and build cell-type networks, revealing changes in aging and neuropsychiatric disorders.

The paper identifies cell-type specific chromatin accessibility QTLs and their functional impact on brain disease risk.

The paper evaluates deconvolution methods on human brain data, finding dtangle and bMIND perform best, and identifies decon-eQTLs that explain more schizophrenia heritability than bulk or single-cell eQTLs.

The paper identifies and validates enhancer regions in primary human neural progenitor cells using capture STARR-seq, linking them to genes involved in psychiatric disease pathways.

The paper profiles gene expression and chromatin accessibility in 45,549 cortical nuclei across 6 developmental timepoints to study cell fate determination and neuropsychiatric disease mechanisms.

This paper applies Bayesian structural time series to biomedical sensor data to evaluate interventions, showing its effectiveness in assessing treatment impacts with complex covariates.

DECODE improves enhancer prediction and boundary detection using deep learning and functional assays, achieving higher accuracy and reduced annotation size.

SCAN-ATAC-Sim simulates scATAC-seq data from bulk ATAC-seq to benchmark analysis methods with known cell-type labels.

The paper introduces STARRPeaker, a processing pipeline for STARR-seq data that improves enhancer detection by addressing sequencing biases and RNA stability effects.

NIMBus identifies mutational hotspots by integrating mutation data with genomic features to account for heterogeneity, revealing known drivers and regulatory disruptions in cancer.

RADAR identifies variants affecting RNA-binding protein function by integrating conservation, RNA structure, and tissue-specific data to prioritize noncoding variants.

The paper develops a custom ENCODE annotation for cancer genomics, highlighting TF-RBP interactions and network rewiring during oncogenesis, and validates findings using experimental assays.

DiNeR identifies significant changes in transcription factor co-regulation networks by analyzing ChIP-seq data from disease and normal samples, revealing key disease-associated regulators.

The paper develops a recurrent neural network to predict splicing outcomes using epigenetic signals, showing that spatiotemporal chromatin context is key to splicing regulation.

TopicNet measures transcriptional regulatory network change by abstracting gene regulatory topics and quantifying rewiring scores between cellular states, revealing TFs with significant network connectivity changes and their impact on cancer survival.

ActivePathways integrates multivariate omics data to identify enriched pathways, revealing cancer driver genes and prognostic pathways in breast cancer.

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.