Guanlan Dong

The paper identifies a shared TOP1-mediated mutagenic mechanism in ALS, FTD, and AD through single-cell genomic analysis and experimental validation.

This study benchmarks six duplex sequencing technologies to evaluate their performance in detecting somatic mutations across different genomic contexts and tissue types.

The paper identifies epistatic interactions between cancer driver mutations and allele-specific copy number variations in tumors, showing their impact on patient survival.

The paper identifies somatic genomic alterations in neurons from CTE patients, finding patterns similar to Alzheimer's disease and suggesting mechanisms beyond repetitive head impacts.

The paper introduces Duplex-Multiome, a method to identify cell-type-specific somatic mutations in brain tissue, revealing age-related mutation patterns and developmental lineage relationships.

The paper examines somatic mutations in neurons across tau pathologic states in Alzheimer's disease and finds increased mutation burden and specific mutational signatures in AD neurons.

This study uses linked-read sequencing to phase somatic mutations in multiple myeloma, extending haplotype blocks and revealing phasing patterns in key mutated genes.

Dictys infers dynamic gene regulatory networks from single-cell multi-omics data, revealing context-specific and developmental GRNs in human blood and mouse skin.

NetBoxR identifies cancer-related biological processes using network analysis, combining multiple data types to find functional modules without relying on curated gene sets.

The paper quantifies protein levels across 32 human tissues and compares them to transcriptome data, revealing insights into post-transcriptional regulation and disease mechanisms.

The paper identifies cancer-specific driver gene mutations associated with immunotherapy outcomes in a multi-cancer cohort of 1,525 patients.