Yongjin Park

G4SCOPE predicts chromatin G-quadruplex occupancy from DNA sequence, revealing epigenetic differences in BRCA1 and BRCA2 deficient cells.

Utanos provides a modular workflow for analyzing shallow whole-genome sequencing data to detect copy number aberrations and signatures.

This study integrates genotype, epigenome, and phenotype data to show immune cells contribute to bipolar disorder pathogenesis, heterogeneity, and therapy through epigenomic and genetic mechanisms.

This paper introduces a scalable topic modeling method for single-cell RNA-seq data that reduces computational demands while maintaining accuracy and enabling integration with bulk data.

This study uses nanopore sequencing to analyze RNA modifications and their correlations with other RNA features in human cells, revealing negative associations and pathway-specific responses.

LaRCH, a tree-structured topic model, improves clustering of single-cell RNA-seq data and reveals transcriptomic differences in SLE by capturing hierarchical immune cell relationships.

The paper identifies synthetic gene network-induced phenotypes by analyzing gene expression in E. coli and validates them through antibiotic resistance experiments.

SPRUCE identifies common cell-cell communication patterns in breast cancer single-cell RNA-seq data, revealing seven interaction signatures and gene networks that explain tumour heterogeneity beyond marker gene expression.

This study analyzes somatic mosaicism in Alzheimer's Dementia using single-cell data, finding increased mutational burden in specific cell types and genes associated with disease processes.

The paper identifies cell-type-specific molecular changes in cerebrovascular cells across brain regions in Alzheimer's Disease, linking them to genetic variants and potential therapeutic targets.

The paper introduces a causal inference framework to prioritize disease genes in single-cell gene expression analysis, identifying 215 causal genes differentially regulated in Alzheimer's disease across cell types.

The paper investigates how genome plasticity and transcriptional organization enable metabolic resilience to nutrient limitation and fasting endurance.

This study identifies exonic variants in ERN1, TACR3, and SPPL2C associated with Alzheimer's disease through exome-wide age-of-onset analysis, with functional insights into their regulatory roles.

The paper deconvolves 3,387 post-mortem brain samples using SPLITR to identify cell-type-specific eQTLs and genetic effects in mental disorders.

The paper presents a large ASE resource from GTEx v8 with 431 million SNP-level and 153 million haplotype-level measurements, and extends phASER to estimate cis regulatory variant effect sizes.

The paper uses bulk RNA-seq data to estimate cell type abundances and identifies cell type interaction QTLs that provide finer resolution of tissue specificity than bulk cis-eQTLs.

The paper examines telomere length variation across 6,391 tissue samples from 952 individuals, showing tissue-specific differences, positive correlations between tissues, and associations with age and ancestry.

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 introduces a method to improve eQTL robustness by correlating gene expression with predicted epigenomic data rather than SNP values.

The paper develops EpiMap, a compendium of 833 epigenomes, to annotate genetic variants and dissect complex traits by analyzing epigenomic data across 800 samples.

The paper identifies genomic variants regulating junction-skipping using RNA-seq and GWAS data to understand their role in complex diseases.

The paper identifies distinct regulatory patterns and trait associations of lncRNA genes compared to protein-coding genes across human tissues.

The paper assesses gene network connectivity's role in disease heritability using LD score regression and finds that network annotations are enriched but largely explained by regulatory annotations.

The paper identifies SNRNP35 as a trauma type-specific PTSD gene through analysis of genetically regulated gene expression in PTSD cases and controls.

This paper introduces a causal inference method to estimate genetic variance and pleiotropy from GWAS summary data, showing improved power and accuracy in simulations and real-world applications.

This paper introduces a Bayesian method to identify causal genes in Alzheimer’s disease by distinguishing mediation from pleiotropy using summary statistics, finding 206 causal genes.

The paper introduces module-based methods to identify differentially regulated network sub-modules using temporal expression data and static network structures, showing improved performance over existing methods in simulations and applications to neural stem cell differentiation.

This paper improves TWAS power by incorporating prediction uncertainty, finding 45 TWAS genes with 17 novel associations and replicating only 2 of 40 previously reported genes.

The paper introduces fQTL, a multi-tissue model for eQTL mapping and gene expression prediction, improving TWAS results for Alzheimer's and schizophrenia.