Boxiang Liu

GHIST+ reconstructs tissue-wide molecular maps from H&E histology, enabling cohort-scale molecular reconstruction from sparse spatial transcriptomic data.

This paper develops a high-quality, scalable workflow for whole-cell single-cell RNA sequencing of adult cardiomyocytes and non-myocytes using in-cell ligation and split-pool barcoding, improving data quality and throughput.

The paper introduces CIMA, a multi-omics atlas of human immune cells, identifying regulatory mechanisms and genetic associations across sex, age, and genetic factors.

EPIC-unmix improves cell-type specific analysis from bulk RNA-seq by integrating single-cell reference profiles, identifying novel genes and eQTLs in Alzheimer's disease.

scPrediXcan improves transcriptome-wide association studies by integrating deep learning for cell-type-specific gene expression prediction, identifying more causal genes and explaining more GWAS loci in type 2 diabetes and systemic lupus erythematosus.

siRNADesign uses a Graph Neural Network to predict siRNA efficacy by analyzing RNA sequences and interactions, achieving state-of-the-art performance in both computational and experimental settings.

The paper presents AIDA, a diverse single-cell RNA-seq atlas of immune cells, revealing sub-continental diversity impacts on immune cell properties and identifying functional genetic variants.

The paper introduces scLinaX, a method to quantify X chromosome inactivation escape using scRNA-seq, revealing heterogeneity across cell types and tissues.

This study identifies PDGFD as a CAD risk gene, showing it promotes SMC expansion, migration, and inflammation, contributing to vascular calcification and plaque recruitment.

LinearTurboFold predicts conserved RNA structures for SARS-CoV-2 homologs efficiently, capturing long-range interactions and enabling global structural analysis of full-length genomes.

The paper uses GTEx data to analyze GWAS loci, showing dose-dependent RNA effects and proposing target genes for 47% of loci using combined methods.

The paper uses single-cell epigenomic data to identify functional SNPs associated with Alzheimer's and Parkinson's disease, revealing novel gene-disease associations and regulatory mechanisms.

CoV-Seq provides a webserver and command-line tool for rapid analysis and visualization of SARS-CoV-2 genomes, automatically predicting gene boundaries and identifying variants.

The paper maps QTLs for TCF21 binding, chromatin accessibility, and chromosomal looping in coronary artery smooth muscle cells and links them to CAD GWAS loci.

The paper introduces LATE and TRANSLATE autoencoder-based methods for imputing single-cell RNA-seq data, showing improved performance over existing methods.

This study identifies fRPE-specific genes and variants associated with AMD and myopia, revealing novel e/sQTL and mechanisms linking a common variant to both diseases.

The paper analyzes gene expression and splicing changes in human aging using longitudinal RNA sequencing data from the PIVUS study, finding stability in most genetic regulation but dynamic changes in a subset of genes.

This study evaluates the use of whole blood transcriptome sequencing for diagnosing rare diseases, finding an 8.5% diagnostic rate across 56 undiagnosed cases.

The paper identifies five genes modulating coronary artery disease risk through smooth muscle cells by integrating eQTL and GWAS data with transcriptomic and epigenomic analyses.

The paper develops S-PrediXcan to compute gene expression associations from GWAS summary data and applies it to 44 GTEx tissues and 100+ phenotypes, finding tissue-specific effects and colocalized associations.

This study compares mutation frequencies between leptomeningeal and solid lung cancer brain metastases, finding higher EGFR mutations in leptomeningeal disease and no KRAS mutations.