Xiuqing Zhang

scPAFA efficiently computes pathway activity scores and identifies multicellular disease-related pathway modules in large-scale scRNA-seq data.

This study identifies and characterizes over one million urinary cell free eccDNAs in healthy adults, revealing their genomic distribution, sequence features, and potential role of direct repeats in their biogenesis.

This study evaluates how blood storage conditions affect plasma cell-free RNA transcript profiles, finding that mRNA and lncRNA stability is highest at 4°C for 6 hours or RT for 2 hours.

The paper introduces TRAP-seq to quantify CRISPR editing efficiency and uses it to design improved gRNAs for Cas9, ABE, and CBE with high accuracy and efficiency.

CNV-PG is a machine-learning framework that improves CNV prediction and genotyping accuracy by reducing false positives and integrating results from multiple callers.

D-GPM uses deep learning to predict promoter methylation levels from landmark gene methylation data, outperforming traditional methods in accuracy and correlation.

The paper creates an ATAC-seq atlas of chromatin accessibility in adult mouse tissues, identifying tissue-specific accessible elements and transcription factor motifs.

This study uses a random forest algorithm to predict gene pathogenicity for Mendelian diseases, achieving 80% accuracy and identifying 10,399 protein-coding genes as potential Mendelian disease genes.

The paper develops a CRISPR on-target activity prediction model called GNL using Bayesian Ridge Regression, showing improved cross-dataset and cross-species performance.

This study demonstrates CRISPR/Cas9 can generate extrachromosomal circular DNA and ring chromosomes in human cells, enabling new tools for genetic engineering and disease modeling.