Charles Lu

The paper introduces DICE, a CRISPR screening method that improves scalability and robustness over SLICE in THP1 cells by using dual-guide RNA infection with Cas9 electroporation.

This study screens all human paralog pairs to identify synthetic lethal interactions and finds that their strength depends on shared protein interaction networks and cellular context.

The paper introduces an R package, gen3DNet, for generating 3D network models from two correlated data objects with shared factors, using non-negative matrix factorization and partial least squares regression.

The paper defines the chromatin landscape of healthy and injured kidney cells using multi-omics data, identifying regulatory elements and transcription factor networks involved in injury response.

A genome-wide CRISPR screen and genetic analysis identify TNRC18 as a novel regulator of IL-1β production and a risk factor for inflammatory diseases.

This study optimizes genomewide CRISPR screens using AsCas12a and multi-guide arrays, finding that three guides per gene construct provide optimal performance.

The paper presents whole-genome and whole-exome sequencing datasets from a multi-center, cross-platform study to benchmark NGS technologies and bioinformatics pipelines in cancer genomics.

The paper establishes reference samples with high-coverage whole-genome sequencing to create truth sets for somatic mutation and germline variant detection using NGS technologies.

This study evaluates reproducibility and accuracy of cancer mutation detection using whole-genome and whole-exome sequencing across multiple sequencing centers and bioinformatics pipelines.