Chunge Zhong

The paper introduces STARK, a toolkit for processing sc3DG-seq data, benchmarks 11 technologies, and establishes scNucleome as a resource for 3D genome research.

This study benchmarks five loss-of-function perturbation tools for studying noncoding genome function, finding dual-SpCas9 is most effective for targeting CREs and recommending RNA interference for noncoding RNA genes.

The paper benchmarks multiple base editors and develops a model to predict editing efficiency, validating edits for disease-associated variants and identifying cancer-related SNVs.

This study identifies convergent therapeutic targets for chemoresistance in cancer using CRISPR screens across multiple drug conditions and cancer models.

The paper introduces BendNet, a neural network that predicts DNA bending at base-resolution from sequences, revealing stiffness patterns and TF binding preferences in the human genome.