Shangzhong Li

The paper investigates the phenotypic and transcriptional effects of glycoengineering in CHO cells, identifying distinct clusters and molecular mechanisms associated with different glycosyltransferase modifications.

This paper develops a virus-free CRISPR screening platform for CHO cells, identifying 338 essential genes and 76 genes involved in ER stress.

The paper introduces a framework to predict how changes in omics data affect metabolic functions in mammalian cells using curated metabolic tasks and genome-scale networks.

The paper revises transcription start site annotations in Chinese hamster genes using experimental data to improve gene engineering and glycosylation in CHO cells.

This study quantifies how repeated freeze-thaw cycles affect RNA-Seq reliability, showing that poly(A)-enriched samples lose reproducibility after three cycles.

The paper updates a genome-scale metabolic model of CHO cells by adding new reactions and genes through gap-filling approaches, improving its predictive power.

The paper improves genome annotation for Chinese hamster by integrating RNA-Seq, proteomics, and Ribo-Seq, identifying new proteins and translational events, and addressing retroviral contamination in CHO cell lines.