scTenifoldKnk is a machine learning workflow designed to perform virtual gene knockout experiments using single-cell RNA sequencing (scRNA-seq) data. By constructing gene regulatory networks (GRNs) from wild-type samples and computationally deleting a target gene, the tool can predict gene functions and identify differentially regulated genes without requiring physical knockout animals. Research demonstrates that this computational approach successfully replicates findings from real-world laboratory experiments, including studies on Mendelian disorders like cystic fibrosis and muscular dystrophy. The system is highly efficient, allowing for systematic, large-scale deletions of thousands of genes to map functional landscapes across various cell types. Ultimately, scTenifoldKnk serves as a powerful resource for prioritizing research targets and anticipating experimental outcomes before conducting costly in vivo studies.

References:

• Daniel Osorio, Yan Zhong, Guanxun Li, Qian Xu, Yongjian Yang, Yanan Tian, Robert S. Chapkin, Jianhua Z. Huang, James J. Cai, scTenifoldKnk: An efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation,Patterns,Volume 3, Issue 3, 2022