GSE175456
Deep Representation Learning Improves Prediction of LacI-mediated Transcriptional Repression
We analyze LacI mutations with the goal to understand and better predict basal transcriptional repression function after mutation. This study utilized lac repressor variants from the Taylor, et al. 2015 Nature Methods study including ten tiles of synthesized single mutations (NCBI GEO Series GSE75009), and an additional six tiles corresponding to higher-order mutations (this study). Overall design: Identification of protein variants depleted after negative selection by next-generation sequencing.