Dataset for genome-wide profiling of autophagy dynamics under nutrient availability in <em>Saccharomyces cerevisiae</em>

This dataset provides the complete quantitative data supporting all main and extended figures of the study “Time-resolved functional genomics using deep learning reveals global hierarchical control of autophagy”. It is part of the AutoDRY resource, a systems-level map of the genetic network controlling activation and inactivation of autophagy in response to nitrogen availability in yeast. The dataset includes time-resolved measurements of autophagy across 5,919 mutants, derived from high-content fluorescent imaging and automated image analysis using deep learning. Data were further processed through UMAP latent-space embedding and Bayesian factor analysis to infer contributions to autophagosome formation and clearance. The files include autophagy quantifications, statistical analyses, network analyses, and cross-omics integration using random forest modeling. Experimental validation data, including quantitative Pho8Δ60 assays, GFP-Atg8 and Ape1 processing, and qPCR analyses, are also provided. Together, these data enable full reproducibility of the study’s analyses and support reuse for further computational analysis or comparative studies of autophagy regulation. Methods High-content fluorescent imaging Automatic image analysis using FIJI/Image J analysis Deep learning analysis Statistical analysis Network analysis UMAP latent-space and Bayes factors analysis Experimental validations and autophagy flux analysis using Western blotting analysis, quantitative Pho8Δ60 assay, and qPCR analysis Cross-omics analysis using Random forest