Computational resources for the AKR1A1-deficiency genome-scale metabolic modeling analyses associated with Crosstalk between S-nitrosylation and glycation defines a novel metabolic vulnerability in liver and renal cancers

Description Computational resources for the AKR1A1 deficiency metabolic modeling analyses associated with Crosstalk between S-nitrosylation and glycation defines a novel metabolic vulnerability in liver and renal cancers. This repository contains the processed data and computational resources required to reproduce the genome-scale metabolic modeling analyses reported in the manuscript Crosstalk between S-nitrosylation and glycation defines a novel metabolic vulnerability in liver and renal cancers. The repository supports the metabolic modeling component of the study and does not contain all datasets, experimental results, or analyses presented in the manuscript. The repository contains all processed datasets, computational models, scripts, statistical analyses, and visualization files required to reproduce the metabolic modeling results, figures, and tables reported in the manuscript. The computational analyses are based on publicly available transcriptomic datasets obtained from the NCBI Gene Expression Omnibus (GEO): GEO accession Description GSE62944 TCGA transcriptomic data GSE310784 769-P renal cancer cell-line RNA-seq data GSE310828 HuH7 hepatocellular carcinoma cell-line RNA-seq data The raw transcriptomic datasets are not included in this repository and should be obtained directly from GEO using the accession numbers listed above. This repository contains: Processed datasets generated for the metabolic modeling analyses. Context-specific genome-scale metabolic models generated during the study. MATLAB scripts for metabolic model reconstruction, simulation, and analysis. Shell scripts used to execute the computational workflow on a high-performance computing (HPC) environment. Statistical analysis outputs. Result tables produced throughout the computational analyses. Figures and visualizations generated from the metabolic modeling workflow. Documentation describing the repository structure and the complete analysis workflow. The scripts expect the following directory structure for the downloaded GEO datasets: data/ └── raw_cell_lines/ ├── GSE310784/ │ └── 769-P/ └── GSE310828/ └── HuH7/ Detailed instructions for reproducing the analyses, software requirements, repository organization, and execution workflow are provided in the accompanying README.md file. This repository is intended to facilitate transparency, reproducibility, and reuse of the computational analyses associated with the metabolic modeling component of the study.