Database of lignin catabolism genes with experimentally validated annotations from MetaCyc and E-Lignin databases

This dataset comprises a manually curated collection of genes involved in lignin catabolism, developed within the framework of the Master's dissertation entitled "Bacterial valorization of pre-treated kraft lignin for biolipids production through metagenomic approach". The study was conducted at the Department of Biotechnology, School of Engineering of Lorena, University of São Paulo (Brazil), under the supervision of Prof. Dr. Anuj Kumar. The dataset was compiled from the MetaCyc (Caspi et al., 2014) and E-Lignin (Brink et al., 2019) databases. All entries included in this resource are supported by experimentally validated evidence, as reported in their respective source databases. A curation strategy was applied to ensure consistency, accuracy, and standardization across all entries. The dataset integrates detailed information on gene identifiers, associated metabolic pathways, protein classification, and source organisms. This resource aims to support research in microbial lignin valorization, enzyme function prediction, and metabolic pathway reconstruction. The database is provided as a structured table with the following fields: Gene_name: Standardized gene name or identifier Involved Pathways: Metabolic pathways associated with the gene Protein Class: Functional classification of the encoded protein Protein ID: Identifier of the protein in the source database DataBase Reference: Source database (e.g., MetaCyc, E-Lignin) DataBase ID: Accession or entry identifier in the source database Organism: Source organism in which the gene was identified Reference: Literature reference supporting experimental validation This dataset provides a reliable reference for studies on lignin degradation, supporting applications in biotechnology, bioinformatics, and enzyme discovery. It may be particularly useful for the development of computational models, including machine learning approaches for enzyme function prediction and pathway annotation.