Metascape is a web-based resource designed to facilitate the comprehensive analysis and interpretation of systems-level datasets, particularly in the context of OMICs (OMICS integrated analysis). It integrates over 40 biological databases and offers a robust analytical pipeline to produce interpretable results. Key features include functional enrichment analysis, interactome analysis, gene annotation, and membership search. Metascape simplifies the user experience with a one-click Express Analysis interface and provides detailed output formats such as Excel spreadsheets, PowerPoint presentations, and graphical summaries. The platform supports multi-gene-list meta-analysis, enabling the identification of common and selective pathways across multiple datasets. Metascape also addresses the issue of outdated data sources by implementing a semi-automatic data synchronization workflow, ensuring regular updates. The tool is designed to be user-friendly and effective for experimental biologists, enhancing the interpretation of complex OMICs data and facilitating the development of actionable hypotheses.Metascape is a web-based resource designed to facilitate the comprehensive analysis and interpretation of systems-level datasets, particularly in the context of OMICs (OMICS integrated analysis). It integrates over 40 biological databases and offers a robust analytical pipeline to produce interpretable results. Key features include functional enrichment analysis, interactome analysis, gene annotation, and membership search. Metascape simplifies the user experience with a one-click Express Analysis interface and provides detailed output formats such as Excel spreadsheets, PowerPoint presentations, and graphical summaries. The platform supports multi-gene-list meta-analysis, enabling the identification of common and selective pathways across multiple datasets. Metascape also addresses the issue of outdated data sources by implementing a semi-automatic data synchronization workflow, ensuring regular updates. The tool is designed to be user-friendly and effective for experimental biologists, enhancing the interpretation of complex OMICs data and facilitating the development of actionable hypotheses.