metaKEGG is a comprehensive software package designed to streamline the visualization and integration of pathway enrichment results from multi-omics data, providing accessible and detailed insights into the molecular mechanisms driving health and disease. Unlike standard pipeline approaches, metaKEGG incorporates novel concepts allowing for clear, granular representation of gene-level or transcript-level expression changes. Beyond transcriptomic analysis, metaKEGG also supports epigenetic and regulatory metadata layers, such as methylation profiles and miRNA target annotations, offering users a versatile solution to depict complex regulatory interactions within a single pathway map. Its modular architecture provides nine analysis pipelines to suit various experimental designs, from comparing gene expression across multiple conditions to the integration of compound-based metabolomics data. Its implementation in Python ensures easy adoption and reproducibility, while a user-friendly web app allows researchers with limited bioinformatics expertise to harness metaKEGG's full potential.
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