The rise of full transcriptome acquisition technologies has fueled the rapid proliferation of molecular-level biological data. These large datasets require interpretation beyond the single-gene level to connect them to meaningful biology and clinical impacts. In 2003, we pioneered the gene set enrichment analysis (GSEA) approach 1 to enable the identification of the coordinate activation or repression of sets of genes that share common biology, thereby distinguishing even subtle differences between phenotypes or cellular states. We first released our GSEA software and a companion resource of gene sets, the Molecular Signatures Database (MSigDB), in 2005 (ref. 2).

Historically, the GSEA/MSigDB resource (https://gsea-msigdb. org) focused on the analysis of human-specific datasets. The gene sets in MSigDB were offered exclusively in the human gene space, and analysis of mouse data was minimally supported through basic ortholog mapping to human genes. In recognition of the importance of model organisms, particularly mice, for research into the mechanisms of human disease, we recently expanded the MSigDB to address this by introducing a database of mouse-native gene sets and by substantially improving MSigDB’s ortholog mapping.