biostudies-literatureUnknownQuackenbush ANHGRI NIH HHSNCI NIH HHSLarge-scale, open-access data sets such as the Genotype Tissue Expression Project (GTEx) and The Cancer Genome Atlas (TCGA) include multi-omic data on large numbers of samples along with extensive clinical and phenotypic information. These datasets provide a unique opportunity to discover correlations among clinical and genomic data features that can lead to testable hypotheses and new discoveries. SEAHORSE (http://seahorse.networkmedicine.org/) is a web-based database and search tool for exploratory data analysis in which we have pre-computed statistical associations between available data elements. An easy-to-use user interface allows users to explore significant associations using tabulated summary statistics, data visualizations, and functional enrichment analyses (using RNA-seq data) bioRxiv : the preprint server for biology2025.08.15.670514https://www.ebi.ac.uk/biostudies/studies/S-EPMC12393347biostudies-literatureSEAHORSE: A Serendipity Engine Assaying Heterogeneous Omics-Related Sampling Experiments.PMC12393347R35 CA220523U24 CA231846R01 HG011393Quackenbush AKolluri JQuackenbush JSaha ENhong SBiju RDeConti DKfalseSEAHORSE: A Serendipity Engine Assaying Heterogeneous Omics-Related Sampling Experiments.Large-scale, open-access data sets such as the Genotype Tissue Expression Project (GTEx) and The Cancer Genome Atlas (TCGA) include multi-omic data on large numbers of samples along with extensive clinical and phenotypic information. These datasets provide a unique opportunity to discover correlations among clinical and genomic data features that can lead to testable hypotheses and new discoveries. SEAHORSE (http://seahorse.networkmedicine.org/) is a web-based database and search tool for exploratory data analysis in which we have pre-computed statistical associations between available data elements. An easy-to-use user interface allows users to explore significant associations using tabulated summary statistics, data visualizations, and functional enrichment analyses (using RNA-seq data) 2025-01-01T00:00:00Z2025 Aug2026-04-08T19:15:09.128Z2026-04-08T12:08:48.739ZS-EPMC123933474089477910.1101/2025.08.15.670514