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SEAHORSE: A Serendipity Engine Assaying Heterogeneous Omics-Related Sampling Experiments
Adam Quackenbush1,2, Jaya Kolluri3,4, Rohan Biju1,5
1Boston University Academy, Boston, MA, USA.
Biorxiv : the Preprint Server for Biology
|September 2, 2025
Summary
SEAHORSE is a new web tool for exploring large biological datasets like GTEx and TCGA. It helps researchers find hidden connections between genes, clinical data, and diseases for new discoveries.
Area of Science:
- Genomics
- Bioinformatics
- Systems Biology
Background:
- Large-scale, open-access datasets like GTEx and TCGA offer multi-omic data with clinical and phenotypic information.
- These resources present opportunities for discovering correlations between clinical and genomic features, driving hypothesis generation and novel insights.
Purpose of the Study:
- To introduce SEAHORSE, a web-based database and search tool designed for exploratory data analysis.
- To facilitate the discovery of statistical associations between diverse data elements within large biological datasets.
Main Methods:
- SEAHORSE pre-computes statistical associations between available data elements.
- It features a user-friendly interface for exploring associations via summary statistics, data visualizations, and functional enrichment analyses (using RNA-seq data).
Main Results:
- The utility of SEAHORSE was demonstrated through the identification of surprising association patterns.
- These patterns were observed across multiple tissues in the Genotype Tissue Expression Project (GTEx) and various cancer types in The Cancer Genome Atlas (TCGA).
Conclusions:
- SEAHORSE provides a valuable platform for exploratory data analysis of multi-omic datasets.
- The tool aids in uncovering complex biological relationships and generating testable hypotheses in genomics and cancer research.
