Currently, there exists an unprecedented amount of publicly available RNA-seq data from cohorts of related and unrelated individuals available through repositories such as the Genotype-Tissue Expression database (GTEx) and the Cancer Genome Atlas (TCGA), amongst others. Concomitantly, there exists an ever-growing number of effective analysis and visualization tools for the interrogation of gene expression data from these public cohort sources. Differential expression analyses are available in many of these applications, most commonly enabling normal-tumor comparisons or comparisons between different cancer subtypes however, more bespoke DEA comparisons are challenging and remain in the domain only for the bioinformatically proficient.

To address this, we developed TRanscriptome ANalysis of StratifiEd CohorTs (TRANSECT), a standalone UNIX application and Web-accessible data mining and exploration tool to facilitate scientific inquiry using publicly available transcriptomic cohort data. The main function of TRANSECT is to stratify individuals within a large cohort into distinct groups based solely on gene expression. The resulting groups are subsequently compared to each other to assess for global expression differences and functional outcomes.

With ongoing global efforts to collect and collate large cohort data on a multitude of different disease and non-disease individuals with progressively larger participant numbers and clinical data content, TRANSECT will prove to be an increasingly powerful instrument toward a better understanding of gene regulation and the perturbations that can occur in a diseased state.

TRANSECT is freely available as a command line application (https://github.com/twobeers75/TRANSECT) or online at https://transect.au.

PUBLICATION: NAR Genomics and Bioinformatics, Volume 7, Issue 2, June 2025, lqaf041, https://doi.org/10.1093/nargab/lqaf041