The Journal of Molecular Diagnostics: Modular RNA-Sequencing Analytics for Exploratory Biomarker Discovery Using Public Data
Framework for extracting validated findings from public RNA-sequencing data at any processing stage, applied across three disease contexts to build biomarker panels, prioritize targets, and locate where signals actually live.
Publicly available RNA-sequencing data provide a cost-effective foundation for biomarker discovery, validation, and hypothesis generation. However, differences in study design, metadata, file formats, and processing can complicate cross-study analyses. We developed a modular, open-source analytics framework that standardizes quality control, differential expression, pathway analysis, and competitive machine learning across disparate datasets. We demonstrated the framework by identifying signatures associated with COVID-19 severity, developing concise biomarker panels from multicohort sepsis data, and examining the tissue- and cell-type specificity of NAPE-PLD in atherosclerosis. These applications show that standardized analysis can reduce noise and extract meaningful biological signals from both bulk and single-cell data. By repurposing public datasets, the framework supports exploratory biomarker discovery, patient stratification, and therapeutic development.
Read the full publication at https://www.jmdjournal.org/article/S1525-1578(26)00129-7/fulltext.
