Abstract
Biologically meaningful interpretation of transcriptomic datasets remains challenging, particularly when context-specific gene sets are either unavailable or too generic to capture the underlying biology. We here present InCURA, an integrative clustering strategy based on transcription factor (TF) motif occurrence patterns in gene promoters. InCURA takes as input lists of (i) all expressed genes, used solely to identify dataset-specific expressed TFs, and (ii) differentially regulated genes (DRGs) used for clustering. Promoter sequences of DRGs are scanned for TF binding motifs, and the resulting counts are compiled into a gene-by-TFBS matrix. InCURA then uses unsupervised clustering to infer gene modules with shared predicted regulatory input. Applying InCURA to diverse biological datasets, we uncovered functionally coherent gene modules revealing upstream regulators and regulatory programs that standard enrichment or co-expression analyses fail to detect. In summary, InCURA provides a user-friendly, regulation-centric tool for dissecting transcriptional responses, particularly in settings lacking context-specific gene sets.