Improving calls of differentially transcribed enhancers and their upstream regulators

Most disease-associated variants reside in transcribed regulatory elements (tREs), whose differential transcription enables identification of upstream regulators and enhancer targets. However, their low and highly variable expression complicates confident detection. Therefore, we present Mu_Counts and TFEA-LE, two algorithms for robust identification of differentially transcribed tREs and their transcription factor regulators. Accurately identifying differentially transcribed tREs requires accurate RNA lengths and therefore counts over these regions. Accordingly, we developed two methods: one for precise length inference (LIET-EMG) and another rapid one for counting reads over tREs (Mu_Counts). Armed with newly quantified tREs, TFEA-LE then integrates motif information to simultaneously identify responsive tREs and their likely upstream regulators. We show improved precision and recall over general-purpose tools (e.g. DESeq2) in detecting p53-responsive tREs. We then clarify TF-specific responses within multi-TF perturbations and from chromatin accessibility data in lung cells. Finally we show that the TFEA-LE approach improves TF activity inference, including in complex perturbations where many TFs respond. TFEA-LE is especially effective in technically challenging datasets, (e.g. highly specific or broad responses, outlier samples, or high GC content). Ultimately, these methods advance the systematic characterization of individual tREs, enabling their integration with regulatory networks and disease-associated variants for translational research.