Transcription factor enrichment analysis (TFEA) quantifies the activity of multiple transcription factors from a single experiment

Detecting changes in the activity of a transcription factor (TF) in response to a perturbation provides insights into the underlying cellular process. Transcription Factor Enrichment Analysis (TFEA) is a robust and reliable computational method that detects positional motif enrichment associated with changes in transcription observed in response to a perturbation. TFEA detects positional motif enrichment within a list of ranked regions of interest (ROIs), typically sites of RNA polymerase initiation inferred from regulatory data such as nascent transcription. Therefore, we also introduce muMerge, a statistically principled method of generating a consensus list of ROIs from multiple replicates and conditions. TFEA is broadly applicable to data that informs on transcriptional regulation including nascent transcription (eg. PRO-Seq), CAGE, histone ChIP-Seq, and accessibility data (e.g., ATAC-Seq). TFEA not only identifies the key regulators responding to a perturbation, but also temporally unravels regulatory networks with time series data. Consequently, TFEA serves as a hypothesis-generating tool that provides an easy, rigorous, and cost-effective means to broadly assess TF activity yielding new biological insights. Rubin et al. introduce transcription factor enrichment analysis (TFEA), a new motif enrichment method specifically aimed at maximizing the informative nature of differential RNA polymerase initiation data. It provides an easy, rigorous, and cost-effective analysis aimed at deciphering the temporal and mechanistic details of complex regulatory networks.

Automated summary

Transcription Factor Enrichment Analysis (TFEA) is a computational method that detects positional motif enrichment associated with changes in transcription, providing insights into cellular processes. TFEA is applicable to various types of transcriptional regulation data, identifying key regulators and unraveling temporal regulatory networks.