Transcription factor enrichment analysis (TFEA): Quantifying the activity of hundreds of transcription factors from a single experiment

Abstract

Detecting differential activation of transcription factors (TFs) in response to perturbation provides insight into cellular processes. Transcription Factor Enrichment Analysis (TFEA) is a robust and reliable computational method that detects differential activity of hundreds of TFs given any set of perturbation data. TFEA draws inspiration from GSEA and detects positional motif enrichment within a list of ranked regions of interest (ROIs). As ROIs are typically inferred from the data, 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 (eg. PRO-Seq), CAGE, ChIP-Seq, and accessibility (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.

Deconvolution of multiplexed transcriptional responses to wood smoke particles defines rapid aryl hydrocarbon receptor signaling dynamics

“…Therefore, to identify transcription factors that control the primary response to WSP exposure, we interrogated enhancer regions whose activity, based on PRO-Seq signatures, changed dynamically in response to WSP exposure. To accomplish this, we identified the origin of bidirectional transcription and applied the transcription factor enrichment analysis (TFEA) tool to these regions to identify differential motif enrichment for specific transcription factors ( 40 ). TFEA quantifies the degree of colocalization of transcription factor motif instances within the center of specific genomic regions, which in this case are sites of bidirectional transcription.…”

Genomics-based deconvolution of multiplexed transcriptional responses to wood smoke particles defines rapid AHR signaling dynamics

“…Therefore, to identify transcription factors that control the primary response to WSP exposure, we interrogated enhancer regions whose activity, based on PRO-seq signatures, changed dynamically in response to WSP exposure. To accomplish this, we identified the origin of bidirectional transcription and applied the Transcription Factor Enrichment Analysis (TFEA) tool to these regions to identify differential motif enrichment (DME) for specific transcription factors (43). TFEA quantifies the degree of co-localization of transcription factor motif instances within the center of specific genomic regions, which in this case are sites of bidirectional transcription.…”

“…Some transcription factors are more associated with chromatin remodeling than directly altering RNA polymerase activity, and TFEA can also be applied to regions based on changes in chromatin structure to identify these regulatory pathways (43). Therefore, to determine effects of WSP exposure on chromatin status, we performed the Assay for Transposase Accessible Chromatin using sequencing (ATAC-seq) in Beas-2B cells following 30 and 120 minutes of WSP exposure (42,56).…”

“…Transcription factor enrichment analysis for both PRO-and ATAC-seq datasets was performed using TFEA (v. 1.0; https://github.com/Dowell-Lab/TFEA; (34)). Sets of consensus regions of interest (ROIs) are first defined for all Tfit-called bidirectionals (for PRO-seq) or all MACS2-called peaks (for ATAC-seq) using the muMerge function of TFEA.…”

“…Some transcription factors are more associated with chromatin remodeling than directly altering RNA polymerase activity, and TFEA can also be applied to regions based on changes in chromatin structure to identify these regulatory pathways (34). Therefore, to determine effects of WSP exposure on chromatin status, we performed the Assay for Transposase Accessible Chromatin using sequencing (ATAC-seq) in Beas-2B cells following 30 and 120 minutes of WSP exposure (33,49).Based on MACS2-defined peaks (50), we partitioned the ATAC-seq data temporally into the time-dependent clusters we had previously defined for nascent transcription (Figure 4A), noting that the number of new peaks occurring after 120 minutes of WSP was significantly greater than the number of new peaks at 30 minutes.…”