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

Abstract

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.

Protocol Variations in Run-On Transcription Dataset Preparation Produce Detectable Signatures in Sequencing Libraries

“…14, see Materials and Methods), while library preparation methods were compared by combining Tfit regions from GRO-LIG and GRO-CIRC libraries (Fig 4E). In every case, regions were combined using muMerge [6] and differential read signal was assessed with DESeq1 analysis (Fig. 4F).…”

“…The protocol-specific nature of both pausing ratios and eRNA recovery led to concerns about whether the choice of experimental preparation influences commonly conducted downstream analyses, such as identifying which genes respond to a perturbation [4] and which transcription factors drive those changes [31,32,5,6]. As such, we used the competitive MDM2 inhibitor Nutlin-3a, which has a known, specific, robust transcription response in human cells induced by the subsequent activation of the transcription factor p53 [33,4,14].…”

“…The second common use of run-on sequencing data is to infer which regulators are driving observed patterns of differential transcription [25,29,5]. Alterations in transcription factor activity can be detected by changes in the locations and levels of sites of bidirectional transcription [5,6], the majority of which reside at enhancers [28]. Therefore we next sought to determine whether the alterations observed in eRNA detection (Fig.…”

“…Therefore we next sought to determine whether the alterations observed in eRNA detection (Fig. 4) impacted TF activity inference [6].…”

“…Capturing and mapping these "nascent" transcripts provides a single base-pair resolution readout of the positions of RNA polymerases throughout the genome [1,2,3]. A portion of these nascent transcripts arise from enhancer regions and have been associated with transcription factor activity [4,5,6]. These enhancer RNAs (eRNAs) are unstable and thus not generally recovered by steady-state assays such as RNA-seq, which sample predominantly from pools of stable transcripts such as mRNAs [7].…”