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

Jonathan D Rubin,Zachary L Maas,Dylan J Taatjes,Cecilia B Levandowski,Robin D Dowell,Rutendo F Sigauke,Jessica Westfall,Jacob T Stanley

doi:10.1038/s42003-021-02153-7

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.

Highlights

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) takes as input a set of RNA polymerase initiation regions and ranks them, preferably by changes in transcription levels between the two conditions
We present here TFEA, a computational method that seamlessly balances the information obtained from differential transcription with the position of a nearby motif, thereby allowing it to be broadly applicable to a variety of datasets that approximate RNA polymerase initiation regions

Summary

Introduction

Detecting changes in the activity of a transcription factor (TF) in response to a perturbation provides insights into the underlying cellular process. 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. In an effort to causally link a TF to observed transcription changes, binding data is often combined with expression, typically measured by RNA-seq[9,10,11]. To infer TF activity, one must solve the assignment problem9—namely linking TF binding sites to stable gene transcripts, which are often both positionally (in the genome) and temporally (RNA processing) distant[17]. Our previous work demonstrated the ability to directly infer causal TF activity from changes in RNA polymerase initiation observed in nascent transcription assays[26]

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Results

Conclusion