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

View ORCID ProfileJonathan D. Rubin, View ORCID ProfileJacob T. Stanley, Rutendo F. Sigauke, View ORCID ProfileCecilia B. Levandowski, Zachary L. Maas, Jessica Westfall, View ORCID ProfileDylan J. Taatjes, View ORCID ProfileRobin D. Dowell
doi: https://doi.org/10.1101/2020.01.25.919738
Jonathan D. Rubin
1Department of Biochemistry, University of Colorado, Boulder CO 80309
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Jacob T. Stanley
2BioFrontiers Institute, University of Colorado, Boulder CO 80309
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Rutendo F. Sigauke
3Computational Bioscience Program, Anschutz Medical Campus, University of Colorado, Aurora, CO 80045
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Cecilia B. Levandowski
1Department of Biochemistry, University of Colorado, Boulder CO 80309
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Zachary L. Maas
2BioFrontiers Institute, University of Colorado, Boulder CO 80309
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Jessica Westfall
4Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder CO 80309
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Dylan J. Taatjes
1Department of Biochemistry, University of Colorado, Boulder CO 80309
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Robin D. Dowell
2BioFrontiers Institute, University of Colorado, Boulder CO 80309
4Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder CO 80309
5Department of Computer Science, University of Colorado, Boulder CO 80309
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  • For correspondence: robin.dowell@colorado.edu
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1 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.

Footnotes

  • Supplemental file and acknowledgements section added. Link to TFEA website fixed

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 06, 2020.
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Transcription factor enrichment analysis (TFEA): Quantifying the activity of hundreds of transcription factors from a single experiment
Jonathan D. Rubin, Jacob T. Stanley, Rutendo F. Sigauke, Cecilia B. Levandowski, Zachary L. Maas, Jessica Westfall, Dylan J. Taatjes, Robin D. Dowell
bioRxiv 2020.01.25.919738; doi: https://doi.org/10.1101/2020.01.25.919738
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Transcription factor enrichment analysis (TFEA): Quantifying the activity of hundreds of transcription factors from a single experiment
Jonathan D. Rubin, Jacob T. Stanley, Rutendo F. Sigauke, Cecilia B. Levandowski, Zachary L. Maas, Jessica Westfall, Dylan J. Taatjes, Robin D. Dowell
bioRxiv 2020.01.25.919738; doi: https://doi.org/10.1101/2020.01.25.919738

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