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Molecules. 2018 May 10;23(5). pii: E1136. doi: 10.3390/molecules23051136.

Detecting Differential Transcription Factor Activity from ATAC-Seq Data.

Author information

1
Computer Science, University of Colorado, Boulder, CO 80305, USA. ignacio.tripodi@colorado.edu.
2
BioFrontiers Institute, University of Colorado, Boulder, CO 80303, USA. ignacio.tripodi@colorado.edu.
3
BioFrontiers Institute, University of Colorado, Boulder, CO 80303, USA. mary.a.allen@colorado.edu.
4
Computer Science, University of Colorado, Boulder, CO 80305, USA. robin.dowell@colorado.edu.
5
BioFrontiers Institute, University of Colorado, Boulder, CO 80303, USA. robin.dowell@colorado.edu.
6
Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO 80305, USA. robin.dowell@colorado.edu.

Abstract

Transcription factors are managers of the cellular factory, and key components to many diseases. Many non-coding single nucleotide polymorphisms affect transcription factors, either by directly altering the protein or its functional activity at individual binding sites. Here we first briefly summarize high-throughput approaches to studying transcription factor activity. We then demonstrate, using published chromatin accessibility data (specifically ATAC-seq), that the genome-wide profile of TF recognition motifs relative to regions of open chromatin can determine the key transcription factor altered by a perturbation. Our method of determining which TFs are altered by a perturbation is simple, is quick to implement, and can be used when biological samples are limited. In the future, we envision that this method could be applied to determine which TFs show altered activity in response to a wide variety of drugs and diseases.

KEYWORDS:

ATAC-seq; DAStk; DNase I cleavage; RNA-seq; motif; open chromatin; perturbation; transcription factor

PMID:
29748466
DOI:
10.3390/molecules23051136
[Indexed for MEDLINE]
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