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Quantification of differential transcription factor activity and multiomics-based classification into activators and repressors: diffTF

Transcription factors (TFs) regulate many cellular processes and can therefore serve as readouts of the signaling and regulatory state. Yet for many TFs, the mode of action—repressing or activating transcription of target genes—is unclear. Here, we present diffTF (https://git.embl.de/grp-zaugg/diffT...

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Main Authors: Berest, Ivan (Author) , Arnold, Christian (Author) , Reyes-Palomares, Armando (Author) , Palla, Giovanni (Author) , Rasmussen, Kasper Dindler (Author) , Giles, Holly (Author) , Bruch, Peter-Martin (Author) , Huber, Wolfgang (Author) , Dietrich, Sascha (Author) , Helin, Kristian (Author) , Zaugg, Judith B. (Author)
Format: Article (Journal)
Language:English
Published: December 3, 2019
In: Cell reports
Year: 2019, Volume: 29, Issue: 10, Pages: 3147-3159.e12
ISSN:2211-1247
DOI:10.1016/j.celrep.2019.10.106
Online Access:Verlag, Volltext: https://doi.org/10.1016/j.celrep.2019.10.106
Verlag, Volltext: http://www.sciencedirect.com/science/article/pii/S2211124719314391
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Author Notes:Ivan Berest, Christian Arnold, Armando Reyes-Palomares, Giovanni Palla, Kasper Dindler Rasmussen, Holly Giles, Peter-Martin Bruch, Wolfgang Huber, Sascha Dietrich, Kristian Helin, and Judith B. Zaugg
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Summary:Transcription factors (TFs) regulate many cellular processes and can therefore serve as readouts of the signaling and regulatory state. Yet for many TFs, the mode of action—repressing or activating transcription of target genes—is unclear. Here, we present diffTF (https://git.embl.de/grp-zaugg/diffTF) to calculate differential TF activity (basic mode) and classify TFs into putative transcriptional activators or repressors (classification mode). In basic mode, it combines genome-wide chromatin accessibility/activity with putative TF binding sites that, in classification mode, are integrated with RNA-seq. We apply diffTF to compare (1) mutated and unmutated chronic lymphocytic leukemia patients and (2) two hematopoietic progenitor cell types. In both datasets, diffTF recovers most known biology and finds many previously unreported TFs. It classifies almost 40% of TFs based on their mode of action, which we validate experimentally. Overall, we demonstrate that diffTF recovers known biology, identifies less well-characterized TFs, and classifies TFs into transcriptional activators or repressors.
Item Description:Gesehen am 21.01.2020
Physical Description:Online Resource
ISSN:2211-1247
DOI:10.1016/j.celrep.2019.10.106

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