Quantification of Differential Transcription Factor Activity and Multiomics-Based Classification into Activators and Repressors: diffTF
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-za...
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Format: | Article |
Language: | English |
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Elsevier
2019-12-01
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Series: | Cell Reports |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2211124719314391 |
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doaj-870a52dda07a413fb5e92cf36d9527dc |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ivan Berest Christian Arnold Armando Reyes-Palomares Giovanni Palla Kasper Dindler Rasmussen Holly Giles Peter-Martin Bruch Wolfgang Huber Sascha Dietrich Kristian Helin Judith B. Zaugg |
spellingShingle |
Ivan Berest Christian Arnold Armando Reyes-Palomares Giovanni Palla Kasper Dindler Rasmussen Holly Giles Peter-Martin Bruch Wolfgang Huber Sascha Dietrich Kristian Helin Judith B. Zaugg Quantification of Differential Transcription Factor Activity and Multiomics-Based Classification into Activators and Repressors: diffTF Cell Reports |
author_facet |
Ivan Berest Christian Arnold Armando Reyes-Palomares Giovanni Palla Kasper Dindler Rasmussen Holly Giles Peter-Martin Bruch Wolfgang Huber Sascha Dietrich Kristian Helin Judith B. Zaugg |
author_sort |
Ivan Berest |
title |
Quantification of Differential Transcription Factor Activity and Multiomics-Based Classification into Activators and Repressors: diffTF |
title_short |
Quantification of Differential Transcription Factor Activity and Multiomics-Based Classification into Activators and Repressors: diffTF |
title_full |
Quantification of Differential Transcription Factor Activity and Multiomics-Based Classification into Activators and Repressors: diffTF |
title_fullStr |
Quantification of Differential Transcription Factor Activity and Multiomics-Based Classification into Activators and Repressors: diffTF |
title_full_unstemmed |
Quantification of Differential Transcription Factor Activity and Multiomics-Based Classification into Activators and Repressors: diffTF |
title_sort |
quantification of differential transcription factor activity and multiomics-based classification into activators and repressors: difftf |
publisher |
Elsevier |
series |
Cell Reports |
issn |
2211-1247 |
publishDate |
2019-12-01 |
description |
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. : Berest et al. present a computational tool (diffTF) to estimate differential TF activity and classify TFs into activators or repressors. It requires active chromatin data (accessibility/ChIP-seq) and integrates with RNA-seq for classification. The authors apply it to two case studies (CLL and hematopoietic differentiation) and validate their predictions experimentally. Keywords: transcription factor, ATAC-seq, CLL, transcriptional activator and repressor, RNA-seq, TF footprint, open chromatin, snakemake, multiomics data integration |
url |
http://www.sciencedirect.com/science/article/pii/S2211124719314391 |
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doaj-870a52dda07a413fb5e92cf36d9527dc2020-11-25T00:26:47ZengElsevierCell Reports2211-12472019-12-01291031473159.e12Quantification of Differential Transcription Factor Activity and Multiomics-Based Classification into Activators and Repressors: diffTFIvan Berest0Christian Arnold1Armando Reyes-Palomares2Giovanni Palla3Kasper Dindler Rasmussen4Holly Giles5Peter-Martin Bruch6Wolfgang Huber7Sascha Dietrich8Kristian Helin9Judith B. Zaugg10Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, GermanyStructural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, GermanyStructural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, GermanyStructural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, GermanySchool of Life Sciences, University of Dundee, Dundee, UK; Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, DenmarkHeidelberg University Hospital, Heidelberg, Germany; Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, GermanyHeidelberg University Hospital, Heidelberg, GermanyStructural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UKHeidelberg University Hospital, Heidelberg, GermanyBiotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark; Novo Nordisk Foundation Center for Stem Cell Biology, Copenhagen, Denmark; Cell Biology Program and Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, NY, USAStructural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK; Corresponding authorSummary: 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. : Berest et al. present a computational tool (diffTF) to estimate differential TF activity and classify TFs into activators or repressors. It requires active chromatin data (accessibility/ChIP-seq) and integrates with RNA-seq for classification. The authors apply it to two case studies (CLL and hematopoietic differentiation) and validate their predictions experimentally. Keywords: transcription factor, ATAC-seq, CLL, transcriptional activator and repressor, RNA-seq, TF footprint, open chromatin, snakemake, multiomics data integrationhttp://www.sciencedirect.com/science/article/pii/S2211124719314391 |