Please use this identifier to cite or link to this item: doi:10.22028/D291-29441
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Title: Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction
Author(s): Schmidt, Florian
Gasparoni, Nina
Gasparoni, Gilles
Gianmoena, Kathrin
Cadenas, Cristina
Polansky, Julia K.
Ebert, Peter
Nordström, Karl
Barann, Matthias
Sinha, Anupam
Fröhler, Sebastian
Xiong, Jieyi
Dehghani Amirabad, Azim
Behjati Ardakani, Fatemeh
Hutter, Barbara
Zipprich, Gideon
Felder, Bärbel
Eils, Jürgen
Brors, Benedikt
Chen, Wei
Hengstler, Jan G.
Hamann, Alf
Lengauer, Thomas
Rosenstiel, Philip
Walter, Jörn Erik
Schulz, Marcel H.
Language: English
Title: Nucleic acids research
Volume: 45
Issue: 1
Startpage: 54
Endpage: 66
Publisher/Platform: Oxford University Press
Year of Publication: 2017
Publikation type: Journal Article
Abstract: The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.
DOI of the first publication: 10.1093/nar/gkw1061
URL of the first publication:
Link to this record: hdl:20.500.11880/27896
ISSN: 0301-5610
Date of registration: 25-Sep-2019
Faculty: NT - Naturwissenschaftlich- Technische Fakultät
Department: NT - Biowissenschaften
Professorship: NT - Prof. Dr. Jörn Walter
Collections:UniBib – Die Universitätsbibliographie

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