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Nucleic Acids Res. 2017 Jan 9;45(1):54-66. doi: 10.1093/nar/gkw1061. Epub 2016 Nov 29.

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

Author information

1
Cluster of Excellence for Multimodal Computing and Interaction, Saarland Informatics Campus, Saarland University, Saarbrücken, 66123, Germany.
2
Computational Biology & Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, 66123, Germany.
3
Department of Genetics, University of Saarland, Saarbrücken, 66123, Germany.
4
Leibniz Research Centre for Working Environment and Human Factors IfADo, Dortmund, 44139, Germany.
5
Experimental Rheumatology, German Rheumatism Research Centre, Berlin, 10117, Germany.
6
International Max Planck Research School for Computer Science, Saarland Informatics Campus, Saarbrücken, 66123, Germany.
7
Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, 24105, Germany.
8
Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Berlin, 13092, Germany.
9
Applied Bioinformatics, Deutsches Krebsforschungszentrum, Heidelberg, 69120, Germany.
10
Data Management and Genomics IT, Deutsches Krebsforschungszentrum, Heidelberg, 69120, Germany.
11
Cluster of Excellence for Multimodal Computing and Interaction, Saarland Informatics Campus, Saarland University, Saarbrücken, 66123, Germany mschulz@mmci.uni-saarland.de.

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.

PMID:
27899623
PMCID:
PMC5224477
DOI:
10.1093/nar/gkw1061
[Indexed for MEDLINE]
Free PMC Article

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