Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction. Schmidt, F., Gasparoni, N., Gasparoni, G., Gianmoena, K., Cadenas, C., Polansky, J. K., Ebert, P., Nordström, K., Barann, M., Sinha, A., Fröhler, S., Xiong, J., Dehghani Amirabad, A., Behjati Ardakani, F., Hutter, B., Zipprich, G., Felder, B., Eils, J., Brors, B., Chen, W., Hengstler, J. G., Hamann, A., Lengauer, T., Rosenstiel, P., Walter, J., & Schulz, M. H. Nucleic Acids Research, 45(1):54–66, January, 2017.
doi  abstract   bibtex   
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.
@article{schmidt_combining_2017,
	title = {Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction},
	volume = {45},
	issn = {1362-4962},
	doi = {10.1093/nar/gkw1061},
	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.},
	language = {eng},
	number = {1},
	journal = {Nucleic Acids Research},
	author = {Schmidt, Florian and Gasparoni, Nina and Gasparoni, Gilles and Gianmoena, Kathrin and Cadenas, Cristina and Polansky, Julia K. and Ebert, Peter and Nordström, Karl and Barann, Matthias and Sinha, Anupam and Fröhler, Sebastian and Xiong, Jieyi and Dehghani Amirabad, Azim and Behjati Ardakani, Fatemeh and Hutter, Barbara and Zipprich, Gideon and Felder, Bärbel and Eils, Jürgen and Brors, Benedikt and Chen, Wei and Hengstler, Jan G. and Hamann, Alf and Lengauer, Thomas and Rosenstiel, Philip and Walter, Jörn and Schulz, Marcel H.},
	month = jan,
	year = {2017},
	pmid = {27899623},
	pmcid = {PMC5224477},
	keywords = {Algorithms, Binding Sites, CD4-Positive T-Lymphocytes, Cell Line, Cell Line, Tumor, Chromatin, Chromatin Assembly and Disassembly, DNA, Gene Expression Regulation, Hep G2 Cells, Hepatocytes, Histones, Human Embryonic Stem Cells, Humans, K562 Cells, Machine Learning, Organ Specificity, Primary Cell Culture, Principal Component Analysis, Protein Binding, Transcription Factors},
	pages = {54--66},
	file = {Volltext:/Users/mschulz/Zotero/storage/3DCIKS5Z/Schmidt et al. - 2017 - Combining transcription factor binding affinities .pdf:application/pdf},
}
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