RT Journal Article SR Electronic T1 Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction JF bioRxiv FD Cold Spring Harbor Laboratory SP 081935 DO 10.1101/081935 A1 Florian Schmidt A1 Nina Gasparoni A1 Gilles Gasparoni A1 Kathrin Gianmoena A1 Cristina Cadenas A1 Julia K. Polansky A1 Peter Ebert A1 Karl Nordström A1 Matthias Barann A1 Anupam Sinha A1 Sebastian Fröhler A1 Jieyi Xiong A1 Azim Dehghani Amirabad A1 Fatemeh Behjati Ardakani A1 Barbara Hutter A1 Gideon Zipprich A1 Bärbel Felder A1 Jürgen Eils A1 Benedikt Brors A1 Wei Chen A1 Jan G. Hengstler A1 Alf Hamann A1 Thomas Lengauer A1 Philip Rosenstiel A1 Jörn Walter A1 Marcel H. Schulz YR 2016 UL http://biorxiv.org/content/early/2016/10/19/081935.abstract AB 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 datasets. 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.