Emilian Jungwirth1, Katrin Panzitt2, Hanns-Ulrich Marschall3, Martin Wagner2, Gerhard G Thallinger1. 1. Institute of Computational Biotechnology, Graz University of Technology, Austria. 2. Research Unit for Translational Nuclear ReceptorResearch, Division of Gastroenterology and Hepatology, Medical University Graz, Graz, Austria. 3. Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden.
Abstract
BACKGROUND: ChIP-seq is a method to identify genome-wide transcription factor (TF) binding sites. The TF FXR is a nuclear receptor that controls gene regulation of different metabolic pathways in the liver. OBJECTIVES: To re-analyze, standardize and combine all publicly available FXR ChIP-seq data sets to create a global FXR signaling atlas. METHODS: All data sets were (re-)analyzed in a standardized manner and compared on every relevant level from raw reads to affected functional pathways. RESULTS: Public FXR data sets were available for mouse, rat and primary human hepatocytes in different treatment conditions. Standardized re-analysis shows that the data sets are surprisingly heterogeneous concerning baseline quality criteria. Combining different data sets increased the depth of analysis and allowed to recover more peaks and functional pathways. CONCLUSION: Published single FXR ChIP-seq data sets do not cover the full spectrum of FXR signaling. Combining different data sets and creating a "FXR super-signaling atlas" enhances understanding of FXR signaling capacities.
BACKGROUND: ChIP-seq is a method to identify genome-wide transcription factor (TF) binding sites. The TF FXR is a nuclear receptor that controls gene regulation of different metabolic pathways in the liver. OBJECTIVES: To re-analyze, standardize and combine all publicly available FXR ChIP-seq data sets to create a global FXR signaling atlas. METHODS: All data sets were (re-)analyzed in a standardized manner and compared on every relevant level from raw reads to affected functional pathways. RESULTS: Public FXR data sets were available for mouse, rat and primary human hepatocytes in different treatment conditions. Standardized re-analysis shows that the data sets are surprisingly heterogeneous concerning baseline quality criteria. Combining different data sets increased the depth of analysis and allowed to recover more peaks and functional pathways. CONCLUSION: Published single FXR ChIP-seq data sets do not cover the full spectrum of FXR signaling. Combining different data sets and creating a "FXR super-signaling atlas" enhances understanding of FXR signaling capacities.