NetSciReg'15 - Network Models in Cellular Regulation
June 1, 2015 - Zaragoza, Spain

 NetSciReg'15 Flyer 
 Important Dates 
 Call for Contributed Talks 
 NetSci 2015 

Time: 6:20 - 6:40 PM

Type: Contributed Talk

1 Babraham Institute, Nuclear Dynamics Programme, Cambridge, United Kingdom
2 Cambridge Institute for Medical Research, Cambridge, United Kingdom
3 EMBL-European Bioinformatics Institute, Cambridge, United Kingdom
4 MRC Biostatistics Unit, Cambridge, United Kingdom
5 University of Liege, GIGA-Research, Liege, Belgium
6 Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, Netherlands
7 University of Cambridge, Department of Haematology, Cambridge, United Kingdom
8 Cambridge Biomedical Research Centre, National Institute for Health Research, Cambridge, United Kingdom
9 National Health Service Blood and Transplant , Cambridge, United Kingdom
10 Wellcome Trust Sanger Institute, Cambridge, United Kingdom
* Joint senior authors; Correspondence:


Remote regulatory regions such as enhancers play a key role in metazoan gene regulation. We have coupled HiC technology with sequence capture to enrich HiC material for interactions involving (at least on one end) ~22,000 known promoters in primary human cells. Combining Promoter-Capture HiC with a peak-calling algorithm (CHiCAGO) developed specifically for this data, we detected hundreds of thousands of putative regulatory interactions across ~20 human primary cell types at a single-restriction fragment resolution. Promoter-Capture HiC enriches the material purely based on sequence on one end of an interacting pair of fragments. Therefore, interactions are detected irrespective of target promoter activity, the identity of recruited transcription factors and across the whole range of distances between interacting fragments. Using this approach, we have assessed the diversity of enhancer-promoter and promoter-promoter interactions across cell types, profiled their dynamics upon lineage commitment and detected the interaction "hallmarks" of active, poised and repressed genes. In addition, we take advantage of promoter interactome data to enhance the annotation of genetic variants mapping to non-coding sequences.