ABSTRACT
Capture Hi-C (CHi-C) is a state-of-the art method for profiling chromosomal interactions involving targeted regions of interest (such as gene promoters) globally and at high resolution. Signal detection in CHi-C data involves a number of statistical challenges that are not observed when using other Hi-C-like techniques. We present a background model, and algorithms for normalisation and multiple testing that are specifically adapted to CHi-C experiments, in which many spatially dispersed regions are captured, such as in Promoter CHi-C. We implement these procedures in CHiCAGO (http://regulatorygenomicsgroup.org/chicago), an open-source package for robust interaction detection in CHi-C. We validate CHiCAGO by showing that promoter-interacting regions detected with this method are enriched for regulatory features and disease-associated SNPs.
Footnotes
(jonathan.cairns{at}babraham.ac.uk), (paulafp{at}babraham.ac.uk), (steven.wingett{at}babraham.ac.uk), (csilla.varnai{at}babraham.ac.uk), (andrew.dimond{at}babraham.ac.uk), (v.plagnol{at}ucl.ac.uk), (zerbino{at}ebi.ac.uk), (stefan.schoenfelder{at}babraham.ac.uk), (biola-maria.javierre{at}babraham.ac.uk), (cameron.osborne{at}kcl.ac.uk), (peter.fraser{at}babraham.ac.uk), (spivakov{at}babraham.ac.uk)
↵* Joint lead authors