Title |
CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data
|
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Published in |
Genome Biology, June 2016
|
DOI | 10.1186/s13059-016-0992-2 |
Pubmed ID | |
Authors |
Jonathan Cairns, Paula Freire-Pritchett, Steven W. Wingett, Csilla Várnai, Andrew Dimond, Vincent Plagnol, Daniel Zerbino, Stefan Schoenfelder, Biola-Maria Javierre, Cameron Osborne, Peter Fraser, Mikhail Spivakov |
Abstract |
Capture Hi-C (CHi-C) is a 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. 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. |
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Members of the public | 22 | 39% |
Science communicators (journalists, bloggers, editors) | 1 | 2% |
Mendeley readers
Geographical breakdown
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Germany | 2 | <1% |
Japan | 2 | <1% |
France | 1 | <1% |
Norway | 1 | <1% |
Sweden | 1 | <1% |
Switzerland | 1 | <1% |
Lithuania | 1 | <1% |
Netherlands | 1 | <1% |
Other | 2 | <1% |
Unknown | 424 | 96% |
Demographic breakdown
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Researcher | 81 | 18% |
Student > Bachelor | 45 | 10% |
Student > Master | 35 | 8% |
Student > Doctoral Student | 23 | 5% |
Other | 61 | 14% |
Unknown | 90 | 20% |
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Computer Science | 21 | 5% |
Medicine and Dentistry | 17 | 4% |
Engineering | 8 | 2% |
Other | 23 | 5% |
Unknown | 96 | 22% |