CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data

被引:260
作者
Cairns, Jonathan [1 ]
Freire-Pritchett, Paula [1 ]
Wingett, Steven W. [1 ,2 ]
Varnai, Csilla [1 ]
Dimond, Andrew [1 ]
Plagnol, Vincent [3 ]
Zerbino, Daniel [4 ]
Schoenfelder, Stefan [1 ]
Javierre, Biola-Maria [1 ]
Osborne, Cameron [5 ]
Fraser, Peter [1 ]
Spivakov, Mikhail [1 ]
机构
[1] Babraham Inst, Nucl Dynam Programme, Cambridge, England
[2] Babraham Inst, Bioinformat Grp, Cambridge, England
[3] UCL Genet Inst, London, England
[4] European Bioinformat Inst, European Mol Biol Lab, Cambridge, England
[5] Kings Coll London, Dept Med & Mol Genet, London, England
来源
GENOME BIOLOGY | 2016年 / 17卷
基金
英国生物技术与生命科学研究理事会; 英国医学研究理事会;
关键词
Gene regulation; Nuclear organisation; Promoter-enhancer interactions; Capture Hi-C; Convolution background model; P value weighting; FALSE DISCOVERY CONTROL; CAJAL BODIES; BIASES;
D O I
10.1186/s13059-016-0992-2
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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.
引用
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页数:17
相关论文
共 55 条
[1]   Chromatin Position Effects Assayed by Thousands of Reporters Integrated in Parallel [J].
Akhtar, Waseem ;
de Jong, Johann ;
Pindyurin, Alexey V. ;
Pagie, Ludo ;
Meuleman, Wouter ;
de Ridder, Jeroen ;
Berns, Anton ;
Wessels, Lodewyk F. A. ;
van Lohuizen, Maarten ;
van Steensel, Bas .
CELL, 2013, 154 (04) :914-927
[2]   Differential expression analysis for sequence count data [J].
Anders, Simon ;
Huber, Wolfgang .
GENOME BIOLOGY, 2010, 11 (10)
[3]   Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts [J].
Ay, Ferhat ;
Bailey, Timothy L. ;
Noble, William Stafford .
GENOME RESEARCH, 2014, 24 (06) :999-1011
[4]   CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING [J].
BENJAMINI, Y ;
HOCHBERG, Y .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) :289-300
[5]   sdef: an R package to synthesize lists of significant features in related experiments [J].
Blangiardo, Marta ;
Cassese, Alberto ;
Richardson, Sylvia .
BMC BIOINFORMATICS, 2010, 11
[6]   Diffusion-Driven Looping Provides a Consistent Framework for Chromatin Organization [J].
Bohn, Manfred ;
Heermann, Dieter W. .
PLOS ONE, 2010, 5 (08)
[7]   Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data [J].
Dekker, Job ;
Marti-Renom, Marc A. ;
Mirny, Leonid A. .
NATURE REVIEWS GENETICS, 2013, 14 (06) :390-403
[8]   Topological domains in mammalian genomes identified by analysis of chromatin interactions [J].
Dixon, Jesse R. ;
Selvaraj, Siddarth ;
Yue, Feng ;
Kim, Audrey ;
Li, Yan ;
Shen, Yin ;
Hu, Ming ;
Liu, Jun S. ;
Ren, Bing .
NATURE, 2012, 485 (7398) :376-380
[9]   Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-C [J].
Dryden, Nicola H. ;
Broome, Laura R. ;
Dudbridge, Frank ;
Johnson, Nichola ;
Orr, Nick ;
Schoenfelder, Stefan ;
Nagano, Takashi ;
Andrews, Simon ;
Wingett, Steven ;
Kozarewa, Lwanka ;
Assiotis, Loannis ;
Fenwick, Kerry ;
Maguire, Sarah L. ;
Campbell, James ;
Natrajan, Rachael ;
Lambros, Maryou ;
Perrakis, Eleni ;
Ashworth, Alan ;
Fraser, Peter ;
Fletcher, Olivia .
GENOME RESEARCH, 2014, 24 (11) :1854-1868
[10]  
Dudoit S, 2008, SPRINGER SER STAT, P1