Faculty Opinions recommendation of CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.

2016 ◽  
Author(s):  
Wolfgang Huber
Keyword(s):  
Dna Looping ◽  
Robust Detection

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

2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Jonathan Cairns ◽  
Paula Freire-Pritchett ◽  
Steven W. Wingett ◽  
Csilla Várnai ◽  
Andrew Dimond ◽  
...  
Keyword(s):  
Dna Looping ◽  
Robust Detection

scholarly journals CHiCAGO: Robust Detection of DNA Looping Interactions in Capture Hi-C data

2015 ◽  
Author(s):  
Jonathan Cairns ◽  
Paula Freire-Pritchett ◽  
Steven W. Wingett ◽  
Csilla Várnai ◽  
Andrew Dimond ◽  
...  
Keyword(s):  
High Resolution ◽  
Signal Detection ◽  
Open Source ◽  
Multiple Testing ◽  
State Of The Art ◽  
Regions Of Interest ◽  
Dna Looping ◽  
Gene Promoters ◽  
Robust Detection ◽  
Interaction Detection

ABSTRACTCapture 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.

scholarly journals SuperTAD: robust detection of hierarchical topologically associated domains with optimized structural information

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yu Wei Zhang ◽  
Meng Bo Wang ◽  
Shuai Cheng Li
Keyword(s):  
Structural Information ◽  
Current Method ◽  
Structural Proteins ◽  
Robust Detection ◽  
Topologically Associating Domains ◽  
Topologically Associated Domains ◽  
Significant Enrichment ◽  
High Consistency ◽  
Public Datasets ◽  
State Of Art

AbstractTopologically associating domains (TADs) are the organizational units of chromosome structures. TADs can contain TADs, thus forming a hierarchy. TAD hierarchies can be inferred from Hi-C data through coding trees. However, the current method for computing coding trees is not optimal. In this paper, we propose optimal algorithms for this computation. In comparison with seven state-of-art methods using two public datasets, from GM12878 and IMR90 cells, SuperTAD shows a significant enrichment of structural proteins around detected boundaries and histone modifications within TADs and displays a high consistency between various resolutions of identical Hi-C matrices.

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