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Nat Methods. 2018 Feb;15(2):119-122. doi: 10.1038/nmeth.4560. Epub 2018 Jan 15.

Detecting hierarchical genome folding with network modularity.

Author information

1
Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
2
Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
3
Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
4
Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
5
Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Abstract

Mammalian genomes are folded in a hierarchy of compartments, topologically associating domains (TADs), subTADs and looping interactions. Here, we describe 3DNetMod, a graph theory-based method for sensitive and accurate detection of chromatin domains across length scales in Hi-C data. We identify nested, partially overlapping TADs and subTADs genome wide by optimizing network modularity and varying a single resolution parameter. 3DNetMod can be applied broadly to understand genome reconfiguration in development and disease.

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