Deciphering 3D Organization of Chromosomes Using Hi-C Data

Andreas Hofmann; Dieter W Heermann

doi:10.1007/978-1-4939-8675-0_19

Deciphering 3D Organization of Chromosomes Using Hi-C Data

Methods Mol Biol. 2018:1837:389-401. doi: 10.1007/978-1-4939-8675-0_19.

Authors

Andreas Hofmann¹, Dieter W Heermann²

Affiliations

¹ Institute for Theoretical Physics, Heidelberg University, Heidelberg, Germany.
² Institute for Theoretical Physics, Heidelberg University, Heidelberg, Germany. heermann@tphys.uni-heidelberg.de.

PMID: 30109620
DOI: 10.1007/978-1-4939-8675-0_19

Abstract

In order to interpret data from Hi-C studies genome-wide contact probability maps need to be translated into models of functional 3D genome organization. Here, we first present an overview of computational methods to analyze contact probability maps in terms of features such as the level and shape of compartmentalization. Next, we describe approaches to modeling 3D genome organization based on Hi-C data.

Keywords: Hi-C; Modeling; Polymer; Simulation.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Chromosomes / chemistry*
Chromosomes / genetics*
Computer Simulation
Genome*
Genomics* / methods
High-Throughput Nucleotide Sequencing*
Imaging, Three-Dimensional
Molecular Conformation*
Polymers

Substances

Polymers