Deciphering 3D organization of chromosomes using Hi-C data
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 o...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Kapitel/Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
15 August 2018
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| In: |
Bacterial chromatin
Year: 2018, Pages: 389-401 |
| DOI: | 10.1007/978-1-4939-8675-0_19 |
| Online-Zugang: | Verlag, Volltext: https://link.springer.com/protocol/10.1007/978-1-4939-8675-0_19 Verlag, Volltext: https://doi.org/10.1007/978-1-4939-8675-0_19 
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| Verfasserangaben: | Andreas Hofmann, Dieter W. Heermann |
| Zusammenfassung: | 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. |
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| Beschreibung: | Gesehen am 04.12.2020 |
| Beschreibung: | Online Resource |
| ISBN: | 9781493986750 |
| DOI: | 10.1007/978-1-4939-8675-0_19 |