Processing and analysis of Hi-C data on bacteria
The study of three-dimensional genome organization has recently gained much attention in the context of novel techniques for detecting genome-wide contacts using next-generation sequencing. These genome-wide chromosome conformation capture-based methods, such as Hi-C, give a deep topological insight...
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: 19-31 |
| DOI: | 10.1007/978-1-4939-8675-0_2 |
| Online-Zugang: | Verlag, Volltext: https://link.springer.com/protocol/10.1007/978-1-4939-8675-0_2 Verlag, Volltext: https://doi.org/10.1007/978-1-4939-8675-0_2 
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| Verfasserangaben: | Andreas Hofmann, Dieter W. Heermann |
| Zusammenfassung: | The study of three-dimensional genome organization has recently gained much attention in the context of novel techniques for detecting genome-wide contacts using next-generation sequencing. These genome-wide chromosome conformation capture-based methods, such as Hi-C, give a deep topological insight into the architecture of the genome inside the cell. This chapter reviews the steps to process next-generation Hi-C sequencing data to generate a final contact probability map. We describe these steps using publicly available Hi-C datasets of different bacteria. We also present strategies to assess the quality of Hi-C datasets. |
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| Beschreibung: | Gesehen am 04.12.2020 |
| Beschreibung: | Online Resource |
| ISBN: | 9781493986750 |
| DOI: | 10.1007/978-1-4939-8675-0_2 |