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  Inferential structure determination of chromosomes from single-cell Hi-C data.

Carstens, S., Nilges, M., & Habeck, M. (2016). Inferential structure determination of chromosomes from single-cell Hi-C data. PLoS Computational Biology, 12(12): e1005292. doi:10.1371/journal.pcbi.1005292.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-6889-1 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-A00C-1
Genre: Journal Article

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Carstens, S., Author
Nilges, M., Author
Habeck, M.1, Author              
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1Research Group of Statistical Inverse-Problems in Biophysics, MPI for Biophysical Chemistry, Max Planck Society, ou_1113580              

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 Abstract: Chromosome conformation capture (3C) techniques have revealed many fascinating insights into the spatial organization of genomes. 3C methods typically provide information about chromosomal contacts in a large population of cells, which makes it difficult to draw conclusions about the three-dimensional organization of genomes in individual cells. Recently it became possible to study single cells with Hi-C, a genome-wide 3C variant, demonstrating a high cell-to-cell variability of genome organization. In principle, restraint-based modeling should allow us to infer the 3D structure of chromosomes from single-cell contact data, but suffers from the sparsity and low resolution of chromosomal contacts. To address these challenges, we adapt the Bayesian Inferential Structure Determination (ISD) framework, originally developed for NMR structure determination of proteins, to infer statistical ensembles of chromosome structures from single-cell data. Using ISD, we are able to compute structural error bars and estimate model parameters, thereby eliminating potential bias imposed by ad hoc parameter choices. We apply and compare different models for representing the chromatin fiber and for incorporating singe-cell contact information. Finally, we extend our approach to the analysis of diploid chromosome data.

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Language(s): eng - English
 Dates: 2016-12-27
 Publication Status: Published online
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 Rev. Method: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1005292
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Title: PLoS Computational Biology
Source Genre: Journal
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Pages: 33 Volume / Issue: 12 (12) Sequence Number: e1005292 Start / End Page: - Identifier: -