Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data.
Simeon CarstensMichael NilgesMichael HabeckPublished in: PLoS computational biology (2016)
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.
Keyphrases
- single cell
- rna seq
- induced apoptosis
- genome wide
- copy number
- cell cycle arrest
- high throughput
- electronic health record
- big data
- healthcare
- magnetic resonance
- high resolution
- molecularly imprinted
- endoplasmic reticulum stress
- cell death
- transcription factor
- machine learning
- bone marrow
- climate change
- stress induced
- molecular dynamics simulations
- human health