Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data

By Job Dekker, Marc A. Martí-Renom, Leonid A. Mirny. Nature reviews genetics, 2013

1,060 citations recorded by the source corpus.

How DNA is organized in three dimensions inside the cell nucleus and how this affects the ways in which cells access, read and interpret genetic information are among the longest standing questions in cell biology. Using newly developed molecular, genomic and computational approaches based on the chromosome conformation capture technology (such as 3C, 4C, 5C and Hi-C), the spatial organization of genomes is being explored at unprecedented resolution. Interpreting the increasingly large chromatin interaction data sets is now posing novel challenges. Here we describe several types of statistical and computational approaches that have recently been developed to analyse chromatin interaction data.

Subfields: Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/2e532c40b6ac987282b46e1f9141609d54f2bceb

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Citations in the corpus

CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data (2015, methodology): “Background Chromosome conformation capture (3C) technology has revolutionised the analysis of nuclear organisation, leading to important insights into gene regulation [1].”
Regulation of disease-associated gene expression in the 3D genome (2016, neutral): “The develop ment of 3C technology 20 and its high throughput variants 55,56 have enabled studies of the 3D shape of the genome.”
Multi-omics single-cell data integration and regulatory inference with graph-linked embedding (2022, methodology): “75 ( d is the genomic distance in kb), which has been proposed to model the probability of chromatin contact 42,43 .”
HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient (2017, neutral): “The three-dimensional (3D) genome organization across a wide range of length scales is important for proper cellular functions (Bickmore 2013; Dekker et al. 2013; Sexton and Cavalli 2015).”
Protein Modelling: What Happened to the “Protein Structure Gap”? (2013, positive): “…modeling based on data from recently developed chromosome conformation capture technology provides unprecedentedly detailed insights into the spatial organization of genomes (Cavalli and Misteli, 2013; Dekker et al., 2013; Engreitz et al., 2013; Gibcus and Dekker, 2013; Kimura et al., 2013).”
The Xist lncRNA exploits three-dimensional genome architecture to spread across the X-chromosome (2013, background): “(ii) Because chromosome conformation is heterogeneous in a cell population (54, 55), the precise order by which Xist spreads to distal sites is likely to differ between individual cells.”
PEDLA: predicting enhancers with a deep learning-based algorithmic framework (2016, background): “In higher eukaryotes, enhancers recruit transcription factors (TFs) and cofactors to orchestrate vital biological processes including development and differentiation (5,6), maintenance of cell identity (7-9), response to stimuli (10-12), and interactions with target genes through promoter-enhancer l”