Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts

By Ferhat Ay, Timothy L. Bailey, William Stafford Noble. Genome Research, 2014

481 citations recorded by the source corpus.

Our current understanding of how DNA is packed in the nucleus is most accurate at the fine scale of individual nucleosomes and at the large scale of chromosome territories. However, accurate modeling of DNA architecture at the intermediate scale of ∼50 kb–10 Mb is crucial for identifying functional interactions among regulatory elements and their target promoters. We describe a method, Fit-Hi-C, that assigns statistical confidence estimates to mid-range intra-chromosomal contacts by jointly modeling the random polymer looping effect and previously observed technical biases in Hi-C data sets. We demonstrate that our proposed approach computes accurate empirical null models of contact probability without any distribution assumption, corrects for binning artifacts, and provides improved statistical power relative to a previously described method. High-confidence contacts identified by Fit-Hi-C preferentially link expressed gene promoters to active enhancers identified by chromatin signatures in human embryonic stem cells (ESCs), capture 77% of RNA polymerase II-mediated enhancer-promoter interactions identified using ChIA-PET in mouse ESCs, and confirm previously validated, cell line-specific interactions in mouse cortex cells. We observe that insulators and heterochromatin regions are hubs for high-confidence contacts, while promoters and strong enhancers are involved in fewer contacts. We also observe that binding peaks of master pluripotency factors such as NANOG and POU5F1 are highly enriched in high-confidence contacts for human ESCs. Furthermore, we show that pairs of loci linked by high-confidence contacts exhibit similar replication timing in human and mouse ESCs and preferentially lie within the boundaries of topological domains for human and mouse cell lines.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4870fc7fdad70722cb833827294f117b50394a8e

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Citations in the corpus

CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data (2015, methodology): “Existing tools model Hi-C background with a broad range of distributions, both discrete (binomial [16, 29], negative binomial [6]) and continuous (Weibull [7, 9], normal [13]).”
HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient (2017, unknown): “To proceed, we identified significant contacts in these subsamples us-ing Fit-Hi-C ( Q -value cutoff = 0.05) (Ay et al. 2014).”
Genome-wide mapping and analysis of chromosome architecture (2016, methodology): “The Fit-HiC algorithm uses a non-parametric spline approach to model the background-chromatin contact frequency 91 .”
A Compendium of Chromatin Contact Maps Reveal Spatially Active Regions in the Human Genome (2016, unknown): “As a means to investigate conserved and tissue-specific chromatin interactions, we first used Fit-Hi-C (Ay et al., 2014) to identify significant chromatin interactions at various significance thresholds (Table S4, Supplemental File).”