Genome-wide mapping and analysis of chromosome architecture

KEY POINTS * Looping of chromatin fibres is an important mechanism for transcription regulation in animals. The past decade has witnessed an explosion of chromosome conformation capture (3C)

technologies aimed at mapping such local or genome-wide chromatin architecture. * Key recent methodological advancements for mapping chromatin conformation include improved methods for

chromatin fragmentation, proximity ligation, single-cell analysis and targeted 3C. These improvements have propelled the field forward with optimized protocols that enhance the efficiency,

scale and resolution of chromatin contact maps. * Improved protocols and advances in ultra-high-throughput DNA-sequencing technology have facilitated the rapid accumulation of 3C data sets.

The need to extract meaningful insight into hierarchical genome architecture has necessitated the development of novel computational algorithms and bioinformatics pipelines. * Accounting for

bias in Hi-C and Capture-HiC data is a critical first step towards appropriately analysing their data sets and reaching grounded conclusions. Current methods to account for bias use either

explicit or implicit assumption models; however, it is recommended that researchers analyse their data using both approaches to ensure the biological relevance of their findings. * Analyses

of chromatin contact maps at various resolutions have revealed principles of hierarchical genome architecture, spanning from chromosome territories, compartments and topologically

associating domains to contact domains, loops and other important contacts mediated by _cis_-regulatory elements. Numerous approaches exist for defining each of these features, and the

selection of each method should be guided by a full understanding of the statistical model used by each approach. * An exhaustive comparison of mapping technologies and analysis methods is

sorely needed. To facilitate the evaluation of the 'accuracy' of each method, future efforts should focus on the development of new interaction data standards that consist of loci,

the interaction tendencies of which have been rigorously characterized using genetic, biochemical and microscopy approaches. ABSTRACT Chromosomes of eukaryotes adopt highly dynamic and

complex hierarchical structures in the nucleus. The three-dimensional (3D) organization of chromosomes profoundly affects DNA replication, transcription and the repair of DNA damage. Thus, a

thorough understanding of nuclear architecture is fundamental to the study of nuclear processes in eukaryotic cells. Recent years have seen rapid proliferation of technologies to

investigate genome organization and function. Here, we review experimental and computational methodologies for 3D genome analysis, with special focus on recent advances in high-throughput

chromatin conformation capture (3C) techniques and data analysis. Access through your institution Buy or subscribe This is a preview of subscription content, access via your institution

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about institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS GPSEQ REVEALS THE RADIAL ORGANIZATION OF CHROMATIN IN THE CELL NUCLEUS

Article 25 May 2020 UNDERSTANDING 3D GENOME ORGANIZATION BY MULTIDISCIPLINARY METHODS Article 05 May 2021 INTEGRATIVE GENOME MODELING PLATFORM REVEALS ESSENTIALITY OF RARE CONTACT EVENTS IN

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Acids Res._ 38, 8164–8177 (2010). Article  CAS  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS The authors dedicate this manuscript in loving memory of Joseph

Schmitt. They would like to give special thanks to members of the Ren laboratory for their suggestions. This work is supported by the Ludwig Institute for Cancer Research, La Jolla,

California, USA, and grants from US National Institutes of Health (NIH; grant U54DK107977 to B.R. and M.H., and grants U54 HG006997 and R01 ES024984 to B.R.). A.D.S. is supported by NIH

genetics training grant T32 GM008666. AUTHOR INFORMATION Author notes * Ming Hu Present address: Department of Cellular and Molecular Medicine, Ludwig Institute for Cancer Research, Moores

Cancer Center and Institute of Genomic Medicine, University of California, San Diego (UCSD) School of Medicine, 9500 Gilman Drive, La Jolla, 92093, California, USA * Bing Ren Present

address: Present address: Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, Ohio 44195, USA., AUTHORS AND

AFFILIATIONS * Ludwig Institute for Cancer Research and the University of California, San Diego (UCSD) Biomedical Sciences Graduate Program, 9500 Gilman Drive, La Jolla, 92093, California,

USA Anthony D. Schmitt * Department of Population Health, Division of Biostatistics, New York University School of Medicine, 650 First Avenue, Room 540, New York, 10016, New York, USA Ming

Hu * Department of Cellular and Molecular Medicine, Ludwig Institute for Cancer Research, Moores Cancer Center and Institute of Genomic Medicine, University of California, San Diego (UCSD)

School of Medicine, 9500 Gilman Drive, La Jolla, 92093, California, USA Bing Ren * Present address: Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic

Foundation, 9500 Euclid Avenue, Cleveland, Ohio 44195, USA., Ming Hu Authors * Anthony D. Schmitt View author publications You can also search for this author inPubMed Google Scholar * Ming

Hu View author publications You can also search for this author inPubMed Google Scholar * Bing Ren View author publications You can also search for this author inPubMed Google Scholar

CORRESPONDING AUTHORS Correspondence to Ming Hu or Bing Ren. ETHICS DECLARATIONS COMPETING INTERESTS B.R. is a co-founder of Arima Genomics, Inc. A.D.S. is a consultant for Arima Genomics,

Inc. POWERPOINT SLIDES POWERPOINT SLIDE FOR FIG. 1 POWERPOINT SLIDE FOR FIG. 2 POWERPOINT SLIDE FOR TABLE 1 POWERPOINT SLIDE FOR TABLE 2 POWERPOINT SLIDE FOR TABLE 3 POWERPOINT SLIDE FOR

TABLE 4 POWERPOINT SLIDE FOR TABLE 5 RELATED LINKS RELATED LINKS FURTHER INFORMATION 4C protocol variant 4D Nucleome Project https://commonfund.nih.gov/4dnucleome/index GLOSSARY * Hi-C A

high-throughput, genome-wide chromosome conformation capture assay using affinity purification of labelled-DNA ligation junctions to measure pairwise interaction frequencies in cell

populations. * Chromosome conformation capture carbon copy (5C). A high-throughput chromosome conformation capture assay that examines the spatial proximity of two defined sets of genomic

regions, measured using a pair of DNA oligos corresponding to the sequences upstream and downstream of the ligation junction. * Target size The cumulative length (in base pairs) targeted by

capture probes in a Capture-HiC experiment. * Bin size A measure of Hi-C data resolution. A bin is a fixed, non-overlapping genomic span to which Hi-C reads are grouped to increase the

signal of chromatin interaction frequency. * Restriction enzyme fragment lengths The total genomic length in each bin that is within 500 bp of restriction enzyme cut sites used in the Hi-C

library preparation. * Mappability The probability of a read-mapping uniquely to the effective fragment length sequence within each bin. * Poisson distribution A probability distribution for

the discrete random variable in which the variance is the same as the mean. * Negative binomial distribution A probability distribution for the discrete random variable in which the

variance is larger than the mean. * Hi-C contact matrices Symmetric, two-dimensional matrices (M), for which each matrix entry (Mij) represents the raw or normalized contact frequency

between bin i and bin j. * Bait-specific bias An experimental bias in the Capture-HiC procedure, referring to the unequal probability of probe hybridization to the target sequence as a

result of variable sequence content and hybridization properties. * Other-end-specific bias An experimental bias in the Capture-HiC procedure, referring to the unequal probability of

ligation between the bait locus and its interacting restriction fragment as a result of variable local genomic features. * Principal component analysis (PCA). A statistical approach for

multivariate data analysis. PCA converts a set of correlated variables into a set of linearly uncorrelated variables named principal components, each of which is a linear combination of the

original correlated variables. * First eigenvector The coefficients of the linear combination in the first principle component, which has the largest variance among all principal components.

In Hi-C data analysis, the sign of the first eigenvector was used to determinate the A and B compartments. * Hidden Markov model (HMM). A statistical model assuming that the observed data

are determined by a set of unobserved (hidden) states with the Markov property: the future state depends on only the current state and is independent of all the previous states. * Heuristic

tuning parameters The parameters in the statistical models and computational pipelines that are not estimated from the observed data but are determined based on prior knowledge and

expectation. * Global background model The statistical model for the expected chromatin contact frequency estimated from genome-wide measurements. It is used to systematically identify

significant pairwise Hi-C interactions throughout the genome. All interacting loci pairs at a given linear distance share the same global background model. * Non-parametric spline A

statistical approach to fit the observed data using a piecewise-defined polynomial function. * Benjamini–Hochberg multiple-testing correction A statistical procedure that uses stringent

statistical significance thresholds to control the false discovery rate when performing multiple comparisons. * Local background model The statistical model for the expected chromatin

contact frequency estimated from local chromatin interaction properties. Each pair of interacting loci has a unique local background model, which depends on the definition of its local

neighbouring regions. RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Schmitt, A., Hu, M. & Ren, B. Genome-wide mapping and analysis of chromosome

architecture. _Nat Rev Mol Cell Biol_ 17, 743–755 (2016). https://doi.org/10.1038/nrm.2016.104 Download citation * Published: 01 September 2016 * Issue Date: December 2016 * DOI:

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