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      Inferring the physical properties of yeast chromatin through Bayesian analysis of whole nucleus simulations

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          Abstract

          Background

          The structure and mechanical properties of chromatin impact DNA functions and nuclear architecture but remain poorly understood. In budding yeast, a simple polymer model with minimal sequence-specific constraints and a small number of structural parameters can explain diverse experimental data on nuclear architecture. However, how assumed chromatin properties affect model predictions was not previously systematically investigated.

          Results

          We used hundreds of dynamic chromosome simulations and Bayesian inference to determine chromatin properties consistent with an extensive dataset that includes hundreds of measurements from imaging in fixed and live cells and two Hi-C studies. We place new constraints on average chromatin fiber properties, narrowing down the chromatin compaction to ~53–65 bp/nm and persistence length to ~52–85 nm. These constraints argue against a 20–30 nm fiber as the exclusive chromatin structure in the genome. Our best model provides a much better match to experimental measurements of nuclear architecture and also recapitulates chromatin dynamics measured on multiple loci over long timescales.

          Conclusion

          This work substantially improves our understanding of yeast chromatin mechanics and chromosome architecture and provides a new analytic framework to infer chromosome properties in other organisms.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-017-1199-x) contains supplementary material, which is available to authorized users.

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          Most cited references52

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          emcee: The MCMC Hammer

          We introduce a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the astrophysics literature. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to \(\sim N^2\) for a traditional algorithm in an N-dimensional parameter space. In this document, we describe the algorithm and the details of our implementation and API. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort. The code is available online at http://dan.iel.fm/emcee under the MIT License.
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            Mapping Nucleosome Resolution Chromosome Folding in Yeast by Micro-C.

            We describe a Hi-C-based method, Micro-C, in which micrococcal nuclease is used instead of restriction enzymes to fragment chromatin, enabling nucleosome resolution chromosome folding maps. Analysis of Micro-C maps for budding yeast reveals abundant self-associating domains similar to those reported in other species, but not previously observed in yeast. These structures, far shorter than topologically associating domains in mammals, typically encompass one to five genes in yeast. Strong boundaries between self-associating domains occur at promoters of highly transcribed genes and regions of rapid histone turnover that are typically bound by the RSC chromatin-remodeling complex. Investigation of chromosome folding in mutants confirms roles for RSC, "gene looping" factor Ssu72, Mediator, H3K56 acetyltransferase Rtt109, and the N-terminal tail of H4 in folding of the yeast genome. This approach provides detailed structural maps of a eukaryotic genome, and our findings provide insights into the machinery underlying chromosome compaction.
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              Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture.

              Hi-C experiments measure the probability of physical proximity between pairs of chromosomal loci on a genomic scale. We report on several systematic biases that substantially affect the Hi-C experimental procedure, including the distance between restriction sites, the GC content of trimmed ligation junctions and sequence uniqueness. To address these biases, we introduce an integrated probabilistic background model and develop algorithms to estimate its parameters and renormalize Hi-C data. Analysis of corrected human lymphoblast contact maps provides genome-wide evidence for interchromosomal aggregation of active chromatin marks, including DNase-hypersensitive sites and transcriptionally active foci. We observe extensive long-range (up to 400 kb) cis interactions at active promoters and derive asymmetric contact profiles next to transcription start sites and CTCF binding sites. Clusters of interacting chromosomal domains suggest physical separation of centromere-proximal and centromere-distal regions. These results provide a computational basis for the inference of chromosomal architectures from Hi-C experiments.
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                Author and article information

                Contributors
                jean-michel.arbona@pasteur.fr
                herbert.sebastien@gmail.com
                emmanuelle-g.fabre@inserm.fr
                czimmer@pasteur.fr
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                3 May 2017
                3 May 2017
                2017
                : 18
                : 81
                Affiliations
                [1 ]ISNI 0000 0001 2353 6535, GRID grid.428999.7, , Unité Imagerie et Modélisation, Institut Pasteur, ; 25 rue du Docteur Roux, 75015 Paris, France
                [2 ]ISNI 0000 0001 2112 9282, GRID grid.4444.0, , UMR 3691, CNRS; C3BI, USR 3756, IP CNRS, ; Paris, France
                [3 ]ISNI 0000 0001 2217 0017, GRID grid.7452.4, , Université Paris Diderot, Sorbonne Paris Cité, Cellule Pasteur, ; 75015 Paris, France
                [4 ]ISNI 0000 0001 2300 6614, GRID grid.413328.f, , Chromosome Biology and Dynamics, Hôpital Saint Louis, ; Paris, France
                Author information
                http://orcid.org/0000-0001-9910-1589
                Article
                1199
                10.1186/s13059-017-1199-x
                5414205
                28468672
                bb730f46-73af-4d23-8ed2-67bd4e6a43b3
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 16 September 2016
                : 23 March 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;
                Award ID: ANR-11-MONU-020–02
                Award Recipient :
                Funded by: Fondation pour la Recherche Médicale (FR)
                Award ID: DEQ20150331762
                Award Recipient :
                Funded by: Institut Pasteur (FR)
                Award ID: Roux-Cantarini fellowship
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Genetics
                chromatin,chromosomes,nuclear architecture,polymer models,yeast
                Genetics
                chromatin, chromosomes, nuclear architecture, polymer models, yeast

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