![]() The power law scaling of the contact probability between two loci as a function of their genomic distance is reproduced well at all genomic distances in a comparison with Hi-C data ( SI Appendix, Figs. S3–S24) confirms that the two sets of maps are correlated exceptionally well. The Pearson’s coefficient is ∼0.9 or higher for all of the chromosomes whether in the training set or test set, and the analysis of the Pearson’s coefficient as a function of genomic distance ( SI Appendix, Figs. 2 for representative chromosomes in the test set (i.e., the even autosomes). The comparison between the simulated and experimental contact maps is shown in Fig. The overall agreement between the experimental and simulated contact probabilities is visually evident. We compare the resulting contact maps from the simulated ensemble of 3D structures with the experimental Hi-C maps reported by Rao et al. The data at each locus are further assumed to be distributed according to a Boltzmann distribution for a Potts model:įrom the ensemble of equilibrium conformations, we calculate the contact probabilities between any pair of loci within each chromosome. The state of the network is represented by the state vector σ → ( l ) = ( C ( l ), Exp 1 ( l ), Exp 2 ( l ), …, Exp L ( l ) ), which represents all of the data available at locus l, with C being the subcompartment annotation and Exp i being the result of the ith ChIP-Seq experiment. We use a neural network in which each data type available at a given locus corresponds to a single neuron ( 30). We then constructed a neural network to uncover the relationship between compartment annotations and epigenetic markings. Next, we discretized each of these profiles, partitioning them into 50-kb loci, each of which is assigned a value from 1 (weakest signal) to 20 (strongest signal). We first obtained ChIP-Seq profiles available from the Encyclopedia of DNA Elements (ENCODE) project for the GM12878 lymphoblastoid cell line, encompassing 84 protein-binding experiments and 11 histone marks. To overcome this difficulty, we use a machine learning approach to extract information from the raw chromatin immunoprecipitation (ChIP-Seq) data. It is therefore impossible to assign any given locus correctly to a specific compartment using the frequency of any single epigenetic modification. 6), the distributions of epigenetic markers found in each compartment are broad and largely overlap. These findings strongly suggest that epigenetic marking patterns encode sufficient information to determine the global architecture of chromosomes and that de novo structure prediction for whole genomes may be increasingly possible.Īlthough the compartments and subcompartments visible in Hi-C maps correlate with a handful of specific epigenetic modifications present at those loci (also ref. Both sets of experiments support the hypothesis of phase separation being the driving process behind compartmentalization. We validate these structural ensembles by using ChIP-Seq tracks alone to predict Hi-C maps, as well as distances measured using 3D fluorescence in situ hybridization (FISH) experiments. After training the model, dubbed Maximum Entropy Genomic Annotation from Biomarkers Associated to Structural Ensembles (MEGABASE), on odd-numbered chromosomes, we predict the sequences of chromatin types and the subsequent 3D conformational ensembles for the even chromosomes. Next, types inferred from this neural network are used as an input to an energy landscape model for chromatin organization to generate an ensemble of 3D chromosome conformations at a resolution of 50 kilobases (kb). ![]() First, a neural network is used to infer the relation between the epigenetic marks present at a locus, as assayed by ChIP-Seq, and the genomic compartment in which those loci reside, as measured by DNA-DNA proximity ligation (Hi-C). Interactions between these chromatin types determine the 3D structural ensemble of chromosomes through a process similar to phase separation. We exploit the idea that chromosomes encode a 1D sequence of chromatin structural types. ![]() Here, we show that this chromatin architecture can be predicted de novo using epigenetic data derived from chromatin immunoprecipitation-sequencing (ChIP-Seq). ![]() Inside the cell nucleus, genomes fold into organized structures that are characteristic of cell type.
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