Abstract
BackgroundGenome structures are dynamic and non-randomly organized in the nucleus of higher eukaryotes. To maximize the accuracy and coverage of three-dimensional genome structural models, it is important to integrate all available sources of experimental information about a genome’s organization. It remains a major challenge to integrate such data from various complementary experimental methods. Here, we present an approach for data integration to determine a population of complete three-dimensional genome structures that are statistically consistent with data from both genome-wide chromosome conformation capture (Hi-C) and lamina-DamID experiments.ResultsOur structures resolve the genome at the resolution of topological domains, and reproduce simultaneously both sets of experimental data. Importantly, this data deconvolution framework allows for structural heterogeneity between cells, and hence accounts for the expected plasticity of genome structures. As a case study we choose Drosophila melanogaster embryonic cells, for which both data types are available. Our three-dimensional genome structures have strong predictive power for structural features not directly visible in the initial data sets, and reproduce experimental hallmarks of the D. melanogaster genome organization from independent and our own imaging experiments. Also they reveal a number of new insights about genome organization and its functional relevance, including the preferred locations of heterochromatic satellites of different chromosomes, and observations about homologous pairing that cannot be directly observed in the original Hi-C or lamina-DamID data.ConclusionsOur approach allows systematic integration of Hi-C and lamina-DamID data for complete three-dimensional genome structure calculation, while also explicitly considering genome structural variability.
Highlights
Genome structures are dynamic and non-randomly organized in the nucleus of higher eukaryotes
Population-based genome structure modeling from data integration Our goal is to determine a population of 3D genome structures for D. melanogaster that is consistent with data from Hi-C and lamina-DamID experiments
The elements of A describe how frequently a given pair of Topological associated domains (TADs) are in contact with each other in an ensemble of cells, and E describes how frequently a given TAD is in contact with the nuclear envelope (NE)
Summary
Genome structures are dynamic and non-randomly organized in the nucleus of higher eukaryotes. We present an approach for data integration to determine a population of complete three-dimensional genome structures that are statistically consistent with data from both genome-wide chromosome conformation capture (Hi-C) and lamina-DamID experiments. Computational approaches can aid in mapping the global 3D structures of genomes at various scales These can be divided into data-driven and physics-based de novo simulation techniques [13]. Data-driven approaches use experimental information, often Hi-C data, to generate 3D genome structures that are constrained to be consistent with the data. These approaches can be divided into three classes [21, 22]. Common to all resampling methods is that the input dataset is applied to each individual structure, and often the restraint violations due to conflicting data lead to structural variance in the resampled ensemble
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