Identification of chromatin loops from Hi-C interaction matrices by CTCF-CTCF topology classification.

Genome-wide profiling of long-range interactions has revealed that the CCCTC-Binding factor (CTCF) often anchors chromatin loops and is enriched at boundaries of the so-called Topologically Associating Domains, which suggests that CTCF is essential in the 3D organization of chromatin. However, the systematic topological classification of pairwise CTCF-CTCF interactions has not been yet explored. Here, we developed a computational pipeline able to classify all CTCF-CTCF pairs according to their chromatin interactions from Hi-C experiments. The interaction profiles of all CTCF-CTCF pairs were further structurally clustered using self-organizing feature maps and their functionality characterized by their epigenetic states. The resulting clusters were then input to a convolutional neural network aiming at the de novo detecting chromatin loops from Hi-C interaction matrices. Our new method, called LOOPbit, is able to automatically detect significant interactions with a higher proportion of enhancer-promoter loops compared to other callers. Our highly specific loop caller adds a new layer of detail to the link between chromatin structure and function.

CHESS enables quantitative comparison of chromatin contact data and automatic feature extraction.

Dynamic changes in the three-dimensional (3D) organization of chromatin are associated with central biological processes, such as transcription, replication and development. Therefore, the comprehensive identification and quantification of these changes is fundamental to understanding of evolutionary and regulatory mechanisms. Here, we present Comparison of Hi-C Experiments using Structural Similarity (CHESS), an algorithm for the comparison of chromatin contact maps and automatic differential feature extraction. We demonstrate the robustness of CHESS to experimental variability and showcase its biological applications on (1) interspecies comparisons of syntenic regions in human and mouse models; (2) intraspecies identification of conformational changes in Zelda-depleted Drosophila embryos; (3) patient-specific aberrant chromatin conformation in a diffuse large B-cell lymphoma sample; and (4) the systematic identification of chromatin contact differences in high-resolution Capture-C data. In summary, CHESS is a computationally efficient method for the comparison and classification of changes in chromatin contact data.

Lamin B1 mapping reveals the existence of dynamic and functional euchromatin lamin B1 domains.

Lamins (A/C and B) are major constituents of the nuclear lamina (NL). Structurally conserved lamina-associated domains (LADs) are formed by genomic regions that contact the NL. Lamins are also found in the nucleoplasm, with a yet unknown function. Here we map the genome-wide localization of lamin B1 in an euchromatin-enriched fraction of the mouse genome and follow its dynamics during the epithelial-to-mesenchymal transition (EMT). Lamin B1 associates with actively expressed and open euchromatin regions, forming dynamic euchromatin lamin B1-associated domains (eLADs) of about 0.3 Mb. Hi-C data link eLADs to the 3D organization of the mouse genome during EMT and correlate lamin B1 enrichment at topologically associating domain (TAD) borders with increased border strength. Having reduced levels of lamin B1 alters the EMT transcriptional signature and compromises the acquisition of mesenchymal traits. Thus, during EMT, the process of genome reorganization in mouse involves dynamic changes in eLADs.