MOTIVATION: Hi-C is currently the method of choice to investigate the global 3D organization of the genome. A major limitation of Hi-C is the sequencing depth required to robustly detect loops in the data. A popular approach used to mitigate this issue, even in single-cell Hi-C data, is genome-wide averaging (piling-up) of peaks, or other features, annotated in high-resolution datasets, to measure their prominence in less deeply sequenced data. However, current tools do not provide a computationally efficient and versatile implementation of this approach. RESULTS: Here, we describe coolpup.py-a versatile tool to perform pile-up analysis on Hi-C data. We demonstrate its utility by replicating previously published findings regarding the role of cohesin and CTCF in 3D genome organization, as well as discovering novel details of Polycomb-driven interactions. We also present a novel variation of the pile-up approach that can aid the statistical analysis of looping interactions. We anticipate that coolpup.py will aid in Hi-C data analysis by allowing easy to use, versatile and efficient generation of pile-ups. AVAILABILITY AND IMPLEMENTATION: Coolpup.py is cross-platform, open-source and free (MIT licensed) software. Source code is available from https://github.com/Phlya/coolpuppy and it can be installed from the Python Packaging Index.

Several recent studies have examined different aspects of mammalian higher order chromatin structure - replication timing, lamina association and Hi-C inter-locus interactions - and have suggested that most of these features of genome organisation are conserved over evolution. However, the extent of evolutionary divergence in higher order structure has not been rigorously measured across the mammalian genome, and until now little has been known about the characteristics of any divergent loci present. Here, we generate a dataset combining multiple measurements of chromatin structure and organisation over many embryonic cell types for both human and mouse that, for the first time, allows a comprehensive assessment of the extent of structural divergence between mammalian genomes. Comparison of orthologous regions confirms that all measurable facets of higher order structure are conserved between human and mouse, across the vast majority of the detectably orthologous genome. This broad similarity is observed in spite of many loci possessing cell type specific structures. However, we also identify hundreds of regions (from 100 Kb to 2.7 Mb in size) showing consistent evidence of divergence between these species, constituting at least 10% of the orthologous mammalian genome and encompassing many hundreds of human and mouse genes. These regions show unusual shifts in human GC content, are unevenly distributed across both genomes, and are enriched in human subtelomeric regions. Divergent regions are also relatively enriched for genes showing divergent expression patterns between human and mouse ES cells, implying these regions cause divergent regulation. Particular divergent loci are strikingly enriched in genes implicated in vertebrate development, suggesting important roles for structural divergence in the evolution of mammalian developmental programmes. These data suggest that, though relatively rare in the mammalian genome, divergence in higher order chromatin structure has played important roles during evolution.

The purpose of this study is to investigate a new technique for image-based cancer cell classification and provide a more quantitative and objective characterization method for a diagnosis, which currently relies on qualitative and empirical judgment of pathologists. For this, a new method for chromatin texture characterization employing a new feature, contour complexity, is proposed and evaluated using nuclear images obtained from paraffin-wax embedded sections of human breast cancer on slides. The proposed feature is calculated on the basis of a contour length of nucleus obtained by setting different threshold values of intensity for a grayscale image, and it is a quantitative measure of chromatin texture. An expectation-maximization (EM) algorithm-based segmentation and an effective initial parameter search method for EM are used for the automatic calculation of the feature. The results for breast cancer cell detection showed that the average contour complexity value for malignant cells (19.6plusmn4.1) is found to be significantly greater (p-6, Kolmogorov-Smirnov test) than that of benign cells (0.35plusmn0.17). By the comparison with the conventional fractal dimension approach, it is shown that the proposed feature is much more sensitive feature than the fractal dimension for the individual cancer cell detection.