Hi-C is commonly used to study three-dimensional genome organization. However, due to the high sequencing cost and technical constraints, the resolution of most Hi-C datasets is coarse, resulting in a loss of information and biological interpretability. Here we develop DeepHiC, a generative adversarial network, to predict high-resolution Hi-C contact maps from low-coverage sequencing data. We demonstrated that DeepHiC is capable of reproducing high-resolution Hi-C data from as few as 1% downsampled reads. Empowered by adversarial training, our method can restore fine-grained details similar to those in high-resolution Hi-C matrices, boosting accuracy in chromatin loops identification and TADs detection, and outperforms the state-of-the-art methods in accuracy of prediction. Finally, application of DeepHiC to Hi-C data on mouse embryonic development can facilitate chromatin loop detection. We develop a web-based tool (DeepHiC, http://sysomics.com/ deephic) that allows researchers to enhance their own Hi-C data with just a few clicks.

Essential genes are those whose loss of function compromises organism viability or results in profound loss of fitness. Recent gene-editing technologies have provided new opportunities to characterize essential genes. Here, we present an integrated analysis that comprehensively and systematically elucidates the genetic and regulatory characteristics of human essential genes. First, we found that essential genes act as ‘hubs’ in protein–protein interaction networks, chromatin structure and epigenetic modification. Second, essential genes represent conserved biological processes across species, although gene essentiality changes differently among species. Third, essential genes are important for cell development due to their discriminate transcription activity in embryo development and oncogenesis. In addition, we developed an interactive web server, the Human Essential Genes Interactive Analysis Platform (http://sysomics.com/HEGIAP/), which integrates abundant analytical tools to enable global, multidimensional interpretation of gene essentiality. Our study provides new insights that improve the understanding of human essential genes.

The Hybrid Single‐Particle Lagrangian Integrated Trajectory platform is employed in this study to simulate trajectories of air parcels in the rainy season in North China during last six decades (1951–2010), with the purpose of investigating moisture sources, their variation, and the eventual relationship with precipitation in North China. Climatological trajectories indicate that moisture in North China originates, respectively, from Eurasia (14.4%), eastern China (10.2%), the Bay of Bengal‐South China Sea (33.8%), the Indian Ocean (10.7%), and the Pacific (30.9%). The spatiotemporal analysis of moisture trajectory based on extended empirical orthogonal function indicates that a decrease of precipitation in North China is caused mainly by a decrease of water vapor sources from the south, the Indian Ocean, the Bay of Bengal, and the South China Sea, which overwhelms an increase of water vapor sources from the North, mainly Eurasia, eastern China, and northern western North Pacific Ocean. In particular, the significant decrease of precipitation in the late 1970s (by 11.6%) is mainly caused by a 10.6% decrease of moisture from all sources. The Bay of Bengal, the South China Sea, and the Indian Ocean are dominant moisture source areas affecting the decadal‐scale variation of precipitation in North China.

The Cancer Genome Atlas (TCGA) is a publicly funded project that aims to catalog and discover major cancer-causing genomic alterations with the goal of creating a comprehensive 'atlas' of cancer genomic profiles. The availability of this genome-wide information provides an unprecedented opportunity to expand our knowledge of tumourigenesis. Computational analytics and mining are frequently used as effective tools for exploring this byzantine series of biological and biomedical data. However, some of the more advanced computational tools are often difficult to understand or use, thereby limiting their application by scientists who do not have a strong computational background. Hence, it is of great importance to build user-friendly interfaces that allow both computational scientists and life scientists without a computational background to gain greater biological and medical insights. To that end, this survey was designed to systematically present available Web-based tools and facilitate the use TCGA data for cancer research.

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