Interactions between regulatory elements are of crucial importance for the understanding of transcriptional regulation and the interpretation of disease mechanisms. Hi-C technique has been developed for genome-wide detection of chromatin contacts. However, unless extremely deep sequencing is performed on a very large number of input cells, which is technically limited and expensive, current Hi-C experiments do not have high enough resolution to resolve contacts between regulatory elements. Here, we develop DeepTACT, a bootstrapping deep learning model, to integrate genome sequences and chromatin accessibility data for the prediction of chromatin contacts between regulatory elements. DeepTACT can infer not only promoter–enhancer interactions, but also promoter–promoter interactions. In tests based on promoter capture Hi-C data, DeepTACT shows better performance over existing methods. DeepTACT analysis also identifies a class of hub promoters, which are correlated with transcriptional activation across cell lines, enriched in housekeeping genes, functionally related to fundamental biological processes, and capable of reflecting cell similarity. Finally, the utility of chromatin contacts in the study of human diseases is illustrated by the association of IFNA2 to coronary artery disease via an integrative analysis of GWAS data and interactions predicted by DeepTACT.

Motivation Accurate prediction of cancer drug response (CDR) is challenging due to the uncertainty of drug efficacy and heterogeneity of cancer patients. Strong evidences have implicated the high dependence of CDR on tumor genomic and transcriptomic profiles of individual patients. Precise identification of CDR is crucial in both guiding anti-cancer drug design and understanding cancer biology. Results In this study, we present DeepCDR which integrates multi-omics profiles of cancer cells and explores intrinsic chemical structures of drugs for predicting CDR. Specifically, DeepCDR is a hybrid graph convolutional network consisting of a uniform graph convolutional network and multiple subnetworks. Unlike prior studies modeling hand-crafted features of drugs, DeepCDR automatically learns the latent representation of topological structures among atoms and bonds of drugs. Extensive experiments showed that DeepCDR outperformed state-of-the-art methods in both classification and regression settings under various data settings. We also evaluated the contribution of different types of omics profiles for assessing drug response. Furthermore, we provided an exploratory strategy for identifying potential cancer-associated genes concerning specific cancer types. Our results highlighted the predictive power of DeepCDR and its potential translational value in guiding disease-specific drug design. Availability and implementation DeepCDR is freely available at https://github.com/kimmo1019/DeepCDR. Supplementary information Supplementary data are available at Bioinformatics online.

Traffic is essential for many dynamic processes on networks. The efficient routing strategy [G. Yan, T. Zhou, B. Hu, Z. Q. Fu, and B. H. Wang, Phys. Rev. E 73, 046108 (2006)] can reach a very high capacity of more than ten times of that with shortest path strategy. In this paper, we propose a global dynamic routing strategy for network systems based on the information of the queue length of nodes. Under this routing strategy, the traffic capacity is further improved. With time delay of updating node queue lengths and the corresponding paths, the system capacity remains constant, while the travel time for packets increases.

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In this paper, a method is proposed to enhance the traffic handling capacity of scale-free networks by closing or cutting some links between some large-degree nodes, for both local routing strategy and global shortest-path routing strategy. The traffic capacity of networks is found to be considerably improved after applying the link-closing strategy, especially in the case of global routing. Due to the strongly improved network capacity, easy realization on networks, and low cost, the strategy may be useful for modern communication networks.

Motivation Single-cell RNA sequencing (scRNA-seq) techniques have revolutionized the investigation of transcriptomic landscape in individual cells. Recent advancements in spatial transcriptomic technologies further enable gene expression profiling and spatial organization mapping of cells simultaneously. Among the technologies, imaging-based methods can offer higher spatial resolutions, while they are limited by either the small number of genes imaged or the low gene detection sensitivity. Although several methods have been proposed for enhancing spatially resolved transcriptomics, inadequate accuracy of gene expression prediction and insufficient ability of cell-population identification still impede the applications of these methods. Results We propose stPlus, a reference-based method that leverages information in scRNA-seq data to enhance spatial transcriptomics. Based on an auto-encoder with a carefully tailored loss function, stPlus performs joint embedding and predicts spatial gene expression via a weighted k-nearest-neighbor. stPlus outperforms baseline methods with higher gene-wise and cell-wise Spearman correlation coefficients. We also introduce a clustering-based approach to assess the enhancement performance systematically. Using the data enhanced by stPlus, cell populations can be better identified than using the measured data. The predicted expression of genes unique to scRNA-seq data can also well characterize spatial cell heterogeneity. Besides, stPlus is robust and scalable to datasets of diverse gene detection sensitivity levels, sample sizes and number of spatially measured genes. We anticipate stPlus will facilitate the analysis of spatial transcriptomics. Availability and implementation stPlus with detailed documents is freely accessible at http://health.tsinghua.edu.cn/software/stPlus/ and the source code is openly available on https://github.com/xy-chen16/stPlus.

Although Cas9 nucleases are remarkably diverse in microorganisms, the range of genomic sequences targetable by a CRISPR/Cas9 system is restricted by the requirement of a short protospacer adjacent motif (PAM) at the target site. Here, we generate a group of chimeric Cas9 (cCas9) variants by replacing the key region in the PAM interaction (PI) domain of Staphylococcus aureus Cas9 (SaCas9) with the corresponding region in a panel of SaCas9 orthologs. By using a functional assay at target sites with different nucleotide recombinations at PAM position 3–6, we identify several cCas9 variants with expanded recognition capability at NNVRRN, NNVACT, NNVATG, NNVATT, NNVGCT, NNVGTG, and NNVGTT PAM sequences. In summary, we provide a panel of cCas9 variants accessible up to 1/4 of all the possible genomic targets in mammalian cells.

The convolutional neural network (CNN) has become a powerful tool for various biomedical image analysis tasks, but there is a lack of visual explanation for the machinery of CNNs. In this paper, we present a novel algorithm, Respond-weighted Class Activation Mapping (Respond-CAM), for making CNN-based models interpretable by visualizing input regions that are important for predictions, especially for biomedical 3D imaging data inputs. Our method uses the gradients of any target concept (e.g. the score of target class) that flows into a convolutional layer. The weighted feature maps are combined to produce a heatmap that highlights the important regions in the image for predicting the target concept. We prove a preferable sum-to-score property of the Respond-CAM and verify its significant improvement on 3D images from the current state-of-the-art approach. Our tests on Cellular Electron Cryo-Tomography 3D images show that Respond-CAM achieves superior performance on visualizing the CNNs with 3D biomedical images inputs, and is able to get reasonably good results on visualizing the CNNs with natural image inputs. The Respond-CAM is an efficient and reliable approach for visualizing the CNN machinery, and is applicable to a wide variety of CNN model families and image analysis tasks. Our code is available at: