Unlocking retinal regeneration in mice: Zebrafish can regenerate damaged retinal tissue, but mice cannot. Hoang et al. found that tracking changes in gene expression and chromatin accessibility upon injury revealed clues as to why retinal glial cells in zebrafish could generate new neurons but the same cell type in mice could not. In zebrafish, activated Müller glial cells shift into a proliferative phase, whereas in mice, a genetic network returns the glial cells to quiescence. A few transcription factors enforce quiescence in the mouse, and disruption of these allowed Müller glia to proliferate and generate new neurons after retinal injury. Science , this issue p. eabb8598

The identification of disease-related microRNAs is vital for understanding the pathogenesis of diseases at the molecular level, and is critical for designing specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses considerable difficulties. Computational analysis of microRNA-disease associations is an important complementary means for prioritizing microRNAs for further experimental examination.

Motivation: Multiple sequence alignment (MSA) is important work, but bottlenecks arise in the massive MSA of homologous DNA or genome sequences. Most of the available state-of-the-art software tools cannot address large-scale datasets, or they run rather slowly. The similarity of homologous DNA sequences is often ignored. Lack of parallelization is still a challenge for MSA research. Results: We developed two software tools to address the DNA MSA problem. The first employed trie trees to accelerate the centre star MSA strategy. The expected time complexity was decreased to linear time from square time. To address large-scale data, parallelism was applied using the hadoop platform. Experiments demonstrated the performance of our proposed methods, including their running time, sum-of-pairs scores and scalability. Moreover, we supplied two massive DNA/RNA MSA datasets for further testing and research. Availability and implementation: The codes, tools and data are accessible free of charge at http://datamining.xmu.edu.cn/software/halign/. Contact:zouquan@nclab.net or ghwang@hit.edu.cn

Motivation: Identification of Drug–Target Interactions (DTIs) is an essential step in drug discovery and repositioning. DTI prediction based on biological experiments is time-consuming and expensive. In recent years, graph learning-based methods have aroused widespread interest and shown certain advantages on this task, where the DTI prediction is often modeled as a binary classification problem of the nodes composed of drug and protein pairs (DPPs). Nevertheless, in many real applications, labeled data are very limited and expensive to obtain. With only a few thousand labeled data, models could hardly recognize comprehensive patterns of DPP node representations, and are unable to capture enough commonsense knowledge, which is required in DTI prediction. Supervised contrastive learning gives an aligned representation of DPP node representations with the same class label. In embedding space, DPP node representations with the same label are pulled together, and those with different labels are pushed apart. Results: We propose an end-to-end supervised graph co-contrastive learning model for DTI prediction directly from heterogeneous networks. By contrasting the topology structures and semantic features of the drug–protein-pair network, as well as the new selection strategy of positive and negative samples, SGCL-DTI generates a contrastive loss to guide the model optimization in a supervised manner. Comprehensive experiments on three public datasets demonstrate that our model outperforms the SOTA methods significantly on the task of DTI prediction, especially in the case of cold start. Furthermore, SGCL-DTI provides a new research perspective of contrastive learning for DTI prediction. Availability and implementation: The research shows that this method has certain applicability in the discovery of drugs, the identification of drug–target pairs and so on.

Drug–target interaction (DTI) is an important step in drug discovery. Although there are many methods for predicting drug targets, these methods have limitations in using discrete or manual feature representations. In recent years, deep learning methods have been used to predict DTIs to improve these defects. However, most of the existing deep learning methods lack the fusion of topological structure and semantic information in DPP representation learning process. Besides, when learning the DPP node representation in the DPP network, the different influences between neighboring nodes are ignored. In this paper, a new model DTI-MGNN based on multi-channel graph convolutional network and graph attention is proposed for DTI prediction. We use two independent graph attention networks to learn the different interactions between nodes for the topology graph and feature graph with different strengths. At the same time, we use a graph convolutional network with shared weight matrices to learn the common information of the two graphs. The DTI-MGNN model combines topological structure and semantic features to improve the representation learning ability of DPPs, and obtain the state-of-the-art results on public datasets. Specifically, DTI-MGNN has achieved a high accuracy in identifying DTIs (the area under the receiver operating characteristic curve is 0.9665).

Prostate cancer is one of the leading causes of cancer death in men. Androgen ablation, the most commonly-used therapy for progressive prostate cancer, is ineffective once the cancer cells become androgen-independent. The regulatory mechanisms that cause this transition (from androgen-dependent to androgen-independent) remain unknown. In this study, based on the microarray data comparing global gene expression patterns in the prostate tissue between androgen-dependent and -independent prostate cancer patients, we indentify a set of transcription factors and microRNAs that potentially cause such difference, using a model-based computational approach.