MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables transcriptome-wide profiling at single-cell resolution, revealing heterogeneous regulatory programs and making gene regulatory network (GRN) inference both central and challenging. Recent graph neural network (GNN)-based approaches for GRN inference typically model edges only implicitly (for example, via concatenated node embeddings), which limits their ability to capture complex regulatory dependencies. In addition, distributional shifts across scRNA-seq datasets make a single fixed GNN architecture poorly suited for broad generalization. RESULTS: We present AutoGERN, a GNN framework tailored for GRN inference from scRNA-seq data. AutoGERN explicitly models regulatory information in the message-passing space and learns expressive link (edge) embeddings, which a lightweight multilayer perceptron uses to score gene-gene regulatory associations. To enhance flexibility and representational power, AutoGERN employs dual message-passing spaces (within-layer and cross-layer) and integrates a robust AutoGNN-based architecture search to adapt the network design to differing dataset distributions. Extensive experiments on multiple real scRNA-seq datasets demonstrate that AutoGERN consistently achieves superior performance and robustness compared with state-of-the-art baselines. AVAILABILITY AND IMPLEMENTATION: The code and data of AutoGERN are available on GitHub at https://github.com/JChander/AutoGERN and on Zenodo at https://doi.org/10.5281/zenodo.18659807.

Abstract Circular RNA (circRNA) is a covalently closed RNA molecule formed by back splicing. The role of circRNAs in posttranscriptional gene regulation provides new insights into several types of cancer and neurological diseases. CircRNAs are associated with multiple diseases and are emerging biomarkers in cancer diagnosis and treatment. The associations prediction is one of the current research hotspots in the field of bioinformatics. Although research on circRNAs has made great progress, the traditional biological method of verifying circRNA-disease associations is still a great challenge because it is a difficult task and requires much time. Fortunately, advances in computational methods have made considerable progress in circRNA research. This review comprehensively discussed the functions and databases related to circRNA, and then focused on summarizing the calculation model of related predictions, detailed the mainstream algorithm into 4 categories, and analyzed the advantages and limitations of the 4 categories. This not only helps researchers to have overall understanding of circRNA, but also helps researchers have a detailed understanding of the past algorithms, guide new research directions and research purposes to solve the shortcomings of previous research.

Reconstructing gene regulatory networks (GRNs) using single-cell RNA sequencing (scRNA-seq) data holds great promise for unraveling cellular fate development and heterogeneity. While numerous machine-learning methods have been proposed to infer GRNs from scRNA-seq gene expression data, many of them operate solely in a statistical or black box manner, limiting their capacity for making causal inferences between genes. In this study, we introduce GRN inference with Accuracy and Causal Explanation (GRACE), a novel graph-based causal autoencoder framework that combines a structural causal model (SCM) with graph neural networks (GNNs) to enable GRN inference and gene causal reasoning from scRNA-seq data. By explicitly modeling causal relationships between genes, GRACE facilitates the learning of regulatory context and gene embeddings. With the learned gene signals, our model successfully decoding the causal structures and alleviates the accurate determination of multiple attributes of gene regulation that is important to determine the regulatory levels. Through extensive evaluations on seven benchmarks, we demonstrate that GRACE outperforms 14 state-of-the-art GRN inference methods, with the incorporation of causal mechanisms significantly enhancing the accuracy of GRN and gene causality inference. Furthermore, the application to human peripheral blood mononuclear cell (PBMC) samples reveals cell type-specific regulators in monocyte phagocytosis and immune regulation, validated through network analysis and functional enrichment analysis.

Virus-encoded circular RNA (circRNA) participates in the immune response to viral infection, affects the human immune system, and can be used as a target for precision therapy and tumor biomarker. The coronaviruses SARS-CoV-1 and SARS-CoV-2 (SARS-CoV-1/2) that have emerged in recent years are highly contagious and have high mortality rates. In coronaviruses, little is known about the circRNA encoded by the SARS-CoV-1/2. Therefore, this study explores whether SARS-CoV-1/2 encodes circRNA and characteristics and functions of circRNA. Based on RNA-seq data of SARS-CoV-1 and SARS-CoV-2 infections, we used circRNA identification tools (circRNA_finder, find_circ and CIRI2) to identify circRNAs. The number of circRNAs encoded by SARS-CoV-1 and SARS-CoV-2 was identified as 151 and 470, respectively. It can be found that SARS-CoV-2 shows more prominent circRNA encoding ability than SARS-CoV-1. Expression analysis showed that only a few circRNAs encoded by SARS-CoV-1/2 showed high expression levels, and the positive strand produced more abundant circRNAs. Then, based on the identified SARS-CoV-1/2-encoded circRNAs, we performed circRNA identification and characterization using the previously developed CirRNAPL. Finally, target gene prediction and functional enrichment analysis were performed. It was found that viral circRNA is closely related to cancer and has a potential role in regulating host cell functions. This study studied the characteristics and functions of viral circRNA encoded by coronavirus SARS-CoV-1/2, providing a valuable resource for further research on the function and molecular mechanism of coronavirus circRNA.

The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool.

Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep learning with a more powerful ability for representation learning enables it to be a promising technology for predicting disease-associated circRNAs. In this review, we mainly introduce the most popular databases related to circRNA, and summarize three types of deep learning-based circRNA-disease associations prediction methods: feature-generation-based, type-discrimination and hybrid-based methods. We further evaluate seven representative models on benchmark with ground truth for both balance and imbalance classification tasks. In addition, we discuss the advantages and limitations of each type of method and highlight suggested applications for future research.