Coral–zooxanthellae holobionts are one of the most productive ecosystems in the ocean. With global warming and ocean acidification, coral ecosystems are facing unprecedented challenges. To save the coral ecosystems, we need to understand the symbiosis of coral–zooxanthellae. Although some Scleractinia (stony corals) transcriptomes have been sequenced, the reliable full-length transcriptome is still lacking due to the short-read length of second-generation sequencing and the uncertainty of the assembly results. Herein, PacBio Sequel II sequencing technology polished with the Illumina RNA-seq platform was used to obtain relatively complete scleractinian coral M. foliosa transcriptome data and to quantify M. foliosa gene expression. A total of 38,365 consensus sequences and 20,751 unique genes were identified. Seven databases were used for the gene function annotation, and 19,972 genes were annotated in at least one database. We found 131 zooxanthellae transcripts and 18,829 M. foliosa transcripts. A total of 6328 lncRNAs, 847 M. foliosa transcription factors (TFs), and 2 zooxanthellae TF were identified. In zooxanthellae we found pathways related to symbiosis, such as photosynthesis and nitrogen metabolism. Pathways related to symbiosis in M. foliosa include oxidative phosphorylation and nitrogen metabolism, etc. We summarized the isoforms and expression level of the symbiont recognition genes. Among the membrane proteins, we found three pathways of glycan biosynthesis, which may be involved in the organic matter storage and monosaccharide stabilization in M. foliosa. Our results provide better material for studying coral symbiosis.

Coral transcriptomic data largely rely on short-read sequencing, which severely limits the understanding of coral molecular mechanisms and leaves many important biological questions unresolved. Here, we sequence the full-length transcriptomes of four common and frequently dominant reef-building corals using the PacBio Sequel II platform. We obtain information on reported gene functions, structures, and expression profiles. Among them, a comparative analysis of biomineralization-related genes provides insights into the molecular basis of coral skeletal density. The gene expression profiles of the symbiont Symbiodiniaceae are also isolated and annotated from the holobiont sequence data. Finally, a phylogenetic analysis of key circadian clock genes among 40 evolutionarily representative species indicates that there are four key members in early metazoans, including cry genes; Clock or Npas2; cyc or Arntl; and tim, while per, as the fifth member, occurs in Bilateria. In summary, this work provides a foundation for further work on the manipulation of skeleton production or symbiosis to promote the survival of these important organisms.

Geloina erosa is an important benthic animal in the mangrove, serving as an indicator organism for coastal environmental pollution. This study aimed to investigate the tissue-specific expression of miRNAs and their regulatory roles in predicted targets in G. erosa. Through miRNA sequencing and co-expression network analysis, we extensively studied the miRNA expression in three tissues: gills, hepatopancreas, and muscle. The results revealed a total of 1412 miRNAs, comprising 1047 known miRNAs, and 365 newly predicted miRNAs. These miRNAs exhibited distinct tissue-specific expression patterns. In the miRNA target gene prediction, a total of 7404 potential predicted targets were identified, representing approximately 33% of all unique transcripts associated with miRNAs. Further co-expression network analysis revealed nine modules, each showing a positive correlation with specific tissues (gills, hepatopancreas, or muscle). The blue module showed a significant correlation with gills (r = 0.83, p-value = 0.006), the black module was significantly related to the hepatopancreas (r = 0.78, p-value = 0.01), and the purple module was significantly correlated with muscle (r = 0.83, p-value = 0.006). Within these modules, related miRNAs tended to cluster together, while their correlations with other modules were relatively weak. Functional enrichment analysis was performed on miRNAs and their predicted targets in each tissue. In the gills, miRNAs primarily regulate immune-related genes, substance transport, and cytoskeletal organization. In the hepatopancreas, miRNAs suppressed genes involved in shell formation and played a role in cellular motor activity and metabolism. In muscle, miRNAs participate in metabolism and photoreceptive processes, as well as immune regulation. In summary, this study provides valuable insights into the tissue-specific regulation of miRNAs in G. erosa, highlighting their potential roles in immune response, metabolism, and environmental adaptation. These findings offer important clues for understanding the molecular mechanisms and biological processes in G. erosa, laying the foundation for further validation and elucidation of these regulatory relationships.

Background: Bulk RNA-seq is a cost-effective method for measuring average gene expression in tissue samples, but its lack of single-cell resolution limits the understanding of cellular heterogeneity. Computational deconvolution aims to infer cell-type proportions from bulk RNA-seq data; however, the accuracy of existing methods needs improvement, especially in complex tissues. Methods: In this study, we introduce DiffFormer, a novel deconvolution model that, for the first time, integrates a conditional diffusion model with a Transformer architecture. We systematically evaluated DiffFormer on four pseudo-bulk datasets and validated it on a gold-standard real-world dataset with FACS-based ground truth. Results: DiffFormer demonstrated consistent and strong performance across all test datasets, outperforming existing methods and a baseline MLP-based diffusion model (DiffMLP). For instance, on the pbmc3k dataset, DiffFormer reduced the Root Mean Square Error (RMSE) from 0.1060 to 0.0120 compared to DiffMLP. This advantage was further confirmed on the real-world dataset, where DiffFormer achieved the highest Pearson Correlation Coefficient (PCC). Conclusions: This work provides a high-precision, reproducible tool for cellular deconvolution. Crucially, the direct comparison with an MLP-based diffusion model provides definitive evidence that the Transformer architecture is key to its success, highlighting the potential of such models for solving complex bioinformatics problems.

Background: Standard single-cell RNA sequencing (scRNA-seq) analysis workflows face significant limitations, particularly the rigidity of clustering-dependent methods that can obscure subtle cellular heterogeneity and the potential loss of biologically meaningful cells during stringent quality control (QC) filtering. This study aims to develop scSelector (v1.0), an interactive software toolkit designed to empower researchers to flexibly select and analyze cell populations directly from low-dimensional embeddings, guided by their expert biological knowledge. Methods: scSelector was developed using Python, relying on core dependencies such as Scanpy (v1.9.0), Matplotlib (v3.4.0), and NumPy (v1.20.0). It integrates an intuitive lasso selection tool with backend analytical modules for differential expression and functional enrichment analysis. Furthermore, it incorporates Large Language Model (LLM) assistance via API integration (DeepSeek/Gemini) to provide automated, contextually informed cell-type and state prediction reports. Results: Validation across multiple public datasets demonstrated that scSelector effectively resolves functional heterogeneity within broader cell types, such as identifying distinct alpha-cell subpopulations with unique remodeling capabilities in pancreatic tissue. It successfully characterized rare populations, including platelets in PBMCs and extremely low-abundance endothelial cells in liver tissue (as few as 53 cells). Additionally, scSelector revealed that cells discarded by standard QC can represent biologically functional subpopulations, and it accurately dissected the states of outlier cells, such as proliferative NK cells. Conclusions: scSelector provides a flexible, researcher-centric platform that moves beyond the constraints of automated pipelines. By combining interactive selection with AI-assisted interpretation, it enhances the precision of scRNA-seq analysis and facilitates the discovery of novel cell types and complex cellular behaviors.

Background: Low sequencing depth causes molecular capture loss and zero inflation in scRNA-seq data, reducing the reliability of downstream analyses. Methods: We propose DepthDiff, a depth-conditional expression enhancement model trained with diffusion-based denoising. Using low-depth expression profiles and sequencing depth ratio as conditions, DepthDiff learns a supervised residual mapping to paired high-depth references. During inference, it requires only a single-step forward prediction. Fixed-UMI downsampling was used to construct benchmarks across three public datasets. Results: DepthDiff outperformed supervised baselines, MAGIC, and unenhanced low-depth data in expression reconstruction and biological signal preservation. Ablation experiments showed that x0 prediction and cosine noise scheduling were important for stability, while full reverse diffusion sampling provided no additional benefit. Cross-dataset transfer and CITE-seq validation supported the generalizability and biological relevance of the recovered signals. Conclusions: DepthDiff is an efficient supervised framework for low-depth scRNA-seq expression enhancement, with gains mainly driven by diffusion-based denoising training rather than generative reverse sampling.