MOTIVATION: Long non-coding RNAs (lncRNAs) have emerged as crucial players in diverse physiological and pathological processes, yet the biological mechanisms of the vast majority of lncRNAs remain elusive. To fill this gap, it is necessary to improve the accuracy of lncRNA identification and functional annotation. RESULTS: Here, we introduce LncADeep 2.0, an integrated deep learning framework designed to meet these needs. In the identification module, LncADeep 2.0 incorporated novel peptide features along with sequence and structural information, demonstrating superior performance over our previous LncADeep and other existing tools on both annotated transcripts from GENCODE and RNA-seq data. For functional annotation, LncADeep 2.0 leveraged lncRNA-centric interaction networks and gene ontology terms through the transfer learning strategy to achieve robust annotation performance with limited functional data. Compared to LncADeep, LncADeep 2.0 could accurately elucidate the general functions of given lncRNA sequences, predict tissue- or cell-type-specific functions from bulk and single-cell RNA-seq data, and establish connections between tumor-associated lncRNAs and genomic markers. Overall, LncADeep 2.0 stands out as an efficient and reliable tool for lncRNA identification and functional annotation across a wide spectrum of biological processes. AVAILABILITY AND IMPLEMENTATION: LncADeep 2.0 is available for use at https://github.com/Jefferson-Chou/LncADeep2 and https://doi.org/10.5281/zenodo.17164767.

With the rapid growth of Lithium-ion (Li-ion) battery usage in modern lifestyles, the increasing risk of fire hazards demands practical temperature monitoring techniques that are massively deployable. The goal of this work is to explore the use of wireless sensing in conjunction with Artificial Intelligence (AI) to enable the detection of the surface temperature of a target object. Our framework for detecting the target surface temperature based on the channel state information (CSI) of WiFi signals utilises a low-complexity classifier algorithm, specifically the linear support vector machine. With a prototype constructed with commodity embedded systems, we experimentally validated the feasibility of our proposed framework for detecting surface temperature levels with more than 93% accuracy at a resolution of 2°C. In addition, we evaluated the robustness of our embedded AI-based WiFI sensing technology. We identified that accuracy is subject to degradation with deployment conditions such as over-range, imbalanced positioning, and inadequate transmit power. Overall, with adequate accuracy, our WiFi CSI-based surface temperature detection framework provides a low-cost, massively deployable, and non-invasive solution for practical temperature surveillance applications involving heat-sensitive products.

Recent advances in neoantigen research have accelerated the development of immunotherapies for cancers, such as glioblastoma (GBM). Neoantigens resulting from genomic mutations and dysregulated alternative splicing have been studied in GBM. However, these studies have primarily focused on annotated alternatively-spliced transcripts, leaving non-annotated transcripts largely unexplored. Circular ribonucleic acids (circRNAs), abnormally regulated in tumors, are correlated with the presence of non-annotated linear transcripts with exon skipping events. But the extent to which these linear transcripts truly exist and their functions in cancer immunotherapies remain unknown. Here, we found the ubiquitous co-occurrence of circRNA biogenesis and alternative splicing across various tumor types, resulting in large amounts of long-range alternatively-spliced transcripts (LRs). By comparing tumor and healthy tissues, we identified tumor-specific LRs more abundant in GBM than in normal tissues and other tumor types. This may be attributable to the upregulation of the protein quaking in GBM, which is reported to promote circRNA biogenesis. In total, we identified 1057 specific and recurrent LRs in GBM. Through in silico translation prediction and MS-based immunopeptidome analysis, 16 major histocompatibility complex class I-associated peptides were identified as potential immunotherapy targets in GBM. This study revealed long-range alternatively-spliced transcripts specifically upregulated in GBM may serve as recurrent, immunogenic tumor-specific antigens.

Rhesus macaque is a unique nonhuman primate model for human evolutionary and translational study, but the error-prone gene models critically limit its applications. Here, we de novo defined full-length macaque gene models based on single molecule, long-read transcriptome sequencing in four macaque tissues (frontal cortex, cerebellum, heart and testis). Overall, 8 588 227 poly(A)-bearing complementary DNA reads with a mean length of 14 106 nt were generated to compile the backbone of macaque transcripts, with the fine-scale structures further refined by RNA sequencing and cap analysis gene expression sequencing data. In total, 51 605 macaque gene models were accurately defined, covering 89.7% of macaque or 75.7% of human orthologous genes. Based on the full-length gene models, we performed a human-macaque comparative analysis on polyadenylation (PA) regulation. Using macaque and mouse as outgroup species, we identified 79 distal PA events newly originated in humans and found that the strengthening of the distal PA sites, rather than the weakening of the proximal sites, predominantly contributes to the origination of these human-specific isoforms. Notably, these isoforms are selectively constrained in general and contribute to the temporospatially specific reduction of gene expression, through the tinkering of previously existed mechanisms of nuclear retention and microRNA (miRNA) regulation. Overall, the protocol and resource highlight the application of bioinformatics in integrating multilayer genomics data to provide an intact reference for model animal studies, and the isoform switching detected may constitute a hitherto underestimated regulatory layer in shaping the human-specific transcriptome and phenotypic changes.