The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory mechanisms in various biological processes. While traditional machine learning approaches have been employed for distinguishing ncRNA, these often necessitate extensive feature engineering. Recently, deep learning algorithms have provided advancements in ncRNA classification. This study presents BioDeepFuse, a hybrid deep learning framework integrating convolutional neural networks (CNN) or bidirectional long short-term memory (BiLSTM) networks with handcrafted features for enhanced accuracy. This framework employs a combination of k-mer one-hot, k-mer dictionary, and feature extraction techniques for input representation. Extracted features, when embedded into the deep network, enable optimal utilization of spatial and sequential nuances of ncRNA sequences. Using benchmark datasets and real-world RNA samples from bacterial organisms, we evaluated the performance of BioDeepFuse. Results exhibited high accuracy in ncRNA classification, underscoring the robustness of our tool in addressing complex ncRNA sequence data challenges. The effective melding of CNN or BiLSTM with external features heralds promising directions for future research, particularly in refining ncRNA classifiers and deepening insights into ncRNAs in cellular processes and disease manifestations. In addition to its original application in the context of bacterial organisms, the methodologies and techniques integrated into our framework can potentially render BioDeepFuse effective in various and broader domains.
Adjuvanticity and delivery are crucial facets of mRNA vaccine design. In modern mRNA vaccines, adjuvant functions are integrated into mRNA vaccine nanoparticles, allowing the co-delivery of antigen mRNA and adjuvants in a unified, all-in-one formulation. In this formulation, many mRNA vaccines utilize the immunostimulating properties of mRNA and vaccine carrier components, including lipids and polymers, as adjuvants. However, careful design is necessary, as excessive adjuvanticity and activation of improper innate immune signalling can conversely hinder vaccination efficacy and trigger adverse effects. mRNA vaccines also require delivery systems to achieve antigen expression in antigen-presenting cells (APCs) within lymphoid organs. Some vaccines directly target APCs in the lymphoid organs, while others rely on APCs migration to the draining lymph nodes after taking up mRNA vaccines. This review explores the current mechanistic understanding of these processes and the ongoing efforts to improve vaccine safety and efficacy based on this understanding.
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