RNAdegformer: accurate prediction of mRNA degradation at nucleotide resolution with deep learning

RegRNA 3.0: expanding regulatory RNA analysis with new features for motif, interaction, and annotation

Functional RNA molecules are crucial for biological processes from gene regulation to protein synthesis, and analyzing functional motifs and elements is essential for understanding RNA regulation. Building on RegRNA 1.0 and 2.0, we present RegRNA 3.0, a sophisticated meta-workflow that integrates 26 computational tools and 28 databases for annotation, enabling one-step and customizable RNA motif predictions. RegRNA streamlines multi-step analysis and enhances result interpretation with interactive visualizations and comprehensive reporting tools. When provided with an RNA sequence, RegRNA 3.0 generates predictions for RNA functional motifs, RNA interaction motifs, and comprehensive RNA annotations. Specifically, RNA functional motifs include core promoter elements, RNA decay, G-quadruplex, and 14 previous types. RNA interaction motifs include newly added RNA-ligand interactions and RNA-binding protein predictions, along with three previous types. RNA annotation includes RNA family classification, blood exosomes RNA, subcellular localizations, A-to-I editing events, modifications, and 3D structures, along with four previously supported features. RegRNA 3.0 accelerates gene regulation and RNA biology discoveries by offering a user-friendly platform for identifying and analyzing RNA motifs and interactions. The web interface has been improved for intuitive visualizations of predicted motifs and structures, with flexible download options in multiple formats. It is available at http://awi.cuhk.edu.cn/∼RegRNA/.

Self-Amplifying RNA: Advantages and Challenges of a Versatile Platform for Vaccine Development

Self-amplifying RNA is synthetic nucleic acid engineered to replicate within cells without generating viral particles. Derived from alphavirus genomes, saRNA retains the non-structural elements essential for replication while replacing the structural elements with an antigen of interest. By enabling efficient intracellular amplification, saRNA offers a promising alternative to conventional mRNA vaccines, enhancing antigen expression while requiring lower doses. However, this advantage comes with challenges. In this review, we highlight the key limitations of saRNA technology and explore potential strategies to overcome them. By identifying these challenges, we aim to provide insights that can guide the future design of saRNA-based therapeutics, extending their potential beyond vaccine applications.

UTR-Insight: integrating deep learning for efficient 5' UTR discovery and design

The 5' UTR is critical for mRNA stability and translation efficiency in therapeutics. We developed UTR-Insight, a model integrating a pretrained language model with a CNN-Transformer architecture, explaining 89.1% of the mean ribosome load (MRL) variation in random 5' UTRs and 82.8% in endogenous 5' UTRs, surpassing existing models. Using UTR-Insight, we performed high-throughput in silico screening of hundreds of thousands of endogenous 5' UTRs from primates, mice, and viruses. The screened sequences increased protein expression by up to 319% compared to the human α-globin 5' UTR, and UTR-Insight-designed sequences achieved even greater expression levels than high-performing endogenous 5' UTRs.

mRNA Vaccines: Design Principles, Mechanisms, and Manufacturing-Insights From COVID-19 as a Model for Combating Infectious Diseases

The full approval of two SARS-CoV-2 mRNA vaccines, Comirnaty and Spikevax, has greatly accelerated the development of numerous mRNA vaccine candidates targeting infectious diseases and cancer. mRNA vaccines provide a rapid, safe, and versatile manufacturing process while eliciting strong humoral and cellular immune responses, making them particularly beneficial for addressing emerging pandemics. Recent advancements in modified nucleotides and lipid nanoparticle delivery systems have further emphasized the potential of this vaccine platform. Despite these transformative opportunities, significant improvements are needed to enhance vaccine efficacy, stability, and immunogenicity. This review outlines the fundamentals of mRNA vaccine design, the manufacturing process, and administration strategies, along with various optimization approaches. It also offers a comprehensive overview of the mRNA vaccine candidates developed since the onset of the COVID-19 pandemic, the challenges posed by emerging SARS-CoV-2 variants, and current strategies to address these variants. Finally, we discuss the potential of broad-spectrum and combined mRNA vaccines and examine the challenges and future prospects of the mRNA vaccine platform.

RNA sequence analysis landscape: A comprehensive review of task types, databases, datasets, word embedding methods, and language models

Deciphering information of RNA sequences reveals their diverse roles in living organisms, including gene regulation and protein synthesis. Aberrations in RNA sequence such as dysregulation and mutations can drive a diverse spectrum of diseases including cancers, genetic disorders, and neurodegenerative conditions. Furthermore, researchers are harnessing RNA's therapeutic potential for transforming traditional treatment paradigms into personalized therapies through the development of RNA-based drugs and gene therapies. To gain insights of biological functions and to detect diseases at early stages and develop potent therapeutics, researchers are performing diverse types RNA sequence analysis tasks. RNA sequence analysis through conventional wet-lab methods is expensive, time-consuming and error prone. To enable large-scale RNA sequence analysis, empowerment of wet-lab experimental methods with Artificial Intelligence (AI) applications necessitates scientists to have a comprehensive knowledge of both DNA and AI fields. While molecular biologists encounter challenges in understanding AI methods, computer scientists often lack basic foundations of RNA sequence analysis tasks. Considering the absence of a comprehensive literature that bridges this research gap and promotes the development of AI-driven RNA sequence analysis applications, the contributions of this manuscript are manifold: It equips AI researchers with biological foundations of 47 distinct RNA sequence analysis tasks. It sets a stage for development of benchmark datasets related to 47 distinct RNA sequence analysis tasks by facilitating cruxes of 64 different biological databases. It presents word embeddings and language models applications across 47 distinct RNA sequence analysis tasks. It streamlines the development of new predictors by providing a comprehensive survey of 58 word embeddings and 70 language models based predictive pipelines performance values as well as top performing traditional sequence encoding based predictors and their performances across 47 RNA sequence analysis tasks.

Personalized cancer vaccine design using AI-powered technologies

Immunotherapy has ushered in a new era of cancer treatment, yet cancer remains a leading cause of global mortality. Among various therapeutic strategies, cancer vaccines have shown promise by activating the immune system to specifically target cancer cells. While current cancer vaccines are primarily prophylactic, advancements in targeting tumor-associated antigens (TAAs) and neoantigens have paved the way for therapeutic vaccines. The integration of artificial intelligence (AI) into cancer vaccine development is revolutionizing the field by enhancing various aspect of design and delivery. This review explores how AI facilitates precise epitope design, optimizes mRNA and DNA vaccine instructions, and enables personalized vaccine strategies by predicting patient responses. By utilizing AI technologies, researchers can navigate complex biological datasets and uncover novel therapeutic targets, thereby improving the precision and efficacy of cancer vaccines. Despite the promise of AI-powered cancer vaccines, significant challenges remain, such as tumor heterogeneity and genetic variability, which can limit the effectiveness of neoantigen prediction. Moreover, ethical and regulatory concerns surrounding data privacy and algorithmic bias must be addressed to ensure responsible AI deployment. The future of cancer vaccine development lies in the seamless integration of AI to create personalized immunotherapies that offer targeted and effective cancer treatments. This review underscores the importance of interdisciplinary collaboration and innovation in overcoming these challenges and advancing cancer vaccine development.

mRNA Fragmentation Pattern Detected by SHAPE

The success of messenger RNA (mRNA) vaccines in controlling COVID-19 has warranted further developments in new technology. Currently, their quality control process largely relies on low-resolution electrophoresis for detecting chain breaks. Here, we present an approach using multi-primer reverse transcription sequencing (MPRT-seq) to identify degradation fragments in mRNA products. Using this in-house-made mRNA containing two antigens and untranslated regions (UTRs), we analyzed the mRNA completeness and degradation pattern at a nucleotide resolution. We then analyzed the sensitive base sequence and its correlation with the secondary structure. Our MPRT-seq mapping shows that certain sequences on the 5' of bulge-stem-loop structures can result in preferential chain breaks. Our results agree with commonly used capillary electrophoresis (CE) integrity analysis but at a much higher resolution, and can improve mRNA stability by providing information to remove sensitive structures or sequences in the mRNA sequence design.

Recent Advancements in mRNA Vaccines: From Target Selection to Delivery Systems

mRNA vaccines are leading a medical revolution. mRNA technologies utilize the host's own cells as bio-factories to produce proteins that serve as antigens. This revolutionary approach circumvents the complicated processes involved in traditional vaccine production and empowers vaccines with the ability to respond to emerging or mutated infectious diseases rapidly. Additionally, the robust cellular immune response elicited by mRNA vaccines has shown significant promise in cancer treatment. However, the inherent instability of mRNA and the complexity of tumor immunity have limited its broader application. Although the emergence of pseudouridine and ionizable cationic lipid nanoparticles (LNPs) made the clinical application of mRNA possible, there remains substantial potential for further improvement of the immunogenicity of delivered antigens and preventive or therapeutic effects of mRNA technology. Here, we review the latest advancements in mRNA vaccines, including but not limited to target selection and delivery systems. This review offers a multifaceted perspective on this rapidly evolving field.

Ribonanza: deep learning of RNA structure through dual crowdsourcing

Prediction of RNA structure from sequence remains an unsolved problem, and progress has been slowed by a paucity of experimental data. Here, we present Ribonanza, a dataset of chemical mapping measurements on two million diverse RNA sequences collected through Eterna and other crowdsourced initiatives. Ribonanza measurements enabled solicitation, training, and prospective evaluation of diverse deep neural networks through a Kaggle challenge, followed by distillation into a single, self-contained model called RibonanzaNet. When fine tuned on auxiliary datasets, RibonanzaNet achieves state-of-the-art performance in modeling experimental sequence dropout, RNA hydrolytic degradation, and RNA secondary structure, with implications for modeling RNA tertiary structure.

Computational design of mRNA vaccines

mRNA technology has emerged as a successful vaccine platform that offered a swift response to the COVID-19 pandemic. Accumulating evidence shows that vaccine efficacy, thermostability, and other important properties, are largely impacted by intrinsic properties of the mRNA molecule, such as RNA sequence and structure, both of which can be optimized. Designing mRNA sequence for vaccines presents a combinatorial problem due to an extremely large selection space. For instance, due to the degeneracy of the genetic code, there are over 10632 possible mRNA sequences that could encode the spike protein, the COVID-19 vaccines' target. Moreover, designing different elements of the mRNA sequence simultaneously against multiple objectives such as translational efficiency, reduced reactogenicity, and improved stability requires an efficient and sophisticated optimization strategy. Recently, there has been a growing interest in utilizing computational tools to redesign mRNA sequences to improve vaccine characteristics and expedite discovery timelines. In this review, we explore important biophysical features of mRNA to be considered for vaccine design and discuss how computational approaches can be applied to rapidly design mRNA sequences with desirable characteristics.

Advancing Cancer Immunotherapy: The Potential of mRNA Vaccines As a Promising Therapeutic Approach

mRNA vaccines have long been recognized for their ability to induce robust immune responses. The discovery that mRNA vaccines may also contribute to antitumor immunity has made them a promising therapeutic approach against cancer. Recent advances in understanding of immune system are precious in developing therapeutic strategies that target pathways involved in tumor survival and progression, leading to the most reliable therapeutic strategies in cancer treatment history. Among all traditional cancer treatments, cancer immunotherapies are less toxic and more effective, even in advanced or recurrent stages of cancer. Recent advancements in genomics and machine learning algorithms give new insight into vaccine development. mRNA vaccines are designed to interfere with stimulator of interferon genes (STING) and tumor‐infiltrating lymphocytes pathways, activating more CD8+ T‐cells involved in destroying tumor cells and inhibiting tumor growth. A stronger immune response can be achieved by incorporating immunological adjuvants alongside mRNA. Nonformulated or vehicle‐based mRNA vaccines, when combined with adjuvants, efficiently express tumor antigens through antigen‐presenting cells and stimulate both innate and adaptive immune responses. Codelivery with additional immunotherapeutic agents, such as checkpoint inhibitors, further enhances the efficacy of mRNA vaccines. This article focuses on the current clinical approaches and challenges to consider when developing mRNA‐based vaccine technology for cancer treatment.

Nucleic Transformer: Classifying DNA Sequences with Self-Attention and Convolutions

Much work has been done to apply machine learning and deep learning to genomics tasks, but these applications usually require extensive domain knowledge, and the resulting models provide very limited interpretability. Here, we present the Nucleic Transformer, a conceptually simple but effective and interpretable model architecture that excels in the classification of DNA sequences. The Nucleic Transformer employs self-attention and convolutions on nucleic acid sequences, leveraging two prominent deep learning strategies commonly used in computer vision and natural language analysis. We demonstrate that the Nucleic Transformer can be trained without much domain knowledge to achieve high performance in Escherichia coli promoter classification, viral genome identification, enhancer classification, and chromatin profile predictions.

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors.