Deep learning tools are top performers in long non-coding RNA prediction

RNA research for drug discovery: Recent advances and critical insight

The field of RNA research has experienced significant changes and is now at the forefront of contemporary drug development. This narrative overview explores the scientific developments and historical turning points in RNA research, emphasising the field's critical significance in the development of novel therapeutics. Important discoveries like antisense oligonucleotides (ASOs), mRNA therapies, and RNA interference (RNAi) have created novel treatment options that can be targeted, such as the ground-breaking mRNA vaccinations against COVID-19. Advances in high-throughput sequencing, single-cell RNA sequencing, and epitranscriptomics have further unravelled the complexity of RNA biology, shedding light on the intricacies of gene regulation and cellular diversity. The integration of computational tools and bioinformatics has propelled the identification of RNA-based biomarkers and the development of RNA therapeutics. Despite significant progress, challenges such as RNA stability, delivery, and off-target effects persist, necessitating continuous innovation and ethical considerations. This review provides a critical insight into the current state and prospects of RNA research, emphasising its transformative potential in drug discovery. By examining the interplay between technological advancements and therapeutic applications, we underscore the promising horizon for RNA-based interventions in treating a myriad of diseases, marking a new era in precision medicine.

Comparative Tissue Identification and Characterization of Long Non-Coding RNAs in the Globally Distributed Blue Shark Prionace glauca

Long non-coding RNAs (lncRNAs) are involved in numerous biological processes and serve crucial regulatory functions in both animals and plants. Nevertheless, there is limited understanding of lncRNAs and their patterns of expression and roles in sharks. In the current study, we systematically identified and characterized lncRNAs in the blue shark (Prionace glauca) from four tissues (liver, spleen, muscle, and kidney) using high-throughput sequencing and bioinformatics tools. A total of 21,932 high-confidence lncRNAs were identified, with 8984 and 3067 stably and tissue-specific expressed lncRNAs, respectively. In addition, a total of 45,007 differentially expressed (DE) lncRNAs were obtained among tissues, with kidney versus muscle having the largest numbers across tissues. DE lncRNAs trans target protein-coding genes were predicted, and functional gene ontology enrichment of these genes showed GO terms such as muscle system processes, cellular/metabolic processes, and stress and immune responses, all of which correspond with the specific biological functions of each tissue analyzed. These results advance our knowledge of lncRNAs in sharks and present novel data on tissue-specific lncRNAs, providing key information to support future functional shark investigations.

Comparison and benchmark of deep learning methods for non-coding RNA classification

The involvement of non-coding RNAs in biological processes and diseases has made the exploration of their functions crucial. Most non-coding RNAs have yet to be studied, creating the need for methods that can rapidly classify large sets of non-coding RNAs into functional groups, or classes. In recent years, the success of deep learning in various domains led to its application to non-coding RNA classification. Multiple novel architectures have been developed, but these advancements are not covered by current literature reviews. We present an exhaustive comparison of the different methods proposed in the state-of-the-art and describe their associated datasets. Moreover, the literature lacks objective benchmarks. We perform experiments to fairly evaluate the performance of various tools for non-coding RNA classification on popular datasets. The robustness of methods to non-functional sequences and sequence boundary noise is explored. We also measure computation time and CO2 emissions. With regard to these results, we assess the relevance of the different architectural choices and provide recommendations to consider in future methods.

A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs

Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body's normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.

Composition, physicochemical property and base periodicity for discriminating lncRNA and mRNA

Annotation of genome data with biological features is a challenging problem. One such problem deals with distinguishing lncRNA from mRNA. In this study, three groups of classification features, namely base periodicity, physicochemical property and nucleotide compositions were considered. We are attempting to propose a simple neural network model to obtain better results using judicious combination of the above said sequence features. Our approach uses balanced dataset, simple prediction model and use of limited features in distinguishing lncRNA and mRNA. Accordingly (a) two properties of base periodicity: peak power spectrum of the signal and noise-to-signal ratio (SNR) of this peak signal (b) three physicochemical properties: solvation, stacking and hydrogen-bonding energy and (c) all dinucleotides and trinucleotides compositions were used. Classification was performed by considering features independently followed by combining these properties for improvement. Classification metric was used to compare the result for seven eukaryotic organisms for various combinations of features. Nucleotide compositions combined with physicochemical property or base periodicity group of features becomes a strong classifier with more than 99 percentage accuracy. Base periodicity analysis with SNR can be used as discriminating feature of lncRNA from mRNA.