Translational regulation plays the most critical role in gene expression. Ribosome profiling sequencing (Ribo-Seq) is one of the methods to study translation and its regulation. It is a high throughput technology based on deep sequencing, which targets ribosome protected mRNA fragments to produce a \'global snapshot\' of translatome. There has been an annual increase in the number of publications incorporating Ribo-seq technology. Because of its importance, we used PubMed database to conduct a comprehensive bibliometric analysis on Ribo-seq. We identified 2744 published articles that utilized the term \'Ribo-seq\' between 2009 and Jan 2024, and 684 articles that contained both Ribo-seq and RNA-seq terms. Based on keywords correlation analysis, we discovered that the primary focus of Ribo-seq articles lies in the areas of translation, transcriptome, and ribosome in the past few years and other topics such as single-cell ribo-seq and crispr within two years, reflecting current areas of interests in Ribo-seq research. The Ribo-seq data analysis applications were also explored and summarized, providing a guide for researchers to choose corresponding tools for different types of analysis. Overall, we highlighted the advances made by Ribo-seq technologies, and the possibilities of utilizing machine learning models to unravel information from multi-omics data. The integration of Ribo-seq with other omics data, such as RNA-seq, is essential to understand the gene expression in complex biological systems.

OBJECTIVE: To elucidate the scientific advances made in the last 12 months within the realm of osteoarthritis genetics, genomics, and epigenetics. This review paper highlights major research publications that enhance our current understanding of the role of genetics, genomics, and epigenetics in osteoarthritis.
METHODS: A systematic literature search was conducted on pubmed.ncbi.nlm.nih.gov on \"March 17, 2023\", using the following keywords: \"osteoarthritis\" in combination with any of these terms: \"genetic(s)\", \"mutation(s)\", \"genomic(s)\", \"epigenetic(s)\", \"DNA methylation\", \"noncoding RNA\", \"lncRNA\", \"circular RNA\", \"microRNA\", \"transcriptomic(s)\", \"RNA sequencing\", \"single cell RNA sequencing\", or \"single nucleus RNA sequencing\". The selection comprised original research articles published in the English language between the OARSI congresses of 2022 and 2023.
RESULTS: A total of 2178 research articles were identified, which subsequently reduced to 67 unique articles relevant to the field. Current trends in osteoarthritis genetics research involve meta-analyses of various cohorts to explore the impact of gene variants on osteoarthritis-related outcomes, such as pain. Early developmental changes within the joint were also found to influence osteoarthritis through genetic variations. Researchers also prioritize testing the mechanisms and functions of miRNAs, circRNAs, and lncRNAs. Potential drug targets began to emerge; however, independent validation studies are lacking. Single cell RNA sequencing studies revealed unique immune cell populations in the knee; however, no study reported single nucleus RNA sequencing analysis.
CONCLUSIONS: This review focused on recent advances in the above-mentioned themes within the field of osteoarthritis. These advances improve our understanding of the disease\'s complexity and guide us toward functional assessments of genetic/epigenetic outcomes and toward their translational and clinical applications.

RNA sequencing has emerged as the standard method for transcriptome profiling of several human diseases. We performed a systematic review detailing the state of RNA-seq analyses in Africa from its inception till February 2022. Our goal was to provide an update on the state of RNA-seq analyses in Africa, including research gaps, funding information, participants information, authorship and collaborations. Following the PRISMA guidelines, we performed an exhaustive literature search for RNA-seq studies conducted in Africa, using PubMed, Scopus and Academic Search Complete (EBSCOhost). The output was exported to Endnote X9 for analyses. The initial literature search yielded 10,369 articles spread across PubMed (4916), Scopus (4847) and EBSCOhost (580). By applying our exclusion criteria, 28 full-text articles remained and were thoroughly analyzed. Overall, 17 human diseases were studied, including cancers (10/28), infectious disease (4/28), parasitic disease (4/28), autoimmune disorders (2/28) and neglected tropical diseases (2/28). Majority of the articles were published in PLoS Pathogens, BioMed Central and Nature. The National Institutes of Health (42.4%), the Bill & Melinda Gates Foundation (7.5%) and the Wellcome Trust (7.5%) were the top funders of the research studies. Eleven African countries contributed to the participant group, with 57% located in Eastern Africa, 23.1% from Western and 16.7% from Southern Africa. The extremely low number of RNA-seq research studies in Africa is worrying and calls for an immediate investment in research by the African governments. The funding agencies and institutional review boards should also ensure that African collaborators are treated equitably in the course of the research projects.

In the last decade, transcriptome research adopting next-generation sequencing (NGS) technologies has gathered incredible momentum amongst functional genomics scientists, particularly amongst clinical/biomedical research groups. The progressive enfoldment/adoption of NGS technologies has incited an abundance of next-generation transcriptomic data harbouring an opulence of new knowledge in public databases. Nevertheless, knowledge discovery from these next-generation RNA-Seq. data analysis necessitates extensive bioinformatics know-how besides elaborate data analysis software packages consistent with the type and context of data analysis. Several reliability and reproducibility concerns continue to impede RNA-Seq. data analysis. Characteristic challenges comprise of data quality, hardware and networking provisions, selection and prioritisation of data analysis tools, and yet significantly implementing of robust machine learning algorithms for maximised exploitation of these experimental transcriptomic data. Over the years, numerous machine learning algorithms have been implemented for improved transcriptomic data analysis executing predominantly shallow learning approaches. More recently, deep learning algorithms are becoming more mainstream, and enactment for next-generation RNA-Seq. data analysis could be revolutionary in the coming years in the biomedical domain. In this scoping review, we attempt to determine the existing literature\'s size and potential nature in deep learning and NGS RNA-Seq. data analysis. An analysis of the contemporary topics of next-generation RNA-Seq. data analysis based on deep learning algorithms is critically reviewed, emphasising open-source resources.

The C4 grass pearl millet is one of the most drought tolerant cereals and is primarily grown in marginal areas where annual rainfall is low and intermittent. It was domesticated in sub-Saharan Africa, and several studies have found that it uses a combination of morphological and physiological traits to successfully resist drought. This review explores the short term and long-term responses of pearl millet that enables it to either tolerate, avoid, escape, or recover from drought stress. The response to short term drought reveals fine tuning of osmotic adjustment, stomatal conductance, and ROS scavenging ability, along with ABA and ethylene transduction. Equally important are longer term developmental plasticity in tillering, root development, leaf adaptations and flowering time that can both help avoid the worst water stress and recover some of the yield losses via asynchronous tiller production. We examine genes related to drought resistance that were identified through individual transcriptomic studies and through our combined analysis of previous studies. From the combined analysis, we found 94 genes that were differentially expressed in both vegetative and reproductive stages under drought stress. Among them is a tight cluster of genes that are directly related to biotic and abiotic stress, as well as carbon metabolism, and hormonal pathways. We suggest that knowledge of gene expression patterns in tiller buds, inflorescences and rooting tips will be important for understanding the growth responses of pearl millet and the trade-offs at play in the response of this crop to drought. Much remains to be learnt about how pearl millet\'s unique combination of genetic and physiological mechanisms allow it to achieve such high drought tolerance, and the answers to be found may well be useful for crops other than just pearl millet.

Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis.

Multiple Sclerosis (MS) is, to date, an incurable disease of the nervous system characterized by demyelination. Several genetic mutations are associated with the disease but they are not able to explain all the diagnosticated cases. Thus, it is suggested that altered gene expression may play a role in human pathologies. In this review, we explored the role of the transcriptomic profile in MS to investigate the main altered biological processes and pathways involved in the disease. Herein, we focused our attention on RNA-seq methods that in recent years are producing a huge amount of data rapidly replacing microarrays, both with bulk and single-cells. The studies evidenced that different MS stages have specific molecular signatures and non-coding RNAs may play a key role in the disease. Sex-dependence was observed before and after treatments used to alleviate symptomatology activating different biological processes in a drug-dependent manner. New pathways, such as neddylation, were found deregulated in MS and inflammation was linked to neuron degeneration areas through spatial transcriptomics. It is evident that the use of RNA-seq in the study of complex pathologies, such as MS, is a valid strategy to shed light on new involved mechanisms.

Analysis of differential gene expression from RNA-seq data has become a standard for several research areas. The steps for the computational analysis include many data types and file formats, and a wide variety of computational tools that can be applied alone or together as pipelines. This paper presents a review of the differential expression analysis pipeline, addressing its steps and the respective objectives, the principal methods available in each step, and their properties, therefore introducing an organized overview to this context. This review aims to address mainly the aspects involved in the differentially expressed gene (DEG) analysis from RNA sequencing data (RNA-seq), considering the computational methods. In addition, a timeline of the computational methods for DEG is shown and discussed, and the relationships existing between the most important computational tools are presented by an interaction network. A discussion on the challenges and gaps in DEG analysis is also highlighted in this review. This paper will serve as a tutorial for new entrants into the field and help established users update their analysis pipelines.

[This corrects the article DOI: 10.3389/fped.2022.917152.].

UNASSIGNED: The links of sedentary behavior and physical activity with health outcomes in children and adolescents is well known. However, the molecular mechanisms involved are poorly understood. We aimed to synthesize the current knowledge of the association of sedentary behavior and physical activity (acute and chronic effects) with gene expression and epigenetic modifications in children and adolescents.
UNASSIGNED: PubMed, Web of Science, and Scopus databases were systematically searched until April 2022. A total of 15 articles were eligible for this review. The risk of bias assessment was performed using the Joanna Briggs Institute Critical Appraisal Tool for Systematic Reviews and/or a modified version of the Downs and Black checklist.
UNASSIGNED: Thirteen studies used candidate gene approach, while only 2 studies performed high-throughput analyses. The candidate genes significantly linked to sedentary behavior or physical activity were: FOXP3, HSD11B2, IL-10, TNF-α, ADRB2, VEGF, HSP70, SOX, and GPX. Non-coding Ribonucleic acids (RNAs) regulated by sedentary behavior or physical activity were: miRNA-222, miRNA-146a, miRNA-16, miRNA-126, miR-320a, and long non-coding RNA MALAT1. These molecules are involved in inflammation, immune function, angiogenic process, and cardiovascular disease. Transcriptomics analyses detected thousands of genes that were altered following an acute bout of physical activity and are linked to gene pathways related to immune function, apoptosis, and metabolic diseases.
UNASSIGNED: The evidence found to date is rather limited. Multidisciplinary studies are essential to characterize the molecular mechanisms in response to sedentary behavior and physical activity in the pediatric population. Larger cohorts and randomized controlled trials, in combination with multi-omics analyses, may provide the necessary data to bring the field forward.
UNASSIGNED: [www.ClinicalTrials.gov], identifier [CRD42021235431].