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MicroRNA-offset RNAs (moRNAs) are microRNA-like small RNAs generated by microRNA precursors. To date, little is known about moRNAs and bioinformatics tools to inspect their expression are still missing. We developed miR&moRe2, the first bioinformatics method to consistently characterize microRNAs, moRNAs, and their isoforms from small RNA sequencing data. To illustrate miR&moRe2 discovery power, we applied it to several published datasets. MoRNAs identified by miR&moRe2 were in agreement with previous research findings. Moreover, we observed that moRNAs and new microRNAs predicted by miR&moRe2 were downregulated upon the silencing of the microRNA-biogenesis pathway. Further, in a sizeable dataset of human blood cell populations, tens of novel miRNAs and moRNAs were discovered, some of them with significantly varied expression levels among the cell types. Results demonstrate that miR&moRe2 is a valid tool for a comprehensive study of small RNAs generated from microRNA precursors and could help to investigate their biogenesis and function.

Background Bacterial small non-coding RNAs (sRNAs) have emerged as important elements in diverse physiological processes, including growth, development, cell proliferation, differentiation, metabolic reactions and carbon metabolism, and attract great attention. Accurate prediction of sRNAs is important and challenging, and helps to explore functions and mechanism of sRNAs. Results In this paper, we utilize a variety of sRNA sequence-derived features to develop ensemble learning methods for the sRNA prediction. First, we compile a balanced dataset and four imbalanced datasets. Then, we investigate various sRNA sequence-derived features, such as spectrum profile, mismatch profile, reverse compliment k-mer and pseudo nucleotide composition. Finally, we consider two ensemble learning strategies to integrate all features for building ensemble learning models for the sRNA prediction. One is the weighted average ensemble method (WAEM), which uses the linear weighted sum of outputs from the individual feature-based predictors to predict sRNAs. The other is the neural network ensemble method (NNEM), which trains a deep neural network by combining diverse features. In the computational experiments, we evaluate our methods on these five datasets by using 5-fold cross validation. WAEM and NNEM can produce better results than existing state-of-the-art sRNA prediction methods. Conclusions WAEM and NNEM have great potential for the sRNA prediction, and are helpful for understanding the biological mechanism of bacteria.

MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. They are suggested to be involved in most biological processes of the cell primarily by targeting messenger RNAs (mRNAs) for cleavage or translational repression. Their binding to their target sites is mediated by the Argonaute (AGO) family of proteins. Thus, miRNA target prediction is pivotal for research and clinical applications. Moreover, transfer-RNA-derived fragments (tRFs) and other types of small RNAs have been found to be potent regulators of Ago-mediated gene expression. Their role in mRNA regulation is still to be fully elucidated, and advancements in the computational prediction of their targets are in their infancy. To shed light on these complex RNA–RNA interactions, the availability of good quality high-throughput data and reliable computational methods is of utmost importance. Even though the arsenal of computational approaches in the field has been enriched in the last decade, there is still a degree of discrepancy between the results they yield. This review offers an overview of the relevant advancements in the field of bioinformatics and machine learning and summarizes the key strategies utilized for small RNA target prediction. Furthermore, we report the recent development of high-throughput sequencing technologies, and explore the role of non-miRNA AGO driver sequences.

The discovery of small noncoding RNAs and their roles in a variety of regulatory mechanisms have led many scientists to look at the principles of functioning of the cells on a completely different side. Small RNA molecules play key roles in important processes such as the co- and posttranscriptional regulation of gene expression, epigenetic modification of DNA and histones and antiviral protection. piRNA is one of the most numerous, although the least-studied class of small noncoding RNAs. piRNA is highly expressed in the germ line of most eukaryotes and its main function is to regulate the activity of mobile elements during embryonic development. Moreover, recent studies reveal moderate activity of piRNA in somatic cells. However, the mechanisms of piRNA biogenesis and function are still poorly understood and are the object of intensive researches. This review presents actual information about the biogenesis and various functions of piRNA, as well as bioinformatical aspects of this field of molecular biology.

Background The success of the CRISPR/Cas9 genome editing technique depends on the choice of the guide RNA sequence, which is facilitated by various websites. Despite the importance and popularity of these algorithms, it is unclear to which extent their predictions are in agreement with actual measurements. Results We conduct the first independent evaluation of CRISPR/Cas9 predictions. To this end, we collect data from eight SpCas9 off-target studies and compare them with the sites predicted by popular algorithms. We identify problems in one implementation but found that sequence-based off-target predictions are very reliable, identifying most off-targets with mutation rates superior to 0.1 %, while the number of false positives can be largely reduced with a cutoff on the off-target score. We also evaluate on-target efficiency prediction algorithms against available datasets. The correlation between the predictions and the guide activity varied considerably, especially for zebrafish. Together with novel data from our labs, we find that the optimal on-target efficiency prediction model strongly depends on whether the guide RNA is expressed from a U6 promoter or transcribed in vitro. We further demonstrate that the best predictions can significantly reduce the time spent on guide screening. Conclusions To make these guidelines easily accessible to anyone planning a CRISPR genome editing experiment, we built a new website ( http://crispor.org ) that predicts off-targets and helps select and clone efficient guide sequences for more than 120 genomes using different Cas9 proteins and the eight efficiency scoring systems evaluated here.

Perinatal probiotic ingestion has been shown to prevent atopic dermatitis (AD) in infancy in a number of randomised trials. The Probiotics in the Prevention of Allergy among Children in Trondheim (ProPACT) trial involved a probiotic supplementation regime given solely to mothers in the perinatal period and demonstrated a ~40% relative risk reduction in the cumulative incidence of AD at 2 years of age. However, the mechanisms behind this effect are incompletely understood. Micro-RNAs (miRNA) are abundant in mammalian milk and may influence the developing gastrointestinal and immune systems of newborn infants. The objectives of this study were to describe the miRNA profile of human breast milk, and to investigate breast milk miRNAs as possible mediators of the observed preventative effect of probiotics. Small RNA sequencing was conducted on samples collected 3 months postpartum from 54 women participating in the ProPACT trial. Differential expression of miRNA was assessed for the probiotic vs placebo and AD vs non-AD groups. The results were further analysed using functional prediction techniques. Human breast milk samples contain a relatively stable core group of highly expressed miRNAs, including miR-148a-3p, miR-22-3p, miR-30d-5p, let-7b-5p and miR-200a-3p. Functional analysis of these miRNAs revealed enrichment in a broad range of biological processes and molecular functions. Although several miRNAs were found to be differentially expressed on comparison of the probiotic vs placebo and AD vs non-AD groups, none had an acceptable false discovery rate and their biological significance in the development of AD is not immediately apparent from their predicted functional consequences. Whilst breast milk miRNAs have the potential to be active in a diverse range of tissues and biological process, individual miRNAs in breast milk 3 months postpartum are unlikely to play a major role in the prevention of atopic dermatitis in infancy by probiotics ingestion in the perinatal period. ClinicalTrials.gov NCT00159523.

Background The pervasive expression of circular RNA is a recently discovered feature of gene expression in highly diverged eukaryotes, but the functions of most circular RNAs are still unknown. Computational methods to discover and quantify circular RNA are essential. Moreover, discovering biological contexts where circular RNAs are regulated will shed light on potential functional roles they may play. Results We present a new algorithm that increases the sensitivity and specificity of circular RNA detection by discovering and quantifying circular and linear RNA splicing events at both annotated and un-annotated exon boundaries, including intergenic regions of the genome, with high statistical confidence. Unlike approaches that rely on read count and exon homology to determine confidence in prediction of circular RNA expression, our algorithm uses a statistical approach. Using our algorithm, we unveiled striking induction of general and tissue-specific circular RNAs, including in the heart and lung, during human fetal development. We discover regions of the human fetal brain, such as the frontal cortex, with marked enrichment for genes where circular RNA isoforms are dominant. Conclusions The vast majority of circular RNA production occurs at major spliceosome splice sites; however, we find the first examples of developmentally induced circular RNAs processed by the minor spliceosome, and an enriched propensity of minor spliceosome donors to splice into circular RNA at un-annotated, rather than annotated, exons. Together, these results suggest a potentially significant role for circular RNA in human development.

Bacterial small RNAs (sRNAs) are pivotal in post-transcriptional regulation, affecting functions like virulence, metabolism, and gene expression by binding specific mRNA targets. Identifying these targets is crucial to understanding sRNA regulation across species. Despite advancements in high-throughput (HT) experimental methods, they remain technically challenging and are limited to detecting sRNA-target interactions under specific environmental conditions. Therefore, computational approaches, especially machine learning (ML), are essential for identifying strong candidates for biological validation. In this paper, we hypothesize that ML models trained on large-scale interaction data from specific conditions can accurately predict new interactions in unseen conditions within the same bacterial strain. To test this, we developed models from two families: (1) graph neural networks (GNNs), including GraphRNA and kGraphRNA , that learn transformed representations of interacting sRNA-mRNA pairs via graph relationships, and (2) decision forests, sInterRF (Random Forest) and sInterXGB (XGBoost), which use various interaction features for prediction. We also proposed Summation Ensemble Models (SEM) that combine scores from multiple models. Across three seen-to-unseen conditions evaluations, our models —particularly kGraphRNA — significantly improved the area under the ROC curve (AUC) and Precision-Recall curve (PR-AUC) compared to sRNARFTarget , CopraRNA , and RNAup . The SEM model combining GraphRNA and CopraRNA outperformed CopraRNA alone on a low-throughput (LT) interactions test set (HT-to-LT evaluation). Beyond enhanced performance, our models enable target prediction for species-specific sRNAs, a capability lacking in some existing tools. Furthermore, GNN models remove the dependency on external tools like RNAplex or RNAup to compute hybridization duplex or energy features, enhancing scalability and runtime efficiency. While this study focuses on E. coli K12 MG1655 interactions, our methods are fully adaptable to predict interactions in other bacterial strains, given sufficient data for training. Our comprehensive feature importance analysis revealed the complexity of sRNA-mRNA interactions across environmental conditions, underscoring the significance of RNA sequence composition and duplex structure characteristics, like base pairing and energy factors; findings that align with biological evidence from previous studies. As HT experiments expand sRNA-target interaction data across conditions in various bacteria, our ML methods with features analysis offer promising advances in sRNA-target prediction and deeper insights into sRNA regulatory mechanisms across diverse species.

Background The non-coding small RNA tRFs (tRNA-derived fragments) and phasiRNAs (plant-specific) exert important roles in plant growth, development and stress resistances. However, whether the tRFs and phasiRNAs respond to the plant important stress hormone abscisic acid (ABA) remain enigma. Results Here, the RNA-sequencing was implemented to decipher the landscape of tRFs and phasiRNAs in tomato ( Solanum lycopersicum ) leaves and their responses when foliar spraying exogenous ABA after 24 h. In total, 733 tRFs and 137 phasiRNAs were detected. The tRFs were mainly derived from the tRNA Ala transporting alanine, which tended to be cleaved at the 5 ’ terminal guanine site and D loop uracil site to produce tRF Ala with length of 20 nt. Most of phasiRNAs originated from NBS-LRR resistance genes. Expression analysis revealed that 156 tRFs and 68 phasiRNAs expressed differentially, respectively. Generally, exogenous ABA mainly inhibited the expression of tRFs and phasiRNAs. Furthermore, integrating analysis of target gene prediction and transcriptome data presented that ABA significantly downregulated the abundance of phsaiRNAs associated with biological and abiotic resistances. Correspondingly, their target genes such as AP2/ERF , WRKY and NBS-LRR , STK and RLK , were mainly up-regulated. Conclusions Combined with the previous analysis of ABA-response miRNAs, it was speculated that ABA can improve the plant resistances to various stresses by regulating the expression and interaction of small RNAs (such as miRNAs, tRFs, phasiRNAs) and their target genes. This study enriches the plant tRFs and phasiRNAs, providing a vital basis for further investigating ABA response-tRFs and phasiRNAs and their functions in biotic and abiotic stresses.

Significance We established a minimum competing endogenous RNA (ceRNA) model to quantitatively analyze the behavior of the ceRNA regulation and implemented multifluorescent synthetic gene circuits in cultured human cells to validate our predictions. Our results suggested that the ceRNA effect is affected by the abundance of microRNA (miRNA) and ceRNAs, the number and affinity of binding sites, and the mRNA degradation pathway determined by the degree of miRNA–mRNA complementarity. Furthermore, we found that a nonreciprocal competing effect between partial and perfect complementary targets is mainly due to different miRNA loss rates in these two types of repressions, which sheds light on utilizing such a competing model for rational design of effective siRNA. Competing endogenous RNAs (ceRNAs) cross-regulate each other at the posttranscriptional level by titrating shared microRNAs (miRNAs). Here, we established a computational model to quantitatively describe a minimum ceRNA network and experimentally validated our model predictions in cultured human cells by using synthetic gene circuits. We demonstrated that the range and strength of ceRNA regulation are largely determined by the relative abundance and the binding strength of miRNA and ceRNAs. We found that a nonreciprocal competing effect between partially and perfectly complementary targets is mainly due to different miRNA loss rates in these two types of regulations. Furthermore, we showed that miRNA-like off targets with high expression levels and strong binding sites significantly diminish the RNA interference efficiency, but the effect caused by high expression levels could be compensated by introducing more small interference RNAs (siRNAs). Thus, our results provided a quantitative understanding of ceRNA cross-regulation via shared miRNA and implied an siRNA design strategy to reduce the siRNA off-target effect in mammalian cells.