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Circular RNAs (circRNAs) are covalently closed, endogenous biomolecules in eukaryotes with tissue-specific and cell-specific expression patterns, whose biogenesis is regulated by specific cis-acting elements and trans-acting factors. Some circRNAs are abundant and evolutionarily conserved, and many circRNAs exert important biological functions by acting as microRNA or protein inhibitors (‘sponges’), by regulating protein function or by being translated themselves. Furthermore, circRNAs have been implicated in diseases such as diabetes mellitus, neurological disorders, cardiovascular diseases and cancer. Although the circular nature of these transcripts makes their detection, quantification and functional characterization challenging, recent advances in high-throughput RNA sequencing and circRNA-specific computational tools have driven the development of state-of-the-art approaches for their identification, and novel approaches to functional characterization are emerging.

Non-coding RNA is an interesting class of gene regulators with diverse functionalities. One large subgroup of non-coding RNAs is the recently discovered class of circular RNAs (circRNAs). CircRNAs are conserved and expressed in a tissue and developmental specific manner, although for the vast majority, the functional relevance remains unclear. To identify and quantify circRNAs expression, several bioinformatic pipelines have been developed to assess the catalog of circRNAs in any given total RNA sequencing dataset. We recently compared five different algorithms for circRNA detection, but here this analysis is extended to 11 algorithms. By comparing the number of circRNAs discovered and their respective sensitivity to RNaseR digestion, the sensitivity and specificity of each algorithm are evaluated. Moreover, the ability to predict circRNA, i.e., circRNAs not derived from annotated splice sites, is also determined as well as the effect of eliminating low quality and adaptor-containing reads prior to circRNA prediction. Finally, and most importantly, all possible pair-wise combinations of algorithms are tested and guidelines for algorithm complementarity are provided. Conclusively, the algorithms mostly agree on highly expressed circRNAs, however, in many cases, algorithm-specific false positives with high read counts are predicted, which is resolved by using the shared output from two (or more) algorithms.

Alternative splicing of eukaryotic messenger RNA transcripts often leads to the production of several mature RNAs — including linear RNAs and circular RNAs (circRNAs) — from a single gene locus. The names given to circRNAs are often ambiguous and lack consistency across studies. This Comment calls on the community to embrace a common nomenclature for naming circRNAs to ensure clarity and reproducibility.

Background Recently, thousands of circular RNAs (circRNAs) have been discovered in various tissues and cell types from human, mouse, fruit fly and nematodes. However, expression of circRNAs across mammalian brain development has never been examined. Results Here we profile the expression of circRNA in five brain tissues at up to six time-points during fetal porcine development, constituting the first report of circRNA in the brain development of a large animal. An unbiased analysis reveals a highly complex regulation pattern of thousands of circular RNAs, with a distinct spatio-temporal expression profile. The amount and complexity of circRNA expression was most pronounced in cortex at day 60 of gestation. At this time-point we find 4634 unique circRNAs expressed from 2195 genes out of a total of 13,854 expressed genes. Approximately 20 % of the porcine splice sites involved in circRNA production are functionally conserved between mouse and human. Furthermore, we observe that “hot-spot” genes produce multiple circRNA isoforms, which are often differentially expressed across porcine brain development. A global comparison of porcine circRNAs reveals that introns flanking circularized exons are longer than average and more frequently contain proximal complementary SINEs, which potentially can facilitate base pairing between the flanking introns. Finally, we report the first use of RNase R treatment in combination with in situ hybridization to show dynamic subcellular localization of circRNA during development. Conclusions These data demonstrate that circRNAs are highly abundant and dynamically expressed in a spatio-temporal manner in porcine fetal brain, suggesting important functions during mammalian brain development.

Circular RNAs (circRNAs) have recently gained substantial attention in the cancer research field where most, including the putative oncogene ciRS-7 (CDR1as), have been proposed to function as competitive endogenous RNAs (ceRNAs) by sponging specific microRNAs. Here, we report the first spatially resolved cellular expression patterns of ciRS-7 in colon cancer and show that ciRS-7 is completely absent in the cancer cells, but highly expressed in stromal cells within the tumor microenvironment. Additionally, our data suggest that this generally apply to classical oncogene-driven adenocarcinomas, but not to other cancers, including malignant melanoma. Moreover, we find that correlations between circRNA and mRNA expression, which are commonly interpreted as evidence of a ceRNA function, can be explained by different cancer-to-stromal cell ratios among the studied tumor specimens. Together, these results have wide implications for future circRNA studies and highlight the importance of spatially resolving expression patterns of circRNAs proposed to function as ceRNAs. The circular RNA named ciRS-7 is overexpressed in human cancers, however, its spatial cellular expression patterns have not been explored. Here, the authors show that ciRS-7 is not expressed in colon cancer cells, but abundant in stromal cells within tumours, and that cancer-to-stromal cell ratios contribute to correlations between ciRS-7 and miR-7 target genes.

MicroRNAs (miRNAs) are short (∼22 nucleotides) regulators of gene expression acting by direct base pairing to 3′-UTR target sites in messenger RNAs. Mature miRNAs are produced by two sequential endonucleolytic cleavages facilitated by Drosha in the nucleus and Dicer in the cytoplasm. A subclass of miRNAs, termed mirtrons, derives from short introns and enters the miRNA biogenesis pathway as Dicer substrates. Here we uncover a third biogenesis strategy that, similar to mirtron biogenesis, initiates from short introns but bypasses Dicer cleavage. These short introns (80–100 nucleotides), coined agotrons, are associated with and stabilized by Argonaute (Ago) proteins in the cytoplasm. Some agotrons are completely conserved in mammalian species, suggesting that they are functionally important. Furthermore, we demonstrate that the agotrons are capable of repressing mRNAs with seed-matching target sequences in the 3′-UTR. These data provide evidence for a novel RNA regulator of gene expression, which bypasses the canonical miRNA biogenesis machinery. MicroRNAs are important small regulatory RNAs produced by sequential Drosha and Dicer cleavage. Here the authors describe 'agotrons', a subclass of small RNAs that bypass the canonical biogenesis machinery.

Circular RNAs are important for many cellular processes but their mechanisms of action remain poorly understood. Here, we map circRNA inventories of mouse embryonic stem cells, neuronal progenitor cells and differentiated neurons and identify hundreds of highly expressed circRNAs. By screening several candidate circRNAs for a potential function in neuronal differentiation, we find that circZNF827 represses expression of key neuronal markers, suggesting that this molecule negatively regulates neuronal differentiation. Among 760 tested genes linked to known neuronal pathways, knockdown of circZNF827 deregulates expression of numerous genes including nerve growth factor receptor ( NGFR ), which becomes transcriptionally upregulated to enhance NGF signaling. We identify a circZNF827 -nucleated transcription-repressive complex containing hnRNP-K/L proteins and show that knockdown of these factors strongly augments NGFR regulation. Finally, we show that the ZNF827 protein is part of the mRNP complex, suggesting a functional co-evolution of a circRNA and the protein encoded by its linear pre-mRNA host.

Circular RNAs (circRNAs) are a subset of noncoding RNAs previously considered as products of missplicing. Now, circRNAs are considered functional molecules, although to date, only few functions have been experimentally validated. Here, based on RNA sequencing from the ENCODE consortium, we identify and characterize a subset of circRNAs, coined AUG circRNAs, encompassing the annotated translational start codon from the protein-coding host genes. AUG circRNAs are more abundantly expressed and conserved than other groups of circRNAs, and they display flanking sequences that suggest an Alu -independent mechanism of biogenesis. The AUG circRNAs contain part of bona fide open reading frame, and in the recent years, several studies have reported cases of circRNA translation. However, using thorough cross-species analysis, extensive ribosome profiling, proteomics analyses, and experimental data on a selected panel of AUG circRNAs, we observe no indications of translation of AUG circRNAs or any other circRNAs. Our data provide a comprehensive classification of circRNAs and, collectively, the data suggest that the AUG circRNAs constitute an abundant subclass of circRNAs produced independently of primate-specific Alu elements.

Sensor drift in Wastewater Treatment Plants (WWTPs) reduces the efficiency of the plants and needs to be handled. Several studies have investigated anomaly detection and fault detection in WWTPs. However, these solutions often remain as academic projects. In this study, the gap between academia and practice is investigated by applying suggested algorithms on real WWTP data. The results show that it is difficult to detect drift in the data to a sufficient level due to missing and imprecise logs, ad hoc changes in control settings, low data quality and the equality in the patterns of some fault types and optimal operation. The challenges related to data quality raise the question of whether the data-driven approach for drift detection is the best solution, as this requires a high-quality data set. Several recommendations are suggested for utilities that wish to bridge the gap between academia and practice regarding drift detection. These include storing data and select data parameters at resolutions which positively contribute to this purpose. Furthermore, the data should be accompanied by sufficient logging of factors affecting the patterns of the data, such as changes in control settings.