MiRTarBase update 2018: a resource for experimentally validated microRNA-target interactions

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally. In animals, this regulation is achieved via base-pairing with partially complementary sequences on mainly 3' UTR region of messenger RNAs (mRNAs). Computational approaches that predict miRNA target interactions (MTIs) facilitate the process of narrowing down potential targets for experimental validation. The availability of new datasets of high-throughput, direct MTIs has led to the development of machine learning (ML) based methods for MTI prediction. To train an ML algorithm, it is beneficial to provide entries from all class labels (i.e., positive and negative). Currently, no high-throughput assays exist for capturing negative examples. Therefore, current ML approaches must rely on either artificially generated or inferred negative examples deduced from experimentally identified positive miRNA-target datasets. Moreover, the lack of uniform standards for generating such data leads to biased results and hampers comparisons between studies. In this comprehensive study, we collected methods for generating negative data for animal miRNA-target interactions and investigated their impact on the classification of true human MTIs. Our study relies on training ML models on a fixed positive dataset in combination with different negative datasets and evaluating their intra- and cross-dataset performance. As a result, we were able to examine each method independently and evaluate ML models' sensitivity to the methodologies utilized in negative data generation. To achieve a deep understanding of the performance results, we analyzed unique features that distinguish between datasets. In addition, we examined whether one-class classification models that utilize solely positive interactions for training are suitable for the task of MTI classification. We demonstrate the importance of negative data in MTI classification, analyze specific methodological characteristics that differentiate negative datasets, and highlight the challenge of ML models generalizing interaction rules from training to testing sets derived from different approaches. This study provides valuable insights into the computational prediction of MTIs that can be further used to establish standards in the field.

MicroRNAs (miRNAs), i.e. small non-coding RNA molecules (∼22 nt), can bind to one or more target sites on a gene transcript to negatively regulate protein expression, subsequently controlling many cellular mechanisms. A current and curated collection of miRNA–target interactions (MTIs) with experimental support is essential to thoroughly elucidating miRNA functions under different conditions and in different species. As a database, miRTarBase has accumulated more than 3500 MTIs by manually surveying pertinent literature after data mining of the text systematically to filter research articles related to functional studies of miRNAs. Generally, the collected MTIs are validated experimentally by reporter assays, western blot, or microarray experiments with overexpression or knockdown of miRNAs. miRTarBase curates 3576 experimentally verified MTIs between 657 miRNAs and 2297 target genes among 17 species. miRTarBase contains the largest amount of validated MTIs by comparing with other similar, previously developed databases. The MTIs collected in the miRTarBase can also provide a large amount of positive samples to develop computational methods capable of identifying miRNA–target interactions. miRTarBase is now available on http://miRTarBase.mbc.nctu.edu.tw/, and is updated frequently by continuously surveying research articles.

MicroRNAs (miRNAs) are small non-coding RNAs of approximately 22 nucleotides, which negatively regulate the gene expression at the post-transcriptional level. This study describes an update of the miRTarBase (http://miRTarBase.mbc.nctu.edu.tw/) that provides information about experimentally validated miRNA-target interactions (MTIs). The latest update of the miRTarBase expanded it to identify systematically Argonaute-miRNA-RNA interactions from 138 crosslinking and immunoprecipitation sequencing (CLIP-seq) data sets that were generated by 21 independent studies. The database contains 4966 articles, 7439 strongly validated MTIs (using reporter assays or western blots) and 348 007 MTIs from CLIP-seq. The number of MTIs in the miRTarBase has increased around 7-fold since the 2014 miRTarBase update. The miRNA and gene expression profiles from The Cancer Genome Atlas (TCGA) are integrated to provide an effective overview of this exponential growth in the miRNA experimental data. These improvements make the miRTarBase one of the more comprehensively annotated, experimentally validated miRNA-target interactions databases and motivate additional miRNA research efforts.

MicroRNAs (miRNAs) serve as critical regulators of gene expression. However, their binding to target genes can be influenced by genetic variability within the miRNA-target interaction (MTI) sites. We performed an in silico sequence reanalysis to identify novel sequence variants within MTIs with potential functional impacts. A literature search of the PubMed and the Web of Science spanning the years 2008 to April 2018 identified 240 articles reporting MTIs in humans. Sequence reanalysis of reported MTI regions was performed using the Ensembl browser. We found 76 sequence variants within 23 MTIs. We present description of MTIs wherein sequence variants are present within both the mature miRNA seed region and the miRNA target, which we termed miR-gene-target-single nucleotide polymorphism (miR-GenTar-SNP). To the best of our knowledge, this is the first report on copresence of sequence variants within both miRNA gene and the target site. In the course our analyses, the need for extension of current terminology emerged and therefore, novel terminology was introduced: miR-indel, miR-double nucleotide polymorphism (DNP), miR-TS-indel, and miR-TS-DNP. Identification of novel MTI sequence variants is a hitherto understudied, but critical dimension in understanding the complexity of interactions and gene deregulation in various complex diseases. Because such variations might profoundly affect miRNA function, they should be taken into consideration in future research that depends on "variability science" such as precision medicine, human genetics, and genomics in the study of complex diseases. The findings presented herein offer a baseline for further systematic reanalysis of all reported MTIs in human and other species.

MicroRNAs (miRNAs) are small non-coding RNAs of approximately 23 nucleotides, which negatively regulate the gene expression at the post-transcriptional level. miRNAs have been considered as good candidates for early detection or prognosis biomarkers for various diseases. Validated miRNA targets are usually reported in literature, necessitating researchers to manually screen through the related literature to keep up-to-date with novel findings. However, the amount of miRNA-related literature is increasing rapidly which makes it difficult for researchers to keep up to date. This study develops a text mining pipeline based on the statistical principle-based approach (SPBA) to detect MiRNA-Target Interactions (MTIs) mentioned in literatures. SPBA uses a collection of principles to represent linguistic concepts or rules used by human for describing MTIs. Each principle is composed of a collection of slots, which can be automatically learned from training data by merging the labeled slot sequences into more representative principles through a dominating set algorithm. Followed by a partial matching algorithm, the proposed approach can successfully recognize miRNA mentions and extract their MTIs in articles with a promising F-score of 98.8% and an accuracy of 71.43%.

MicroRNAs (miRNAs) are small non-coding RNAs (typically consisting of 18–25 nucleotides) that negatively control expression of target genes at the post-transcriptional level. Owing to the biological significance of miRNAs, miRTarBase was developed to provide comprehensive information on experimentally validated miRNA–target interactions (MTIs). To date, the database has accumulated >13,404 validated MTIs from 11,021 articles from manual curations. In this update, a text-mining system was incorporated to enhance the recognition of MTI-related articles by adopting a scoring system. In addition, a variety of biological databases were integrated to provide information on the regulatory network of miRNAs and its expression in blood. Not only targets of miRNAs but also regulators of miRNAs are provided to users for investigating the up- and downstream regulations of miRNAs. Moreover, the number of MTIs with high-throughput experimental evidence increased remarkably (validated by CLIP-seq technology). In conclusion, these improvements promote the miRTarBase as one of the most comprehensively annotated and experimentally validated miRNA–target interaction databases. The updated version of miRTarBase is now available at http://miRTarBase.cuhk.edu.cn/.

BackgroundMicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally via base-pairing with complementary sequences on messenger RNAs (mRNAs). Due to the technical challenges involved in the application of high-throughput experimental methods, datasets of direct bona fide miRNA targets exist only for a few model organisms. Machine learning (ML)-based target prediction models were successfully trained and tested on some of these datasets. There is a need to further apply the trained models to organisms in which experimental training data are unavailable. However, it is largely unknown how the features of miRNA–target interactions evolve and whether some features have remained fixed during evolution, raising questions regarding the general, cross-species applicability of currently available ML methods.ResultsWe examined the evolution of miRNA–target interaction rules and used data science and ML approaches to investigate whether these rules are transferable between species. We analyzed eight datasets of direct miRNA–target interactions in four species (human, mouse, worm, cattle). Using ML classifiers, we achieved high accuracy for intra-dataset classification and found that the most influential features of all datasets overlap significantly. To explore the relationships between datasets, we measured the divergence of their miRNA seed sequences and evaluated the performance of cross-dataset classification. We found that both measures coincide with the evolutionary distance between the compared species.ConclusionsThe transferability of miRNA–targeting rules between species depends on several factors, the most associated factors being the composition of seed families and evolutionary distance. Furthermore, our feature-importance results suggest that some miRNA–target features have evolved while others remained fixed during the evolution of the species. Our findings lay the foundation for the future development of target prediction tools that could be applied to “non-model” organisms for which minimal experimental data are available.Availability and implementationThe code is freely available at https://github.com/gbenor/TPVOD.

BackgroundMiRNA are about 22nt long small noncoding RNAs that post transcriptionally regulate gene expression in animals, plants and protozoa. Confident identification of MiRNA-Target Interactions (MTI) is vital to understand their function. Currently, several integrated computational programs and databases are available for animal miRNAs, the mechanisms of which are significantly different from plant miRNAs.MethodsHere we present an integrated MTI prediction and analysis toolkit (imiRTP) for Arabidopsis thaliana. It features two important functions: (i) combination of several effective plant miRNA target prediction methods provides a sufficiently large MTI candidate set, and (ii) different filters allow for an efficient selection of potential targets. The modularity of imiRTP enables the prediction of high quality targets on genome-wide scale. Moreover, predicted MTIs can be presented in various ways, which allows for browsing through the putative target sites as well as conducting simple and advanced analyses.ResultsResults show that imiRTP could always find high quality candidates compared with single method by choosing appropriate filter and parameter. And we also reveal that a portion of plant miRNA could bind target genes out of coding region. Based on our results, imiRTP could facilitate the further study of Arabidopsis miRNAs in real use. All materials of imiRTP are freely available under a GNU license at (http://admis.fudan.edu.cn/projects/imiRTP.htm).

MicroRNA Target Recognition: Insights from Transcriptome-Wide Non-Canonical Interactions

MicroRNAs (miRNAs) regulate gene expression by binding to mRNAs, inhibiting translation, or promoting mRNA degradation. Accurate identification of functional miRNA-target interactions (MTIs), typically validated by methods like western blot or reporter assay, remains challenging due to the scarcity of experimental data compared to the vast number of sequence-based predictions. This study pioneers a novel approach focusing solely on the disease association degree between miRNAs and their target genes. We propose that this single feature is sufficient for distinguishing experimentally validated functional MTIs from sequence-based predicted MTIs in a binary classification task. To quantify miRNA-gene disease association, we fine-tuned Sentence-BERT to generate disease description embeddings and compute their semantic similarity. Remarkably, using only disease association features, miRTarDS achieved an F1 score of 0.88 on the task of distinguishing functional from predicted MTIs in the external validation set. The approach also exhibits generalizability across different gene-disease association databases. This study demonstrates disease association as a powerful, independent dimension for prioritizing high-confidence functional MTIs.

Simple SummarymiRNAs are omnipresent short non-coding RNA molecules, which post-transcriptionally fine-tune the expression of most protein-coding genes in health and disease. In cancer, miRNAs have been described to directly regulate the amounts of targeted tumor suppressors and oncogenes. Although their canonical mechanism of action is well understood, the correct prediction of miRNA target genes is still a challenge. We describe here the unbiased investigation of the miRNA targetome in cancer (melanoma) cells using a technique, which allows to physically link the miRNA to its target gene. The herein identified miRNA-target interactions reveal further layers of complexity of post-transcriptional gene regulation in cancer cells and shed new light on miRNA-target gene interactions.MicroRNAs are key post-transcriptional gene regulators often displaying aberrant expression patterns in cancer. As microRNAs are promising disease-associated biomarkers and modulators of responsiveness to anti-cancer therapies, a solid understanding of their targetome is crucial. Despite enormous research efforts, the success rates of available tools to reliably predict microRNAs (miRNA)-target interactions remains limited. To investigate the disease-associated miRNA targetome, we have applied modified cross-linking ligation and sequencing of hybrids (qCLASH) to BRAF-mutant melanoma cells. The resulting RNA-RNA hybrid molecules provide a comprehensive and unbiased snapshot of direct miRNA-target interactions. The regulatory effects on selected miRNA target genes in predicted vs. non-predicted binding regions was validated by miRNA mimic experiments. Most miRNA–target interactions deviate from the central dogma of miRNA targeting up to 60% interactions occur via non-canonical seed pairing with a strong contribution of the 3′ miRNA sequence, and over 50% display a clear bias towards the coding sequence of mRNAs. miRNAs targeting the coding sequence can directly reduce gene expression (miR-34a/CD68), while the majority of non-canonical miRNA interactions appear to have roles beyond target gene suppression (miR-100/AXL). Additionally, non-mRNA targets of miRNAs (lncRNAs) whose interactions mainly occur via non-canonical binding were identified in melanoma. This first application of CLASH sequencing to cancer cells identified over 8 K distinct miRNA–target interactions in melanoma cells. Our data highlight the importance non-canonical interactions, revealing further layers of complexity of post-transcriptional gene regulation in melanoma, thus expanding the pool of miRNA–target interactions, which have so far been omitted in the cancer field.

Regulation and functional specialization of small RNA–target nodes during plant development

In plants, many mRNAs and non-coding RNAs are cleaved by RNA-induced silencing complexes. After cleavage, only a limited number of RNAs are processed into trans-acting siRNAs (tasiRNAs). One reason is that 22 nt small RNAs, but not the more common 21 nt small RNAs, can efficiently trigger tasiRNA formation. The characteristics of the target transcripts may also affect tasiRNA production. Here we report the effects of target site location and sequence complementarity on tasiRNA formation. A synthetic sequence that included a miR173 target site and two siRNAs targeting an endogenous mRNA encoding PHYTOENE DESATURASE3 was introduced into a protein-coding (GFP) gene in the coding region or 3' UTR. tasiRNAs were generated in the transgenic seedlings, and the PDS3 mRNA level was reduced, leading to a photobleaching phenotype. It was found that tasiRNAs were most efficiently produced when the miR173 target site was placed immediately after the stop codon. Introducing premature stop codons caused a dramatic reduction of tasiRNAs and over-accumulation of 3' cleavage products, suggesting positive effects of translation on processing the 3' cleavage products into tasiRNAs. By systematically mutating the miR173 target site, we found that perfect complementarity between the 3' end of miR173 and the 5' end of the target sequence was crucial. Mismatches at that position abolished tasiRNA formation, but mismatches at the 5' end of miR173 had less effect. These data suggest important roles for translation and specific sequence complementarity in tasiRNA formation, providing new insights into tasiRNA biogenesis as well as a strategy for improving the efficiency of RNA interference (RNAi) using tasiRNAs.

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.

Integration into the host genome is one of the hallmarks of the retroviral life cycle and is catalyzed by virus-encoded integrases. While integrase has strict sequence requirements for the viral DNA ends, target site sequences have been shown to be very diverse. We carefully examined a large number of integration target site sequences from several retroviruses, including human immunodeficiency virus type 1, simian immunodeficiency virus, murine leukemia virus, and avian sarcoma-leukosis virus, and found that a statistical palindromic consensus, centered on the virus-specific duplicated target site sequence, was a common feature at integration target sites for these retroviruses.