A curated human cellular microRNAome based on 196 primary cell types.

BACKGROUND: An incomplete picture of the expression distribution of microRNAs (miRNAs) across human cell types has long hindered our understanding of this important regulatory class of RNA. With the continued increase in available public small RNA sequencing datasets, there is an opportunity to more fully understand the general distribution of miRNAs at the cell level. RESULTS: From the NCBI Sequence Read Archive, we obtained 6,054 human primary cell datasets and processed 4,184 of them through the miRge3.0 small RNA sequencing alignment software. This dataset was curated down, through shared miRNA expression patterns, to 2,077 samples from 196 unique cell types derived from 175 separate studies. Of 2,731 putative miRNAs listed in miRBase (v22.1), 2,452 (89.8%) were detected. Among reasonably expressed miRNAs, 108 were designated as cell specific/near specific, 59 as infrequent, 52 as frequent, 54 as near ubiquitous, and 50 as ubiquitous. The complexity of cellular microRNA expression estimates recapitulates tissue expression patterns and informs on the miRNA composition of plasma. CONCLUSIONS: This study represents the most complete reference, to date, of miRNA expression patterns by primary cell type. The data are available through the human cellular microRNAome track at the UCSC Genome Browser (https://genome.ucsc.edu/cgi-bin/hgHubConnect) and an R/Bioconductor package (https://bioconductor.org/packages/microRNAome/).

The limits of human microRNA annotation have been met.

Over the last few years, the number of microRNAs in the human genome has become a controversially debated issue. Several publications reported thousands of putative novel microRNAs not included in the curated microRNA gene database MirGeneDB and the repository miRBase. Recently, by using sequencing of ∼300 human tissues and cell lines, the human RNA atlas, an expanded inventory of human RNA annotations, was published, reporting thousands of putative microRNAs. We, the developers of established microRNA prediction tools and hosts of MirGeneDB, raise concerns about the frequently applied prediction and functional validation strategies, briefly discussing the drawbacks of false positive detections. By means of quantifying well-established biogenesis-derived features, we show that the reported novel microRNAs essentially represent false-positives and argue that the human microRNA complement, at about 550 microRNA genes, is already near complete. Output of available tools must be curated as false predictions will misguide scientists looking for biomarkers or therapeutic targets.

In Silico Analysis of Micro-RNA Sequencing Data.

High-throughput sequencing for micro-RNAs (miRNAs) to obtain expression estimates is a central method of molecular biology. Surprisingly, there are a number of different approaches to converting sequencing output into micro-RNA counts. Each has their own strengths and biases that impact on the final data that can be obtained from a sequencing run. This chapter serves to make the reader aware of the trade-offs one must consider in analyzing small RNA sequencing data. It then compares two methods, miRge2.0 and the sRNAbench and the steps utilized to output data from their tools.

Improving the Quality of Positive Datasets for the Establishment of Machine Learning Models for pre-microRNA Detection.

MicroRNAs (miRNAs) are involved in the post-transcriptional regulation of protein abundance and thus have a great impact on the resulting phenotype. It is, therefore, no wonder that they have been implicated in many diseases ranging from virus infections to cancer. This impact on the phenotype leads to a great interest in establishing the miRNAs of an organism. Experimental methods are complicated which led to the development of computational methods for pre-miRNA detection. Such methods generally employ machine learning to establish models for the discrimination between miRNAs and other sequences. Positive training data for model establishment, for the most part, stems from miRBase, the miRNA registry. The quality of the entries in miRBase has been questioned, though. This unknown quality led to the development of filtering strategies in attempts to produce high quality positive datasets which can lead to a scarcity of positive data. To analyze the quality of filtered data we developed a machine learning model and found it is well able to establish data quality based on intrinsic measures. Additionally, we analyzed which features describing pre-miRNAs could discriminate between low and high quality data. Both models are applicable to data from miRBase and can be used for establishing high quality positive data. This will facilitate the development of better miRNA detection tools which will make the prediction of miRNAs in disease states more accurate. Finally, we applied both models to all miRBase data and provide the list of high quality hairpins.

A Uniform System for the Annotation of Vertebrate microRNA Genes and the Evolution of the Human microRNAome.

Although microRNAs (miRNAs) are among the most intensively studied molecules of the past 20 years, determining what is and what is not a miRNA has not been straightforward. Here, we present a uniform system for the annotation and nomenclature of miRNA genes. We show that less than a third of the 1,881 human miRBase entries, and only approximately 16% of the 7,095 metazoan miRBase entries, are robustly supported as miRNA genes. Furthermore, we show that the human repertoire of miRNAs has been shaped by periods of intense miRNA innovation and that mature gene products show a very different tempo and mode of sequence evolution than star products. We establish a new open access database--MirGeneDB ( http://mirgenedb.org )--to catalog this set of miRNAs, which complements the efforts of miRBase but differs from it by annotating the mature versus star products and by imposing an evolutionary hierarchy upon this curated and consistently named repertoire.