MirGeneDB 2.0: The metazoan microRNA complement

Abstract

Small non-coding RNAs have gained substantial attention due to their roles in animal development and human disorders. Among them, microRNAs are unique because individual gene sequences are conserved across the animal kingdom. In addition, unique and mechanistically well understood features can clearly distinguish bona fide miRNAs from the myriad other small RNAs generated by cells. However, making this separation is not a common practice and, thus, not surprisingly, the heterogeneous quality of available miRNA complements has become a major concern in microRNA research. We addressed this by extensively expanding our curated microRNA gene database MirGeneDB to 45 organisms that represent the full taxonomic breadth of Metazoa. By consistently annotating and naming more than 10,900 microRNA genes in these organisms, we show that previous microRNA annotations contained not only many false positives, but surprisingly lacked more than 2,100 bona fide microRNAs. Indeed, curated microRNA complements of closely related organisms are very similar and can be used to reconstruct Metazoan evolution. MirGeneDB represents a robust platform for microRNA-based research, providing deeper and more significant insights into the biology and evolution of miRNAs but also biomedical and biomarker research. MirGeneDB is publicly and freely available at http://mirgenedb.org/.

Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API

“…Only three lines are mandatory: the mirGFF3 format version, the database used for annotation, and the sample name ( Figure 1A). The database line can point to any of the already published resources: miRBase [49], miRCarta [50], miRGeneDB [51], or a custom database.…”

xMD-miRNA-seq to generate near in vivo miRNA expression estimates in colon epithelial cells

“…Adapters and random base linkers were removed with Cutadapt version 1.14 and trimmed FASTQ files were processed using both miRge and a miRBase v21 miRNA library and miRge 2.0 with a MirGeneDB library of miRNAs. [21][22][23][24] Using the more restrictive MirGeneDB dataset, we identified between 260 and 321 miRNAs for each sample. Setting the minimum threshold to 10 RPM for a given miRNA, the range was 236-285 miRNAs per sample, with 203 miRNAs consistently above 10 RPM.…”

Too Many False Targets for MicroRNAs: Challenges and Pitfalls in Prediction of miRNA Targets and Their Gene Ontology in Model and Non‐model Organisms

“…We chose two evolutionary very distant organisms that are not known to interact with one another, do not share common miRNAs, but share a similar number of genes: Arabidopsis thaliana ( A. thaliana ) (thale cress) and Rattus norvegicus ( R. norvegicus ) (brown rat). For each of these organisms we chose 20 highly confident miRNAs, which were used for target prediction in a reciprocal manner (self‐miRNA vs self‐transcripts, and self‐miRNAs vs non‐self‐transcripts) (Figure A). The number of predicted targets of each miRNA was normalized to the number of nucleotides in the transcriptome of each dataset.…”

“…The number of targets is presented as targets per mega base (targets per Mb). Rat miRNAs with high confidence were chosen from MirGeneDB2.0 . Arabidopsis highly confident miRNAs were chosen according to Axtell et al and Arribas‐Hernandez et al The data used for these analyses are available at https://figshare.com/s/7db0263cf3e3085443b6.…”