miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades

Abstract

microRNAs (miRNAs) are a large class of small non-coding RNAs which post-transcriptionally regulate the expression of a large fraction of all animal genes and are important in a wide range of biological processes. Recent advances in high-throughput sequencing allow miRNA detection at unprecedented sensitivity, but the computational task of accurately identifying the miRNAs in the background of sequenced RNAs remains challenging. For this purpose, we have designed miRDeep2, a substantially improved algorithm which identifies canonical and non-canonical miRNAs such as those derived from transposable elements and informs on high-confidence candidates that are detected in multiple independent samples. Analyzing data from seven animal species representing the major animal clades, miRDeep2 identified miRNAs with an accuracy of 98.6–99.9% and reported hundreds of novel miRNAs. To test the accuracy of miRDeep2, we knocked down the miRNA biogenesis pathway in a human cell line and sequenced small RNAs before and after. The vast majority of the >100 novel miRNAs expressed in this cell line were indeed specifically downregulated, validating most miRDeep2 predictions. Last, a new miRNA expression profiling routine, low time and memory usage and user-friendly interactive graphic output can make miRDeep2 useful to a wide range of researchers.

Global characterization of the Dicer-like protein DrnB roles in miRNA biogenesis in the social amoeba Dictyostelium discoideum

“…For this, programs designed for detection of small/miRNA in animals and plants were used, i.e. miRDeep2 and ShortStack [43,44]. In addition, the small RNA-seq data was also analyzed utilizing our pipeline designed for D. discoideum miRNA discovery [28,45].…”

“…Small RNA sequencing reads (≥18nt) were mapped to the genome sequence using Bowtie 1.1.1 [82] allowing for two mismatches. miRNA prediction were performed with miRDeep2 [43], ShortStack 3.6 [44] and the pipeline for high-throughput identification of miRNAs in D. discoideum [28] using all wt small RNA sequencing data as input. Small RNA reads were mapped to the predicted miRNA hairpin-sequences and visualized using in-house python script.…”

Characterization of Trichuris muris secreted proteins and extracellular vesicles provides new insights into host–parasite communication

“…The miRDeep2 package [35] was used to identify known and putative novel miRNAs present in both miRNA samples. As there are no T. muris miRNAs available in miRBase release 21 [36], the miRNAs from the nematodes Ascaris suum, Brugia malayi, Caenorhabditis elegans, Caenorhabditis brenneri, Caenorhabditis briggsae, Caenorhabditis remanei, Haemonchus contortus, Pristionchus pacificus, Panagrellus redivivus and Strongyloides ratti were utilized as a training set for the algorithm.…”

Circulating microRNA signature of genotype‐by‐age interactions in the long‐lived Ames dwarf mouse

“…Sequencing reads were analyzed with miRDeep2 (Friedlander et al ., 2012), which is probabilistic algorithm based on the miRNA biogenesis model and designed to detect miRNAs from deep sequencing reads. Briefly, miRDeep2 removes the 3′ adapter sequence and discards reads shorter than 18 nucleotides, before aligning them to the mouse mm10 genome.…”