Discovering microRNAs from deep sequencing data using miRDeep

Friedländer, Marc R.; Chen, Wei; Adamidi, Catherine; Maaskola, Jonas; Einspanier, Ralf; Knespel, Signe; Rajewsky, Nikolaus

doi:10.1038/nbt1394

Cited by 1,094 publications

(1,086 citation statements)

References 41 publications

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“…For circRNA analysis, any circRNA detected that contained <3 reads in all individuals was excluded. miRNAs were identified using mirDeep2 software (Friedlander et al., 2008). Differential miRNA and gene expression between young and old was determined using DESeq2 (Love et al., 2014) algorithm in BRB‐Arraytools.…”

Section: Methodsmentioning

confidence: 99%

Extracellular RNA profiles with human age

Dluzen

Hooten

et al. 2018

Aging Cell

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SummaryCirculating extracellular RNAs (exRNAs) are potential biomarkers of disease. We thus hypothesized that age‐related changes in exRNAs can identify age‐related processes. We profiled both large and small RNAs in human serum to investigate changes associated with normal aging. exRNA was sequenced in 13 young (30–32 years) and 10 old (80–85 years) African American women to identify all RNA transcripts present in serum. We identified age‐related differences in several RNA biotypes, including mitochondrial transfer RNAs, mitochondrial ribosomal RNA, and unprocessed pseudogenes. Age‐related differences in unique RNA transcripts were further validated in an expanded cohort. Pathway analysis revealed that EIF2 signaling, oxidative phosphorylation, and mitochondrial dysfunction were among the top pathways shared between young and old. Protein interaction networks revealed distinct clusters of functionally‐related protein‐coding genes in both age groups. These data provide timely and relevant insight into the exRNA repertoire in serum and its change with aging.

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Section: Methodsmentioning

confidence: 99%

Extracellular RNA profiles with human age

Dluzen

Hooten

et al. 2018

Aging Cell

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“…Annotating mature products from small RNA data libraries Small RNA reads were collected from several previously published studies of human (Landgraf et al 2007;Azuma-Mukai et al 2008;Ender et al 2008;Friedlander et al 2008;Morin et al 2008), mouse (Calabrese et al 2007;Babiarz et al 2008;Baek et al 2008;Marson et al 2008;Stark et al 2008;Tam et al 2008;Watanabe et al 2008), dog , and chicken (Glazov et al 2008;Rathjen et al 2009). Small RNA reads were clipped if necessary and mapped to known miRNA precursors (miRBase) using custom python scripts.…”

Section: Methodsmentioning

confidence: 99%

Widespread regulatory activity of vertebrate microRNA* species

Yang

Phillips

Betel

et al. 2010

RNA

313

242

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An obligate intermediate during microRNA (miRNA) biogenesis is an~22-nucleotide RNA duplex, from which the mature miRNA is preferentially incorporated into a silencing complex. Its partner miRNA* species is generally regarded as a passenger RNA, whose regulatory capacity has not been systematically examined in vertebrates. Our bioinformatic analyses demonstrate that a substantial fraction of miRNA* species are stringently conserved over vertebrate evolution, collectively exhibit greatest conservation in their seed regions, and define complementary motifs whose conservation across vertebrate 39-UTR evolution is statistically significant. Functional tests of 22 miRNA expression constructs revealed that a majority could repress both miRNA and miRNA* perfect match reporters, and the ratio of miRNA:miRNA* sensor repression was correlated with the endogenous ratio of miRNA:miRNA* reads. Analysis of microarray data provided transcriptome-wide evidence for the regulation of seedmatched targets for both mature and star strand species of several miRNAs relevant to oncogenesis, including mir-17, mir-34a, and mir-19. Finally, 39-UTR sensor assays and mutagenesis tests confirmed direct repression of five miR-19* targets via star seed sites. Overall, our data demonstrate that miRNA* species have demonstrable impact on vertebrate regulatory networks and should be taken into account in studies of miRNA functions and their contribution to disease states.

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“…Having consulted with the previously developed programs, such as miRDeep (Friedlander et al, 2008) and PIPmiR (Breakfield et al, 2012), the following miRNA prediction pipeline was constructed. The automated pipeline of miRAuto consists of four modules: (1) data pre-processing, (2) classification of other small RNAs, (3) identification of conserved and novel miRNA candidates, and (4) prediction of their target genes (Fig.…”

Section: Work Flow and Data Processingmentioning

confidence: 99%

miRAuto: An Automated User-Friendly MicroRNA Prediction Tool Utilizing Plant Small RNA Sequencing Data

Lee

Kim

Park

et al. 2013

Molecules and Cells

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MicroRNAs (miRNAs) are a class of small RNAs that posttranscriptionally regulate gene expression in animals and plants. The recent rapid advancement in miRNA biology, including high-throughput sequencing of small RNA libraries, inspired the development of a bioinformatics software, miRAuto, which predicts putative miRNAs in model plant genomes computationally. Furthermore, miRAuto enables users to identify miRNAs in non-model plant species whose genomes have yet to be fully sequenced. miRAuto analyzes the expression of the 5′-end position of mapped small RNAs in reference sequences to prevent the possibility of mRNA fragments being included as candidate miRNAs. We validated the utility of miRAuto on a small RNA dataset, and the results were compared to other publicly available miRNA prediction programs. In conclusion, miRAuto is a fully automated user-friendly tool for predicting miRNAs from small RNA sequencing data in both model and non-model plant species. miRAuto is available at http://nature.snu.ac.kr/software/miRAuto.htm.

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Discovering microRNAs from deep sequencing data using miRDeep

Cited by 1,094 publications

References 41 publications

Extracellular RNA profiles with human age

Extracellular RNA profiles with human age

Widespread regulatory activity of vertebrate microRNA* species

miRAuto: An Automated User-Friendly MicroRNA Prediction Tool Utilizing Plant Small RNA Sequencing Data

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