Discovering microRNAs from deep sequencing data using miRDeep

By Marc R. Friedländer, Wei Chen, C. Adamidi, J. Maaskola, R. Einspanier, S. Knespel, Nikolaus Rajewsky. Nature Biotechnology, 2008

1,195 citations recorded by the source corpus.

The capacity of highly parallel sequencing technologies to detect small RNAs at unprecedented depth suggests their value in systematically identifying microRNAs (miRNAs). However, the identification of miRNAs from the large pool of sequenced transcripts from a single deep sequencing run remains a major challenge. Here, we present an algorithm, miRDeep, which uses a probabilistic model of miRNA biogenesis to score compatibility of the position and frequency of sequenced RNA with the secondary structure of the miRNA precursor. We demonstrate its accuracy and robustness using published Caenorhabditis elegans data and data we generated by deep sequencing human and dog RNAs. miRDeep reports altogether ∼230 previously unannotated miRNAs, of which four novel C. elegans miRNAs are validated by northern blot analysis.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4476b44a22156a6ab4a4c659cacc5859b70901e0

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Citations in the corpus

Metazoan MicroRNAs (2018, background): “The exception is the MIR1306 gene (Friedlander et al., 2008; Morin et al., 2008), which was subsequently shown to be a negative-control element that overlaps the DGCR8 coding sequence (Han et al., 2009).”
A human snoRNA with microRNA-like functions. (2008, methodology): “The dog data set was produced by sequencing small RNAs from dog lymphocytes using the Solexa technology (GEO accession number GSE10825) (Friedlander et al., 2008).”
TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support (2011, background): “High-throughput techniques are now playing an important role in miRNA identiﬁcation (3).”
Expression profiling of microRNAs by deep sequencing (2009, methodology): “Publicly available tools for miRNA discovery from deep sequencing of the short RNAome include miRDeep [17], CD-miRNA [18], MiRank [19] and”
Identifying miRNAs, targets and functions (2012, methodology): “With the advance of next-generation sequencing (NGS), deep sequencing has been also used to discover miRNAs systematically at a phenomenal rate [28, 31], and predicted miRNAs from deep sequencing have been incorporated into miRNA databases [23].”
miREvo: an integrative microRNA evolutionary analysis platform for next-generation sequencing experiments (2012, background): “Additionally, NGS can also be adopted to discover novel miRNAs [15,16].”
Characterization of human plasma-derived exosomal RNAs by deep sequencing (2013, background): “The ever growing number of novel sequences in the miRNA database implies that human miRNA annotation is far from complete [64].”
miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades (2011, methodology): “If the structure resembles a miRNA hairpin and the reads fall in the hairpin as would be expected from Dicer processing, then the potential precursor is assigned a score that reﬂects the likelihood of it being a genuine miRNA (11).”
miRBase: annotating high confidence microRNAs using deep sequencing data (2013, methodology): “For example, miRDeep is a well-used tool to predict microRNAs from small RNAseq data sets, which returns a score for each putative microRNA annotation (14).”
Transcriptome-wide analysis of regulatory interactions of the RNA-binding protein HuR. (2011, methodology): “Moreover, according to an analysis of the small RNA sequencing data with miRDeep (Friedländer et al., 2008), all reads aligning to miR-7-2/3 can be explained by expression of miR-7-1, but not the other way around.”