Computational identification of Drosophila microRNA genes

By Eric C. Lai, Pavel Tomancak, Robert W. Williams, Gerald M. Rubin. Genome Biology, 2003

820 citations recorded by the source corpus.

BackgroundMicroRNAs (miRNAs) are a large family of 21-22 nucleotide non-coding RNAs with presumed post-transcriptional regulatory activity. Most miRNAs were identified by direct cloning of small RNAs, an approach that favors detection of abundant miRNAs. Three observations suggested that miRNA genes might be identified using a computational approach. First, miRNAs generally derive from precursor transcripts of 70-100 nucleotides with extended stem-loop structure. Second, miRNAs are usually highly conserved between the genomes of related species. Third, miRNAs display a characteristic pattern of evolutionary divergence.ResultsWe developed an informatic procedure called 'miRseeker', which analyzed the completed euchromatic sequences of Drosophila melanogaster and D. pseudoobscura for conserved sequences that adopt an extended stem-loop structure and display a pattern of nucleotide divergence characteristic of known miRNAs. The sensitivity of this computational procedure was demonstrated by the presence of 75% (18/24) of previously identified Drosophila miRNAs within the top 124 candidates. In total, we identified 48 novel miRNA candidates that were strongly conserved in more distant insect, nematode, or vertebrate genomes. We verified expression for a total of 24 novel miRNA genes, including 20 of 27 candidates conserved in a third species and 4 of 11 high-scoring, Drosophila-specific candidates. Our analyses lead us to estimate that drosophilid genomes contain around 110 miRNA genes.ConclusionsOur computational strategy succeeded in identifying bona fide miRNA genes and suggests that miRNAs constitute nearly 1% of predicted protein-coding genes in Drosophila, a percentage similar to the percentage of miRNAs recently attributed to other metazoan genomes.

Subfields: Cancer Research, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/61f9b76ff4fdb87cc179a216786e188e9ae7699f

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Citations in the corpus

Computational prediction of miRNAs in Arabidopsis thaliana. (2005, methodology): “This pattern of divergence has been exploited in one algorithm involving the comparison of two or more genomes, and has resulted in the estimation of 110 miRNA genes within Drosophila (Lai et al. 2003).”
Plant microRNA: a small regulatory molecule with big impact. (2006, methodology): “This approach is based on a genome sequence (Lim et al., 2003a,b; Lai et al., 2003; Grad et al., 2003; Wang et al., 2004; Bonnet et al., 2004a,b; Jones-Rhoades and Bartel, 2004; Bentwich et al., 2005; Brown and Sanseau, 2005; Sunkar et al., 2005; Li et al., 2005; Berezikov et al., 2005; Adai et al.,”
microPrimer: the biogenesis and function of microRNA (2005, background): “Now more than 1600 miRNAs have been identified in plants, animals and viruses (Lai et al., 2003; Lim et al., 2003a; Lim et al., 2003b).”
MicroRNAS and their regulatory roles in plants. (2006, background): “, miRNA stem-loop precursors) between species, searching for patterns that are characteristic of miRNAs (63, 72, 74).”
Identification of microRNAs of the herpesvirus family (2005, background): “This observation indicated that, unlike host miRNAs, viral miRNAs are difficult to identify using existing miRNA gene prediction software, which heavily relies on cross-species sequence conservatio”
microRNAs as oncogenes and tumor suppressors. (2007, background): “Computational analysis indicates that the total number of miRNAs may be more than 1% of the total protein coding genes (Lai et al., 2003; Lim et al., 2003a,b); more than 30% of protein-coding genes may be targeted by miRNAs (Berezikov et al., 2005; Lewis et al., 2005).”
Novel and Stress-Regulated MicroRNAs and Other Small RNAs from Arabidopsis (2004, background): “The presence of multiple miRNA complementary sites is known in animal targets (Lee et al., 1993; Wightman et al., 1993; Lai, 2003; Bartel, 2004).”
Bioinformatic discovery of microRNA precursors from human ESTs and introns (2006, background): “Several studies have reported serial pipelines to predict miRNAs from Caenorhabditis elegans, Drosophila, Arabidopsis and human [6-9].”
Identifying miRNAs, targets and functions (2012, methodology): “Many algorithms, for example, miRScan [25], miRSeeker [26], miRank [27], miRDeep [28], miRDeep2 [29] and miRanalyzer [30], have been proposed.”
Prediction and validation of microRNAs and their targets (2005, neutral): “[16] identified 48 microRNA candidates in Drosophila, 24 of which were validated, using a computational microRNA detection program called miRseeker.”
Discovery of functional elements in 12 Drosophila genomes using evolutionary signatures (2007, background): “MicroRNA hairpins show a characteristic conservation profile with high conservation in the stem and mutations in loop regions 10,11 (Fig.”
Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences (2004, background): “This kind of approach has been proven to be very successful, especially in animal genomes, where all known miRNA genes are of comparable length and contain few unpaired bases (Lai et al., 2003; Lim et al., 2003a,b).”
FlyAtlas 2: a new version of the Drosophila melanogaster expression atlas with RNA-Seq, miRNA-Seq and sex-specific data (2017, neutral): “In undertaking this new work, we were able to address some other limitations of the original: the lack of information for Drosophila microRNAs (6), the absence of separate data for the somatic tissues of male and female flies, and the unsuitability of the web interface for viewing on the mobile…”