Evolutionary flux of canonical microRNAs and mirtrons in Drosophila

Abstract

Next-generation sequencing technologies generate vast catalogs of short RNA sequences from which to mine microRNAs. However, such data must be vetted to appropriately categorize microRNA precursors and interpret their evolution. A recent study annotated hundreds of microRNAs in three Drosophila species on the basis of singleton reads of heterogeneous length1. Our multi-million read datasets indicated that most of these were not substrates of RNAse III cleavage, and comprised many mRNA degradation fragments. We instead identified a distinct and smaller set of novel microRNAs supported by confident cloning signatures, including a high proportion of evolutionarily nascent mirtrons. Our data support a much lower rate in the emergence of lineage-specific microRNAs than previously inferred1, with a net flux of ~1 microRNA/million years of Drosophilid evolution.

Evolution and Functional Diversification of MIRNA Genes

“…This estimate overlaps the 0.8 to 1.6 genes per million years estimated for the Drosophila lineage MIRNA flux rate (Berezikov et al, 2010).…”

Discovery of new microRNAs by small RNAome deep sequencing in childhood acute lymphoblastic leukemia

“…This pipeline was used to distinguish miRNA sequences from other small RNAs. The criteria used to identify miRNA sequences and predict precursor hairpin structures were based on previous work by Berezikov et al [22][23][24] In short, sequences were mapped to the human genome available in Ensembl (release 56/GRCh37 assembly) and UCSC (GRCh37/hg19). Sequences that did not map to protein-coding mRNA or to known small RNAs (including transfer RNA, ribosomal RNA, small nuclear and small nucleolar RNA) were further explored.…”

“…The potential miRNA precursors were then computationally folded into hairpin structures and tested for a set of features derived from known miR genes in order to identify putative novel miRNAs (Table 1 and Figure 1). The features used to identify miRNAs were based on experience in the identification of miRNAs by the previous work of Berezikov et al [22][23][24] Details can also be found on the website www.internagenomics.com. [22][23][24] We have normalized the read frequencies of miRNAs by dividing the number of absolute read sequences (numerator) by the sum of total miRNA sequence reads (denominator) per subtype.…”

“…The features used to identify miRNAs were based on experience in the identification of miRNAs by the previous work of Berezikov et al [22][23][24] Details can also be found on the website www.internagenomics.com. [22][23][24] We have normalized the read frequencies of miRNAs by dividing the number of absolute read sequences (numerator) by the sum of total miRNA sequence reads (denominator) per subtype. The total sum of sequence reads represents the reads of known miRNAs, novel miRNAs and candidate novel miRNAs.…”

The use of high-throughput sequencing methods for plant microRNA research

“…In recent years, atypical miRNA species such as mirtrons [76][77][78][79][80][81][82][83][84] and reverse complementary miRNA genes 85 were identified based on computational predictions, sRNA-seq data and further experimental validations. Besides, based on sRNA-seq and degradome-seq data, we previously proposed that in plants, the intronic regions of the pre-mRNAs (precursors of mRNAs) might be targeted by specific miRNAs for cleavages.…”