Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests. Issue 21 (25th September 2017)

• Record Type: Journal Article
• Authors: Vitsios, Dimitrios M.; Kentepozidou, Elissavet; Quintais, Leonor; Benito-Gutiérrez, Elia; van Dongen, Stijn; Davis, Matthew P.; Enright, Anton J.
• Is Part Of: Nucleic acids research. Volume 45:Issue 21(2017)
• Page Start: e177
• Page End: e177
• Publication Date: 2017-09-25
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkx836
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: The discovery of microRNAs (miRNAs) remains an important problem, particularly given the growth of high-throughput sequencing, cell sorting and single cell biology. While a large number of miRNAs have already been annotated, there may well be large numbers of miRNAs that are expressed in very particular cell types and remain elusive. Sequencing allows us to quickly and accurately identify the expression of known miRNAs from small RNA-Seq data. The biogenesis of miRNAs leads to very specific characteristics observed in their sequences. In brief, miRNAs usually have a well-defined 5′ end and a more flexible 3′ end with the possibility of 3′ tailing events, such as uridylation. Previous approaches to the prediction of novel miRNAs usually involve the analysis of structural features of miRNA precursor hairpin sequences obtained from genome sequence. We surmised that it may be possible to identify miRNAs by using these biogenesis features observed directly from sequenced reads, solely or in addition to structural analysis from genome data. To this end, we have developed mirnovo, a machine learning based algorithm, which is able to identify known and novel miRNAs in animals and plants directly from small RNA-Seq data, with or without a reference genome. This method performs comparably to existing tools, however is simpler to use with reduced run time. Its performance and accuracy has been tested on multiple datasets, including species with poorly assembled genomes, RNaseIII (Drosha and/or Dicer) deficient samples and single cells (at both embryonic and adult stage).