Your browser doesn't support javascript.
loading
PAREameters: a tool for computational inference of plant miRNA-mRNA targeting rules using small RNA and degradome sequencing data.
Thody, Joshua; Moulton, Vincent; Mohorianu, Irina.
Afiliação
  • Thody J; School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK.
  • Moulton V; School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK.
  • Mohorianu I; School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK.
Nucleic Acids Res ; 48(5): 2258-2270, 2020 03 18.
Article em En | MEDLINE | ID: mdl-31943065
MicroRNAs (miRNAs) are short, non-coding RNAs that modulate the translation-rate of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to sequence-specific targets. In plants, this typically results in cleavage and subsequent degradation of the mRNA. Degradome sequencing is a high-throughput technique developed to capture cleaved mRNA fragments and thus can be used to support miRNA target prediction. The current criteria used for miRNA target prediction were inferred on a limited number of experimentally validated A. thaliana interactions and were adapted to fit these specific interactions; thus, these fixed criteria may not be optimal across all datasets (organisms, tissues or treatments). We present a new tool, PAREameters, for inferring targeting criteria from small RNA and degradome sequencing datasets. We evaluate its performance using a more extensive set of experimentally validated interactions in multiple A. thaliana datasets. We also perform comprehensive analyses to highlight and quantify the differences between subsets of miRNA-mRNA interactions in model and non-model organisms. Our results show increased sensitivity in A. thaliana when using the PAREameters inferred criteria and that using data-driven criteria enables the identification of additional interactions that further our understanding of the RNA silencing pathway in both model and non-model organisms.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / RNA Mensageiro / Arabidopsis / RNA de Plantas / Regulação da Expressão Gênica de Plantas / Biologia Computacional / MicroRNAs Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / RNA Mensageiro / Arabidopsis / RNA de Plantas / Regulação da Expressão Gênica de Plantas / Biologia Computacional / MicroRNAs Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article