ArticleNucleic acids research2020
PAREameters: a tool for computational inference of plant miRNA-mRNA targeting rules using small RNA and degradome sequencing data.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Application of Artificial Intelligence Technology in Plant MicroRNA Research: Progress, Challenges, and Prospects.International journal of molecular sciences · 2025Review
- Long Non-Coding RNAs in the Cold-Stress Response of Horticultural Plants: Molecular Mechanisms and Potential Applications.International journal of molecular sciences · 2025Review
- Prediction of miRNA Targets in Plants.Methods in molecular biology (Clifton, N.J.) · 2025Article
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- Article
- From single- to multi-omics: future research trends in medicinal plants.Briefings in bioinformatics · 2023Article
- miR-18a-5p derived from mesenchymal stem cells-extracellular vesicles inhibits ovarian cancer cell proliferation, migration, invasion, and chemotherapy resistance.Journal of translational medicine · 2022Article
- MicroRNA-125a/b-5p promotes malignant behavior in multiple myeloma cells and xenograft tumor growth by targeting DIS3.The Kaohsiung journal of medical sciences · 2022Article
- TRUEE; a bioinformatic pipeline to define the functional microRNA targetome of Arabidopsis.The Plant journal : for cell and molecular biology · 2022Article
- The Multiverse of Plant Small RNAs: How Can We Explore It?International journal of molecular sciences · 2022Review
- MicroRNA-mediated bioengineering for climate-resilience in crops.Bioengineered · 2021Review
- noisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise.Nucleic acids research · 2021Article
- NATpare: a pipeline for high-throughput prediction and functional analysis of nat-siRNAs.Nucleic acids research · 2020Article
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3 authors.
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Abstract
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.
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