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.

中文翻译：

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PAReameters：一种使用小 RNA 和降解组测序数据计算推断植物 miRNA-mRNA 靶向规则的工具。


MicroRNA (miRNA) 是短的非编码 RNA，通过将 RNA 诱导的沉默复合物引导至序列特异性靶标来调节信使 RNA (mRNA) 的翻译速率。在植物中，这通常会导致 mRNA 的裂解和随后的降解。降解组测序是一种高通量技术，旨在捕获切割的 mRNA 片段，因此可用于支持 miRNA 靶点预测。目前用于 miRNA 靶标预测的标准是根据有限数量的经实验验证的拟南芥相互作用推断出来的，并进行了调整以适应这些特定的相互作用；因此，这些固定标准可能并非在所有数据集（生物体、组织或治疗）中都是最佳的。我们提出了一种新工具 PAReameters，用于从小 RNA 和降解组测序数据集中推断靶向标准。我们使用多个拟南芥数据集中更广泛的经过实验验证的相互作用来评估其性能。我们还进行全面的分析，以突出和量化模型和非模型生物体中 miRNA-mRNA 相互作用子集之间的差异。我们的结果表明，当使用 PAReameters 推断标准时，拟南芥的敏感性增加，并且使用数据驱动的标准能够识别额外的相互作用，从而进一步加深我们对模型和非模型生物体中 RNA 沉默途径的理解。