ICAR-National Institute for Plant Biotechnology, LBS Centre, Pusa, New Delhi 110012, India.
School of Interdisciplinary Sciences and Technology, Jamia Hamdard (Hamdard University), Hamdard Nagar, New Delhi 110062, India.
Brief Bioinform. 2022 Jan 17;23(1). doi: 10.1093/bib/bbab448.
In recent years, microRNAs (miRNAs) and tRNA-derived RNA fragments (tRFs) have been reported extensively following different approaches of identification and analysis. Comprehensively analyzing the present approaches to overcome the existing variations, we developed a benchmarking methodology each for the identification of miRNAs and tRFs, termed as miRNA Prediction Methodology (miRPreM) and tRNA-induced small non-coding RNA Prediction Methodology (tiRPreM), respectively. We emphasized the use of respective genome of organism under study for mapping reads, sample data with at least two biological replicates, normalized read count support and novel miRNA prediction by two standard tools with multiple runs. The performance of these methodologies was evaluated by using Oryza coarctata, a wild rice species as a case study for model and non-model organisms. With organism-specific reference genome approach, 98 miRNAs and 60 tRFs were exclusively found. We observed high accuracy (13 out of 15) when tested these genome-specific miRNAs in support of analyzing the data with respective organism. Such a strong impact of miRPreM, we have predicted more than double number of miRNAs (186) as compared with the traditional approaches (79) and with tiRPreM, we have predicted all known classes of tRFs within the same small RNA data. Moreover, the methodologies presented here are in standard form in order to extend its applicability to different organisms rather than restricting to plants. Hence, miRPreM and tiRPreM can fulfill the need of a comprehensive methodology for miRNA prediction and tRF identification, respectively, for model and non-model organisms.
近年来，通过不同的鉴定和分析方法，microRNAs（miRNAs）和 tRNA 衍生的 RNA 片段（tRFs）得到了广泛的报道。为了全面分析克服现有差异的现有方法，我们分别针对 miRNA 和 tRF 的鉴定开发了一种基准测试方法，分别称为 miRNA 预测方法学（miRPreM）和 tRNA 诱导的小非编码 RNA 预测方法学（tiRPreM）。我们强调使用研究中生物体的各自基因组进行读段映射，使用至少两个生物学重复的样本数据，归一化的读段计数支持，以及使用两个标准工具进行多次运行的新 miRNA 预测。通过以野生稻种 Oryza coarctata 作为模型和非模型生物的案例研究，评估了这些方法学的性能。使用生物体特异性参考基因组方法，专门鉴定到了 98 个 miRNA 和 60 个 tRF。当在支持使用各自生物体分析数据的情况下测试这些基因组特异性 miRNA 时，我们观察到了高准确性（13 个中有 15 个）。miRPreM 的这种强大影响，使我们预测的 miRNA 数量比传统方法（79 个）多了两倍以上，并且通过 tiRPreM，我们在同一小 RNA 数据中预测到了所有已知类别的 tRF。此外，这里提出的方法学采用标准形式，以便将其应用扩展到不同的生物体，而不限于植物。因此，miRPreM 和 tiRPreM 可以分别满足模型和非模型生物的 miRNA 预测和 tRF 鉴定的综合方法学的需求。
