Parikh Rohan, Wilson Briana, Marrah Laine, Su Zhangli, Saha Shekhar, Kumar Pankaj, Huang Fenix, Dutta Anindya
Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22901, USA.
Biocomplexity Institute, University of Virginia, Charlottesville, VA 22901, USA.
NAR Genom Bioinform. 2022 May 30;4(2):lqac037. doi: 10.1093/nargab/lqac037. eCollection 2022 Jun.
tRNA fragments (tRFs) are small RNAs comparable to the size and function of miRNAs. tRFs are generally Dicer independent, are found associated with Ago, and can repress expression of genes post-transcriptionally. Given that this expands the repertoire of small RNAs capable of post-transcriptional gene expression, it is important to predict tRF targets with confidence. Some attempts have been made to predict tRF targets, but are limited in the scope of tRF classes used in prediction or limited in feature selection. We hypothesized that established miRNA target prediction features applied to tRFs through a random forest machine learning algorithm will immensely improve tRF target prediction. Using this approach, we show significant improvements in tRF target prediction for all classes of tRFs and validate our predictions in two independent cell lines. Finally, Gene Ontology analysis suggests that among the tRFs conserved between mice and humans, the predicted targets are enriched significantly in neuronal function, and we show this specifically for tRF-3009a. These improvements to tRF target prediction further our understanding of tRF function broadly across species and provide avenues for testing novel roles for tRFs in biology. We have created a publicly available website for the targets of tRFs predicted by tRForest.
转运RNA片段（tRFs）是一类小RNA，其大小和功能与微小RNA（miRNAs）相当。tRFs通常不依赖于Dicer酶，与AGO蛋白相关联，并且能够在转录后抑制基因表达。鉴于这扩展了能够进行转录后基因表达调控的小RNA种类，准确预测tRFs的靶标具有重要意义。虽然已经有人尝试预测tRFs的靶标，但这些尝试在预测所使用的tRF类别范围上存在限制，或者在特征选择方面存在局限。我们推测，通过随机森林机器学习算法将已确立的miRNA靶标预测特征应用于tRFs，将极大地改善tRFs靶标的预测。使用这种方法，我们在所有类别的tRFs靶标预测中都显示出显著改进，并在两个独立的细胞系中验证了我们的预测。最后，基因本体分析表明，在小鼠和人类之间保守的tRFs中，预测的靶标在神经元功能方面显著富集，我们以tRF - 3009a为例进行了具体展示。这些对tRFs靶标预测的改进进一步加深了我们对tRFs在跨物种广泛功能的理解，并为测试tRFs在生物学中的新作用提供了途径。我们创建了一个公开可用的网站，用于展示通过tRForest预测的tRFs靶标。
