tRFTars: predicting the targets of tRNA-derived fragments

Abstract

Background: tRNA-derived fragments (tRFs) are 14–40-nucleotide-long, small non-coding RNAs derived from specific tRNA cleavage events with key regulatory functions in many biological processes. Many studies have shown that tRFs are associated with Argonaute (AGO) complexes and inhibit gene expression in the same manner as miRNAs. However, there are currently no tools for accurately predicting tRF target genes.Methods: We used tRF-mRNA pairs identified by crosslinking, ligation, and sequencing of hybrids (CLASH) and covalent ligation of endogenous AGO-bound RNAs (CLEAR)-CLIP to assess features that may participate in tRF targeting, including the sequence context of each site and tRF-mRNA interactions. We applied genetic algorithm (GA) to select key features and support vector machine (SVM) to construct tRF prediction models.Results: We first identified features that globally influenced tRF targeting. Among these features, the most significant were the minimum free folding energy (MFE), position 8 match, number of bases paired in the tRF-mRNA duplex, and length of the tRF, which were consistent with previous findings. Our constructed model yielded an area under the receiver operating characteristic (ROC) curve (AUC) = 0.980 (0.977-0.983) in the training process and an AUC = 0.847 (0.83-0.861) in the test process. The model was applied to all the sites with perfect Watson-Crick complementarity to the seed in the 3' untranslated region (3'-UTR) of the human genome. Seven of nine target/nontarget genes of tRFs confirmed by reporter assay were predicted. We also validated the predictions via quantitative real-time PCR (qRT-PCR). Thirteen potential target genes from the top of the predictions were significantly down-regulated at the mRNA levels by overexpression of the tRFs (tRF-3001a, tRF-3003a or tRF-3009a).Conclusions: Predictions can be obtained online, tRFTars, freely available at http://trftars.cmuzhenninglab.org:3838/tar/, which is the first tool to predict targets of tRFs in humans with a user-friendly interface.

tRForest: a novel random forest-based algorithm for tRNA-derived fragment target prediction

“…tRFTarget predicted targets were obtained from tRFTarget (http://trftarget.net/) (10). tRFTars predicted targets were obtained from tRFTars (http://trftars.cmuzhenninglab.org:3838/tar/) (9). The random forest classifier used in tRForest was from scikit-learn in Python (https://scikit-learn.org/) (17).…”

“…Accurate identification of tRF targets will improve our understanding of the role of tRFs in biology and disease. Recognizing this need, tRF specific target prediction tools have become available recently (9)(10)(11). Despite the increase in the number of tools over the past couple of years, these tools are lacking in one way or another.…”

PACeR: a bioinformatic pipeline for the analysis of chimeric RNA-seq data

“…As most mammalian mRNAs are targets of miRNAs (Friedman et al 2009), the potential for miRNAs to coordinately influence gene expression and impact cell biology is immense. In addition to miRNAs, other sncRNAs such as tRNA fragments (tRFs) have also been shown to associate with Ago and mediate post-transcriptional gene regulation (Kumar et al 2014; Kuscu et al 2018; Guan et al 2020; Xiao et al 2021; Zuo et al 2021; Wang et al 2022).…”