Correlation Engine 2.0
Clear Search sequence regions

  • algorithm (1)
  • CLIP (1)
  • free (1)
  • humans (2)
  • hybrids (1)
  • mirnas (1)
  • mirnas (2)
  • mrna (4)
  • pcr (1)
  • rna (4)
  • rna transfer (12)
  • rna transfer (2)
  • target genes (3)
  • Sizes of these terms reflect their relevance to your search.

    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. 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. 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). Predictions can be obtained online, tRFTars, freely available at , which is the first tool to predict targets of tRFs in humans with a user-friendly interface.


    Qiong Xiao, Peng Gao, Xuanzhang Huang, Xiaowan Chen, Quan Chen, Xinger Lv, Yu Fu, Yongxi Song, Zhenning Wang. tRFTars: predicting the targets of tRNA-derived fragments. Journal of translational medicine. 2021 Feb 25;19(1):88

    Expand section icon Mesh Tags

    Expand section icon Substances

    PMID: 33632236

    View Full Text