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

Abstract

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.

sRNA-Effector: A tool to expedite discovery of small RNA regulators

“…miRNA targets were obtained from mirdb. 15 The effect size of the knockdown is calculated as the log2 fold change of targets minus the log2 fold change of the non-targets (as defined in 67 ) at the 50 th percentile on the Y-axis of CDF plots (Fraction of genes). This statistic serves to summarize the strength of target repression or derepression.…”

tRNA-derived small RNAs in plant response to biotic and abiotic stresses

“…tRFTar enables various functions like custom searching, co-expressed TGI filtering, genome browser and TGI-based tRF functional enrichment analysis (Rawal et al, 2022). Recently, using cross-linking, Ligation, and Sequencing of Hybrids (CLASH) data as the training and testing dataset, a novel tsRNAs target prediction tool, tRForest, was developed based on the random forest machine learning algorithm (Parikh et al, 2022) (https://trforest.com). However, no specific target prediction tools are currently available for plant tsRNAs, so the development of tsRNAs target prediction tools is an urgent issue for plant tsRNAs study.…”