m5UPred: A Web Server for the Prediction of RNA 5-Methyluridine Sites from Sequences

Abstract

As one of the widely occurring RNA modifications, 5-methyluridine (m 5 U) has recently been shown to play critical roles in various biological functions and disease pathogenesis, such as under stress response and during breast cancer development. Precise identification of m 5 U sites on RNA is vital for the understanding of the regulatory mechanisms of RNA life. We present here m5UPred, the first web server for in silico identification of m 5 U sites from the primary sequences of RNA. Built upon the support vector machine (SVM) algorithm and the biochemical encoding scheme, m5UPred achieved reasonable prediction performance with the area under the receiver operating characteristic curve (AUC) greater than 0.954 by 5-fold cross-validation and independent testing datasets. To critically test and validate the performance of our newly proposed predictor, the experimentally validated m 5 U sites were further separated by high-throughput sequencing techniques (mi-CLIP-Seq and FICC-Seq) and cell types (HEK293 and HAP1). When tested on cross-technique and cross-cell-type validation using independent datasets, m5UPred achieved an average AUC of 0.922 and 0.926 under mature mRNA mode, respectively, showing reasonable accuracy and reliability. The m5UPred web server is freely accessible now and it should make a useful tool for the researchers who are interested in m 5 U RNA modification.

Recent advances in functional annotation and prediction of the epitranscriptome

“…The first step of functional epitranscriptome prediction is to identify modification sites, either directly from high-throughput sequencing data or by using sequenced-based computational prediction tools. There exist a large number of sequencing technologies and software tools that can serve this purpose, including but not limited to those based on reverse transcription signature [55] , [56] , [57] , [58] , [59] , bisulfite treatment [39] , [60] , [61] , [62] , [63] , [64] , [65] , [66] , antibody [11] , [12] and the primary sequences of RNA molecules [67] , [68] , [69] , [70] , [71] , [72] , [73] , [74] , [75] , [76] , [77] , [78] , [79] , [80] , [81] , [82] , [83] , [84] . In the following paragraph, we cover primarily two most widely used approaches, including site detection from MeRIP-Seq data and sequence-based in silico prediction methods.…”

Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications

“…To date, a number of computational approaches have been proposed for in silico prediction of RNA modification sites from the primary RNA sequences, including: the iRNA toolkits [3][4][5][6][7][8][9][10][11] , SRAMP 12 , DeepPromise 13 , WHISTLE 14 , Gene2vec 15 , m6A-Atlas 16 , RMDisease 17 , PEA 18 , PPUS 19 , BERMP 20 , m5Upred 21 , and m6AmPred 22 . Special attention has also been paid to the prediction of RNA modifications in introns 23 , lncRNAs 24 as well as various tissues and cell lines [25][26][27] .…”

The Regulation of RNA Modification Systems: The Next Frontier in Epitranscriptomics?

“…Notable exceptions are studies on the functional consequences of RNA editing, which can draw on reproducible mapping data produced by multiple (and independent) laboratories [49][50][51][52]. Inaccurate mapping of any RNA modification will greatly affect all experimental conclusions, hypothesis building, and importantly, ongoing bioinformatics efforts to predict RNA modification patterns in silico [53][54][55][56][57][58][59][60][61]. A persistent question, therefore, is how to reliably map specific epitranscriptomes, not only in reproducible fashion but also sufficiently robust to technical variation.…”