LITHOPHONE: Improving lncRNA Methylation Site Prediction Using an Ensemble Predictor

Abstract

N 6-methyladenosine (m 6 A) is one of the most widely studied epigenetic modifications, which plays an important role in many biological processes, such as splicing, RNA localization, and degradation. Studies have shown that m 6 A on lncRNA has important functions, including regulating the expression and functions of lncRNA, regulating the synthesis of pre-mRNA, promoting the proliferation of cancer cells, and affecting cell differentiation and many others. Although a number of methods have been proposed to predict m 6 A RNA methylation sites, most of these methods aimed at general m 6 A sites prediction without noticing the uniqueness of the lncRNA methylation prediction problem. Since many lncRNAs do not have a polyA tail and cannot be captured in the polyA selection step of the most widely adopted RNA-seq library preparation protocol, lncRNA methylation sites cannot be effectively captured and are thus likely to be significantly underrepresented in existing experimental data affecting the accuracy of existing predictors. In this paper, we propose a new computational framework, LITHOPHONE, which stands for long noncoding RNA methylation sites prediction from sequence characteristics and genomic information with an ensemble predictor. We show that the methylation sites of lncRNA and mRNA have different patterns exhibited in the extracted features and should be differently handled when making predictions. Due to the used experiment protocols, the number of known lncRNA m 6 A sites is limited, and insufficient to train a reliable predictor; thus, the performance can be improved by combining both lncRNA and mRNA data using an ensemble predictor. We show that the newly developed LITHOPHONE approach achieved a reasonably good performance when tested on independent datasets (AUC: 0.966 and 0.835 under full transcript and mature mRNA modes, respectively), marking a substantial improvement compared with existing methods. Additionally, LITHOPHONE was applied to scan the entire human lncRNAome for all possible lncRNA m 6 A sites, and the results are freely accessible at: http://180.208.58.19/lith/.

RMDisease: a database of genetic variants that affect RNA modifications, with implications for epitranscriptome pathogenesis

“…One possible explanation is that, since many biological features are correlated, the base-pairing information may be indirectly and inexplicitly captured by the model after including additional genomic features, such as, secondary structure of RNA and genomic conservation. Additionally, our previous studies showed that, including additional genomic features can effectively improve the accuracy of a predictor, for example, for m 6 A on mRNAs ( 42 ), lncRNAs ( 102 ) and introns ( 103 ), as well as for m 1 A ( 77 ), Pseudouridine ( 101 ) and m 7 G ( 74 ) site prediction. It may be worth noting that, although existing methods did not explicitly model the base-pairing mechanisms between target RNAs and snoRNPs, they may still vaguely capture the relevant patterns.…”

Comprehensive Analysis of Differences of N6-Methyladenosine of Lncrnas Between Atrazine-Inducedand Normal Xenopus Laevis Testis

“…M 6 A has been shown to be the abundant internal modi cation in eukaryotic mRNAs [28]. Emerging ndings have shed light on the involvement of m 6 A modi cation of lncRNAs [29,30]. A recent study showed that m 6 A methylation regulatory network regulates RNA processing and participates in various cellular biological processes, such as biological rhythm, immune modulation, fat metabolism, reproductive development [31].…”

PSI-MOUSE: Predicting Mouse Pseudouridine Sites From Sequence and Genome-Derived Features

“…With the massive amount of data generated from various types of high-throughput sequencing techniques, many computational methods have been developed to facilitate the research of RNA modification, such as site prediction and data collection works, [49][50][51][52][53] RNA modification-associated genetic variants analysis tools, 54,55 as well as functional annotation tools. [56][57][58][59] In this study, we innovatively represented 38 RNA topological features on the mouse genome, and a prediction framework PSI-MOUSE was built upon them.…”