Table 3 M6A prediction methods
From: Emerging role of tumor-related functional peptides encoded by lncRNA and circRNA
| Name | Characteristics | Website |
|---|---|---|
| DeepM6ASeq [125] | It is based on miCLIP-Seq data at single-base resolution and detects m6A sites. It can recognize new reader FMR1. | https://github.com/rreybeyb/DeepM6ASeq |
| M6APred-EL [126] | It uses position-specific k-mer nucleotide propensity, physicochemical properties, and ring function hydrogen chemical properties to optimize m6A position recognition accuracy. | http://server.malab.cn/M6APred-EL/ |
| M6AMRFS [127] | It uses dinucleotide binary encoding and local position-specific dinucleotide frequencies to encode RNA sequences. It can identify m6A sites in multiple species. | http://server.malab.cn/M6AMRFS/ |
| SRAMP [128] | It identifies mammalian m6A sites at single-nucleotide resolution and builds m6A site predictors. SRAMP = sequence-based RNA adenosine methylation site predictor. | http://www.cuilab.cn/sramp/ |
| iRNA-Methyl [129] | Identifying m6A sites by incorporating the global and long-range sequence pattern information of RNA via the pseudo k-tupler nucleotide composition (PseKNC) approach. | http://lin.uestc.edu.cn/server/iRNA-Methyl |
| iRNA (m6A)-PseDNC [130] | It uses the Euclidean distance-based method and pseudodinucleotide composition to identify m6A sites in the Saccharomyces cerevisiae (yeast) genome. | http://lin-group.cn/server/iRNA (m6A)-PseDNC.php |
| m6Acomet [131] | It is based on the RNA co-methylation network comprising 339,158 putative gene ontology functions associated with 1,446 identified human m6A sites. | http://www.xjtlu.edu.cn/biologicalsciences/m6acomet |
| WHISTLE [132] | It integrates 35 genome-derived and conventional sequence-derived features. It enable direct queries of predicted RNA-methylation sites, their putative functions, and their associations with other methylation sites or genes. | http://whistle-epitranscriptome.com |
| pRNAm-PC [133] | It predicts m6A sites in RNA sequences based on physicochemical properties. RNA sequence samples are expressed by pseudodinucleotide composition (PseDNC). | http://www.jci-bioinfo.cn/pRNAm-PC |
| TargetM6A [134] | It identifies m6A sites from RNA sequences via position-specific nucleotide propensities (PSNP) and a support vector machine (SVM). | http://csbio.njust.edu.cn/bioinf/TargetM6A |
| AthMethPre [135] | It trains the SVM classifier using the positional flanking nucleotide sequence and the position-independent k-mer nucleotide spectrum to predict m6A sites in Arabidopsis thaliana. | http://bioinfo.tsinghua.edu.cn/AthMethPre/index.html |
| RNAMethPre [136] | It predicts m6A sites by integrating multiple mRNA features and training the SVM classifier in mammalian mRNA sequences. | http://bioinfo.tsinghua.edu.cn/RNAMethPre/index.html |