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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