Skip to main content

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.
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.
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.
SRAMP [128] It identifies mammalian m6A sites at single-nucleotide resolution and builds m6A site predictors. SRAMP = sequence-based RNA adenosine methylation site predictor.
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.
iRNA (m6A)-PseDNC [130] It uses the Euclidean distance-based method and pseudodinucleotide composition to identify m6A sites in the Saccharomyces cerevisiae (yeast) genome. (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.
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.
pRNAm-PC [133] It predicts m6A sites in RNA sequences based on physicochemical properties. RNA sequence samples are expressed by pseudodinucleotide composition (PseDNC).
TargetM6A [134] It identifies m6A sites from RNA sequences via position-specific nucleotide propensities (PSNP) and a support vector machine (SVM).
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.
RNAMethPre [136] It predicts m6A sites by integrating multiple mRNA features and training the SVM classifier in mammalian mRNA sequences.