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Table 1 Summary of m6A detection methods

From: When animal viruses meet N6-methyladenosine (m6A) modifications: for better or worse?

Method

Resolution

Advantages

Disadvantages

Main applications

MeRIP-seq (m6A-seq)

Low

Provides a broad overview of m6A distribution; widely applicable

Requires antibodies; complex background in results

Transcriptome-wide studies of m6A distribution

PA-m6A-seq

 ~30 nts

Low background noise

Multiple complex steps; requires 4-thiouridine and UV crosslinking

Studying m6A modifications in viral and cellular RNA, detecting various kinds of RNA modifications

MiCLIP-m6A-seq

Single-nucleotide

Highly specific

Complicated procedure; low UV crosslinking efficiency

Precise mapping of m6A modifications transcriptome-wide

High-Resolution Melting (HRM)

Single-nucleotide

Simple and quick; antibody-independent

Requires prior knowledge or speculation of m6A modification sites

Detecting m6A modifications in specific RNA sequences

m6A-REF-seq

Single-nucleotide

High-throughput; antibody-independent

Only applicable to specific sequences (ACA)

Precise identification of m6A modification sites and methylation levels

MAZTER-seq

Single-nucleotide

Quantitative and high-throughput; antibody-independent

Only applicable to ACA sites; sensitivity and specificity need calibration

Studying m6A dynamics in yeast and mammalian systems

BstI DNA Polymerase dependent methods

Single-nucleotide

Simple and quick; antibody-independent

Limited reverse transcription fragment length; requires prior knowledge of m6A modification range

Detecting m6A modifications in structurally complex RNAs such as viral RNA

Nano-m6A

Single-nucleotide

High precision; Direct detection of native RNA

Complex data processing; expensive equipment

Detailed analysis of RNA modifications, including m6A and other RNA modifications

ONT Direct RNA Sequencing (DRS)

Single-nucleotide

Preserves native RNA state; no reverse transcription needed; detects multiple RNA modifications

Expensive equipment; complex data processing; requires extensive data analysis

Transcriptome-wide RNA modification detection, including long transcripts and identification of native RNA 3′ ends