What is the difference between MeRIP-seq and m6A-seq?

MeRIP-seq and m6A-seq refer to essentially the same method — both use anti-m6A antibodies to enrich methylated RNA fragments for sequencing. The method can also be adapted to detect other RNA modifications using modification-specific antibodies.

What is the recommended sequencing depth for MeRIP-seq?

We recommend ≥ 30 million paired-end reads per IP sample and ≥ 30 million reads per input control. Higher depth improves detection sensitivity for low-abundance m6A peaks.

Can MeRIP-seq detect modifications other than m6A?

Yes. The method can be applied to detect m5C, m1A, ac4C, and other RNA modifications using modification-specific antibodies. The core workflow remains the same; only the antibody is changed.

MeRIP-Seq (m6A) Service: Transcriptome-Wide RNA Methylation Profiling

MeRIP-Seq (Methylated RNA Immunoprecipitation Sequencing), also known as m6A-seq, is an antibody-based method for transcriptome-wide detection of N6-methyladenosine (m6A) — the most abundant internal RNA modification in eukaryotic mRNA. Using a highly specific anti-m6A antibody, methylated RNA fragments are selectively enriched, reverse-transcribed, and sequenced, enabling researchers to map m6A modification sites across the entire transcriptome. Beyond m6A, the method can also be adapted to profile other RNA modifications (m5C, m1A, ac4C) with corresponding antibodies, offering a versatile platform for epitranscriptomic discovery.

Key Highlights of Our MeRIP-Seq Service:

High-Specificity m6A Enrichment — Validated anti-m6A antibody with stringent IP conditions ensures high signal-to-noise ratio and reproducible peak calling across biological replicates.
Comprehensive Transcriptome Coverage — Genome-wide detection captures m6A modifications on mRNA, lncRNA, and other polyadenylated transcripts, providing an unbiased epitranscriptomic overview.
Flexible Input & Sample Compatibility — Optimized protocols for total RNA, mRNA, cell pellets, and tissue, with input as low as 1–5 μg total RNA using enriched mRNA or rRNA-depleted strategies.
Integrated Bioinformatics Pipeline — From raw data QC and peak calling to differential methylation, motif discovery, and functional enrichment, our pipeline delivers publication-ready deliverables.

Available MeRIP-Seq Study Options:

CD Genomics supports standard mRNA MeRIP-Seq, mRNA + lncRNA MeRIP-Seq, low-input MeRIP-Seq for limited RNA samples, and high-throughput MeRIP-Seq project support for large-scale or multi-condition studies.

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3D illustration of anti-m6A antibody binding to methylated adenosine residues on fragmented mRNA for MeRIP-seq enrichment

Advantages

Core Technical Advantages of MeRIP-Seq

MeRIP-Seq combines antibody-based immunoprecipitation with high-throughput sequencing for transcriptome-wide m6A detection. The following features make it the method of choice for epitranscriptomic profiling.

MeRIP-seq enrichment principle: fragmented RNA with m6A sites bound by anti-m6A antibody, captured by magnetic beads, enriched methylated fragments vs input control

Transcriptome-Wide Detection at Peak-Level Resolution — By fragmenting RNA to ~100 nt and enriching methylated fragments with anti-m6A antibody, MeRIP-seq enables identification of m6A peaks across the entire transcriptome. Paired with input RNA sequencing, robust peak calling with MACS2, exomePeak2, or MeTPeak provides both the location and enrichment level of m6A modifications.
Validated Antibody Specificity — The anti-m6A antibody specifically recognizes N6-methyladenosine without cross-reactivity to unmodified adenosine or other RNA modifications. Stringent IP-wash conditions and input normalization ensure that detected peaks represent true m6A enrichment rather than non-specific background.
Broad Sample Compatibility — MeRIP-seq has been successfully applied to mammalian cell lines, primary tissues, plant samples, and clinical specimens. The method is compatible with total RNA, rRNA-depleted RNA, or polyA-enriched mRNA, providing flexibility for diverse research contexts and RNA quality scenarios.
Rich Multi-Dimensional Output — In addition to peak calling and annotation, MeRIP-seq supports motif discovery (identifying the canonical RRACH m6A consensus), differential methylation analysis, metagene profiling, and functional enrichment. This enables researchers to move directly from modification maps to mechanistic hypotheses.

Applications

Applications in Epigenetics Research

RNA methylation profiling is central to understanding post-transcriptional gene regulation. MeRIP-seq provides a transcriptome-wide view of m6A modifications that influence RNA stability, splicing, translation, and localization.

Cancer Biology & Tumorigenesis

Profile m6A modification patterns across tumor and normal tissues to identify aberrant RNA methylation events driving oncogene activation or tumor suppressor silencing. MeRIP-seq reveals how dysregulated m6A writers, erasers, and readers contribute to cancer progression.

Developmental & Stem Cell Biology

Track dynamic m6A methylation changes during embryonic development, cell differentiation, and reprogramming. MeRIP-seq captures temporal and cell-type-specific RNA modification patterns governing cell fate decisions.

Stress Response & Environmental Adaptation

Investigate how external stimuli — heat shock, hypoxia, or oxidative stress — reshape the m6A epitranscriptome. MeRIP-seq enables comparison of RNA methylation landscapes under diverse conditions.

RNA Metabolism & Post-Transcriptional Control

Combine MeRIP-seq with RNA-seq to link m6A modification status to downstream effects on mRNA stability, splicing efficiency, and translation. Integrated epitranscriptomic analysis provides a comprehensive view of RNA-level regulation.

Workflow

Optimized MeRIP-Seq Workflow and Stringent QC Metrics

CD Genomics maintains a rigorous end-to-end workflow to ensure data integrity and biological relevance. Every project undergoes multiple layers of quality control from RNA extraction to reporting.

RNA QC & Fragmentation: Total RNA integrity assessed via Bioanalyzer (RIN ≥ 7.0 recommended). RNA fragmented to ~100 nt using chemical fragmentation to ensure uniform fragment size for efficient immunoprecipitation and sequencing.
m6A Immunoprecipitation: Fragmented RNA incubated with validated anti-m6A antibody. m6A-containing fragments captured using Protein A/G magnetic beads. Input control retained for normalization. Stringent wash conditions minimize non-specific binding.
Library Preparation: Both IP-enriched and input RNA reverse-transcribed to cDNA. Strand-specific library preparation with unique dual-index adapters. PCR cycles optimized to minimize duplication bias.
Illumina Sequencing: High-throughput paired-end sequencing (PE150) on Illumina platforms. Recommended ≥ 30M reads per IP sample + ≥ 30M reads per input control.
Bioinformatics Analysis: Executing the comprehensive pipeline from raw data QC to peak calling, differential methylation, motif discovery, and functional annotation. Both IP and input libraries processed in parallel.

Sample Requirements

Proper sample preparation is critical for successful MeRIP-seq. The table below summarizes recommended input guidelines for common sample types.

Sample Type	Recommended Input	Container & Shipping	QC Requirements	Notes
Total RNA	≥ 5 μg (standard); ≥ 1 μg (low-input)	RNase-free tube, dry ice	RIN ≥ 7.0; OD260/280 = 1.8–2.1	rRNA depletion or polyA enrichment recommended
mRNA (polyA-enriched)	≥ 500 ng	RNase-free tube, dry ice	Clear size distribution; no rRNA	Optimal for coding transcript m6A profiling
Cell Pellets	≥ 2 × 10^6 cells	RNase-free tube, dry ice	Viability ≥ 85%	PBS-washed; freeze immediately in liquid N2
Tissue	≥ 50 mg	RNase-free cryovial, dry ice	Flash-frozen in liquid N2	RNAlater preservation acceptable; avoid repeated freeze-thaw

Demo Results

Publication-Ready Demo Results

Our data deliverables are structured to drop directly into your next high-impact manuscript. We provide comprehensive, high-resolution visual outputs.

Peak Distribution & Genomic Annotation: Pie chart showing the distribution of m6A peaks across transcript regions (5’UTR, CDS, 3’UTR). Typical m6A enrichment near stop codons and 3’UTR regions validates data quality.
m6A Motif Enrichment: Sequence logo plot identifying the canonical RRACH (R=G/A, H=A/C/U) consensus motif enriched within peak regions, confirming antibody specificity.
Metagene Profile: Average m6A enrichment profile across a standardized gene body, showing characteristic enrichment patterns near stop codons and 3’UTRs.
Differential Methylation Volcano Plot: Instantly highlights statistically significant hyper- and hypo-methylated m6A peaks between experimental and control groups.
Functional Enrichment Analysis: GO/KEGG bar charts mapping differentially methylated genes to relevant biological pathways, providing mechanistic context.
IGV Genome Browser Tracks: Representative IGV screenshots showing m6A peak enrichment in both IP and input tracks across specific genes of interest.

Bioinformatics

Comprehensive MeRIP-Seq Bioinformatics Pipeline

Transforming raw MeRIP-seq reads into meaningful biological insights requires specialized computational expertise for epitranscriptomic data. We provide an end-to-end bioinformatics pipeline structured to deliver clear, actionable, and visually compelling results.

Standard Epitranscriptomic Profiling:

Raw Data QC & Alignment: Quality control via FastQC, adapter trimming with Cutadapt. Reads aligned to reference genome using STAR or HISAT2 with transcriptome-aware parameters.
Peak Calling: Identification of m6A-enriched regions using MACS2 (with input as control), exomePeak2, or MeTPeak. Input normalization ensures accurate enrichment quantification.
Peak Annotation: Annotation of m6A peaks to transcript features (5’UTR, CDS, 3’UTR, intron) and gene-level assignment.
Differential Methylation Analysis: Statistical comparison of m6A enrichment between conditions using MeTDiff, QNB, or RADAR (FDR < 0.05, |log2FC| > 0.5).

Advanced Mechanistic Analysis:

Motif Discovery: De novo motif analysis (HOMER, MEME) to identify enriched sequence motifs within m6A peak regions.
Functional Enrichment: GO biological process and KEGG pathway enrichment of m6A-modified genes.
Multi-Omics Integration: Correlation analysis between MeRIP-seq and matched RNA-seq data to link m6A modification to gene expression changes.
Custom Visualizations: Publication-ready figures including metagene plots, distribution charts, IGV tracks, and motif logos tailored to manuscript requirements.

Comparison

MeRIP-Seq vs. Other Epitranscriptomics Methods

Understanding the technical differences between available RNA modification detection methods helps researchers select the most appropriate approach for their study.

Feature	MeRIP-Seq (m6A-seq)	miCLIP / eCLIP	Direct RNA-seq (Nanopore)	LC-MS/MS
Principle	Antibody IP + NGS	Antibody crosslink + truncation	Native RNA direct sequencing	Mass spectrometry quantification
Resolution	~100–200 nt (peak-level)	Single-nucleotide	Single-nucleotide (emerging)	Nucleoside level (global)
Coverage	Transcriptome-wide	Antibody-targeted sites	All modifications detectable	All modifications simultaneously
Modifications Detected	m6A (primary); m5C, m1A (with specific antibodies)	Antibody-dependent	m6A, m5C, pseudouridine, inosine	All known modifications
Input Required	≥ 1–5 μg total RNA	≥ 50 μg total RNA	≥ 500 ng polyA RNA	≥ 50 ng RNA
Bioinformatics	Mature (MACS2, exomePeak2)	Specialized (CLIPper, PureCLIP)	Developing (Tombo, m6Anet)	Standard MS workflows
Best For	Genome-wide m6A discovery, cohort studies	Single-nucleotide mechanistic studies	Multi-modification, isoform-level	Global modification quantification

Selection Strategy: Choose MeRIP-seq when you need genome-wide m6A profiling with mature bioinformatics support and moderate input requirements. Select miCLIP for single-nucleotide resolution studies of specific targets. Direct RNA-seq via Nanopore is emerging as a powerful multi-modification platform. LC-MS/MS is ideal for quantifying global modification levels without positional information.

Case Study

Published Case Study: MeRIP-Seq in Lung Adenocarcinoma m6A Profiling

Comprehensive Analysis of the Transcriptome-wide m6A Methylome in Lung Adenocarcinoma by MeRIP Sequencing

Background: N6-methyladenosine (m6A) is the most abundant internal modification on eukaryotic mRNAs and plays a key role in tumor progression. However, the transcriptome-wide m6A modification landscape in lung adenocarcinoma (LUAD) had not been comprehensively characterized.

Methods: MeRIP-seq was performed on three paired LUAD tumor and adjacent normal tissue samples. The study integrated m6A peak calling, differential methylation analysis, and correlation with gene expression to identify m6A-modified genes involved in LUAD pathogenesis.

Results: MeRIP-seq identified thousands of m6A peaks across the LUAD transcriptome, with characteristic enrichment near stop codons and 3’UTR regions. Differential methylation analysis revealed numerous hyper- and hypo-methylated m6A peaks in tumor versus normal tissue. Functional enrichment analysis mapped these differentially methylated genes to cancer-relevant pathways including cell adhesion, transcriptional regulation, and signal transduction.

Figure: MeRIP-seq reveals distinct m6A methylation patterns between LUAD tumor and normal tissues, with differential peaks enriched in cancer-related pathways.

Conclusion: This study demonstrates that MeRIP-seq can comprehensively map the m6A epitranscriptomic landscape in clinical cancer samples. The identification of differentially methylated m6A-modified genes provides a valuable resource for understanding the role of RNA methylation in lung adenocarcinoma pathogenesis.

Source: Wang, Y., et al. "Comprehensive Analysis of the Transcriptome-wide m6A Methylome in Lung Adenocarcinoma by MeRIP Sequencing." Frontiers in Oncology, vol. 12, 2022, 791332.

FAQ

Frequently Asked Questions (FAQ)

References

Dominissini, D., et al. "Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq." Nature, vol. 485, 2012, pp. 201–206.
Meyer, K. D., et al. "Comprehensive analysis of mRNA methylation reveals enrichment in 3’ UTRs and near stop codons." Cell, vol. 149, 2012, pp. 1635–1646.
Wang, Y., et al. "Comprehensive analysis of the transcriptome-wide m6A methylome in lung adenocarcinoma by MeRIP sequencing." Frontiers in Oncology, vol. 12, 2022, 791332.