m6A (N6-methyladenosine) is one of the most studied internal RNA modifications in epitranscriptomics. In practice, the hardest part is rarely whether to measure m6A—it's choosing a method that matches your real question, your sample constraints, and the kind of result you need to publish.

This article compares three widely discussed routes—MeRIP-seq, GLORI, and CAM-seq—in reader-first language: what each method really outputs (peaks vs sites vs quantitative fractions), where each one fits, and what to plan for so you don't redo the experiment later.

Figure 1. Quick picker for m6A mapping methods.

The 30-Second Method Picker

• Pick MeRIP-seq if you need a transcriptome-wide discovery scan (enriched regions/"peaks") and you're okay with region-level resolution.
• Pick GLORI if you need single-base resolution and want absolute quantification (stoichiometry) at each site (how much of the RNA population is methylated at that base).
• Consider CAM-seq if you want base-resolution profiling built around a mild chemical deamination strategy, and you're comfortable adopting a newer approach with a validation plan.

Quick glossary (so you can choose faster):

• Peak-level mapping: identifies enriched regions (not the exact base).
• Single-base mapping: calls the modified nucleotide position.
• Stoichiometry: methylation fraction at a site (e.g., 10% vs 40%).
• IP vs Input: enrichment sample vs background control.

Start With Your Research Question

Most "method confusion" disappears when you choose the minimum method that answers what you actually need:

1. "Where are m6A-enriched regions?"

   You want a discovery map and broad comparisons.

2. "Which exact nucleotide is modified?"

   You're doing mechanism work and need single-base calls.

3. "Does the methylation fraction change?"

   You care about stoichiometry (e.g., 10% → 40%), not just "a peak got higher."

4. "How limited is my input?"

   Practical constraints can decide the method faster than theory.

A common, efficient strategy is wide → narrow: discover candidates first, then validate or quantify the few sites that matter biologically.

Three Ways to Map m6A

Route A — Enrich then sequence (MeRIP-seq)

MeRIP-seq uses an m6A-specific antibody to pull down RNA fragments that carry m6A, then sequences both IP and a matched Input sample to call enriched regions ("peaks").

Route B — Convert unmodified A so m6A "stands out" (GLORI)

GLORI uses a chemical conversion logic: unmodified adenosines are efficiently converted and show up differently in sequencing reads after reverse transcription, while m6A resists conversion and stays as "A." That makes it possible to read m6A at single-base resolution and estimate methylation fraction at each site.

Route C — Mild cooperative-catalysis deamination (CAM-seq)

CAM-seq is another chemical strategy designed for base-resolution mapping. Conceptually, it also relies on selective deamination so unmodified A shifts in the readout, while m6A remains unchanged, helping pinpoint modified bases.

If you're comparing broader "single-base m6A" options beyond GLORI/CAM-seq, this overview is a useful side reference: Three Methods Comparison: GLORI-seq, miCLIP and Mazter-seq.

MeRIP-seq: Peak-Level Discovery

Figure 2. MeRIP-seq enriches m6A-marked RNA fragments with an m6A antibody; sequencing IP vs Input reveals m6A-enriched regions as peaks.

What you'll get back

A peak map—enriched regions along transcripts—often summarized as:

• a peak list (genomic/transcript coordinates),
• peak-to-gene annotations,
• differential peaks between conditions.

When it's the best choice

MeRIP-seq is a strong first step when your goal is discovery and prioritization:

• you're screening conditions,
• you need a global view of m6A remodeling,
• you want candidate transcripts for follow-up.

The tradeoff (why it matters)

MeRIP-seq typically does not pinpoint the exact modified base. Peaks can span multiple nearby sites, and antibody/enrichment behavior can affect reproducibility if not controlled carefully.

If you want the most MeRIP-specific practical context (workflow, strengths, limitations), keep this open while you plan: MeRIP-seq for Detecting RNA methylation: An Overview.

A realistic example:

You perturb an m6A writer/eraser and ask, "Which transcripts show the biggest shifts overall?" MeRIP-seq gives you a fast shortlist—then you switch to a base-resolution method on the handful of transcripts/sites you care about.

GLORI: Single-Base + Stoichiometry

Figure 3. GLORI uses selective conversion so unmodified A is read as G while m6A remains A, enabling single-base mapping and methylation fraction estimation.

What you'll get back

A site list (single-nucleotide calls) with a quantitative methylation fraction per site—so you can move from "this region is enriched" to "this base is 35% methylated in condition A and 12% in condition B."

Why GLORI feels different in real projects

For many studies, "peak changes" aren't the final story. What reviewers often push for is:

• exact sites,
• site-level changes,
• functional interpretation tied to specific nucleotides.

That's where GLORI's stoichiometry framing is especially useful.

Where it fits best

• mechanism studies that need the exact base,
• projects focused on methylation fraction shifts (not just presence/absence),
• workflows that prefer an antibody-independent mapping logic.

What to plan for (so quantification stays trustworthy)

With base-resolution quantification, your conclusions become more sensitive to conversion behavior, library quality, and calling/filtering rules—so treat those as part of study design, not "just bioinformatics."

If you want an on-ramp that reads like a mini-series, here's a natural learning path:

• GLORI 1.0 to 2.0: A Leap in Gold Standard Evolution
• GLORI-seq: Core Principles and Three Detailed Steps
• GLORI-seq Optimization: Five Key Technical Steps Involved
• GLORI-seq: Four Critical Applications Beyond Detection

A realistic example:

You're studying an m6A enzyme and you suspect the phenotype depends on whether a specific site moves from ~10% to ~40% methylation. A peak-level result may not answer that. GLORI is built to.

CAM-seq: Single-Base Profiling With Mild Chemistry

Figure 4. CAM-seq applies a mild chemical strategy that converts unmodified A (read as G) while m6A remains A, enabling base-resolution site calling.

What you'll get back

A base-resolution profile where the readout logic is again "unmodified A shifts; m6A stays," enabling site-level mapping.

Why people consider it

CAM-seq is positioned around a milder chemical route to base-resolution profiling—attractive if you want nucleotide-level answers while avoiding antibody pull-down.

A practical way to adopt CAM-seq (without over-committing)

If CAM-seq is new to your lab or sample type, the most convincing rollout is:

1. use MeRIP-seq (or existing knowledge) to define candidates, then
2. run CAM-seq on a subset, and
3. confirm that site-level calls align directionally with expected biology before scaling.

MeRIP vs GLORI vs CAM-seq (Comparison Table)

Study Design Tips That Prevent Rework

Don't skip the right controls

• For MeRIP-seq, a well-matched Input is essential for interpreting enrichment.
• For base-resolution chemical methods, treat conversion behavior and QC as part of design, not an afterthought.

If you want the most actionable "what to optimize" checklist for GLORI-style workflows, this deep dive is the best internal link to send teammates: GLORI-seq Optimization: Five Key Technical Steps Involved.

Be explicit about what "difference" means

• In MeRIP-seq, difference often means peak enrichment shifts.
• In GLORI/CAM-seq, difference can mean methylation fraction shifts at a specific base.

FAQ

Can MeRIP-seq tell me the exact m6A nucleotide?

Usually not. MeRIP-seq is interpreted at the peak/region level because enrichment happens on RNA fragments rather than directly reporting a single modified base. If you need exact sites, plan a base-resolution follow-up.

What does "absolute quantification" mean in GLORI?

It means you estimate methylation as a fraction at a given base—so you can track stoichiometry shifts across conditions (not only "a peak exists").

Are GLORI and CAM-seq antibody-free? Why does that matter?

They are built around chemical conversion logic rather than antibody pull-down. In practice, that can reduce antibody-driven variability, while increasing the importance of conversion/QC and calling strategy.

Which method is best for comparing treated vs control?

All three can compare conditions, but they measure "difference" differently: MeRIP-seq compares enrichment peaks; GLORI/CAM-seq can compare site-level fractions. Pick based on what your biological claim requires.

Conclusion

In m6A projects, the "best" method is the one that supports the exact statement you want to make in your Results section. If you need a transcriptome-wide discovery scan, start with a peak-based map (MeRIP-seq). If you need to defend a site-specific mechanism—or quantify how methylation fraction shifts at a specific nucleotide—choose a base-resolution strategy such as GLORI, and adopt newer base-resolution options like CAM-seq with a sensible validation plan.

Work with CD Genomics

If you'd like a publication-ready workflow with experimental and bioinformatics support, CD Genomics can help you choose and execute the right m6A strategy based on your study goal and sample constraints:

• MeRIP-Seq (transcriptome-wide discovery / peak-level comparison)
• GLORI-seq (single-base mapping + quantitative interpretation)
• Targeted follow-up after discovery (validate a shortlist of sites efficiently)

  SELECT-m6A sequencing

  MazF-qPCR (motif-dependent validation)

For more RNA modification methods and planning guides, visit the Epigenetics Article Hub.

References

1. Liu, Lian, et al. "Bioinformatics approaches for deciphering the epitranscriptome: recent progress and emerging topics." Computational and structural biotechnology journal 18 (2020): 1587-1604.
2. Dominissini, Dana, et al. "Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq." Nature, vol. 485, 2012, pp. 201–206.
3. Meyer, Kate D., et al. "Comprehensive Analysis of mRNA Methylation Reveals Enrichment in 3′ UTRs and near Stop Codons." Cell, vol. 149, no. 7, 2012, pp. 1635–1646.
4. Liu, Cong, et al. "Absolute quantification of single-base m6A methylation in the mammalian transcriptome using GLORI." Nature Biotechnology, vol. 41, 2023, pp. 355–366.
5. Wang, Pingluan, et al. "Small-molecule-catalysed deamination enables transcriptome-wide profiling of N6-methyladenosine in RNA." Nature Chemistry, vol. 17, 2025, pp. 1042–1052.
6. Linder, Bastian, et al. "Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome." Nature Methods, vol. 12, 2015, pp. 767–772.
7. Garcia-Campos, Miguel Angel, et al. "Deciphering the 'm6A Code' via Antibody-Independent Quantitative Profiling." Cell, vol. 178, no. 3, 2019, pp. 731–747.e16.