How much RNA do I need for CAM-seq?

Provided materials report feasibility with 10 ng poly(A)+ RNA; practical requirements depend on RNA quality and study design.

Is CAM-seq antibody-free? Why does that matter?

Yes—CAM-seq’s signal comes from chemistry, not IP enrichment, helping reduce antibody/IP-driven bias.

Can CAM-seq be used across species?

Provided materials describe successful application in human cells and plants, supporting cross-species profiling.

How is CAM-seq different from MeRIP-seq?

MeRIP-seq is enrichment-based and typically provides regional peaks, while CAM-seq is chemistry-based and targets base-resolution site identification.

What are common limitations to consider with chemical deamination approaches?

Provided materials note potential off-target deamination risks (e.g., at C under certain conditions) and RT-related artifacts, emphasizing careful workflow design and analysis filters.

Do you provide bioinformatic analysis and interpretation?

Yes—deliverables include QC, mapping summaries, candidate site tables, and optional advanced reporting (see Bioinformatics table).

What timeline should I expect?

Timelines vary by study scope and sequencing design; many projects are completed on a multi-week schedule.

CAM-seq m6A Sequencing Service

Antibody-free, chemically mild, base-resolution mapping of transcriptome-wide m6A—designed for site-focused interpretation when you need more than peak-level enrichment.

Move beyond peak-level signals: Designed to support single-nucleotide m6A site localization, helping you interpret biology at the site level rather than broad regions.
Support limited material: Low-input positioning can help when sample amount is a constraint (reported feasibility down to very low poly(A)+ RNA input in published summaries).
Reduce IP-related bias risk: A chemistry-based, antibody-free approach minimizes dependence on antibody performance and enrichment variability.
Actionable deliverables: Receive core QC summaries and site-focused outputs suitable for downstream interpretation and reporting.

Request a CAM-seq Quote

CAM-seq is an antibody-free m6A profiling workflow designed to support site-focused interpretation with clear QC and reporting-ready outputs (RUO).

Overview

What is CAM-seq for m6A Sequencing? Base-Resolution, Antibody-Free Profiling

CAM-seq is a chemically mild approach that converts adenine to hypoxanthine (read as G) while m6A resists deamination and remains A, enabling base-resolution detection logic without antibody enrichment.

m6A research has shifted from "is it present?" to "which exact sites change, and how does that connect to phenotype?" Yet many workflows still return region-level enrichment rather than site-level answers—making it hard to distinguish nearby events, prioritize follow-up targets, or explain mechanistic outcomes with confidence.

What this means for your study

Frame hypotheses around specific sites (not just enriched regions).
Compare conditions using site-level readouts for more interpretable biology.
Reduce dependence on antibody specificity and IP variability in m6A workflows.

Principle

How CAM-seq Works: Mild Chemical Deamination and A-to-G Readout for m6A Sites

CAM-seq infers m6A using an A-to-G readout: unmodified A is chemically deaminated to hypoxanthine/inosine (read as G), while m6A remains A.

CAM-seq is based on cooperative catalysis combining a carbonyl organocatalyst with a Lewis acid catalyst to enable selective deamination under mild conditions. In this workflow:

Adenine → hypoxanthine, which is read as guanine by reverse transcriptases or polymerases.
m6A resists deamination and remains identified as adenine, creating a direct base-resolution signal logic for m6A detection.

Portfolio

CAM-seq Service Options: Site Discovery and Multi-Condition Comparison

CAM-seq projects are typically designed for site discovery (build a base-resolution map) or comparative profiling (evaluate site-level differences across conditions).

1) CAM-seq Site Discovery (Single Condition)

Best for

Building a first-pass site-level m6A map
Generating candidate sites for mechanistic follow-up

2) CAM-seq Comparative Profiling (Multi-Condition)

Best for

Contrasting conditions (e.g., experimental condition vs control; perturbation vs WT)
Prioritizing site-level differences for downstream validation

Why CAM-seq

Base-resolution logic: supports site-level biology rather than broad regions.
Antibody-free workflow: reduces reliance on antibody specificity and IP efficiency.
Mild deamination strategy: designed to be more compatible with biological macromolecules than harsh acid-based deamination.

Applications

When to Use CAM-seq: Mechanism Studies, Low-Input Samples, and Cross-Species m6A Mapping

Choose CAM-seq when your question requires site-level m6A interpretation, especially for mechanism studies, limited-input projects, or cross-species mapping.

CAM-seq is a good fit when your question needs site-level answers:

Mechanism studies: connect m6A changes to RNA metabolism/translation regulation by focusing on specific sites.
Condition comparisons: assess how perturbations shift site-level signals across transcripts.
Cross-species mapping: published summaries highlight use across human and plant contexts (useful for motif/deposition differences).
Limited-input projects: when material is scarce and you need a workflow positioned for low input.

Workflow

CAM-seq Workflow: From poly(A)+ RNA to Illumina Libraries and Site Calling

CAM-seq follows a poly(A)+ RNA workflow—enrichment → fragmentation → chemical conversion → library prep → sequencing → site reporting.

Below is a service-ready representation aligned to the published method logic (high-level, RUO):

RNA preparation
- Total RNA extraction and poly(A) enrichment (poly(A)+ RNA), followed by fragmentation (commonly ~200 nt in method summaries).
Chemical conversion (CAM-seq core)
- Cooperative catalysis drives A→I conversion under mild conditions; m6A resists deamination.
Library construction
- Adapter ligation → reverse transcription → amplification → sequencing-ready library.
Sequencing
- Illumina-compatible sequencing.
Bioinformatics
- Map reads → quantify A/G outcomes → derive site-oriented outputs and study-level summaries.

CAM-seq workflow from poly(A)+ RNA to Illumina sequencing and site-level m6A reporting

Bioinformatics

Bioinformatics for CAM-seq: QC, Alignment, A/G Metrics, and m6A Site-Level Reporting

CAM-seq bioinformatics converts sequencing data into site-focused outputs by performing QC, alignment, extracting A/G metrics, and generating annotated site tables and summary plots.

Bioinformatic Analysis Table (Standard vs. Advanced)

Module	Standard (Included)	Advanced (Optional)
Data QC	Read quality summary; adapter/quality trimming summary; mapping rates	Sensitivity checks across QC thresholds; batch notes (if multi-batch)
Alignment	Reference alignment + core mapping statistics	Genome/transcriptome cross-checks (when relevant); expanded annotation layers
Site-oriented extraction	Site-level A/G outcome summaries and candidate site list	Artifact-focused filtering; configurable thresholds by sample type
Annotation & summaries	Transcript feature summaries; motif summaries; distribution plots	Condition overlays; motif stratification; targeted panels for manuscripts
Reporting	Structured report + tables for interpretation	Publication-format figure set and executive summary (RUO)

Samples

Sample Requirements for CAM-seq: RNA Type, Input Guidance, and Quality

CAM-seq is positioned around poly(A)+ RNA with fragmentation as part of the workflow; feasibility is driven primarily by RNA integrity, cleanliness, and achieving the target poly(A)+ input.

Sample Types & Submission Guidance

Sample Type	Accepted Format	Submission Guidance
Extracted RNA	poly(A)+ RNA	10 ng poly(A)+ RNA is reported as feasible starting input in provided summaries; practical requirements depend on study design and RNA quality.
Extracted RNA	Total RNA	Used as upstream material for poly(A) enrichment; amount depends on yield needs (project-specific).
Cells	Cell pellet	RNA-yield dependent; submit sufficient material to obtain the required poly(A)+ input.
Tissue	Fresh/frozen tissue	RNA-yield dependent; submit sufficient material to obtain the required poly(A)+ input.

Important: CAM-seq outcomes depend on RNA quality and library construction. Fragmentation is part of the described workflow, and mild reaction conditions are intended to reduce degradation risk relative to harsher chemistries.

Deliverables

Deliverables and Demo Results: What You Receive From a CAM-seq Project

Deliverables are designed to be decision-ready, combining QC, site-focused tables, and summary plots that support interpretation and reporting.

What You'll Receive (Deliverables)

Sequencing data files (FASTQ)
Alignment files (BAM) + mapping/QC summary (where included in your package)
Site-focused output tables supporting downstream interpretation
Motif and distribution summaries (transcript feature–oriented reporting)

Base-resolution m6A site table deliverable preview

CAM-seq motif enrichment and transcript distribution for m6A sites

CAM-seq QC snapshot showing key sequencing and alignment metrics

CAM-seq transcript feature distribution summary for site-level reporting

Comparison

CAM-seq vs MeRIP-seq vs GLORI: Resolution, Bias, Input, and Best-Fit Use Cases

Use MeRIP-seq for region-level discovery, GLORI when absolute site-level quantification is the priority, and CAM-seq when you want an antibody-free, site-oriented readout positioned for mild chemistry and low-input feasibility.

Dimension	MeRIP-seq	GLORI	CAM-seq
Core principle	Antibody IP enrichment + sequencing	Chemical A→I; m6A resists; A/(A+G) for quant	Cooperative-catalysis A→I; m6A resists; base-resolution site-oriented calling
Resolution	Regional (~100–200 nt)	Single-base	Single-base
Bias risk	Antibody specificity + IP bias	Chemistry/RT constraints	Antibody-free; mild chemistry emphasis
Input (as described)	Higher input typical	Higher input typical	Low-input poly(A)+ RNA positioning (e.g., 10 ng in provided summary)
Best fit	Transcriptome screening	Absolute quant focus	Low-input, low-background, site-focused profiling

Case

Case Study

Why Base-Resolution m6A Matters

Reference: Gao, Y., Liu, X., Wu, B. et al. Genome Biology (2021). Quantitative profiling of N6-methyladenosine at single-base resolution in stem-differentiating xylem of Populus trichocarpa using Nanopore direct RNA sequencing.

1) Background

Alternative polyadenylation (APA) generates transcript isoforms with different 3' ends; understanding whether isoforms differ in m6A patterns requires base-resolution and quantitative measurement rather than region-level peaks.

2) Methods

The authors built a pipeline (Nanom6A) to identify and quantify m6A at single-base resolution from Nanopore direct RNA sequencing signals, enabling estimation of modification ratios across transcripts.

3) Results

They report that distal vs proximal poly(A) site usage can show different methylation-ratio distributions, and provide representative examples illustrating isoform-associated m6A differences.

4) Conclusions

This case illustrates that quantitative, base-resolution m6A profiling can resolve isoform-level regulatory patterns that are difficult to infer from regional enrichment alone—supporting the rationale for choosing base-resolution approaches when the biological question requires precise localization and quantitative interpretation.

Differential m6A modification associated with alternative polyadenylation sites (Fig. 6e–f).

FAQ

CAM-seq FAQ: Inputs, Accuracy Considerations, and Study Design Questions

References

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.
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.
Shen, W., et al. "GLORI for absolute quantification of transcriptome-wide m6A at single-base resolution." Nature Protocols, vol. 19, 2024, pp. 1252–1287. Nature
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.
Dominissini, Dan, et al. "Topology of the Human and Mouse m6A RNA Methylomes Revealed by m6A-seq." Nature, vol. 485, 2012, pp. 201–206.
Werner, Stephan, et al. "NOseq: Amplicon sequencing evaluation method for RNA m6A sites after chemical deamination." Nucleic Acids Research, vol. 49, no. 4, 2021, e23.
Mahdavi-Amiri, Yasaman, Kimberley Chung Kim Chung, and Ryan Hili. "Single-nucleotide resolution of N6-adenine methylation sites in DNA and RNA by nitrite sequencing." Chemical Science, vol. 12, 2021, pp. 606–612.
Gao, Y., et al. "Quantitative profiling of N6-methyladenosine at single-base resolution in stem-differentiating xylem of Populus trichocarpa using Nanopore direct RNA sequencing." Genome Biology, 2021.

! For research purposes only. Not intended for clinical diagnosis, treatment, or individual health assessments.