What does AlkAniline-Seq detect?

It is designed for high-throughput, single-nucleotide mapping of m7G and m3C using an N+1 read-start cleavage signature.

Can AlkAniline-Seq detect both m7G and m3C in one run?

Yes. AlkAniline-Seq is designed to generate single-nucleotide site maps for both m7G and m3C from one workflow using an N+1 read-start signature.

How does AlkAniline-Seq achieve single-nucleotide resolution?

The workflow generates cleavage products where Read1 begins at N+1, and mapped 5'-end read-start pileups identify site positions and signal intensity.

Is AlkAniline-Seq the same as MeRIP-seq?

No. MeRIP is an antibody enrichment strategy that typically yields region-level peaks, while AlkAniline-Seq is chemistry-driven and designed for site-level mapping.

What sample types are accepted for AlkAniline-Seq?

Cells, tissue, or purified RNA are supported; other sample types are assessed case-by-case based on RNA yield and integrity.

How much sequencing depth do I need?

Depth depends on organism, RNA class focus (tRNA/rRNA vs transcriptome), and whether you need differential analysis across conditions. We recommend a design review to set the plan.

What bioinformatics outputs will I receive?

Site tables (m7G and m3C), summary QC, and visualization-ready tracks; advanced options include differential site analysis and stratified reporting.

Is this service for clinical diagnosis?

No. This service is provided for research use only.

AlkAniline-Seq for Single-Nucleotide m7G/m3C RNA Modification Mapping

Most RNA methylation assays enrich regions. AlkAniline-Seq is designed for a different question: exactly where are the m7G and m3C sites—at single-nucleotide resolution—across diverse RNA classes?

Single-nucleotide localization via N+1 read-start signatures (site-level, not peaks)
Positive-enrichment chemistry to reduce background and improve signal-to-noise
One workflow delivers dual m7G + m3C site maps at single-nucleotide resolution
Supports tRNA/rRNA/mRNA and cytosolic + mitochondrial RNA studies

Start Your AlkAniline-Seq Project

AlkAniline-Seq workflow diagram showing chemistry-driven positive enrichment and N+1 read-start mapping for single-nucleotide m7G and m3C site calling

Overview

AlkAniline-Seq Overview: Single-Nucleotide m7G+m3C Mapping

Key takeaways:

Targets: m7G (N7-methylguanosine) and m3C (3-methylcytidine)
Resolution: single-nucleotide (N+1 read-start signal)
Mechanism: alkali/NaBH₄ → phosphatase → aniline cleavage → positive-enrichment ligation
Primary output: separate m7G and m3C site tables + N+1 pileup tracks

RNA modifications are widespread (150+ described), yet only a small subset has mature, high-throughput sequencing readouts. AlkAniline-Seq is a chemistry-based approach developed to enable high-throughput detection and single-nucleotide localization of m7G (N7-methylguanosine) and m3C (3-methylcytidine).

At a glance: What is AlkAniline-Seq?

AlkAniline-Seq is a sequencing workflow that uses sequential chemical processing to generate cleavage products whose Read1 starts at the N+1 nucleotide adjacent to a modified base. After alignment, 5'-end read-start counts indicate cleavage position and intensity, enabling site-level calling for m7G and m3C.

Applications

Use Cases: When to Choose AlkAniline-Seq for m7G+m3C

AlkAniline-Seq is a fit when your study needs site precision and chemical specificity:

Site-level maps of m7G and m3C across the transcriptome (high-throughput detection + single-nucleotide localization)
Structured RNA-centric projects: tRNA and rRNA mapping in cytosolic and mitochondrial compartments
Low-abundance RNA contexts, where positive enrichment helps concentrate informative fragments
Studies that benefit from one dataset yielding two modification outputs (m7G + m3C) from a single workflow

Common companion assays for biological interpretation include RNA-seq (expression context) and downstream validation workflows.

Principle

Method Principle: N+1 Read-Start Chemistry for m7G+m3C

AlkAniline-Seq uses three consecutive chemistry steps to create an alignment-readable signature:

Alkaline hydrolysis / lesion induction
Alkaline conditions promote formation of abasic (AP) sites associated with chemically labile positions (workflow descriptions include NaBH₄ handling in this step).
Broad 5' and 3' dephosphorylation (end normalization)
Alkaline phosphatase removes existing phosphate groups to convert fragment ends to 5'-OH and 3'-OH, reducing background from unrelated breaks and standardizing ends before enrichment.
Aniline cleavage at abasic sites (enrichment handle creation)
Aniline cleaves at abasic sites, exposing a 5'-phosphate on the N+1 nucleotide. Adapter ligation then preferentially captures these cleavage fragments, enabling positive enrichment of modification-proximal products.

Sequencing and readout

Illumina library construction produces dsDNA amplicons where Read1 begins at N+1, so mapped read-start pileups report cleavage position and strength.

AlkAniline-Seq method principle diagram showing N+1 read-start chemistry and positive enrichment for single-nucleotide m7G and m3C site mapping

Advantages

Advantages: Why AlkAniline-Seq vs Region-Level Enrichment Approaches

Single-nucleotide resolution

site calls are anchored by Read1 starts at N+1 rather than broad enrichment peaks.

Low-background design

positive enrichment focuses library complexity on cleavage-derived fragments, improving signal-to-noise.

Dual-mod output from one experiment

m7G and m3C can be distinguished in analysis to generate separate result tables from one workflow.

Broad RNA coverage

designed for multiple RNA classes, including tRNA/rRNA and compartment-specific mapping (cytosolic/mitochondrial).

Sensitivity logic

enrichment supports detection in contexts where low abundance makes site discovery difficult.

Workflow

Project Workflow: From Samples to m7G+m3C Site Tables

Wet-lab workflow

Project intake & study design (organism, RNA class focus, replicates/controls)
Sample QC (RNA quantity/quality review; acceptance criteria)
Sequential chemistry (alkali/NaBH₄ handling → phosphatase → aniline cleavage)
Adapter ligation & library construction
Illumina sequencing
Bioinformatics site calling and reporting for m7G and m3C

Recommended experimental design

Use biological replicates for condition comparisons
Include appropriate controls for interpretation of enrichment-derived signals (study-design dependent)
Pre-specify whether your primary focus is mRNA/lncRNA or tRNA/rRNA, and whether you need cytosolic vs mitochondrial stratification

Vertical workflow diagram of AlkAniline-Seq from sample intake and chemistry steps to Illumina sequencing and m7G/m3C site calling, with recommended study design tips.

Bioinformatics

Bioinformatics: N+1 Pileups, Site Calling

AlkAniline-Seq analysis is centered on:

Alignment to appropriate references
Quantification of 5'-end read-start pileups at N+1 positions
Generation of site-level tables and summary statistics for m7G and m3C

Bioinformatic Analysis Table (Standard vs. Advanced)

Module	Standard (Included)	Advanced (Add-on)
Data QC	Read quality checks, adapter/quality trimming	Cohort-level QC across many batches/conditions
Mapping	Alignment to appropriate references for the study scope	Specialized mapping strategies for structured RNAs (tRNA/rRNA-focused)
Signal extraction	5'-end read-start counts and N+1 signal metrics	Enhanced background modeling and signal normalization
Site calling	Candidate site tables with signal metrics and annotations	Differential site analysis across conditions/groups
Annotation	RNA biotype and feature annotation, summary distributions	Compartment- or RNA-class–stratified reporting (e.g., mitochondrial vs cytosolic)
Reporting	Tables + publication-ready summary figures	Customized visuals, integration guidance with RNA-seq

Sample

Sample Requirements: Accepted Sample Types, Inputs, and Shipping

Sample Requirement Table (Cells / Tissue / Fluid)

Sample Type	Input Requirement	Quality Notes
Cells	1 × 10⁷	Preserve RNA integrity; ship frozen on dry ice
Tissue	500 mg – 5 g	Fresh/frozen tissue; stabilize RNA (e.g., RNA protectant or TRIzol)
Purified RNA	100–300 µg	OD260/280 1.6–2.3; no obvious degradation
Other samples	Consult required	Other sample types are supported upon consultation; requirements depend on RNA yield and integrity

Shipping & storage

Ship samples in 1.5 mL tubes, sealed and packed on dry ice
Store at −80°C, avoid repeated freeze–thaw
Cells/fresh tissues may be processed in TRIzol or RNA preservative prior to freezing

Deliverables

Deliverables & Demo Outputs: What You'll Receive from AlkAniline-Seq

Core deliverables:

Separate m7G and m3C site tables (coordinates, signal metrics, annotations)
N+1 read-start pileup tracks for visualization and site inspection
QC summary (library complexity, mapping overview, replicate concordance)
Standard report + complete data package for downstream analysis

What the selected demo outputs demonstrate:

Clear N+1 read-start pileups supporting single-nucleotide site calls
Clean separation of m7G vs m3C results in separate deliverable tables
RNA-class composition summaries across tRNA/rRNA/mRNA/lncRNA
Replicate concordance views to support high-confidence site calls

IGV-style track view showing an N+1 read-start pileup used for single-nucleotide site calling in AlkAniline-Seq.

Side-by-side m7G and m3C site tables showing coordinates, signal metrics, and annotations from AlkAniline-Seq.

QC summary dashboard showing library complexity, mapping overview, RNA-class composition, and read length distribution for AlkAniline-Seq data.

Replicate concordance plots showing correlation of site-level signals for m7G and m3C calls in AlkAniline-Seq.

Comparison

Method Selection: AlkAniline-Seq vs m7G MeRIP vs TRAC-Seq

Decision Factor	AlkAniline-Seq	*RNA m7G Methylation Sequencing (MeRIP + Single-Base options)*	TRAC-Seq
Primary question it answers	"Where exactly are the m7G and m3C sites?"	"Where is m7G enriched (MeRIP) or where are m7G sites at base resolution (single-base option)?"	"Where are m7G sites in tRNA at single-base resolution?"
Typical resolution	Single-nucleotide (N+1 read-start signature)	Two modes: peak/region-level (MeRIP) or single-nucleotide (single-base option)	Single-nucleotide (tRNA-focused)
Key principle	Chemistry-driven positive enrichment via cleavage-derived ligation products	Two modes: antibody enrichment (MeRIP) or single-base route described in the service	tRNA-optimized single-base mapping workflow
Outputs	Separate m7G and m3C site tables from one workflow	m7G peaks (MeRIP) and/or m7G site-level outputs (single-base option)	tRNA m7G site list with tRNA-centric QC
Best-fit RNA classes	Broad RNA classes; structured RNAs supported	Broad (especially mRNA/lncRNA for MeRIP); single-base option positioned for internal m7G mapping	tRNA-first projects
When to choose	You want single-nucleotide m7G+m3C site maps from one workflow	You want either fast peak screening (MeRIP) or a single-base m7G option within one service family	You need tRNA-specialized depth and analysis

Quick selection rules

Choose AlkAniline-Seq for single-nucleotide m7G+m3C mapping with a positive-enrichment readout.
Choose RNA m7G Methylation Sequencing when your decision is between peak screening (MeRIP) and an m7G single-base option.
Choose TRAC-Seq when the biology is tRNA-centric.

Case

Case Study

Chemical Sequencing Maps tRNA Modifications in Vibrio cholerae

Paper: Hardy et al., 2025, PLOS Genetics — "The tRNA epitranscriptomic landscape and RNA modification enzymes in Vibrio cholerae."

Background

The study built the first experimentally validated, genome-wide map of tRNA modifications in V. cholerae to connect RNA chemical marks with translation regulation, stress responses, and pathogen biology.

Methods

They combined chemical sequencing approaches with a panel of targeted gene deletions to assign modifications to enzymes across tRNA isodecoders. Notably, parts of their RT-signature analysis leveraged existing datasets including AlkAnilineSeq.

Results

Generated a comprehensive, isodecoder-level tRNA modification map that can serve as a reference framework.
Demonstrated that several modifications are dispensable in basal growth but become important under proteotoxic stress, affecting translation fidelity measures such as stop-codon readthrough.

Conclusion

This study illustrates why base-precise chemical sequencing is increasingly used to map modification landscapes and connect them to genotype and phenotype—exactly the type of question AlkAniline-Seq is designed to support.

Mapping of m7G/D/ho5C by AlkAnilineSeq. (from paper Figure 2).

FAQ

FAQ: AlkAniline-Seq Service Questions

References

Marchand, Virginie, et al. "AlkAniline-Seq: Profiling of m7G and m3C RNA Modifications at Single Nucleotide Resolution." Angewandte Chemie International Edition, vol. 57, no. 51, 2018, pp. 16785–16790.
Marchand, Virginie, et al. "AlkAniline-Seq: A Highly Sensitive and Specific Method for Simultaneous Mapping of 7-Methyl-guanosine (m7G) and 3-Methyl-cytosine (m3C) in RNAs by High-Throughput Sequencing." In RNA Modifications, edited by Mary McMahon, Methods in Molecular Biology, vol. 2298, Humana, 2021, pp. 77–95.
Zhang, Li Sheng, et al. "Transcriptome-wide Mapping of Internal N7-Methylguanosine Methylome in Mammalian mRNA." Molecular Cell, vol. 74, no. 6, 2019, pp. 1304–1316.e8.
Pandolfini, Luca, et al. "METTL1 Promotes let-7 MicroRNA Processing via m7G Methylation." Molecular Cell, vol. 74, no. 6, 2019, pp. 1278–1290.e9.
Hardy, Léo, et al. "The tRNA Epitranscriptomic Landscape and RNA Modification Enzymes in Vibrio cholerae." PLOS Genetics, vol. 21, no. 10, 31 Oct. 2025, e1011937.
Sordyl, Dominik, et al. "MODOMICS: a Database of RNA Modifications and Related Information. 2025 Update and 20th Anniversary." Nucleic Acids Research, 24 Nov. 2025, gkaf1284.
Lin, Shuibin, et al. "Nucleotide Resolution Profiling of m7G tRNA Modification by TRAC-Seq." Nature Protocols, vol. 14, no. 11, 2019, pp. 3220–3242.