TRAC-Seq Service for Single-Nucleotide tRNA m⁷G Mapping
CD Genomics' TRAC-Seq service provides high-resolution tRNA m⁷G sequencing to map N⁷-methylguanosine at single-nucleotide resolution across the tRNA transcriptome.
By applying tRNA reduction and cleavage sequencing, we generate unbiased tRNA m⁷G profiles that help you link RNA methylation to translational control, stress responses, and disease models.
- Chemistry-based, antibody-free TRAC-Seq minimizes pull-down bias and provides precise tRNA m7G site calls at single-nucleotide resolution.
- Small-RNA enrichment and optimized library preparation improve coverage of structured tRNAs and increase confidence in low-abundance modifications.
- Our end-to-end TRAC-Seq solution, from RNA extraction to bioinformatics, removes in-house optimization burden so your team can move from raw reads to interpretable tRNA m⁷G insights in a single project.
What Is TRAC-Seq
TRAC-Seq, also written TRAC-seq, is a tRNA reduction and cleavage sequencing method designed to map N7-methylguanosine (m7G) on tRNA at single-nucleotide resolution. In our TRAC-Seq service, we use chemical reactions rather than antibodies to detect m7G, so you obtain site-specific information on tRNA methylation instead of broad enrichment peaks.
In practical terms, TRAC-Seq is a specialized tRNA m7G sequencing approach. It focuses on transfer RNA, identifies which guanosine residues carry m7G, and quantifies how strongly each site is modified across your samples or conditions. This makes TRAC-Seq particularly useful when you want to link m7G tRNA modification to translation efficiency, codon usage, or disease-related changes.
TRAC-Seq works by selectively converting m7G into a chemical signal that can be read by next-generation sequencing. Demethylation and cleavage reactions generate characteristic ends at m7G-containing positions, which are then captured in the sequencing library. When the reads are aligned back to tRNA genes, these signals reveal the exact sites and relative levels of tRNA m7G modification.
Key questions answered by TRAC-Seq
- What is TRAC-Seq used for?
TRAC-Seq is used to profile tRNA m7G sites at single-nucleotide resolution and compare their modification levels across biological conditions.
- What does TRAC-Seq measure?
It measures the location and relative abundance of m7G on tRNA molecules, providing a detailed tRNA m7G map for each sample.
- How is TRAC-Seq different from general m7G RNA methylation sequencing?
TRAC-Seq is tRNA-focused, antibody-independent, and site-resolved, while general m7G RNA methylation sequencing often targets many RNA types with lower positional resolution.
Why Use TRAC-Seq for tRNA m7G Studies
TRAC-Seq is most useful when you need site-resolved, quantitative information on tRNA m7G, rather than a broad view of RNA methylation.
Instead of surveying many RNA types at once, TRAC-Seq concentrates on tRNA and reports which positions are modified and how strongly in each condition. This makes it well suited for projects that aim to connect tRNA m7G to translation control, codon usage, or pathway activation in disease models.
Because the chemistry is optimized for structured tRNAs, you gain high coverage per tRNA species and more stable estimates of modification levels. This is important when you are comparing subtle changes, for example between wild-type and METTL1/WDR4-perturbed cells, treatment versus control, or early versus advanced disease stages.
For many translational research teams, TRAC-Seq is a decision tool: it helps you see which tRNAs are affected, which codons are most impacted, and which mRNAs are likely to change in translation, before committing to larger mechanistic or in vivo studies.
When TRAC-Seq is a good fit
- You need single-nucleotide tRNA m7G maps rather than peak-level signals.
- You want to rank tRNAs by change in m7G across treatments or genotypes.
- You plan to overlay tRNA m7G data with RNA-seq or Ribo-seq to interpret shifts in translation.
- You prefer a chemistry-based tRNA methylation readout to simplify cross-study comparison.
Applications of TRAC-Seq
TRAC-Seq is designed for studies where tRNA m7G is not just a marker but a regulatory variable you want to quantify and interpret. Typical TRAC-Seq applications range from basic epitranscriptomic profiling to disease-focused projects that link N7-methylguanosine tRNA modification to translational outcomes.
Global tRNA m7G landscape profiling
TRAC-Seq can be used to build baseline tRNA m7G maps in key cell types, tissues, or model systems.
You can compare:
- Different tissues or developmental stages
- Primary cells versus immortalized lines
- Normal versus transformed or drug-treated samples
Our analysis reports the distribution of m7G across tRNA families, isoacceptors, and isodecoders, giving you a structured view of the tRNA epitranscriptome instead of a single global metric.
Writer enzyme and pathway perturbation studies
Many groups use TRAC-Seq to characterize the impact of writer enzymes such as METTL1 and WDR4 or upstream signaling pathways. Typical designs include knockdown, knockout, overexpression, or inhibitor treatment.
- You can pinpoint which tRNAs lose or gain m7G after perturbation.
- You can stratify tRNAs by the magnitude of change and link them to their decoded codons.
Our integrated TRAC-Seq data processing and statistical pipeline converts raw sequencing reads into ranked lists of affected tRNAs and codon sets, helping you quickly identify the most relevant candidates for downstream functional experiments.
Cancer and disease research
In oncology and disease modelling, TRAC-Seq supports projects that investigate how altered tRNA m7G contributes to selective translation of oncogenic or stress-response mRNAs. Examples include:
- Comparing tumor versus matched non-tumor tissues or cell models
- Studying chemoresistance by profiling tRNA m7G before and after drug exposure
- Examining how pathway activation (for example WNT/β-catenin signaling) coincides with changes in specific m7G-modified tRNAs
By combining TRAC-Seq tRNA m7G results with your existing pathway and phenotypic data, you can move from descriptive observations to mechanistic hypotheses about how translation is rewired in disease.
Codon usage and translational reprogramming
TRAC-Seq is also suited to studies focused on codon usage bias and translational efficiency. When combined with RNA-seq or ribosome profiling, tRNA m7G data can be used to:
- Identify mRNAs enriched for codons decoded by m7G-modified tRNAs
- Test whether changes in tRNA m7G correlate with altered translation of specific gene sets
- Prioritize codon patterns and pathways that are most sensitive to tRNA methylation changes
Our reporting includes matrices and summary tables that can be plugged directly into your codon usage or translation models, reducing the time your team spends on data wrangling.
Dynamic responses: stress, differentiation, and treatment time courses
Because the assay is compatible with cells, tissues, and purified RNA, TRAC-Seq can be applied to time-course experiments that track how tRNA m7G responds to:
- Cellular stress (for example oxidative stress, nutrient deprivation)
- Differentiation or reprogramming protocols
- Drug treatment or withdrawal
This allows you to capture not only which sites are modified, but also how quickly and in what direction those modifications change over time, supporting more nuanced models of tRNA-driven regulation.
TRAC-Seq Workflow – From Sample to Data
This section explains how our TRAC-Seq tRNA m7G sequencing service runs in practice, from the moment your samples arrive to the point you receive analyzed results.
1. Sample receipt and RNA preparation
We accept cells, tissues, or purified RNA and register each sample under standard SOPs. Our team extracts or verifies total RNA and checks integrity, so downstream tRNA m7G measurements are based on reliable input material.
2. tRNA-focused chemistry
After QC, we enrich the small-RNA fraction to increase the proportion of tRNA reads. TRAC-Seq-specific chemistry prepares tRNAs for modification readout, supporting efficient cDNA synthesis from structured tRNAs and robust detection of tRNA m7G sites..
3. Library construction and sequencing
Prepared RNA fragments are converted into indexed libraries on an Illumina-compatible platform. We select read length and depth to match your study design, balancing sensitivity for low-abundance tRNAs with efficient use of sequencing capacity.
4. Primary data processing
Raw reads are filtered, trimmed, and aligned to curated tRNA references. Position-specific signals are quantified to generate site-level tRNA m7G metrics that can be integrated directly into your internal analysis pipelines.
5. Project-level review and delivery
We review mapping quality, library complexity, and signal distribution before release. You receive raw data, processed files, and summary reports, allowing your team to move straight to interpretation and follow-up experiments.
Sample Requirements and Supported Species
To obtain reliable TRAC-Seq tRNA m7G data, please follow the sample guidelines below. This layout is designed for quick on-page scanning and easy copying into lab SOPs.
1. Accepted sample types
We can start your TRAC-Seq project from:
- Cultured cells
- Adherent or suspension cells
- Tissue samples
- Fresh or frozen tissues (e.g., tumor, normal, model tissues)
- Purified total RNA
- Column- or TRIzol-extracted RNA that retains the tRNA fraction
If your material is different (e.g., small biopsies, FFPE alternatives), contact us to discuss feasibility.
2. Recommended input amounts and RNA quality
Per sample, we recommend at least:
- Cells: ≥ 2 × 10⁷ cells
- Tissue: ≥ 100 mg
- Total RNA: ≥ 50 µg, with RIN ≥ 6
More input is helpful for:
- Low-yield tissues
- Highly heterogeneous samples
- Projects where you expect subtle differences in tRNA m7G
3. Storage and shipping
Please ensure consistent handling across all groups in your study.
Cells and tissues
- Stabilize in TRIzol or another RNA protectant when possible
- Snap-freeze and store at −80 °C
- Ship on dry ice in sealed, clearly labelled 1.5 mL tubes
Purified RNA
- Dissolve in RNase-free water or buffer
- Store at −80 °C
- Avoid repeated freeze–thaw cycles
- Ship on dry ice in well-sealed tubes
4. Supported species
Our TRAC-Seq workflow is routinely used for:
- Human
- Mouse
- Rat
We can often support other mammalian or model species after checking genome and tRNA annotations. For non-routine species, a brief pre-project consultation is recommended.
5. Quick checklist before shipping
Before you send samples, please verify:
- ✅ Input amount meets or exceeds the minimum for cells, tissue, or RNA
- ✅ RNA integrity is acceptable (RIN ≥ 6 or visibly intact on a gel or trace)
- ✅ Labels are clear (sample ID, species, sample type, treatment group)
- ✅ All tubes are sealed and packed on dry ice for transit
Following this checklist helps us generate consistent TRAC-Seq tRNA m7G data and shortens the time from sample receipt to report delivery.
TRAC-Seq Sequencing Strategy
Our TRAC-Seq tRNA m7G sequencing strategy is designed to generate enough depth for confident site-level analysis while keeping run costs reasonable.
Platforms and read design
We typically run TRAC-Seq libraries on Illumina high-throughput platforms with short-read chemistry:
- Read type: single-end or paired-end, selected according to your study design and downstream analysis plans
- Read length: usually 50–150 bp, sufficient to capture tRNA-derived fragments and map them uniquely to tRNA references
This configuration balances accurate mapping of structured tRNA fragments with efficient use of sequencing capacity.
Depth tailored to project goals
Per-sample depth is matched to your biological questions:
- Baseline tRNA m7G profiling: moderate depth to define site positions and relative occupancy
- Complex cohorts or subtle effects: higher depth to support robust statistics and group comparisons
By tuning depth rather than applying a one-size-fits-all rule, we help you obtain reliable tRNA m7G estimates without overspending on redundant reads.
TRAC-Seq Data Analysis and Deliverables
Our TRAC-Seq bioinformatics workflow turns raw sequencing reads into interpretable tRNA m7G maps and comparison results.
- Read trimming and QC
- Mapping
- Chemical cleavage detection
-
tRNA m7G site calling, including:
- tRNA / site-level quantification
- Differential analysis
- Motif analysis
- Integrative analysis with RNA-seq / Ribo-seq
You typically receive:
- Site-level results table
- tRNA-level summary matrix
- Differential analysis output
- Motif analysis file
- QC and analysis summary
Example TRAC-Seq cleavage score profile
Example motif enrichment table for TRAC-Seq m⁷G sites
Example volcano plot of differential tRNA m⁷G modification
Quality Control and Study Design Support
Robust TRAC-Seq tRNA m7G results depend on both data quality and a sound experimental plan. We help on both fronts.
Key QC checkpoints
- RNA integrity and input amount
- Library complexity and duplication
- Mapping performance to tRNA references
- Reproducibility across biological replicates
Recommended study design
- ≥ 3 biological replicates per condition where possible
- Clear control group (e.g., wild-type, vehicle, baseline)
- Defined perturbation (e.g., METTL1/WDR4 knockdown, drug treatment, time points)
- Option to pair TRAC-Seq with RNA-seq or Ribo-seq for translation-focused projects
Support we provide
- Feedback on sample plans before project start
- Suggestions on replicate numbers and contrasts
- QC-based comments to guide data interpretation and follow-up experiments
Choosing the Right m⁷G Sequencing Method
TRAC-Seq is one of several options for studying m⁷G RNA methylation. The best choice depends on whether you need tRNA-specific, single-nucleotide information or broader transcriptome coverage.
| Comparison Dimensions | m7G Mass Spectrometry | m7G-MeRIP-seq | TRAC-seq |
|---|---|---|---|
| Core Principle | Mass spectrometry detects the molecular mass of m7G characteristic molecules | Antibody capture of modified RNA + high-throughput sequencing | Chemical-specific cleavage + library construction for localization |
| Spatial Resolution | Nucleic molecule level (no transcriptome localization) | Regional level (100-200nt) | Single-base level |
| Quantitative Ability | Accurate, absolute quantification | Relative quantification of modification enrichment | Site-level relative quantification |
| Coverage Range | Sample-wide level | Whole transcriptome RNA (global) | tRNA |
| Key Advantages | High specificity, absolute quantification, no antibody dependence | Whole transcriptome site screening | Single-base resolution, high specificity, no antibody dependence |
| Main Limitations | Inability to localize modification sites | Low resolution, dependence on antibody specificity | Low throughput, high cost, high technical requirements, high sample volume demand |
| Core Application Scenarios | Modification verification and total level quantification | Whole transcriptome modification profiling, differential site screening | Fine site analysis, modification heterogeneity analysis |
Case Study
Why Choose CD Genomics for TRAC-Seq
Specialized experience in RNA modifications
We have extensive hands-on experience with tRNA and RNA methylation assays, which helps reduce failed libraries and gives more interpretable tRNA m7G datasets.
Integrated lab and bioinformatics team
Wet-lab scientists and bioinformaticians work together from the start of your project, so sample handling, sequencing settings, and analysis plans are aligned with your biological questions.
Project-level consultation, not just sequencing
We support you in refining groups, replicate numbers, and optional companion assays, helping TRAC-Seq become a decision-making tool rather than a standalone dataset.
Clear communication and transparent QC
Dedicated project contacts and concise QC summaries keep you informed at each key stage, making it easier to explain the data internally and plan the next experiments.
TRAC-Seq Service FAQs
References
- Lin, S., Liu, Q., Lelyveld, V.S. et al. Mettl1/Wdr4-mediated m7G tRNA methylome is required for normal mRNA translation and embryonic stem cell self-renewal and differentiation. Molecular Cell 71, 244–255.e5 (2018).
- Lin, S., Liu, Q., Jiang, Y.-Z. & Gregory, R.I. Nucleotide resolution profiling of m7G tRNA modification by TRAC-Seq. Nature Protocols 14, 3220–3242 (2019).
- Dai, Z., Liu, H., Liao, J. et al. N7-methylguanosine tRNA modification enhances oncogenic mRNA translation and promotes intrahepatic cholangiocarcinoma progression. Molecular Cell 81, 3339–3355.e8 (2021).
- Ma, J., Han, H., Huang, Y. et al. METTL1/WDR4-mediated m7G tRNA modifications and m7G codon usage promote mRNA translation and lung cancer progression. Molecular Therapy 29, 3422–3435 (2021).
- Han, H., Yang, C., Ma, J. et al. N7-methylguanosine tRNA modification promotes esophageal squamous cell carcinoma tumorigenesis via the RPTOR/ULK1/autophagy axis. Nature Communications 13, 1478 (2022).
- Zhang, X., Zhu, W.-Y., Shen, S.-Y. et al. Biological roles of RNA m7G modification and its implications in cancer. Biology Direct 18, 58 (2023).
- Wu, Q., Fu, X., Liu, G. et al. N7-methylguanosine modification in cancers: from mechanisms to therapeutic potential. Journal of Hematology & Oncology 18, 12 (2025).