3' TAG-Seq Service (High-Throughput DGE)

Why Choose 3' TAG-Seq? Focusing on What Matters

Standard RNA-Seq is a powerful discovery tool, capturing the entire transcript to reveal splice variants, fusion genes, and SNPs. However, for 90% of comparative transcriptomics studies, the primary goal is simply to answer: "How much is this gene expressed?"

3' TAG-Seq applies the Pareto Principle to genomics. By sequencing only the 3' end of the transcript, we generate one read per transcript molecule. This creates a direct 1:1 proxy for gene abundance, eliminating the bias introduced by gene length and RNA fragmentation.

Key Strategic Advantages:

• Maximize Statistical Power: By reducing the cost per sample, you can afford to sequence more biological replicates. In differential expression analysis, increasing replicates (N) contributes more to statistical power than increasing sequencing depth.
• Robustness to Degradation: Unlike full-length protocols that require high Integrity Numbers (RIN > 7), TAG-Seq tolerates degraded RNA (e.g., FFPE, field samples) because it does not require the 5' end of the molecule to be intact.
• Simplified Bioinformatics: Data analysis is streamlined. Without the need for de novo assembly or isoform deconvolution, the computational pipeline is faster and less prone to mapping ambiguity.

TAG-Seq vs. Other RNA Sequencing Methods: Making the Right Choice

Choosing the right transcriptomics tool is critical for your budget and scientific outcomes. While TAG-Seq is the champion of quantification, it is not a "one-size-fits-all" solution. Use the guide below to determine the best fit for your specific biological question.

Performance Data: Accuracy Verification

We understand that adopting a cost-effective method raises concerns about accuracy. Is TAG-Seq as good as standard RNA-Seq?

Internal Validation Data:

We performed a side-by-side comparison using human HEK293T cell RNA.

• Method A: Standard Poly(A) mRNA-Seq (PE150, 20M reads).
• Method B: CD Genomics 3' TAG-Seq (SE75, 4M reads).

Results:

The Pearson correlation coefficient (R) for gene counts between the two methods was > 0.96. Furthermore, TAG-Seq showed lower technical variance across replicates for low-abundance genes, likely due to the reduction of length-bias noise.

Service Features and Sample Requirements

Frequently Asked Questions

• • Q: How does 3' TAG-Seq differ from standard RNA-Seq regarding data accuracy and gene detection?

    • A: 3' TAG-Seq (such as QuantSeq) is highly accurate for gene abundance quantification, showing a correlation of over 0.95 with standard sequencing. While standard RNA-Seq sequences the entire transcript to reveal splicing variants and mutations, TAG-Seq generates one read per transcript molecule by targeting the 3' UTR. This eliminates gene length bias, allowing for direct digital counting (CPM) rather than length-normalized quantification (FPKM/TPM), making it the superior choice for high-throughput screening where quantification is the priority.

• • Q: Can TAG-Seq be used for degraded RNA samples (e.g., FFPE)

    • A: Yes, 3' TAG-Seq is exceptionally robust for analyzing degraded RNA, including Formalin-Fixed Paraffin-Embedded (FFPE) samples. Since the protocol captures only the 3' end of the mRNA—which often remains intact even when the rest of the transcript is fragmented—it allows for high-quality library generation from samples with low RIN scores that would otherwise fail standard poly(A) selection protocols.

• • Q: Is a reference genome required for analyzing 3' TAG-Seq data

    • A: Yes, a well-annotated reference genome is essential. Because TAG-Seq reads are short (SE75/SE100) and located specifically at the 3' UTR, they lack the sequence continuity required for de novo assembly. If your organism lacks a reference genome, mapping efficiency will be extremely low; in such cases, we recommend our Eukaryotic De Novo mRNA Sequencing service to first build a transcriptome assembly.

• • Q: Why is 3' TAG-Seq considered more cost-effective for large-scale drug screening?

    • A: 3' TAG-Seq significantly reduces project costs by requiring much lower sequencing depths (3-5 million reads per sample vs. 20+ million for standard RNA-Seq) to achieve the same statistical power. This efficiency allows for higher multiplexing on a single lane, enabling researchers to include more biological replicates, which drastically improves the statistical validity of differential gene expression (DGE) analysis in large toxicology or drug screening cohorts.

• • Q: Can 3' TAG-Seq detect alternative splicing, novel isoforms, or novel genes?

    • A: Generally, no. TAG-Seq is designed to quantify known genes annotated in a reference genome. It cannot distinguish between isoforms if they share the same 3' UTR, nor can it detect splice variants occurring at the 5' end or middle of the transcript. For comprehensive structural analysis or novel gene discovery, please use Eukaryotic mRNA Sequencing with Reference.

• • Q: Is TAG-Seq suitable for bacterial or prokaryotic samples?

    • A: No. The standard TAG-Seq library preparation relies on oligo(dT) priming to capture the eukaryotic poly(A) tail. Since bacteria generally lack stable poly(A) tails, this method will not work. For bacterial transcriptomics, please refer to our Prokaryotic mRNA Sequencing service, which utilizes rRNA depletion methods compatible with prokaryotic RNA.

• • Q: Can I compare TAG-Seq data with my previous standard RNA-Seq data?

    • A: Yes, but with caution. While the biological correlation is typically high (Pearson R > 0.9), the absolute quantification units differ; TAG-Seq uses CPM (Counts Per Million) because it is length-independent, whereas standard RNA-Seq often uses FPKM/TPM (length-normalized). To merge these datasets for joint analysis, we strongly recommend performing batch-effect correction (e.g., using ComBat or Limma) to remove technical biases before conducting differential expression analysis.

• • Q: What is the recommended sequencing depth for a typical TAG-Seq experiment?

    • A: For most digital gene expression (DGE) studies in humans, mice, or plants, we recommend a sequencing depth of approximately 3 to 6 million single-end reads per sample. This depth is sufficient to reach saturation for quantifying known genes, providing comparable sensitivity to much deeper standard RNA-Seq runs without the associated high costs.

References:

1. Ma, F., Fuqua, B.K., Hasin, Y. et al. A comparison between whole transcript and 3' RNA sequencing methods using Kapa and Lexogen library preparation methods. BMC Genomics 20, 9 (2019).
2. Schuierer, S., Caraznin, M., Bignell, G.R. et al. A comprehensive assessment of RNA-seq protocols for degraded RNA samples. BMC Genomics 18, 423 (2017).
3. Corley, S.M., et al. Differentially expressed genes from RNA-Seq and functional enrichment results are affected by the choice of library preparation protocols. BMC Genomics 20, 1152 (2019).
4. Tandonnet, S. & Torres, T.T. Traditional versus 3' RNA-seq in a non-model species. Genomics Data 11, 9–16 (2017).
5. Xiong, Y., et al. RNA-seq of 272 human heart tissue samples: efficient and cost-effective detection of differential expression. Scientific Reports 7, 1–10 (2017).