QuantSeq technology- offers a streamlined, cost-effective alternative to whole transcriptome sequencing (WTS), primarily by focusing sequencing reads on the 3' ends of polyadenylated transcripts.
The following are the key advantages of QuantSeq over whole transcriptome sequencing:
Cost-Efficiency and High Throughput
Lower Sequencing Depth: QuantSeq generates only one fragment per transcript, meaning fewer reads are required to accurately quantify gene expression compared to WTS, which covers the entire transcript length.
Increased Multiplexing: Because fewer reads are needed per sample, many more samples (up to 36,864 with certain versions) can be pooled and sequenced in a single run, significantly reducing the cost per sample.
No Length Normalisation: Since only one read is produced per transcript, the number of reads is directly proportional to the gene's expression, eliminating the need for length-based normalisation (e.g., FPKM/TPM).
Superior Performance with Challenging Samples
Degraded RNA and FFPE: QuantSeq is highly robust and performs well with low-quality or degraded RNA, such as that from Formalin-Fixed Paraffin-Embedded (FFPE) tissues. Because the method targets the 3' end near the poly(A) tail, it is less affected by fragmentation than full-length protocols.
Low Input Requirements: The technology can handle very low amounts of total RNA, with input requirements starting as low as 0.5 ng to 1 ng. 3.
Streamlined Workflow and Analysis
Faster Turnaround: The entire library preparation can be completed in approximately 4.5 hours with less than 2 hours of hands-on time.
No Pre-processing: It does not require prior mRNA enrichment or ribosomal RNA (rRNA) depletion, as it uses oligo(dT) priming to directly target polyadenylated mRNA from total RNA.
Simplified Data Analysis: With fewer reads to map and no isoform-level complexity, mapping and quantification are significantly faster and require less computational power than WTS.
Technical Precision
Strand Specificity: QuantSeq maintains high strand specificity (>99.9%), which allows for the accurate identification and quantification of antisense transcripts and overlapping genes.
Reduced Bias: Focusing on a single end of the transcript avoids the gene length bias often seen in whole transcriptome methods, where longer transcripts typically attract more reads.
