QuantSeq is a robust and simple mRNA sequencing method. It highly suitable for differential gene expression analysis. At lower read depths, such focus on the 3' end results in higher stability of differential gene expression measurements. QuantSeq is ideal for increasing the degree of multiplexing in NGS gene expression experiments and is the method of choice for accurately determining gene expression at the lowest cost. And QuantSeq 3'mRNA Seq is more effective in building a database for low-quality samples than other mRNA sorting processes using Poly (A).
Alternative polyadenylation (APA) of mRNAs will result in 3 ' UTR isomers. For a specific gene, it not only produces multiple subtypes of this gene, but also ' There are cis regulatory elements in UTR, which will also affect the regulation of this transcript. QuantSeq 3'mRNA seq is enriched in mRNA 3'CDS and UTR regions, so it can be used for APA researchers to study the regulation of miRNA, the stability and localization of mRNA, and the translation of mRNA in more detail.
QuantSeq 3'mRNA Seq shows its unique advantages in many aspects, especially in gene expression analysis, 3'UTR and APA site analysis and other aspects. Therefore, QuantSeq 3'mRNA Seq is the best choice for RNA sequencing research applications that focus on the above points.
Fig1. The QuantSeq workflow (Moll, 2014)
One of the main advantages of this method is its targeted enrichment of the 3' end of mRNA, which enables efficient sequencing of mRNA and accurate quantification of gene expression levels. In addition, the library construction for QuantSeq 3' mRNA sequencing is relatively simple and involves fewer steps compared to other RNA sequencing methods. QuantSeq 3' mRNA sequencing is a highly cost-effective method that requires only small amounts of starting material, making it an ideal choice for studies with limited samples or budgets, and detecting alternative polyadenylation (APA) sites with high efficiency.
Library Construction of Our QuantSeq 3' mRNA Sequencing Service
|High Compatibility||Low Noise||High Accuracy||More Flexible|
|Less sensitive to RNA sample quality/ integrity variations.||Low noise gene expression profiling.||Just one fragment per transcript is produced; therefore, no length normalization is required.||Much higher dynamic range compared to microarrays.|