QuantSeq provides an easy protocol to generate highly strand-specific next-generation sequencing (NGS) libraries close to the 3′ end of polyadenylated RNAs within 4.5 h. Only one fragment per transcript is generated, directly linking the number of reads mapping to a gene to its expression. QuantSeq reduces data analysis time and enables a higher level of multiplexing per run. QuantSeq is the RNA sample preparation method for accurate and affordable gene expression measurement.
With the rapid development of NGS technologies, RNA-seq has become the new standard for transcriptome analysis. Although the price per base has been substantially reduced, sample preparation, sequencing and data processing are major cost factors in high-throughput screenings. QuantSeq reduces the expenditures in these areas.
Only One Read per Molecule
The key feature of QuantSeq is that "One molecule of transcript is sequenced only one time". This means that you can get direct measurements of the expression level simply by counting the number of sequence reads of the genes. Together with massive data generated by NGS, both qualitative and quantitative analysis of the transcriptome is possible with QuantSeq.
Digital Expression Profiling
QuantSeq provides RNA-Seq library for 3’ end sequencing on major NGS platforms. Earlier, this kind of approach was only possible by massive SAGE (Serial Analysis of Gene Expression) or EST (Expressed Sequence Tag) sequencing. However, thanks to Lexogen's proprietary technology used in QuantSeq and development of NGS, digital gene expression has become easier and more affordable than ever before.
High Level of Multiplexing
Sequencing of generic RNA-Seq libraries tend to generate vast amount of information about the sample, most of which is often unnecessary. If you are not interested in detection of splice variants or junctions, a large portion of the data generated is simply wasted, and these sequence reads could have been better used for your sole objective of expression profiling, such as by multiplexing more samples or getting more reads to detect low-abundant transcripts.
QuantSeq is specifically designed for this purpose. Up to 96 different barcodes are available for this high level of multiplexing to accommodate as many samples as possible in one lane of sequencers.
Pico-level of input RNA amount
As low as 500pg of total RNA is enough for input amount. Poly(A) selection is already included in the protocol and additional Ribo-depletion is not required. As a result, QuantSeq has very good applicability for most of the samples, even for FFPE or near-single-cell samples.
Easy Data Analysis
The sequence data from QuantSeq does not require any read-number normalization (RPKM or FPKM) where huge portion of computational resources is consumed. Everything finishes simply by counting the reads; there is no need for highly sophisticated analytic tools.