RNA sequencing (RNAseq) is a common tool for measuring relative gene expression levels. It is a versatile method that can be utilized to detect and characterize gene expression, mutations, gene fusions, and noncoding RNAs. Targeted RNA sequencing is a highly accurate method for selecting and sequencing specific transcripts of interest that offer both quantitative and qualitative information. Targeted RNA sequencing is a sequencing-based gene expression profiling method that allows quantification of messenger RNA (mRNA) or targeted non-coding RNA (ncRNA) levels for up to 300 customer-defined genes in a single amplification reaction. The ability to target a portion of the genome has revolutionized next-generation sequencing experiments. The analysis of exomes has exploded, custom panels for exome-style pull-down are being used to great effect for analyzing several thousand samples, and amplicon analysis is making it possible to run these samples in a single experiment.
Targeted RNA Sequencing Market: Dynamics
Targeted RNAseq is increasingly the method of choice for researchers studying the transcriptome. The clinical potential of RNAs as disease and treatment markers is fueling advances in RNA analysis methods. It offers numerous advantages over gene expression arrays as it is compatible with difficult samples such as formalin-fixed paraffin embedded (FFPE) tissue, requires a low input of 10ng of total RNA or 20 – 100ng of FFPE RNA. It can also be applied to any species, even if reference sequencing is not available. Broader dynamic range enables more sensitive and accurate measurement of gene expression and a better value often delivering advantages at a comparable or lower price per sample than many arrays which are costlier in comparison to these. But as RNA sequencing data accumulates, and researchers compare findings, standardization has become a major issue. To assess RNA sequence performance across laboratories, the U.S. Food and Drug Administration has coordinated the international Sequencing Quality Control (SEQC) project. The findings offer guidance for interpreting data, such as being cautious about using RNAseq for absolute quantitation.