RNA Sequencing

The transcriptome is comprised of different populations of RNA molecules, including mRNA, rRNA, tRNA, and other non-coding RNA (such as microRNA, lncRNA). RNA sequencing (RNA-Seq) using next-generation sequencing (NGS) technology allows us to profile the entire transcriptome, including both the coding and the noncoding regions. It also aids in the identification of genes that are differentially expressed in distinct cell populations and provides information on their relative abundance. In addition, it allows us to determine the effects of genetic variant splicing events, identify novel transcripts, detect gene fusions, isoforms and other structural variants, and call single nucleotide variants (SNVs).

• Enables the evaluation of RNA expression levels, variant splicing events, single nucleotide polymorphisms (SNPs), and insertion deletion (indels) events throughout the transcriptome
• Provides the flexibility to evaluate specific populations of RNA through different technologies (targeted RNA sequencing, enrichment of mRNA or depletion of rRNA)
• Facilitates the detection of low-abundance transcripts and isoforms through targeted sequencing

Selecting the optimal RNA-Seq workflow for your experiment is dependent on many factors, including sample quality, input amount and the transcripts of interest. RNA-Seq workflows that are currently supported can be grouped into three main categories:

Whole transcriptome sequencing involves the measurement of the complete complement of transcripts in a sample, at a given time. Through interrogation of the whole transcriptome, researchers are able to determine global expression levels of each transcript (both coding and non-coding) and identify exons, introns and their junctions.

Find out more about the following RNA-Seq applications:

• Whole transcriptome sequencing from degraded inputs
• Selective transcript depletion

Sequencing of mRNA transcripts opens the door to identifying the molecular basis of phenotypic differences between biological or experimental conditions. Gene expression analysis provides valuable insights beyond the static information of a genome sequence by allowing researchers to detect and quantify the expression levels of multiple coding transcripts.

For Research Use Only. Not for use in diagnostic procedures.