RNA Exome Capture Sequencing

Introduction to RNA Exome Capture Sequencing

Achieve cost-effective, accurate, and sensitive RNA exome analysis of even difficult samples without sacrificing gene fusion discovery power. Many RNA exome sequencing methods focus on a defined number of known transcripts or require expensive deep sequencing. RNA exome capture sequencing overcomes these challenges by combining RNA-Seq with exome enrichment.

This method captures only the coding regions of the transcriptome, allowing higher throughput and requiring lower sequencing depth than non-exome capture methods. Sequence-specific capture of the RNA exome does not rely on the presence of a poly-A tail. This makes RNA exome capture sequencing ideal for RNA-Seq with low-quality samples or limited starting material.

Advantages of RNA Exome Capture Sequencing

Isolating transcriptome coding regions maximizes discovery power at a fraction of the read depth of total RNA sequencing.

  • Enables high sample throughput and cost efficiency
  • Focuses on high-value content for affordability
  • Achieves high-quality data from degraded samples, including formalin-fixed, paraffin-embedded (FFPE) tissues
  • Requires low sample input (as little as 10 ng total RNA) while maintaining high sensitivity
Transcriptome Analysis with NGS

Illumina Distinguished Scientist Dr. Gary Schroth demonstrates the latest advances in RNA-Seq technology. Learn how the latest RNA library prep methods allow transcriptome analysis from challenging samples, like single cells or FFPE tissues.

View Video

RNA Exome Capture Sequencing Workflow

In addition to industry-leading data quality, Illumina offers integrated RNA exome capture workflows that simplify the entire process, from library preparation to data analysis and biological interpretation.

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Additional Resources

NextSeq 1000 and NextSeq 2000 single-cell RNA sequencing solution

This cost-effective, flexible workflow measures gene expression in single cells and offers high-resolution analysis to discover cellular differences usually masked by bulk sampling methods. This app note outlines the workflow, provides example study designs and throughput charts, and more.