Clariom Assays for RNA Biomarker Discovery
The power of Clariom Assays
Translational scientists don’t have the luxury of time. Accelerate your expression biomarker discovery research with Applied Biosystems Clariom Assays, the next generation of transcriptome profiling tools offering a fast path to results. Recent advanced transcriptome analyses have uncovered thousands of splice variants and long noncoding (lnc) RNAs, providing new sources for biomarker discovery. Given the complexity of the transcriptome, however, finding informative expression biomarkers can be challenging, time-consuming, and costly. Clariom Assays, built using the latest transcriptome knowledge from multiple public data sources, are simple and fast tools for finding expression biomarkers. They are compatible with challenging and precious samples, available in scalable formats for different throughout needs, and include intuitive software for fast and simple analysis. Get the comprehensive coverage you need, the reproducibility you require, and the insights you want to act quickly on your discoveries.
Get the data you need
Confidently identify complex biomarker signatures and investigate significantly altered pathways.
- Quickly find key biomarkers with transcriptome-level assays that detect coding and long noncoding genes, exons, and alternative splicing events, including rare transcripts
- Get answers fast with gene-level assays that measure changes in well-annotated genes and pathways
- Gain important insights quickly from large-scale cohort studies
When you have precious or challenging samples
- Generate expression profiles from as little as 100 pg of total RNA—as few as 10 cells
- Analyze RNA from a wide variety of sample types including cells, whole blood, and fresh/fresh-frozen or FFPE tissues
- Preserve sample integrity and reduce variability with no need for globin or ribosomal RNA removal
Clariom Assays brochure
Clariom Assays are built on the latest transcriptomic knowledge to accelerate RNA biomarker discovery even from challenging samples, and without the need for bioinformatics resources.