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Updated: Jul 17, 2025

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
Published on: June 8, 2020
Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling.
Ying Yu1, Wanwan Hou1, Yaqing Liu1
1State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
Certified RNA reference materials from a twin family aid RNA sequencing quality control. These resources help detect subtle biological differences for improved clinical diagnostics and research.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Reliable RNA sequencing is crucial for detecting subtle biological variations in clinical settings, such as disease subtyping.
- Quality control and data integration are essential for multi-omics profiling in research and clinical applications.
Purpose of the Study:
- To establish certified RNA reference materials for assessing RNA sequencing reliability.
- To create ratio-based transcriptome-wide reference datasets for cross-platform and cross-laboratory validation.
- To enable sensitive assessment of transcriptomic data integration and quality.
Main Methods:
- Development of four RNA reference materials from immortalized B-lymphoblastoid cell lines of monozygotic twins (Quartet Project).
- Construction of ratio-based transcriptome-wide reference datasets between sample pairs.
- Utilizing subtle biological differences among Quartet samples for quality assessment.
Main Results:
- Established certified RNA reference materials and ratio-based transcriptome datasets.
- Demonstrated the utility of these materials for assessing cross-batch integration of transcriptomic data at the ratio level.
- Provided a 'ground truth' for transcriptome-wide comparisons across platforms and laboratories.
Conclusions:
- The Quartet RNA reference materials and associated datasets are valuable resources for quality assessment in transcriptomics.
- These materials support the improvement of transcriptomic data quality in both clinical and biological research.
- Enhances the reliability of RNA sequencing for detecting small biological differences, aiding molecular subtyping and diagnostics.
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