Quartet RNA reference materials and ratio-based reference datasets for reliable transcriptomic profiling

Abstract

As an indispensable tool for transcriptome-wide analysis of differential gene expression, RNA sequencing (RNAseq) has demonstrated great potential in clinical applications such as companion diagnostics and prognostics. However, there is a lack of certified RNA reference materials and the corresponding reference datasets of differential expression for assessing the reliability of RNAseq for its intended use in detecting intrinsically small biological differences in clinical settings such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multiomics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two reference samples, providing "ground truth" for cross-platform and cross-lab proficiency test. Moreover, Quartet-sample-based quality metrics were developed for assessing the reliability of RNAseq technology in terms of intra-batch measurement and cross-batch data integration. The small intrinsic biological differences among the Quartet samples enabled sensitive assessment of performance of transcriptomic measurements and their cross-batch integration at the ratio level. The Quartet RNA reference materials combined with the ratio-based reference datasets can serve as unique resources for assessing data quality and improving reliability of transcriptomic profiling.

Quartet metabolite reference materials for assessing inter-laboratory reliability and data integration of metabolomic profiling

“…Even if the metabolomic methods were developed using different wet-lab operation procedures on different LC-MS instruments, the ratio-based metabolomic data integration was reliable enough for differentiating the various Quartet reference materials. The intrinsic batch-effect resistant characteristics of the ratio-based profiling is also demonstrated by other quantitative omics profiling technologies, such as methylomics, transcriptomics, and proteomics 41, 43, 45 .…”

“…SNR is calculated as the ratio of the averaged distance between different Quartet samples (“signal”) to the averaged distances between technical replicates for each sample (“noise”) on a 2D-PCA scatter plot ( Fig. 3c ) 41,43 , where a higher SNR indicates better reproducibility and discriminability ( Fig. 3 ).…”

“…As part of the Quartet project 41 , we provide the community the first suite of renewable metabolite reference materials, with matched DNA 42 , RNA 43 , and protein 44 isolated from the same immortalized cell lines. The intended use of the Quartet metabolite reference materials includes intra-laboratory quality control, inter-laboratory proficiency test, and quality assurance of large-scale metabolomic data generation and integration.…”

Plasma-free samples for transcriptomic analysis: a potential alternative to whole blood samples

“…The copyright holder for this preprint this version posted April 28, 2023. ; https://doi.org/10.1101/2023.04.27.538178 doi: bioRxiv preprint reference datasets (Yu et al 2022), which suggested the successful alignment of the reads to the reference genome. The gene region distribution was consistent with the expected characteristics of RiboZero libraries in the Quartet RNA reference datasets (Yu et al 2022) across the three blood types (Fig. 2c).…”