bioRxivGeneticsPreprint
Quartet RNA reference materials and ratio-based reference datasets for reliable transcriptomic profiling
Ying YuiD, Wanwan HouiD, Haiyan WangiD, Lianhua DongiD, Yaqing LiuiD, Shanyue SuniD, Jingcheng YangiD, Zehui CaoiD, Peipei ZhangiD, Yi ZiiD, Zhihui LiiD, Ruimei Liu, Jian GaoiD, Qingwang CheniD, Naixin ZhangiD, Jingjing LiiD, Luyao ReniD, He JiangiD, Jun ShangiD, Sibo ZhuiD, Xiaolin WangiD, Tao QingiD, Bao Fu DingiD, Bingying LiiD, Bin LiiD, , Yuanting ZhengiD
Abstract
As an indispensable tool for transcriptome-wide analysis of differential gene expression, RNA sequencing (RNAseq) has demonstrated great potential in clinical applications. However, the lack of multi-group RNA reference materials of biological relevance and the corresponding reference datasets for assessing the reliability of RNAseq hampers its wide clinical applications wherein the underlying biological differences among study groups are often small. As part of the Quartet Project for quality control and data integration of multiomic 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 using multi-batch RNAseq datasets, providing "ground truth" for benchmarking. Moreover, Quartet-sample-based quality metrics were developed for assessing reliability of RNAseq technology in terms of intra-batch proficiency and cross-batch reproducibility. The small intrinsic biological differences among the Quartet samples enable sensitive assessment of performance of transcriptomic measurements. The Quartet RNA reference materials combined with the reference datasets can be served as unique resources for assessing data quality and improving reliability of transcriptomic profiling.
- organism: human
- assay: bulk RNA-seq
- research focus: benchmarking
- research focus: variant calling
- research focus: variant effect prediction
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 8
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