期刊
GIGASCIENCE
卷 10, 期 3, 页码 -出版社
OXFORD UNIV PRESS
DOI: 10.1093/gigascience/giab011
关键词
RNA-Seq; sequencing; depth; duplicate; unmapped; exonic; quality
资金
- American Association for Cancer Research NextGen Grant for Transformative Cancer Research Award
- Emily Beazley Kures for Kids Fund St. Baldrick's Consortium Grant
- Alex's Lemonade Stand Foundation for Childhood Cancer Research
- Unravel Pediatric Cancer
- Team G Childhood Cancer Foundation
- California Initiative to Advance Precision Medicine
- Live for Others Foundation
- Schmidt Futures Foundation
The reproducibility of gene expression measured by RNA-Seq is dependent on sequencing depth, with MEND reads being a useful measure for reproducibility. The fraction of reads contributing to reproducibility varies greatly among datasets, suggesting the importance of reporting sequencing depth in MEND reads.
Background: The reproducibility of gene expression measured by RNA sequencing (RNA-Seq) is dependent on the sequencing depth. While unmapped or non-exonic reads do not contribute to gene expression quantification, duplicate reads contribute to the quantification but are not informative for reproducibility. We show that mapped, exonic, non-duplicate (MEND) reads are a useful measure of reproducibility of RNA-Seq datasets used for gene expression analysis. Findings: In bulk RNA-Seq datasets from 2,179 tumors in 48 cohorts, the fraction of reads that contribute to the reproducibility of gene expression analysis varies greatly. Unmapped reads constitute 1-77% of all reads (median [IQR], 3% [3-6%]); duplicate reads constitute 3-100% of mapped reads (median [IQR], 27% [13-43%]); and non-exonic reads constitute 4-97% of mapped, non-duplicate reads (median [IQR], 25% [16-37%]). MEND reads constitute 0-79% of total reads (median [IQR], 50% [30-61%]). Conclusions: Because not all reads in an RNA-Seq dataset are informative for reproducibility of gene expression measurements and the fraction of reads that are informative varies, we propose reporting a dataset's sequencing depth in MEND reads, which definitively inform the reproducibility of gene expression, rather than total, mapped, or exonic reads. We provide a Docker image containing (i) the existing required tools (RSeQC, sambamba, and samblaster) and (ii) a custom script to calculate MEND reads from RNA-Seq data files. We recommend that all RNA-Seq gene expression experiments, sensitivity studies, and depth recommendations use MEND units for sequencing depth.
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