The case for using mapped exonic non-duplicate reads when reporting RNA-sequencing depth: examples from pediatric cancer datasets

Author:

Beale Holly C12ORCID,Roger Jacquelyn M3ORCID,Cattle Matthew A3ORCID,McKay Liam T3ORCID,Thompson Drew K A3ORCID,Learned Katrina2ORCID,Lyle A Geoffrey12ORCID,Kephart Ellen T2ORCID,Currie Rob2ORCID,Lam Du Linh2ORCID,Sanders Lauren1ORCID,Pfeil Jacob2ORCID,Vivian John2ORCID,Bjork Isabel2,Salama Sofie R45ORCID,Haussler David45ORCID,Vaske Olena M12ORCID

Affiliation:

1. UC Santa Cruz, Molecular, Cell and Developmental Biology, 1156 High Street, Santa Cruz, CA 95064, USA

2. UC Santa Cruz, Genomics Institute, 1156 High Street, Santa Cruz, CA 95064, USA

3. UC Santa Cruz, School of Engineering, 1156 High Street, Santa Cruz, CA 95064, USA

4. UC Santa Cruz, Department of Biomolecular Engineering, 1156 High Street, Santa Cruz, CA 95064, USA

5. Howard Hughes Medical Institute, 1156 High Street, Santa Cruz, CA 95064, USA

Abstract

Abstract 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.

Funder

American Association for Cancer Research NextGen

Alex's Lemonade Stand Foundation for Childhood Cancer Research

Unravel Pediatric Cancer, Team G Childhood Cancer Foundation

Howard Hughes Medical Institute Investigator

Publisher

Oxford University Press (OUP)

Subject

Computer Science Applications,Health Informatics

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