A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Xiong, Yuguang; Soumillon, Magali; Wu, Jie; Hansen, Jens; Hu, Bin; Hasselt, Johan; Jayaraman, Gomathi; Lim, Ryan; Bouhaddou, Mehdi; Ornelas, Loren; Bochicchio, Jim; Lenaeus, Lindsay; Stocksdale, Jennifer; Shim, Jaehee; Gomez, Emilda; Sareen, Dhruv; Svendsen, Clive; Thompson, Leslie; Mahajan, Milind; Iyengar, Ravi; Sobie, Eric; Azeloglu, Evren; Birtwistle, Marc
• Is Part Of: Scientific reports. Volume 7:Issue 1(2017)
• Page Start: 1
• Page End: 12
• Publication Date: 2017-12
• Subjects: Natural history -- Research -- Periodicals; Biology -- Research -- Periodicals; Physical sciences -- Research -- Periodicals; Earth sciences -- Research -- Periodicals; Environmental sciences -- Research -- Periodicals; 502.85
• DOI: 10.1038/s41598-017-14892-x
• Languages: English
• ISSNs: 2045-2322

Abstract

Abstract Creating a cDNA library for deep mRNA sequencing (mRNAseq) is generally done by random priming, creating multiple sequencing fragments along each transcript. A 3′-end-focused library approach cannot detect differential splicing, but has potentially higher throughput at a lower cost, along with the ability to improve quantification by using transcript molecule counting with unique molecular identifiers (UMI) that correct PCR bias. Here, we compare an implementation of such a 3′-digital gene expression (3′-DGE) approach with "conventional" random primed mRNAseq. Given our particular datasets on cultured human cardiomyocyte cell lines, we find that, while conventional mRNAseq detects ~15% more genes and needs ~500, 000 fewer reads per sample for equivalent statistical power, the resulting differentially expressed genes, biological conclusions, and gene signatures are highly concordant between two techniques. We also find good quantitative agreement at the level of individual genes between two techniques for both read counts and fold changes between given conditions. We conclude that, for high-throughput applications, the potential cost savings associated with 3′-DGE approach are likely a reasonable tradeoff for modest reduction in sensitivity and inability to observe alternative splicing, and should enable many larger scale studies focusing on not only differential expression analysis, but also quantitative transcriptome profiling.