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.
Highlights
Parallel sequencing of mRNA, or mRNA sequencing (mRNAseq), was first introduced in 20081–3, and since has rapidly become the preferred method for whole transcriptome measurements[4,5,6,7,8,9,10,11], culminating recently with the announcement by Illumina of the discontinuation of the Human Expression Array BeadChip (HumanHT-12 v4 as of 9 Dec 2016)
Because it is not yet clear to what extent the results from this 3′-digital gene expression (3′-digital gene expression (DGE)) library preparation method overlap with the current gold-standard of random primed conventional mRNA sequencing, we performed a comprehensive comparison of data obtained by both methods, from the same RNA samples
We find that 3′-DGE has only about ~15% lower sensitivity than conventional random primed mRNAseq, good quantitative agreement, and high overlap in ranked lists of differentially expressed genes
Summary
Received: 14 February 2017 Accepted: 18 October 2017 Published online: 07 November 2017. We conclude that the 3′-DGE approach used here is likely to be a viable alternative to conventional random-primed mRNAseq for high-throughput applications, when looking for differentially expressed genes between treatment conditions, as is a common goal for transcriptomic studies. This is relevant for more simple expression profiling which is becoming more commonplace in single-cell mRNAseq[17,18,19,20] or tissue-level examinations[21,22,23]. We conclude from these data that despite the fact that reads are 3′-end-focused, a large and sufficient majority can be reliably mapped to individual genes with high fidelity This feature may be facilitated by the strand-specificity of the 3′-DGE library preparation method. Read counts were removed from each gene with probability proportional to that gene’s overall representation in the dataset, and expression of a gene was considered detected with four or greater
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