TY - JOUR
T1 - A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries
AU - Xiong, Yuguang
AU - Soumillon, Magali
AU - Wu, Jie
AU - Hansen, Jens
AU - Hu, Bin
AU - Van Hasselt, Johan G.C.
AU - Jayaraman, Gomathi
AU - Lim, Ryan
AU - Bouhaddou, Mehdi
AU - Ornelas, Loren
AU - Bochicchio, Jim
AU - Lenaeus, Lindsay
AU - Stocksdale, Jennifer
AU - Shim, Jaehee
AU - Gomez, Emilda
AU - Sareen, Dhruv
AU - Svendsen, Clive
AU - Thompson, Leslie M.
AU - Mahajan, Milind
AU - Iyengar, Ravi
AU - Sobie, Eric A.
AU - Azeloglu, Evren U.
AU - Birtwistle, Marc R.
N1 - Funding Information:
We acknowledge R01GM104184 (MRB) and U54HG008098 (LINCS Center), the NIH Grant P50GM071558 (Systems Biology Center New York) (MRB and RI), and the NIH Grant U54NS091046 (NeuroLINCS-LMT). MB was supported by a NIGMS-funded Integrated Pharmacological Sciences Training Program grant (T32GM062754). We also thank Marc Hafner for helpful discussions and facilitating our work with the Broad Institute team.
Publisher Copyright:
© 2017 The Author(s).
PY - 2017/12/1
Y1 - 2017/12/1
N2 - reating 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.
AB - reating 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.
UR - http://www.scopus.com/inward/record.url?scp=85033373409&partnerID=8YFLogxK
U2 - 10.1038/s41598-017-14892-x
DO - 10.1038/s41598-017-14892-x
M3 - Article
C2 - 29116112
AN - SCOPUS:85033373409
SN - 2045-2322
VL - 7
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 14626
ER -