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A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries

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 a...

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Autores principales: Xiong, Yuguang, Soumillon, Magali, Wu, Jie, Hansen, Jens, Hu, Bin, van Hasselt, Johan G. C., Jayaraman, Gomathi, Lim, Ryan, Bouhaddou, Mehdi, Ornelas, Loren, Bochicchio, Jim, Lenaeus, Lindsay, Stocksdale, Jennifer, Shim, Jaehee, Gomez, Emilda, Sareen, Dhruv, Svendsen, Clive, Thompson, Leslie M., Mahajan, Milind, Iyengar, Ravi, Sobie, Eric A., Azeloglu, Evren U., Birtwistle, Marc R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5676863/
https://www.ncbi.nlm.nih.gov/pubmed/29116112
http://dx.doi.org/10.1038/s41598-017-14892-x
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author Xiong, Yuguang
Soumillon, Magali
Wu, Jie
Hansen, Jens
Hu, Bin
van Hasselt, Johan G. C.
Jayaraman, Gomathi
Lim, Ryan
Bouhaddou, Mehdi
Ornelas, Loren
Bochicchio, Jim
Lenaeus, Lindsay
Stocksdale, Jennifer
Shim, Jaehee
Gomez, Emilda
Sareen, Dhruv
Svendsen, Clive
Thompson, Leslie M.
Mahajan, Milind
Iyengar, Ravi
Sobie, Eric A.
Azeloglu, Evren U.
Birtwistle, Marc R.
author_facet Xiong, Yuguang
Soumillon, Magali
Wu, Jie
Hansen, Jens
Hu, Bin
van Hasselt, Johan G. C.
Jayaraman, Gomathi
Lim, Ryan
Bouhaddou, Mehdi
Ornelas, Loren
Bochicchio, Jim
Lenaeus, Lindsay
Stocksdale, Jennifer
Shim, Jaehee
Gomez, Emilda
Sareen, Dhruv
Svendsen, Clive
Thompson, Leslie M.
Mahajan, Milind
Iyengar, Ravi
Sobie, Eric A.
Azeloglu, Evren U.
Birtwistle, Marc R.
author_sort Xiong, Yuguang
collection PubMed
description 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.
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spelling pubmed-56768632017-11-15 A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries Xiong, Yuguang Soumillon, Magali Wu, Jie Hansen, Jens Hu, Bin van Hasselt, Johan G. C. Jayaraman, Gomathi Lim, Ryan Bouhaddou, Mehdi Ornelas, Loren Bochicchio, Jim Lenaeus, Lindsay Stocksdale, Jennifer Shim, Jaehee Gomez, Emilda Sareen, Dhruv Svendsen, Clive Thompson, Leslie M. Mahajan, Milind Iyengar, Ravi Sobie, Eric A. Azeloglu, Evren U. Birtwistle, Marc R. Sci Rep Article 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. Nature Publishing Group UK 2017-11-07 /pmc/articles/PMC5676863/ /pubmed/29116112 http://dx.doi.org/10.1038/s41598-017-14892-x Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Xiong, Yuguang
Soumillon, Magali
Wu, Jie
Hansen, Jens
Hu, Bin
van Hasselt, Johan G. C.
Jayaraman, Gomathi
Lim, Ryan
Bouhaddou, Mehdi
Ornelas, Loren
Bochicchio, Jim
Lenaeus, Lindsay
Stocksdale, Jennifer
Shim, Jaehee
Gomez, Emilda
Sareen, Dhruv
Svendsen, Clive
Thompson, Leslie M.
Mahajan, Milind
Iyengar, Ravi
Sobie, Eric A.
Azeloglu, Evren U.
Birtwistle, Marc R.
A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries
title A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries
title_full A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries
title_fullStr A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries
title_full_unstemmed A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries
title_short A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries
title_sort comparison of mrna sequencing with random primed and 3′-directed libraries
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5676863/
https://www.ncbi.nlm.nih.gov/pubmed/29116112
http://dx.doi.org/10.1038/s41598-017-14892-x
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