Comparison of library construction kits for mRNA sequencing in the Illumina platform

Abstract

Background

The emergence of next-generation sequencing (NGS) technologies has made a tremendous contribution to the deciphering and significance of transcriptome analysis in biological fields. Since the advent of NGS technology in 2007, Illumina, Inc. has provided one of the most widely used sequencing platforms for NGS analysis.

Objective

Although reagents and protocols provided by Illumina are adequately performed in transcriptome sequencing, recently, alternative reagents and protocols which are relatively cost effective are accessible. However, the kits derived from various manufacturers have advantages and disadvantages when researchers carry out the transcriptome library construction.

Methods

We compared them using a variety of protocols to produce Illumina-compatible libraries based on transcriptome. Three different mRNA sequencing kits were selected for this study: TruSeq® RNA Sample Preparation V2 (Illumina, Inc., USA), Universal Plus mRNA-Seq (NuGEN, Ltd., UK), and NEBNext® Ultra™ Directional RNA Library Prep Kit for Illumina® (New England BioLabs, Ltd., USA). We compared them focusing on cost, experimental time, and data output.

Results

The quality and quantity of sequencing data obtained through the NGS technique were strongly influenced by the type of the sequencing library kits. It suggests that for transcriptome studies, researchers should select a suitable library construction kit according to the goal and resources of experiments.

Conclusion

The present work will help researchers to choose the right sequencing library construction kit for transcriptome analyses.

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References

  1. Alberti A, Belser C, Engelen S, Bertrand L, Orvain C, Brinas L, Cruaud C, Giraut L, Da Silva C, Firmo C et al (2014) Comparison of library preparation methods reveals their impact on interpretation of metatranscriptomic data. BMC Genom 15:912

    Article  Google Scholar 

  2. Casamassimi A, Federico A, Rienzo M, Esposito S, Ciccodicola A (2017) Transcriptome profiling in human diseases: new advances and perspectives. Int J Mol Sci 18:1652

    Article  Google Scholar 

  3. Chaitankar V, Karakulah G, Ratnapriya R, Giuste FO, Brooks MJ, Swaroop A (2016) Next generation sequencing technology and genomewide data analysis: perspectives for retinal research. Prog Retin Eye Res 55:1–31

    CAS  Article  Google Scholar 

  4. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szczesniak MW, Gaffney DJ, Elo LL, Zhang X et al (2016) A survey of best practices for RNA-Seq data analysis. Genome Biol 17:13

    Article  Google Scholar 

  5. Cui P, Lin Q, Ding F, Xin C, Gong W, Zhang L, Geng J, Zhang B, Yu X, Yang J et al (2010) A comparison between ribo-minus RNA-sequencing and polyA-selected RNA-sequencing. Genomics 96:259–265

    CAS  Article  Google Scholar 

  6. Everaert C, Luypaert M, Maag JLV, Cheng QX, Dinger ME, Hellemans J, Mestdagh P (2017) Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data. Sci Rep 7:1559

    Article  Google Scholar 

  7. Fang Z, Cui X (2011) Design and validation issues in RNA-Seq experiments. Br Bioinform 12:280–287

    CAS  Article  Google Scholar 

  8. Guo Y, Zhao S, Sheng Q, Guo M, Lehmann B, Pietenpol J, Samuels DC, Shyr Y (2015) RNAseq by total RNA library identifies additional RNAs compared to poly(A) RNA library. Biomed Res Int 2015:862130

    PubMed  PubMed Central  Google Scholar 

  9. Head SR, Komori HK, LaMere SA, Whisenant T, Van Nieuwerburgh F, Salomon DR, Ordoukhanian P (2014) Library construction for next-generation sequencing: overviews and challenges. Biotechniques 56:61–64 (66, 68, passim)

    CAS  Article  Google Scholar 

  10. Hong H, Zhang W, Shen J, Su Z, Ning B, Han T, Perkins R, Shi L, Tong W (2013) Critical role of bioinformatics in translating huge amounts of next-generation sequencing data into personalized medicine. Sci China Life Sci 56:110–118

    CAS  Article  Google Scholar 

  11. Hrdlickova R, Toloue M, Tian B (2017) RNA-Seq methods for transcriptome analysis. Wiley Interdiscip Rev RNA 8:e1364

    Article  Google Scholar 

  12. Kissopoulou A, Jonasson J, Lindahl TL, Osman A (2013) Next generation sequencing analysis of human platelet PolyA+ mRNAs and rRNA-depleted total RNA. PLoS One 8:e81809

    Article  Google Scholar 

  13. Kukurba KR, Montgomery SB (2015) RNA sequencing and analysis. Cold Spring Harb Protoc 2015:951–969

    Article  Google Scholar 

  14. Kumar A, Kankainen M, Parsons A, Kallioniemi O, Mattila P, Heckman CA (2017) The impact of RNA sequence library construction protocols on transcriptomic profiling of leukemia. BMC Genom 18:629

    Article  Google Scholar 

  15. Levy SE, Myers RM (2016) Advancements in next-generation sequencing. Annu Rev Genom Hum Genet 17:95–115

    CAS  Article  Google Scholar 

  16. Lockhart DJ, Winzeler EA (2000) Genomics, gene expression and DNA arrays. Nature 405:827–836

    CAS  Article  Google Scholar 

  17. Mills JD, Kawahara Y, Janitz M (2013) Strand-specific RNA-Seq provides greater resolution of transcriptome profiling. Curr Genom 14:173–181

    CAS  Article  Google Scholar 

  18. Oliver GR, Hart SN, Klee EW (2015) Bioinformatics for clinical next generation sequencing. Clin Chem 61:124–135

    CAS  Article  Google Scholar 

  19. O’Neil D, Glowatz H, Schlumpberger M (2013) Ribosomal RNA depletion for efficient use of RNA-Seq capacity. Curr Protoc Mol Biol Chapter 4:Unit 4 19

  20. Parkhomchuk D, Borodina T, Amstislavskiy V, Banaru M, Hallen L, Krobitsch S, Lehrach H, Soldatov A (2009) Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res 37:e123

    Article  Google Scholar 

  21. Rohland N, Reich D (2012) Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res 22:939–946

    CAS  Article  Google Scholar 

  22. Schwartz S, Motorin Y (2017) Next-generation sequencing technologies for detection of modified nucleotides in RNAs. RNA Biol 14:1124–1137

    Article  Google Scholar 

  23. Sultan M, Amstislavskiy V, Risch T, Schuette M, Dokel S, Ralser M, Balzereit D, Lehrach H, Yaspo ML (2014) Influence of RNA extraction methods and library selection schemes on RNA-Seq data. BMC Genom 15:675

    Article  Google Scholar 

  24. Sun Z, Asmann YW, Nair A, Zhang Y, Wang L, Kalari KR, Bhagwate AV, Baker TR, Carr JM, Kocher JP et al (2013) Impact of library preparation on downstream analysis and interpretation of RNA-Seq data: comparison between Illumina PolyA and NuGEN Ovation protocol. PLoS One 8:e71745

    CAS  Article  Google Scholar 

  25. Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25:1105–1111

    CAS  Article  Google Scholar 

  26. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L (2012) Differential gene and transcript expression analysis of RNA-Seq experiments with TopHat and Cufflinks. Nat Protoc 7:562–578

    CAS  Article  Google Scholar 

  27. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63

    CAS  Article  Google Scholar 

  28. Wolf JB (2013) Principles of transcriptome analysis and gene expression quantification: an RNA-Seq tutorial. Mol Ecol Resour 13:559–572

    CAS  Article  Google Scholar 

  29. Yang IS, Kim S (2015) Analysis of whole transcriptome sequencing data: workflow and software. Genom Inform 13:119–125

    Article  Google Scholar 

  30. Zhao S, Fung-Leung WP, Bittner A, Ngo K, Liu X (2014a) Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PLoS One 9:e78644

    Article  Google Scholar 

  31. Zhao W, He X, Hoadley KA, Parker JS, Hayes DN, Perou CM (2014b) Comparison of RNA-Seq by poly(A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling. BMC Genom 15:419

    Article  Google Scholar 

  32. Zhao S, Zhang Y, Gamini R, Zhang B, von Schack D (2018) Evaluation of two main RNA-Seq approaches for gene quantification in clinical RNA sequencing: polyA+ selection versus rRNA depletion. Sci Rep 8:4781

    Article  Google Scholar 

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Acknowledgements

This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science & ICT (2016M3A9B6026776).

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Correspondence to Wonseok Shin or Kyudong Han.

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Yong-Soo Park, Songmi Kim, Dong-Guk Park, Dong Hee Kim, Kyeong-Wook Yoon, Wonseok Shin and Kyudong Han declare that we have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee.

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Park, YS., Kim, S., Park, DG. et al. Comparison of library construction kits for mRNA sequencing in the Illumina platform. Genes Genom 41, 1233–1240 (2019). https://doi.org/10.1007/s13258-019-00853-3

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Keywords

  • Transcriptome sequencing
  • RNA-Seq
  • Library prep kit
  • Poly A selection