RNA Sequencing Best Practices: Experimental Protocol and Data Analysis

  • Andrew R. HeskethEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2049)


The genome-wide analysis of gene transcription using RNA sequencing (RNA-seq) has become the method of choice for characterizing and understanding transcriptional regulation in yeasts. RNA-seq has largely supplanted microarray based approaches in recent years due to improved accuracy and flexibility in the high-throughput identification and quantification of transcripts. The improvements associated with a sequencing approach compared to one based on hybridization, however, are accompanied by new experimental considerations related to both the collection and the analysis of the transcriptome data. Consensus approaches for processing and analysing the RNA-seq data in particular have yet to be arrived at, and it is possible to feel overwhelmed when surveying all the software tools that have been developed and recommended for these tasks. This chapter considers these issues in the context of providing general guidelines to help achieve best practice in yeast RNA-seq studies, and recommends a small number of the best performing tools that are currently available.

Key words

Transcriptomics RNA sequencing Yeast 


  1. 1.
    Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63CrossRefGoogle Scholar
  2. 2.
    Hrdlickova R, Toloue M, Tian B (2017) RNA-Seq methods for transcriptome analysis. Wiley Interdiscip Rev RNA 8.
  3. 3.
    Law CW, Chen Y, Shi W, Smyth GK (2014) voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15:R29CrossRefGoogle Scholar
  4. 4.
    Schurch NJ, Schofield P, Gierliński M, Cole C, Sherstnev A, Singh V et al (2016) How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? RNA 22:839–851CrossRefGoogle Scholar
  5. 5.
    Tsuzuki M, Watanabe Y (2017) Profiling new small RNA sequences. Methods Mol Biol 1456:177–188CrossRefGoogle Scholar
  6. 6.
    Reynoso MA, Juntawong P, Lancia M, Blanco FA, Bailey-Serres J, Zanetti MEE (2015) Translating ribosome affinity purification (TRAP) followed by RNA sequencing technology (TRAP-SEQ) for quantitative assessment of plant translatomes. Methods Mol Biol 1284:185–207CrossRefGoogle Scholar
  7. 7.
    Martinez-Nunez RT, Sanford JR (2016) Studying isoform-specific mRNA recruitment to polyribosomes with Frac-seq. Methods Mol Biol 1358:99–108CrossRefGoogle Scholar
  8. 8.
    McGlincy NJ, Ingolia NT (2017) Transcriptome-wide measurement of translation by ribosome profiling. Methods 126:112–129CrossRefGoogle Scholar
  9. 9.
    Duncan C, Mata J (2017) Ribosome profiling for the analysis of translation during yeast meiosis. Methods Mol Biol 1471:99–122CrossRefGoogle Scholar
  10. 10.
    Hart SN, Therneau TM, Zhang Y, Poland GA, Kocher J-PP (2013) Calculating sample size estimates for RNA sequencing data. J Comput Biol 20:970–978CrossRefGoogle Scholar
  11. 11.
    Busby MA, Stewart C, Miller CA, Grzeda KR, Marth GT (2013) Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression. Bioinformatics 29:656–657CrossRefGoogle Scholar
  12. 12.
    Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13CrossRefGoogle Scholar
  13. 13.
    Yu L, Fernandez S, Brock G (2017) Power analysis for RNA-Seq differential expression studies. BMC Bioinformatics 18:234CrossRefGoogle Scholar
  14. 14.
    Ching T, Huang S, Garmire LX (2014) Power analysis and sample size estimation for RNA-Seq differential expression. RNA 20:1684–1696CrossRefGoogle Scholar
  15. 15.
    Ewels P, Magnusson M, Lundin S, Käller M (2016) MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32:3047–3048CrossRefGoogle Scholar
  16. 16.
    Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21CrossRefGoogle Scholar
  17. 17.
    Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12:357–360CrossRefGoogle Scholar
  18. 18.
    Liao Y, Smyth GK, Shi W (2013) The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res 41:e108CrossRefGoogle Scholar
  19. 19.
    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079CrossRefGoogle Scholar
  20. 20.
    Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G et al (2011) Integrative genomics viewer. Nat Biotechnol 29:24–26CrossRefGoogle Scholar
  21. 21.
    Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W et al (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47CrossRefGoogle Scholar
  22. 22.
    Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140CrossRefGoogle Scholar
  23. 23.
    Liu Y, Zhou J, White KP (2014) RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30:301–304CrossRefGoogle Scholar
  24. 24.
    Levin JZ, Yassour M, Adiconis X, Nusbaum C, Thompson DA, Friedman N et al (2010) Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat Methods 7:709–715CrossRefGoogle Scholar
  25. 25.
    Williams CR, Baccarella A, Parrish JZ, Kim CC (2016) Trimming of sequence reads alters RNA-Seq gene expression estimates. BMC Bioinformatics 17:103CrossRefGoogle Scholar
  26. 26.
    Pertea M, Pertea GM, Antonescu CM, Chang T-CC, Mendell JT, Salzberg SL (2015) StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol 33:290–295CrossRefGoogle Scholar
  27. 27.
    Klepikova AV, Kasianov AS, Chesnokov MS, Lazarevich NL, Penin AA, Logacheva M (2017) Effect of method of deduplication on estimation of differential gene expression using RNA-seq. PeerJ 5:e3091CrossRefGoogle Scholar
  28. 28.
    Law CW, Alhamdoosh M, Su S, Smyth GK, Ritchie ME (2016) RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. F1000Res 5:1408CrossRefGoogle Scholar
  29. 29.
    Chen Y, Lun AT, Smyth GK (2016) From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Res 5:1438PubMedPubMedCentralGoogle Scholar
  30. 30.
    Lun AT, Chen Y, Smyth GK (2016) It’s DE-licious: a recipe for differential expression analyses of RNA-seq experiments using quasi-likelihood methods in edgeR. Methods Mol Biol 1418:391–416CrossRefGoogle Scholar
  31. 31.
    Liu R, Holik AZ, Su S, Jansz N, Chen K, Leong HS et al (2015) Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic Acids Res 43:e97CrossRefGoogle Scholar
  32. 32.
    Nueda MJJ, Tarazona S, Conesa A (2014) Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series. Bioinformatics 30:2598–2602CrossRefGoogle Scholar
  33. 33.
    Conesa A, Nueda MJJ, Ferrer A, Talón M (2006) maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics 22:1096–1102CrossRefGoogle Scholar
  34. 34.
    Spies D, Renz PF, Beyer TA, Ciaudo C (2019) Comparative analysis of differential gene expression tools for RNA sequencing time course data. Brief Bioinformatics 20(1):288–298CrossRefGoogle Scholar
  35. 35.
    Young MD, Wakefield MJ, Smyth GK, Oshlack A (2010) Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol 11:R14CrossRefGoogle Scholar
  36. 36.
    Wang X, Cairns MJ (2014) SeqGSEA: a Bioconductor package for gene set enrichment analysis of RNA-Seq data integrating differential expression and splicing. Bioinformatics 30:1777–1779CrossRefGoogle Scholar
  37. 37.
    Alhamdoosh M, Ng M, Wilson NJ, Sheridan JM, Huynh H, Wilson MJ et al (2017) Combining multiple tools outperforms individual methods in gene set enrichment analyses. Bioinformatics 33:414–424PubMedGoogle Scholar
  38. 38.
    Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley D et al (2014) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7:562–578CrossRefGoogle Scholar
  39. 39.
    Anders S, Pyl PT, Huber W (2015) HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31:166–169CrossRefGoogle Scholar
  40. 40.
    Love MI, Anders S, Kim V, Huber W (2015) RNA-Seq workflow: gene-level exploratory analysis and differential expression. F1000Res 4:1070CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Cambridge Systems Biology CentreUniversity of CambridgeCambridgeUK
  2. 2.School of Pharmacy and Biomolecular ScienceUniversity of BrightonBrightonUK

Personalised recommendations