RNA sequencing: the teenage years


Over the past decade, RNA sequencing (RNA-seq) has become an indispensable tool for transcriptome-wide analysis of differential gene expression and differential splicing of mRNAs. However, as next-generation sequencing technologies have developed, so too has RNA-seq. Now, RNA-seq methods are available for studying many different aspects of RNA biology, including single-cell gene expression, translation (the translatome) and RNA structure (the structurome). Exciting new applications are being explored, such as spatial transcriptomics (spatialomics). Together with new long-read and direct RNA-seq technologies and better computational tools for data analysis, innovations in RNA-seq are contributing to a fuller understanding of RNA biology, from questions such as when and where transcription occurs to the folding and intermolecular interactions that govern RNA function.

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Fig. 1: Short-read, long-read and direct RNA-seq technologies and workflows.
Fig. 2: RNA-seq data analysis workflow for differential gene expression.
Fig. 3: The key concepts of single-cell and spatial RNA-seq.
Fig. 4: The key concepts of nascent RNA and translatome analysis.
Fig. 5: The key concepts of translatome analysis.
Fig. 6: The key concepts of RNA structure and RNA–protein interaction analysis.


  1. 1.

    Emrich, S. J., Barbazuk, W. B., Li, L. & Schnable, P. S. Gene discovery and annotation using LCM-454 transcriptome sequencing. Genome Res. 17, 69–73 (2007).

  2. 2.

    Lister, R. et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523–536 (2008).

  3. 3.

    Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344–1350 (2008).

  4. 4.

    Mortazavi, A., Williams, B. A., Mccue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

  5. 5.

    Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M. & Gilad, Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008).

  6. 6.

    Cloonan, N. et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat. Methods 5, 613–619 (2008).

  7. 7.

    Wang, E. T. et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008).

  8. 8.

    Djebali, S. et al. Landscape of transcription in human cells. Nature 489, 101–108 (2012).

  9. 9.

    Morris, K. V. & Mattick, J. S. The rise of regulatory RNA. Nat. Rev. Genet. 15, 423–437 (2014).

  10. 10.

    Li, W., Notani, D. & Rosenfeld, M. G. Enhancers as non-coding RNA transcription units: recent insights and future perspectives. Nat. Rev. Genet. 17, 207–223 (2016).

  11. 11.

    Illumina. For all you seq. Illumina https://emea.illumina.com/techniques/sequencing/ngs-library-prep/library-prep-methods.html (2014). A tour de force that includes a graphical abstract, a brief description and primary references for most sequencing methods.

  12. 12.

    Garalde, D. R. et al. Highly parallel direct RNA sequencing on an array of nanopores. Nat. Methods 15, 201–206 (2018). The first report of Oxford Nanopore direct sequencing of RNA molecules without reverse transcription or amplification. It reports full-length, strand-specific RNA sequencing and detection of RNA nucleotide analogues.

  13. 13.

    Smith, A. M. Reading canonical and modified nucleotides in 16S ribosomal RNA using nanopore direct RNA sequencing. Preprint at bioRxiv https://doi.org/10.1101/132274 (2017).

  14. 14.

    Byrne, A. et al. Nanopore long-read RNAseq reveals widespread transcriptional variation among the surface receptors of individual B cells. Nat. Commun. 8, 16027 (2017).

  15. 15.

    Sharon, D., Tilgner, H., Grubert, F. & Snyder, M. A single-molecule long-read survey of the human transcriptome. Nat. Biotechnol. 31, 1009–1014 (2013).

  16. 16.

    Cartolano, M., Huettel, B., Hartwig, B., Reinhardt, R. & Schneeberger, K. cDNA library enrichment of full length transcripts for SMRT long read sequencing. PLOS ONE 11, e0157779 (2016). A paper comparing the performance of reverse transcriptases for long-read RNA-seq, using Pacific Biosciences Iso-Seq, and discussing the challenges of sequencing full-length transcripts, due to RNA degradation, shearing and incomplete cDNA synthesis.

  17. 17.

    Dard-Dascot, C. et al. Systematic comparison of small RNA library preparation protocols for next-generation sequencing. BMC Genomics 19, 118 (2018).

  18. 18.

    Giraldez, M. D. et al. Comprehensive multi-center assessment of small RNA-seq methods for quantitative miRNA profiling. Nat. Biotechnol. 36, 746–757 (2018).

  19. 19.

    Creecy, J. P. & Conway, T. Quantitative bacterial transcriptomics with RNA-seq. Curr. Opin. Microbiol. 23, 133–140 (2015).

  20. 20.

    Hör, J., Gorski, S. A. & Vogel, J. Bacterial RNA biology on a genome scale. Mol. Cell 70, 785–799 (2018).

  21. 21.

    Saletore, Y. et al. The birth of the Epitranscriptome: deciphering the function of RNA modifications. Genome Biol. 13, 175 (2012).

  22. 22.

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

  23. 23.

    Leinonen, R., Sugawara, H. & Shumway, M. The sequence read archive. Nucleic Acids Res. 39, D19–D21 (2011).

  24. 24.

    Su, Z. et al. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 32, 903–914 (2014). A thorough comparison of RNA-seq platforms and methods, which assesses multiple performance and quality metrics using cell line and control RNAs across multiple sequencing instruments and multiple laboratories.

  25. 25.

    Li, S. et al. Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study. Nat. Biotechnol. 32, 915–925 (2014).

  26. 26.

    Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47, D766–D773 (2019).

  27. 27.

    Piovesan, A., Caracausi, M., Antonaros, F., Pelleri, M. C. & Vitale, L. GeneBase 1.1: a tool to summarize data from NCBI Gene datasets and its application to an update of human gene statistics. Database 2016, baw153 (2016).

  28. 28.

    Gazzoli, I. et al. Non-sequential and multi-step splicing of the dystrophin transcript. RNA Biol. 13, 290–305 (2016).

  29. 29.

    Tilgner, H. et al. Microfluidic isoform sequencing shows widespread splicing coordination in the human transcriptome. Genome Res. 28, 231–242 (2018).

  30. 30.

    Wu, I., Ben-yehezkel, T., Genomics, L. & Jose, S. A. Single-molecule long-read survey of human transcriptomes using LoopSeq synthetic long read sequencing. Preprint at bioRxiv https://doi.org/10.1101/532135 (2019).

  31. 31.

    Fu, G. K., Hu, J., Wang, P.-H. & Fodor, S. P. Counting individual DNA molecules by the stochastic attachment of diverse labels. Proc. Natl Acad. Sci. USA 108, 9026–9031 (2011).

  32. 32.

    Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat. Methods 9, 72–74 (2011).

  33. 33.

    Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

  34. 34.

    Smith, G. R. & Birtwistle, M. R. A mechanistic beta-binomial probability model for mRNA sequencing data. PLOS ONE 11, e0157828 (2016).

  35. 35.

    Oikonomopoulos, S., Wang, Y. C., Djambazian, H., Badescu, D. & Ragoussis, J. Benchmarking of the Oxford Nanopore MinION sequencing for quantitative and qualitative assessment of cDNA populations. Sci. Rep. 6, 31602 (2016).

  36. 36.

    Engström, P. G. et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat. Methods 10, 1185–1191 (2013).

  37. 37.

    Thomas, S., Underwood, J. G., Tseng, E. & Holloway, A. K. Long-read sequencing of chicken transcripts and identification of new transcript isoforms. PLOS ONE 9, e94650 (2014).

  38. 38.

    Matz, M. et al. Amplification of cDNA ends based on template-switching effect and step-out PCR. Proc. Natl Acad. Sci. USA 27, 1558–1560 (1999).

  39. 39.

    Ramsköld, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).

  40. 40.

    Ardui, S., Ameur, A., Vermeesch, J. R. & Hestand, M. S. Single molecule real-time (SMRT) sequencing comes of age: applications and utilities for medical. Nucleic Acids Res. 46, 2159–2168 (2018).

  41. 41.

    Bolisetty, M. T., Rajadinakaran, G. & Graveley, B. R. Determining exon connectivity in complex mRNAs by nanopore sequencing. Genome Biol. 16, 204 (2015).

  42. 42.

    Prazsák, I. et al. Long-read sequencing uncovers a complex transcriptome topology in varicella zoster virus. BMC Genomics 19, 873 (2018).

  43. 43.

    Jain, M., Olsen, H. E., Paten, B. & Akeson, M. The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community. Genome Biol. 17, 239 (2016).

  44. 44.

    Jain, M. et al. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nat. Biotechnol. 36, 338–345 (2018).

  45. 45.

    Workman, R. E. et al. Nanopore native RNA sequencing of a human poly(A) transcriptome. Preprint at bioRxiv https://doi.org/10.1101/459529 (2018).

  46. 46.

    Weirather, J. L. et al. Comprehensive comparison of Pacific Biosciences and Oxford Nanopore Technologies and their applications to transcriptome analysis. F1000Res. 6, 100 (2017). A paper providing an assessment of the power of long-read sequencing in transcriptome analysis. It reports hybrid sequencing through the combination of Illumina short reads with Pacific Biosciences or Nanopore long reads.

  47. 47.

    Wongsurawat, T., Jenjaroenpun, P., Wassenaar, T. M. & Taylor, D. Decoding the epitranscriptional landscape from native RNA sequences. Preprint at bioRxiv https://doi.org/10.1101/487819 (2018).

  48. 48.

    Tilgner, H., Grubert, F., Sharon, D. & Snyder, M. P. Defining a personal, allele-specific, and single-molecule long-read transcriptome. Proc. Natl Acad. Sci. USA 111, 9869–9874 (2014).

  49. 49.

    Au, K. F. et al. Characterization of the human ESC transcriptome by hybrid sequencing. Proc. Natl Acad. Sci. USA 110, E4821–E4830 (2013).

  50. 50.

    Sahraeian, S. M. E. et al. Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis. Nat. Commun. 8, 59 (2017). A paper that assesses RNA-seq workflows that incorporate RNA variant calling, editing and fusion detection, covering both short- and long-read RNA-seq methods, and that benchmarks 39 analysis tools.

  51. 51.

    Kohli, M. et al. Androgen receptor variant AR-V9 is coexpressed with AR-V7 in prostate cancer metastases and predicts abiraterone resistance. Clin. Cancer Res. 23, 4704–4715 (2017).

  52. 52.

    Quail, M. A. et al. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics 13, 341 (2012).

  53. 53.

    Minoche, A. E. et al. Exploiting single-molecule transcript sequencing for eukaryotic gene prediction. Genome Biol. 16, 184 (2015).

  54. 54.

    Rhoads, A. & Au, K. F. PacBio sequencing and its applications. Genomics Proteomics Bioinformatics 13, 278–289 (2015).

  55. 55.

    Nottingham, R. M. et al. RNA-seq of human reference RNA samples using a thermostable group II intron reverse transcriptase. RNA 22, 597–613 (2016).

  56. 56.

    Zhao, C., Liu, F. & Pyle, A. M. An ultra-processive, accurate reverse transcriptase encoded by a metazoan group II intron. RNA 24, 185–193 (2017).

  57. 57.

    Antipov, D., Korobeynikov, A., McLean, J. S. & Pevzner, P. A. HybridSPAdes: an algorithm for hybrid assembly of short and long reads. Bioinformatics 32, 1009–1015 (2016).

  58. 58.

    Robert, C. & Watson, M. The incredible complexity of RNA splicing. Genome Biol. 17, 265 (2016).

  59. 59.

    Parkhomchuk, D. V. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. 37, e123 (2009).

  60. 60.

    Levin, J. Z. et al. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat. Methods 7, 709–715 (2010).

  61. 61.

    Morlan, J. D., Qu, K. & Sinicropi, D. V. Selective depletion of rRNA enables whole transcriptome profiling of archival fixed tissue. PLOS ONE 7, e42882 (2012).

  62. 62.

    Hafner, M. et al. Identification of microRNAs and other small regulatory RNAs using cDNA library sequencing. Methods 44, 3–12 (2008).

  63. 63.

    Chen, Z. & Duan, X. Ribosomal RNA depletion for massively parallel bacterial RNA-sequencing applications. Methods Mol. Biol. 733, 93–103 (2011).

  64. 64.

    Herbert, Z. T. et al. Cross-site comparison of ribosomal depletion kits for Illumina RNAseq library construction. BMC Genomics 19, 199 (2018).

  65. 65.

    Zhao, W. et al. Comparison of RNA-Seq by poly (A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling. BMC Genomics 15, 419 (2014).

  66. 66.

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

  67. 67.

    Tian, B. & Manley, J. L. Alternative polyadenylation of mRNA precursors. Nat. Rev. Mol. Cell. Biol. 18, 18–30 (2016).

  68. 68.

    Fullwood, M. J., Wei, C., Liu, E. T. & Ruan, Y. Next-generation DNA sequencing of paired-end tags (PET) for transcriptome and genome analyses. Genome Res. 19, 521–532 (2009).

  69. 69.

    Morrissy, A. S. et al. Next-generation tag sequencing for cancer gene expression profiling. Genome Res. 19, 1825–1835 (2009).

  70. 70.

    Moll, P., Ante, M., Seitz, A. & Reda, T. Q. QuantSeq 3΄ mRNA sequencing for RNA quantification. Nat. Methods 11, 972 (2014).

  71. 71.

    Herzog, V. A. et al. Thiol-linked alkylation of RNA to assess expression dynamics. Nat. Methods 14, 1198–1204 (2017).

  72. 72.

    Chen, W. et al. Alternative polyadenylation: methods, findings, and impacts. Genomics Proteomics Bioinformatics 15, 287–300 (2017).

  73. 73.

    Shepard, P. J., Choi, E., Lu, J., Flanagan, L. A. & Hertel, K. J. Complex and dynamic landscape of RNA polyadenylation revealed by PAS-Seq. RNA 17, 761–772 (2011).

  74. 74.

    Chang, H., Lim, J., Ha, M. & Kim, V. N. TAIL-seq: genome-wide determination of poly(A) tail length and 3΄ end modifications. Mol. Cell 53, 1044–1052 (2014).

  75. 75.

    Licatalosi, D. D. et al. HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature 456, 464–469 (2008).

  76. 76.

    Murata, M. et al. Detecting expressed genes using CAGE. Methods Mol. Biol. 1164, 67–85 (2014).

  77. 77.

    Batut, P., Dobin, A., Plessy, C., Carninci, P. & Gingeras, T. R. High-fidelity promoter profiling reveals widespread alternative promoter usage and transposon-driven developmental gene expression. Genome Res. 23, 169–180 (2013).

  78. 78.

    Islam, S. et al. Highly multiplexed and strand-specific single-cell RNA 5΄ end sequencing. Nat. Protoc. 7, 813–828 (2012).

  79. 79.

    The FANTOM Consortium & The RIKEN PMI and CLST (DGT). A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).

  80. 80.

    Adiconis, X. et al. Comprehensive comparative analysis of 5΄-end RNA-sequencing methods. Nat. Methods 15, 505–511 (2018). A primary reference for users considering CAGE or similar methods.

  81. 81.

    Parekh, S., Ziegenhain, C., Vieth, B., Enard, W. & Hellmann, I. The impact of amplification on differential expression analyses by RNA-seq. Sci. Rep. 6, 25533 (2016).

  82. 82.

    Hong, J. & Gresham, D. Incorporation of unique molecular identifiers in TruSeq adapters improves the accuracy of quantitative sequencing. Biotechniques 63, 221–226 (2017).

  83. 83.

    Fu, Y., Wu, P.-H., Beane, T., Zamore, P. D. & Weng, Z. Elimination of PCR duplicates in RNA-seq and small RNA-seq using unique molecular identifiers. BMC Genomics 19, 531 (2018). A paper reporting that the majority of RNA-seq duplicates are driven by RNA input rather than sequencing depth and PCR cycles. It also shows that computational removal of duplicates can have unintended consequences on the analysis results.

  84. 84.

    Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65, 631–643 (2017). A comparison of six scRNA-seq methods that describes the pros and cons of the various approaches and is an excellent introduction to scRNA-seq.

  85. 85.

    Wang, L. et al. Measure transcript integrity using RNA-seq data. BMC Bioinformatics 17, 58 (2016).

  86. 86.

    Romero, I. G., Pai, A. A., Tung, J. & Gilad, Y. RNA-seq: impact of RNA degradation on transcript quantification. BMC Biol. 12, 42 (2014).

  87. 87.

    Cieslik, M. et al. The use of exome capture RNA-seq for highly degraded RNA with application to clinical cancer sequencing. Genome Res. 25, 1372–1381 (2015).

  88. 88.

    Adiconis, X. et al. Comparative analysis of RNA sequencing methods for degraded or low-input samples. Nat. Methods 10, 623–629 (2013). A paper covering many of the factors that users with low-quality samples must consider before starting RNA-seq experiments.

  89. 89.

    Schuierer, S. et al. A comprehensive assessment of RNA-seq protocols for degraded and low-quantity samples. BMC Genomics 18, 442 (2017).

  90. 90.

    Hodges, E. et al. Genome-wide in situ exon capture for selective resequencing. Nat. Genet. 39, 1522–1527 (2007).

  91. 91.

    Sigurgeirsson, B., Emanuelsson, O. & Lundeberg, J. Sequencing degraded RNA addressed by 3΄ tag counting. PLOS ONE 9, e91851 (2014).

  92. 92.

    Li, W. et al. Comprehensive evaluation of AmpliSeq transcriptome, a novel targeted whole transcriptome RNA sequencing methodology for global gene expression analysis. BMC Genomics 16, 1069 (2015).

  93. 93.

    Lamarre, S. et al. Optimization of an RNA-Seq differential gene expression analysis depending on biological replicate number and library size. Front. Plant Sci. 9, 108 (2018).

  94. 94.

    Hansen, K. D., Wu, Z., Irizarry, R. A. & Leek, J. T. Sequencing technology does not eliminate biological variability. Nat. Biotechnol. 29, 572–573 (2011). Required reading for anyone considering RNA-seq or other -omics technologies. A well-written reminder of why quantitative RNA experiments will always need replicates, even if RNA assay technologies were perfect. The authors caution users against being over-enthusiastic about new technologies and discarding lessons learned about experimental design.

  95. 95.

    Norton, S. S., Vaquero-Garcia, J., Lahens, N. F., Grant, G. R. & Barash, Y. Outlier detection for improved differential splicing quantification from RNA-Seq experiments with replicates. Bioinformatics 34, 1488–1497 (2017).

  96. 96.

    Busby, M. A., Stewart, C., Miller, C. A., Grzeda, K. R. & Marth, G. T. Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression. Bioinformatics 29, 656–657 (2013).

  97. 97.

    Wu, Z. & Wu, H. in Statistical Genomics: Methods and Protocols (eds Mathé, E. & Davis, S.) 379–390 (Humana Press, 2016).

  98. 98.

    Wu, H., Wang, C. & Wu, Z. PROPER: comprehensive power evaluation for differential expression using RNA-seq. Bioinformatics 31, 233–241 (2015).

  99. 99.

    Gaye, A. Extending the R Library PROPER to enable power calculations for isoform-level analysis with EBSeq. Front. Genet. 7, 225 (2017).

  100. 100.

    Schurch, N. J. et al. How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? RNA 22, 1641–1641 (2016).

  101. 101.

    Montgomery, S. B. et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 464, 773–777 (2010).

  102. 102.

    The ENCODE Consortium. Standards, guidelines and best practices for RNA-Seq — V1.0 (June 2011). UCSC https://genome.ucsc.edu/ENCODE/protocols/dataStandards/ENCODE_RNAseq_Standards_V1.0.pdf (2011).

  103. 103.

    Conesa, A. et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 17, 13 (2016). An overview of computational tools and methods used in RNA-seq analysis.

  104. 104.

    Lei, R., Ye, K., Gu, Z. & Sun, X. Diminishing returns in next-generation sequencing (NGS) transcriptome data. Gene 557, 82–87 (2014).

  105. 105.

    Li, B. & Dewey, C. N. RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

  106. 106.

    Chhangawala, S., Rudy, G., Mason, C. E. & Rosenfeld, J. A. The impact of read length on quantification of differentially expressed genes and splice junction detection. Genome Biol. 16, 131 (2015).

  107. 107.

    Katz, Y., Wang, E. T., Airoldi, E. M. & Burge, C. B. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7, 1009–1015 (2010).

  108. 108.

    Alamancos, G. P., Agirre, E. & Eyras, E. Methods to study splicing from high-throughput RNA sequencing data. Methods Mol. Biol. 1126, 357–397 (2014).

  109. 109.

    Seyednasrollah, F., Laiho, A. & Elo, L. L. Comparison of software packages for detecting differential expression in RNA-seq studies. Brief. Bioinform. 16, 59–70 (2013).

  110. 110.

    Williams, C. R., Baccarella, A., Parrish, J. Z. & Kim, C. C. Empirical assessment of analysis workflows for differential expression analysis of human samples using RNA-seq. BMC Bioinformatics 18, 38 (2017). A useful overview of several popular computational analysis tools and how they can be used in combination.

  111. 111.

    Cock, P. J. A., Fields, C. J., Goto, N., Heuer, M. L. & Rice, P. M. The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res. 38, 1767–1771 (2010).

  112. 112.

    Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

  113. 113.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, (15–21 (2013).

  114. 114.

    Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

  115. 115.

    Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

  116. 116.

    Pertea, M., Kim, D., Pertea, G., Leek, J. T. & Steven, L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie, and Ballgown. Nat. Protoc. 11, 1650–1667 (2017).

  117. 117.

    Xie, Y. et al. SOAPdenovo-Trans: De novo transcriptome assembly with short RNA-Seq reads. Bioinformatics 30, 1660–1666 (2014).

  118. 118.

    Patro, R., Mount, S. M. & Kingsford, C. Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat. Biotechnol. 32, 462–464 (2014).

  119. 119.

    Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 4–8 (2016).

  120. 120.

    Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

  121. 121.

    Wu, D. C., Yao, J., Ho, K. S., Lambowitz, A. M. & Wilke, C. O. Limitations of alignment-free tools in total RNA-seq quantification. BMC Genomics 19, 510 (2018). A useful comparison of popular mRNA-seq analysis methods, with particular emphasis on alignment-free tools.

  122. 122.

    Yang, C., Wu, P.-Y., Tong, L., Phan, J. H. & Wang, M. D. The impact of RNA-seq aligners on gene expression estimation. ACM BMB 9, 462–471 (2016).

  123. 123.

    Robert, C. & Watson, M. Errors in RNA-Seq quantification affect genes of relevance to human disease. Genome Biol. 16, 177 (2015). An experimental demonstration of the importance of read mapping and quantification in the computational analysis of mRNA-seq experiments. This paper clearly describes the impact that different alignments and quantification methods can have on biological conclusions.

  124. 124.

    Zytnicki, M. mmquant: how to count multi-mapping reads? BMC Bioinformatics 18, 411 (2017).

  125. 125.

    McDermaid, A. et al. A new machine learning-based framework for mapping uncertainty analysis in RNA-Seq read alignment and gene expression estimation. Front. Genet. 9, 313 (2018).

  126. 126.

    Fonseca, N. A., Marioni, J. C. & Brazma, A. RNA-Seq gene profiling — a systematic empirical comparison. PLOS ONE 9, e107026 (2014).

  127. 127.

    Teng, M. et al. A benchmark for RNA-seq quantification pipelines. Genome Biol. 17, 74 (2016).

  128. 128.

    Quinn, T. P., Crowley, T. M. & Richardson, M. F. Benchmarking differential expression analysis tools for RNA-Seq: normalization-based versus log-ratio transformation-based methods. BMC Bioinformatics 19, 274 (2018).

  129. 129.

    Vijay, N., Poelstra, J. W., Künstner, A. & Wolf, J. B. W. Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensive in silico assessment of RNA-seq experiments. Mol. Ecol. 22, 620–634 (2013).

  130. 130.

    Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 4, 1521 (2016).

  131. 131.

    Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

  132. 132.

    Turro, E. et al. Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads. Genome Biol. 12, R13 (2011).

  133. 133.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq — a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

  134. 134.

    Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

  135. 135.

    Risso, D., Schwartz, K., Sherlock, G. & Dudoit, S. GC-content normalization for RNA-seq data. BMC Bioinformatics 12, 480 (2011).

  136. 136.

    Wagner, G. P., Kin, K. & Lynch, V. J. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 131, 281–285 (2012).

  137. 137.

    Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902 (2014).

  138. 138.

    Bourgon, R., Gentleman, R. & Huber, W. Independent filtering increases detection power for high-throughput experiments. Proc. Natl Acad. Sci. USA 107, 9456–9551 (2010).

  139. 139.

    Bullard, J. H., Purdom, E., Hansen, K. D. & Dudoit, S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC 11, 94–107 (2010).

  140. 140.

    Dillies, M. A. et al. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief. Bioinform. 14, 671–683 (2013).

  141. 141.

    Li, X. et al. A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data. PLOS ONE 12, e0176185 (2017).

  142. 142.

    Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

  143. 143.

    Robinson, M. D., Mccarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

  144. 144.

    Chen, K. et al. The overlooked fact: fundamental need for spike-in control for virtually all genome-wide analyses. Mol. Cell. Biol. 36, 662–667 (2016).

  145. 145.

    Hardwick, S. A., Deveson, I. W. & Mercer, T. R. Reference standards for next-generation sequencing. Nat. Rev. Genet. 18, 473–484 (2017). A review of the use of spike-in controls and their associated statistical principles. It introduces readers to the concept of commutability: the ability of a spike-in control to perform comparably to experimental RNA samples.

  146. 146.

    Pine, P. S. et al. Evaluation of the External RNA Controls Consortium (ERCC) reference material using a modified Latin square design. BMC Biotechnol. 16, 54 (2016).

  147. 147.

    Paul, L. et al. SIRVs: spike-in RNA variants as external isoform controls in RNA-sequencing. Preprint at bioRxiv https://doi.org/10.1101/080747 (2016).

  148. 148.

    Hardwick, S. A. et al. Spliced synthetic genes as internal controls in RNA sequencing experiments. Nat. Methods 13, 792–798 (2016).

  149. 149.

    Lovén, J. et al. Revisiting global gene expression analysis. Cell 151, 476–482 (2012).

  150. 150.

    Risso, D., Ngai, J., Speed, T. & Dudoit, S. in Statistical Analysis of Next Generation Sequencing Data (eds Datta, S. & Nettleton, D.) 169–190 (Springer, 2014).

  151. 151.

    Qing, T., Yu, Y., Du, T. T. & Shi, L. M. mRNA enrichment protocols determine the quantification characteristics of external RNA spike-in controls in RNA-Seq studies. Sci. China Life Sci. 56, 134–142 (2013).

  152. 152.

    Leshkowitz, D. et al. Using synthetic mouse spike-in transcripts to evaluate RNA-seq analysis tools. PLOS ONE 11, e0153782 (2016).

  153. 153.

    Lun, A. T. L., Calero-nieto, F. J., Haim-vilmovsky, L., Göttgens, B. & Marioni, J. C. Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data. Genome Res. 27, 1795–1806 (2017).

  154. 154.

    Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

  155. 155.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  156. 156.

    Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

  157. 157.

    Frazee, A. et al. Ballgown bridges the gap between transcriptome assembly and expression analysis. Nat. Biotechnol. 33, 243–246 (2015).

  158. 158.

    Rapaport, F. et al. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol. 14, R95 (2013).

  159. 159.

    Montoro, D. T. et al. A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Nature 560, 319–324 (2018).

  160. 160.

    Asp, M. et al. Spatial detection of fetal marker genes expressed at low level in adult human heart tissue. Sci. Rep. 7, 12941 (2017).

  161. 161.

    Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

  162. 162.

    Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015). This review provides an overview and in-depth discussion of scRNA-seq transcript quantitation methods. The authors highlight the analytical challenges that are unique to single-cell experiments.

  163. 163.

    Svensson, V., Vento-Tormo, R. & Teichmann, S. A. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13, 599–604 (2018). This review is an excellent introduction to the full range of single-cell sequencing methods.

  164. 164.

    Leelatian, N. et al. Single cell analysis of human tissues and solid tumors with mass cytometry. Cytometry B 92, 68–78 (2018). A useful description of the pitfalls of tissue dissociation for users of single-cell sequencing to consider.

  165. 165.

    Hines, W. C., Su, Y., Kuhn, I., Polyak, K. & Bissell, M. J. Sorting out the FACS: a devil in the details. Cell Rep. 6, 779–781 (2014).

  166. 166.

    Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011).

  167. 167.

    Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1098 (2013).

  168. 168.

    Goldstein, L. D. et al. Massively parallel nanowell-based single-cell gene expression profiling. BMC Genomics 18, 519 (2017).

  169. 169.

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

  170. 170.

    Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

  171. 171.

    Cao, J. et al. Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing. Science 357, 661–667 (2017).

  172. 172.

    Rosenberg, A. B. et al. Single cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

  173. 173.

    Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17, 77 (2016).

  174. 174.

    Sena, J. A. et al. Unique molecular identifiers reveal a novel sequencing artefact with implications for RNA-Seq based gene expression analysis. Sci. Rep. 8, 13121 (2018).

  175. 175.

    Dal Molin, A. & Di Camillo, B. How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives. Brief. Bioinform. https://doi.org/10.1093/bib/bby007 (2018).

  176. 176.

    Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

  177. 177.

    Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

  178. 178.

    10x Genomics. Application note. Chromium™ — transcriptional profiling of 1.3 million brain cells with the Chromium single cell 3΄ solution. 10x Genomics http://go.10xgenomics.com/l/172142/2017-06-09/bsylz/172142/31729/LIT000015_Chromium_Million_Brain_Cells_Application_Note_Digital_RevA.pdf (2018).

  179. 179.

    Regev, A. et al. The human cell atlas. eLife 6, e27041 (2017).

  180. 180.

    Insel, T. R., Landis, S. C. & Collins, F. S. The NIH BRAIN initiative. 340, 687–689 (2013).

  181. 181.

    Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 599, 594–599 (2018).

  182. 182.

    Hui Ryu, K., Huang, L., Min Kang, H. & Schiefelbein, J. Single-cell RNA sequencing resolves molecular relationships among individual plant cells. Plant Physiol. 179, 1444–1456 (2019).

  183. 183.

    Chen, J. et al. Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq. Nat. Protoc. 12, 566–580 (2017).

  184. 184.

    Stahl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

  185. 185.

    Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 1467, 1463–1467 (2019).

  186. 186.

    Crosetto, N., Bienko, M. & van Oudenaarden, A. Spatially resolved transcriptomics and beyond. Nat. Rev. Genet. 16, 57–66 (2015).

  187. 187.

    Moor, A. E. & Itzkovitz, S. Spatial transcriptomics: paving the way for tissue-level systems biology. Curr. Opin. Biotechnol. 46, 126–133 (2017). This review of spatial RNA-seq methods introduces the main methods in more detail and discusses some of the technical challenges that remain to be resolved.

  188. 188.

    Lein, E., Borm, L. E. & Linnarsson, S. The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science 358, 64–69 (2017).

  189. 189.

    Datta, S. et al. Laser capture microdissection: big data from small samples. Histol. Histopathol. 30, 1255–1269 (2015).

  190. 190.

    Lovatt, D., Bell, T. & Eberwine, J. Single-neuron isolation for RNA analysis using pipette capture and laser capture microdissection. Cold Spring Harb. Protoc. 2015, 60–68 (2015).

  191. 191.

    Cubi, R. et al. Laser capture microdissection enables transcriptomic analysis of dividing and quiescent liver stages of Plasmodium relapsing species. Cell. Microbiol. 19, e12735 (2017).

  192. 192.

    Giacomello, S. et al. Spatially resolved transcriptome profiling in model plant species. Nat. Plants 3, 17061 (2017).

  193. 193.

    Moncada, R. et al. Integrating single-cell RNA-Seq with spatial transcriptomics in pancreatic ductal adenocarcinoma using multimodal intersection analysis. Preprint at bioRxiv https://doi.org/10.1101/254375 (2018).

  194. 194.

    Ke, R. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013).

  195. 195.

    Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

  196. 196.

    Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M. & Cai, L. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360–361 (2014).

  197. 197.

    Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

  198. 198.

    Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).

  199. 199.

    Wang, G., Moffitt, J. R. & Zhuang, X. Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy. Sci. Rep. 8, 4847 (2018).

  200. 200.

    Pichon, X., Lagha, M., Mueller, F. & Bertrand, E. A. Growing toolbox to image gene expression in single cells: sensitive approaches for demanding challenges. Mol. Cell 71, 468–480 (2018).

  201. 201.

    Maniatis, S. et al. Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science 93, 89–93 (2019).

  202. 202.

    Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).

  203. 203.

    Edsgärd, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).

  204. 204.

    Core, L. J., Waterfall, J. & Lis, J. Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science 322, 1845–1848 (2008).

  205. 205.

    Core, L. J. & Lis, J. T. Transcription regulation through promoter-proximal pausing of RNA polymerase II. Science 319, 1791–1792 (2008).

  206. 206.

    Skalska, L., Beltran-nebot, M., Ule, J. & Jenner, R. G. Regulatory feedback from nascent RNA to chromatin and transcription. Nat. Rev. Mol. Cell. Biol. 18, 331–337 (2017).

  207. 207.

    Tani, H. et al. Genome-wide determination of RNA stability reveals hundreds of short-lived noncoding transcripts in mammals. Genome Res. 22, 947–956 (2012).

  208. 208.

    Paulsen, M. T. et al. Coordinated regulation of synthesis and stability of RNA during the acute TNF-induced proinflammatory response. Proc. Natl Acad. Sci. USA 110, 2240–2245 (2013).

  209. 209.

    Kwak, H., Fuda, N. J., Core, L. J. & Lis, J. T. Precise maps of RNA polymerase reveal how promoters direct initiation and pausing. Science 339, 950–953 (2013).

  210. 210.

    Nojima, T., Gomes, T., Carmo-fonseca, M. & Proudfoot, N. J. Mammalian NET-seq analysis defines nascent RNA profiles and associated RNA processing genome-wide. Nat. Protoc. 11, 413–428 (2016).

  211. 211.

    Nagari, A., Murakami, S., Malladi, V. S. & Kraus, W. L. Computational approaches for mining GRO-Seq data to identify and characterize active enhancers. Methods Mol. Biol. 1468, 121–138 (2017).

  212. 212.

    Kruesi, W. S., Core, L. J., Waters, C. T., Lis, J. T. & Meyer, B. J. Condensin controls recruitment of RNA polymerase II to achieve nematode X-chromosome dosage compensation. eLife 18, e00808 (2013).

  213. 213.

    Scruggs, B. S. et al. Bidirectional transcription arises from two distinct hubs of transcription factor binding and active chromatin. Mol. Cell 58, 1101–1112 (2015).

  214. 214.

    Churchman, L. S. & Weissman, J. S. Nascent transcript sequencing visualizes transcription at nucleotide resolution. Nature 469, 368–373 (2011).

  215. 215.

    Nojima, T. et al. Mammalian NET-Seq reveals genome-wide nascent transcription coupled to RNA processing. Cell 161, 526–540 (2015).

  216. 216.

    Wallace, E. W. J. & Beggs, J. D. Extremely fast and incredibly close: cotranscriptional splicing in budding yeast. RNA 23, 601–610 (2017).

  217. 217.

    Rabani, M. et al. Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells. Nat. Biotechnol. 29, 436–442 (2011).

  218. 218.

    Schwalb, B. et al. TT-seq maps the human transient transcriptome. Science 352, 1225–1228 (2016).

  219. 219.

    Marzi, M. J. & Nicassio, F. Uncovering the stability of mature miRNAs by 4-thio-uridine metabolic labeling. Methods Mol. Biol. 1823, 141–152 (2018).

  220. 220.

    Riml, C. et al. Osmium-mediated transformation of 4-thiouridine to cytidine as key to study RNA dynamics by sequencing. Angew. Chem. Int. Ed. 56, 13479–13483 (2017).

  221. 221.

    Schofield, J. A., Duffy, E. E., Kiefer, L., Sullivan, M. C. & Simon, M. D. TimeLapse-seq: adding a temporal dimension to RNA sequencing through nucleoside recoding. Nat. Methods 15, 221–225 (2018).

  222. 222.

    Muhar, M. et al. SLAM-seq defines direct gene-regulatory functions of the BRD4-MYC axis. Science 360, 800–805 (2018).

  223. 223.

    Matsushima, W. et al. SLAM-ITseq: sequencing cell type-specific transcriptomes without cell sorting. Development 145, dev164640 (2018).

  224. 224.

    Jürges, C., Dölken, L. & Erhard, F. Dissecting newly transcribed and old RNA using GRAND-SLAM. Bioinformatics 34, 218–226 (2018).

  225. 225.

    Shah, S. et al. Dynamics and spatial genomics of the nascent transcriptome by intron seqFISH. Cell 174, 363–376 (2018).

  226. 226.

    Johannes, G., Carter, M. S., Eisen, M. B., Brown, P. O. & Sarnow, P. Identification of eukaryotic mRNAs that are translated at reduced cap binding complex eIF4F concentrations using a cDNA microarray. Proc. Natl Acad. Sci. USA 96, 13118–13123 (1999).

  227. 227.

    Yamashita, R. et al. Genome-wide characterization of transcriptional start sites in humans by integrative transcriptome analysis. Genome Res. 21, 775–789 (2011).

  228. 228.

    Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. S. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).

  229. 229.

    Wang, E. T. et al. Dysregulation of mRNA localization and translation in genetic disease. J. Neurosci. 36, 11418–11426 (2016).

  230. 230.

    Parker, M. W. et al. Fibrotic extracellular matrix activates a profibrotic positive feedback loop. J. Clin. Invest. 124, 1622–1635 (2014).

  231. 231.

    Moreno, J. A. et al. Sustained translational repression by eIF2a–P mediates prion neurodegeneration. Nature 485, 507–511 (2012).

  232. 232.

    Bhat, M. et al. Targeting the translation machinery in cancer. Nat. Rev. Drug Discov. 14, 261–278 (2015).

  233. 233.

    Leibovitch, M. & Topisirovic, I. Dysregulation of mRNA translation and energy metabolism in cancer. Adv. Biol. Regul. 67, 30–39 (2018).

  234. 234.

    Liang, S. et al. Polysome-profiling in small tissue samples. Nucleic Acids Res. 46, e3 (2017).

  235. 235.

    Picelli, S. et al. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res. 24, 2033–2040 (2014).

  236. 236.

    Floor, S. N., Doudna, J. A., States, U. & Initiative, I. G. Tunable protein synthesis by transcript isoforms in human cells. eLife 5, e10921 (2016).

  237. 237.

    Blair, J. et al. Widespread translational remodeling during human neuronal differentiation. Cell Rep. 21, 2005–2016 (2017).

  238. 238.

    Steitz, J. Polypeptide chain initiation: nucleotide sequences of the three ribosomal binding sites in bacteriophage R17 RNA. Nature 224, 957–964 (1969).

  239. 239.

    Hsu, P. Y. et al. Super-resolution ribosome profiling reveals unannotated translation events in Arabidopsis. Proc. Natl Acad. Sci. USA 113, E7126–E7135 (2016).

  240. 240.

    McGlincy, N. J. & Ingolia, N. T. Transcriptome-wide measurement of translation by ribosome profiling. Methods 126, 112–129 (2017).

  241. 241.

    Calviello, L. & Ohler, U. Beyond read-counts: ribo-seq data analysis to understand the functions of the transcriptome. Trends Genet. 33, 728–744 (2017).

  242. 242.

    Erhard, F. et al. Improved Ribo-seq enables identification of cryptic translation events. Nat. Methods 15, 363–366 (2018).

  243. 243.

    Li, W., Wang, W., Uren, P. J., Penalva, L. O. F. & Smith, A. D. Riborex: fast and flexible identification of differential translation from Ribo-seq data. Bioinformatics 33, 1735–1737 (2017).

  244. 244.

    Zhong, Y. et al. RiboDiff: Detecting changes of mRNA translation efficiency from ribosome footprints. Bioinformatics 33, 139–141 (2017).

  245. 245.

    Paulet, D., David, A. & Rivals, E. Ribo-seq enlightens codon usage bias. DNA Res. 24, 303–310 (2017).

  246. 246.

    Gao, X. et al. Quantitative profiling of initiating ribosomes in vivo. Nat. Methods 12, 147–153 (2015).

  247. 247.

    Archer, S. K., Shirokikh, N. E., Beilharz, T. H. & Preiss, T. Dynamics of ribosome scanning and recycling revealed by translation complex profiling. Nature 535, 570–574 (2016).

  248. 248.

    Iwasaki, S. & Ingolia, N. T. The growing toolbox for protein synthesis studies. Trends Biochem. Sci. 42, 612–624 (2017).

  249. 249.

    Kwok, C. K., Tang, Y., Assmann, S. M. & Bevilacqua, P. C. The RNA structurome: transcriptome-wide structure probing with next-generation sequencing. Trends Biochem. Sci. 40, 221–232 (2015).

  250. 250.

    Holley, R. W. et al. Structure of a ribonucleic acid. Science 147, 1462–1465 (1965).

  251. 251.

    Merino, E. J., Wilkinson, K. A., Coughlan, J. L. & Weeks, K. M. RNA structure analysis at single nucleotide resolution by selective 2΄-hydroxyl acylation and primer extension (SHAPE). J. Am. Chem. Soc. 127, 4223–4231 (2005).

  252. 252.

    Strobel, E. J., Yu, A. M. & Lucks, J. B. High-throughput determination of RNA structures. Nat. Rev. Genet. 19, 615–634 (2018). A good introduction to RNA structural analysis using RNA-seq.

  253. 253.

    Kertesz, M. et al. Genome-wide measurement of RNA secondary structure in yeast. Nature 467, 103–107 (2010).

  254. 254.

    Underwood, J. G. et al. FragSeq: Transcriptome-wide RNA structure probing using high-throughput sequencing. Nat. Methods 7, 995–1001 (2010).

  255. 255.

    Lucks, J. B. et al. Multiplexed RNA structure characterization with selective 2΄-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). Proc. Natl Acad. Sci. USA 108, 11063–11068 (2011).

  256. 256.

    Ding, Y. et al. In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features. Nature 505, 696–700 (2014).

  257. 257.

    Rouskin, S., Zubradt, M., Washietl, S., Kellis, M. & Weissman, J. S. Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo. Nature 505, 701–705 (2014).

  258. 258.

    Siegfried, N. A., Busan, S., Rice, G. M., Nelson, J. A. E. & Weeks, K. M. RNA motif discovery by SHAPE and mutational profiling (SHAPE-MaP). Nat. Methods 11, 959–965 (2014).

  259. 259.

    Zubradt, M. et al. DMS-MaPseq for genome-wide or targeted RNA structure probing in vivo. Nat. Methods 14, 75–82 (2017).

  260. 260.

    Incarnato, D. et al. In vivo probing of nascent RNA structures reveals principles of cotranscriptional folding. Nucleic Acids Res. 45, 9716–9725 (2017).

  261. 261.

    Novoa, E. M., Beaudoin, J., Giraldez, A. J., Mattick, J. S. & Kellis, M. Best practices for genome-wide RNA structure analysis: combination of mutational profiles and drop-off information. Preprint at bioRxiv https://doi.org/10.1101/176883 (2017).

  262. 262.

    Lee, B. et al. Comparison of SHAPE reagents for mapping RNA structures inside living cells. RNA 23, 169–174 (2017).

  263. 263.

    Tang, Y., Assmann, S. M. & Bevilacqua, P. C. Protein structure is related to RNA structural reactivity in vivo. J. Mol. Biol. 428, 758–766 (2016).

  264. 264.

    Jain, A. & Vale, R. D. RNA phase transitions in repeat expansion disorders. Nature 546, 243–247 (2017).

  265. 265.

    Warner, K. D., Hajdin, C. E. & Weeks, K. M. Principles for targeting RNA with drug-like small molecules. Nat. Rev. Drug Discov. 17, 547–558 (2018).

  266. 266.

    Kudla, G., Granneman, S., Hahn, D., Beggs, J. D. & Tollervey, D. Cross-linking, ligation, and sequencing of hybrids reveals RNA–RNA interactions in yeast. Proc. Natl Acad. Sci. USA 108, 10010–10015 (2011).

  267. 267.

    Kretz, M. et al. Control of somatic tissue differentiation by the long non-coding RNA TINCR. Nature 493, 231–235 (2013).

  268. 268.

    Engreitz, J. M. et al. RNA-RNA interactions enable specific targeting of noncoding RNAs to nascent pre-mRNAs and chromatin sites. Cell 159, 188–199 (2014).

  269. 269.

    Lu, Z. et al. RNA duplex map in living cells reveals higher-order transcriptome structure. Cell 165, 1267–1279 (2016).

  270. 270.

    Aw, J. G. et al. In vivo mapping of eukaryotic RNA interactomes reveals principles of higher-order organization and regulation. Mol. Cell 62, 603–617 (2016).

  271. 271.

    Sharma, E. et al. Global mapping of human RNA-RNA interactions. Mol. Cell 62, 618–626 (2016).

  272. 272.

    Gong, J. et al. RISE: a database of RNA interactome from sequencing experiments. Nucleic Acids Res. 46, 194–201 (2018).

  273. 273.

    Zhang, X. et al. RAID: a comprehensive resource for human RNA-associated (RNA–RNA/RNA–protein) interaction. RNA 20, 989–993 (2014).

  274. 274.

    Schönberger, B., Schaal, C., Schäfer, R. & Voß, B. RNA interactomics: recent advances and remaining challenges. F1000Res. 7, 1824 (2018).

  275. 275.

    Johnson, D. S., Mortazavi, A., Myers, R. M. & Wold, B. Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 1497–1502 (2007).

  276. 276.

    Tenenbaum, S. A., Carson, C. C., Lager, P. J. & Keene, J. D. Identifying mRNA subsets in messenger ribonucleoprotein complexes by using cDNA arrays. Proc. Natl Acad. Sci. USA 97, 14085–14090 (2000).

  277. 277.

    Zhao, J. et al. Genome-wide Identification of Polycomb-Associated RNAs by RIP-seq. Mol. Cell 40, 939–953 (2010).

  278. 278.

    Mili, S. & Steitz, J. Evidence for reassociation of RNA-binding proteins after cell lysis: Implications for the interpretation of immunoprecipitation analyses. RNA 10, 1692–1694 (2004).

  279. 279.

    Niranjanakumari, S., Lasda, E. & Brazas, R. Reversible cross-linking combined with immunoprecipitation to study RNA–protein interactions in vivo. Methods 26, 182–190 (2002).

  280. 280.

    Hendrickson, G., Kelley, D., Tenen, D., Bernstein, D. & Rinn, J. Widespread RNA binding by chromatin-associated proteins. Genome Biol. 17, 28 (2016).

  281. 281.

    Ule, J. et al. CLIP identifies Nova-regulated RNA networks in the brain. Science 302, 1212–1215 (2003).

  282. 282.

    König, J. et al. iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution. Nat. Struct. Mol. Biol. 17, 909–915 (2010).

  283. 283.

    Hafner, M. et al. Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141, 129–141 (2010).

  284. 284.

    Garzia, A., Meyer, C., Morozov, P., Sajek, M. & Tuschl, T. Optimization of PAR-CLIP for transcriptome-wide identification of binding sites of RNA-binding proteins. Methods 118, 24–40 (2017).

  285. 285.

    Zarnegar, B. J. et al. IrCLIP platform for efficient characterization of protein-RNA interactions. Nat. Methods 13, 489–492 (2016).

  286. 286.

    Van Nostrand, E. L. et al. Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods 13, 508–514 (2016).

  287. 287.

    Nostrand, E. L. Van et al. A large-scale binding and functional map of human RNA binding proteins. Preprint at bioRxiv https://doi.org/10.1101/179648 (2017).

  288. 288.

    Chakrabarti, A. M., Haberman, N., Praznik, A., Luscombe, N. M. & Ule, J. Data science issues in studying protein–RNA interactions with CLIP technologies. Annu. Rev. 1, 235–261 (2018).

  289. 289.

    Lee, F. C. Y. & Ule, J. Advances in CLIP technologies for studies of protein-RNA interactions. Mol. Cell 69, 354–369 (2018). A review of RNA–protein interaction methods, with a 5-page table describing the methodological advances of each. Vital reading for anyone considering CLIP–seq analysis.

  290. 290.

    Buenrostro, J. D. et al. Quantitative analysis of RNA-protein interactions on a massively parallel array reveals biophysical and evolutionary landscapes. Nat. Biotechnol. 32, 562–568 (2014).

  291. 291.

    Cook, K. B., Hughes, T. R. & Morris, Q. D. High-throughput characterization of protein-RNA interactions. Brief. Funct. Genomics 14, 74–89 (2015).

  292. 292.

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

  293. 293.

    Doebele, R. C. et al. An oncogenic NTRK fusion in a patient with soft-tissue sarcoma with response to the tropomyosin-related kinase inhibitor. Cancer Discov. 5, 1049–1057 (2015).

  294. 294.

    Byron, S. A., Van Keuren-Jensen, K. R., Engelthaler, D. M., Carpten, J. D. & Craig, D. W. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat. Rev. Genet. 17, 257–271 (2016).

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We thank J. Marioni and J. Ule for their valuable comments on the manuscript.

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J.H., R.S. and M.G. researched the literature. J.H. and R.S. discussed the content and wrote and edited the article.

Correspondence to James Hadfield.

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Supplementary information

Supplementary Information


Differential gene expression

(DGE). The analysis methods that together allow users to determine the quantitative changes in expression levels between experimental groups.

Read depth

The total number of sequencing reads obtained for a sample. This should not be confused with coverage, or sequencing depth, in genome sequencing, which refers to how many times individual nucleotides are sequenced.


Sequencing technologies that generate reads of up to 500 bp, more commonly 100–300 bp, that represent fragmented or degraded mRNAs.


Sequencing technologies that generate reads of over 1,000 bp that represent either full-length or near-full-length mRNAs.

Direct RNA sequencing

(dRNA-seq). Sequencing technologies that generate reads by directly sequencing RNA without modification or reverse transcription, usually with the aim of sequencing full-length or near-full-length mRNAs.

Multi-mapped reads

Sequencing reads from homologous regions of the transcriptome that cannot be unambiguously mapped to the transcriptome or genome.

Synthetic long reads

A method for generating long reads from multiple short reads by assembly.

Unique molecular identifiers

(UMIs). Short sequences or barcodes usually added during RNA sequencing (RNA-seq) library preparation (but also by direct RNA ligation), before amplification, that mark a sequence read as coming from a specific starting molecule. The approach is used to reduce the quantitative biases of RNA-seq and is particularly useful in low-input or single-cell experiments.

Read length

The length of the individual sequencing reads, which is usually 50–150 bp for short-read RNA sequencing.


A measure of the proportion of transcripts present in the sample that are detected. It is affected by sample handling, library preparation, sequencing and computational biases.


A measure of the proportion of differentially expressed transcripts that are correctly identified. It is affected by sample handling, library preparation, sequencing and computational biases.

Tag read

A read that is unique to a transcript, usually from the 3΄ end of mRNA, for differential gene expression analysis, or the 5΄ end, for analysis of transcription start sites and promoters.

Duplication rates

The frequencies at which sequencing reads for an RNA sequencing (RNA-seq) sample map to the same location in the transcriptome. In RNA-seq libraries, duplication rates can seem high for some transcripts because they are present at wildly different levels in the sample. Highly expressed genes will have high duplication rates, while low expressors may have minimal duplication. RNA-seq presents a particular challenge, as much of the duplication may be genuine signal from highly expressed transcripts, while some may be attributable to amplification and sequencing biases.

Single-end sequencing

Short-read sequencing performed from one end of the cDNA fragment, commonly used for differential gene expression experiments, due to its low cost.

Paired-end sequencing

Short-read sequencing performed from both ends of the cDNA fragment, often used for differential gene expression experiments, where maximum sensitivity to splicing is required because more bases of the individual cDNAs will be sequenced.

Biological replicates

Parallel measurements of biologically distinct samples, such as tissue from three subjects, that capture natural biological variation, which may itself be either a subject of study or a source of noise. By contrast, technical replicates are repeated measurements of the same sample — for example, the same tissue processed three times.

Expression matrix

Matrix of values capturing the essential data for a differential-expression RNA-seq experiment. Rows are RNA features, such as genes or transcripts, with one column per sequenced sample. Values are generally counts of the number of reads associated with each RNA feature; these may be estimated for isoform features and are often transformed via normalization before subsequent analysis.

Spike-in control

A pool of exogenous nucleic acids added at known concentration to a sample before processing. They are usually synthetic RNAs pre-pooled at varying concentrations and used to monitor reaction efficiency and to identify methodological bias and false-negative results.


Transcriptome analysis methods that preserve the spatial information of individual transcripts within a given sample, usually a tissue section.

Nascent RNA

RNA that has just been transcribed, as opposed to RNA that has been processed and transported to the cytoplasm.


(4 sU). A thio-substituted nucleoside not naturally found in eukaryotic mRNAs, which is easily incorporated into nucleic acids and is used in nascent RNA analysis.


The complete set of proteins translated from mRNA in a cell, tissue or organism.


The complete set of secondary and tertiary RNA structures in a cell, tissue or organism.


The complete set of molecular interactions in a cell, tissue or organism, including RNA–RNA or RNA–protein interactions.

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