1932

Abstract

Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq data sets, as well as the performance of the myriad of methods developed. In this review, we give an overview of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on the quantification of gene expression and statistical approachesfor differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-biodatasci-072018-021255
2019-07-20
2024-03-29
Loading full text...

Full text loading...

/deliver/fulltext/biodatasci/2/1/annurev-biodatasci-072018-021255.html?itemId=/content/journals/10.1146/annurev-biodatasci-072018-021255&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Lister R, O'Malley RC, Tonti-Filippini J, Gregory BD, Berry CC et al. 2008. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133:523–36
    [Google Scholar]
  2. 2. 
    Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D et al. 2008. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320:1344–49
    [Google Scholar]
  3. 3. 
    Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B 2008. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5:621–28
    [Google Scholar]
  4. 4. 
    Cloonan N, Forrest ARR, Kolle G, Gardiner BBA, Faulkner GJ et al. 2008. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat. Methods 5:613–19
    [Google Scholar]
  5. 5. 
    Wilhelm BT, Marguerat S, Watt S, Schubert F, Wood V et al. 2008. Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature 453:1239–43
    [Google Scholar]
  6. 6. 
    Palazzo AF, Lee ES. 2015. Non-coding RNA: What is functional and what is junk. Front. Genet. 6:2
    [Google Scholar]
  7. 7. 
    Zhao W, He X, Hoadley KA, Parker JS, Hayes DN, Perou CM 2014. Comparison of RNA-Seq by poly (A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling. BMC Genom 15:419
    [Google Scholar]
  8. 8. 
    Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J et al. 2008. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456:53–59
    [Google Scholar]
  9. 9. 
    Ju J, Kim DH, Bi L, Meng Q, Bai X et al. 2006. Four-color DNA sequencing by synthesis using cleavable fluorescent nucleotide reversible terminators. PNAS 103:19635–40
    [Google Scholar]
  10. 10. 
    Levin JZ, Yassour M, Adiconis X, Nusbaum C, Thompson DA et al. 2010. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat. Methods 7:709–15
    [Google Scholar]
  11. 11. 
    Zhao S, Zhang Y, Gordon W, Quan J, Xi H et al. 2015. Comparison of stranded and non-stranded RNA-seq transcriptome profiling and investigation of gene overlap. BMC Genom 16:675
    [Google Scholar]
  12. 12. 
    Parkhomchuk D, Borodina T, Amstislavskiy V, Banaru M, Hallen L et al. 2009. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res 37:e123
    [Google Scholar]
  13. 13. 
    Mamanova L, Turner DJ. 2011. Low-bias, strand-specific transcriptome Illumina sequencing by on-flowcell reverse transcription (FRT-seq). Nat. Protoc. 6:1736–47
    [Google Scholar]
  14. 14. 
    Wang B, Tseng E, Regulski M, Clark TA, Hon T et al. 2016. Unveiling the complexity of the maize transcriptome by single-molecule long-read sequencing. Nat. Commun. 7:11708
    [Google Scholar]
  15. 15. 
    Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA et al. 2017. Reproducible RNA-seq analysis using recount2. Nat. Biotechnol 35:319–21
    [Google Scholar]
  16. 16. 
    Lazic SE. 2017. Experimental Design for Laboratory Biologists: Maximising Information and Improving Reproducibility Cambridge, UK: Cambridge Univ. Press. , 1st ed..
  17. 17. 
    Hart SN, Therneau TM, Zhang Y, Poland GA, Kocher J-P 2013. Calculating sample size estimates for RNA sequencing data. J. Comput. Biol. 20:970–78
    [Google Scholar]
  18. 18. 
    Guo Y, Zhao S, Li CI, Sheng Q, Shyr Y 2014. RNAseqPS: a web tool for estimating sample size and power for RNAseq experiment. Cancer Inform 13:1–5
    [Google Scholar]
  19. 19. 
    Zhao S, Li C-I, Guo Y, Sheng Q, Shyr Y 2018. RnaSeqSampleSize: real data based sample size estimation for RNA sequencing. BMC Bioinform 19:191
    [Google Scholar]
  20. 20. 
    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 29:656–57
    [Google Scholar]
  21. 21. 
    Poplawski A, Binder H. 2018. Feasibility of sample size calculation for RNA-seq studies. Brief. Bioinform. 19:713–20
    [Google Scholar]
  22. 22. 
    Oshlack A, Wakefield MJ. 2009. Transcript length bias in RNA-seq data confounds systems biology. Biol. Direct. 4:14
    [Google Scholar]
  23. 23. 
    Liu Y, Zhou J, White KP 2014. RNA-seq differential expression studies: More sequence or more replication. Bioinformatics 30:301–4
    [Google Scholar]
  24. 24. 
    Schurch NJ, Schofield P, Gierliński M, Cole C, Sherstnev A et al. 2016. How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use. RNA 22:839–51
    [Google Scholar]
  25. 25. 
    Mercer TR, Clark MB, Crawford J, Brunck ME, Gerhardt DJ et al. 2014. Targeted sequencing for gene discovery and quantification using RNA CaptureSeq. Nat. Protoc. 9:989–1009
    [Google Scholar]
  26. 26. 
    Cabanski CR, Magrini V, Griffith M, Griffith OL, McGrath S et al. 2014. cDNA hybrid capture improves transcriptome analysis on low-input and archived samples. J. Mol. Diagn. 16:440–51
    [Google Scholar]
  27. 27. 
    Irimia M, Weatheritt RJ, Ellis JD, Parikshak NN, Gonatopoulos-Pournatzis T et al. 2014. A highly conserved program of neuronal microexons is misregulated in autistic brains. Cell 159:1511–23
    [Google Scholar]
  28. 28. 
    Eom T, Zhang C, Wang H, Lay K, Fak J et al. 2013. NOVA-dependent regulation of cryptic NMD exons controls synaptic protein levels after seizure. eLife 2:e00178
    [Google Scholar]
  29. 29. 
    Fratta P, Sivakumar P, Humphrey J, Lo K, Ricketts T et al. 2018. Mice with endogenous TDP-43 mutations exhibit gain of splicing function and characteristics of amyotrophic lateral sclerosis. EMBO J 37:e98684
    [Google Scholar]
  30. 30. 
    Salzman J, Gawad C, Wang PL, Lacayo N, Brown PO 2012. Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types. PLOS ONE 7:e30733
    [Google Scholar]
  31. 31. 
    Kim T-K, Hemberg M, Gray JM 2015. Enhancer RNAs: a class of long noncoding RNAs synthesized at enhancers. Cold Spring Harb. Perspect. Biol. 7:a018622
    [Google Scholar]
  32. 32. 
    Parker BC, Zhang W. 2013. Fusion genes in solid tumors: an emerging target for cancer diagnosis and treatment. Chin. J. Cancer 32:594–603
    [Google Scholar]
  33. 33. 
    Frye M, Jaffrey SR, Pan T, Rechavi G, Suzuki T 2016. RNA modifications: What have we learned and where are we headed. Nat. Rev. Genet. 17:365–72
    [Google Scholar]
  34. 34. 
    Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS et al. 2000. Molecular portraits of human breast tumours. Nature 406:747–52
    [Google Scholar]
  35. 35. 
    Climente-González H, Porta-Pardo E, Godzik A, Eyras E 2017. The functional impact of alternative splicing in cancer. Cell Rep 20:2215–26
    [Google Scholar]
  36. 36. 
    Cieślik M, Chinnaiyan AM. 2017. Cancer transcriptome profiling at the juncture of clinical translation. Nat. Rev. Genet. 19:93–109
    [Google Scholar]
  37. 37. 
    Pedersen G, Kanigan T. 2016. Clinical RNA sequencing in oncology: Where are we. Per Med 13:209–13
    [Google Scholar]
  38. 38. 
    Ståhl PL, Salmén F, Vickovic S, Lundmark A, Navarro JF et al. 2016. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353:78–82
    [Google Scholar]
  39. 39. 
    Westermann AJ, Gorski SA, Vogel J 2012. Dual RNA-seq of pathogen and host. Nat. Rev. Microbiol. 10:618–30
    [Google Scholar]
  40. 40. 
    Piskol R, Ramaswami G, Li JB 2013. Reliable identification of genomic variants from RNA-seq data. Am. J. Hum. Genet. 93:641–51
    [Google Scholar]
  41. 41. 
    Park E, Williams B, Wold BJ, Mortazavi A 2012. RNA editing in the human ENCODE RNA-seq data. Genome Res 22:1626–33
    [Google Scholar]
  42. 42. 
    Uszczynska-Ratajczak B, Lagarde J, Frankish A, Guigó R, Johnson R 2018. Towards a complete map of the human long non-coding RNA transcriptome. Nat. Rev. Genet. 19:535–48
    [Google Scholar]
  43. 43. 
    Bashiardes S, Zilberman-Schapira G, Elinav E 2016. Use of metatranscriptomics in microbiome research. Bioinform. Biol. Insights 10:19–25
    [Google Scholar]
  44. 44. 
    Racle J, de Jonge K, Baumgaertner P, Speiser DE, Gfeller D 2017. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. eLife 6:e26476
    [Google Scholar]
  45. 45. 
    Martin JA, Wang Z. 2011. Next-generation transcriptome assembly. Nat. Rev. Genet. 12:671–82
    [Google Scholar]
  46. 46. 
    Castel SE, Levy-Moonshine A, Mohammadi P, Banks E, Lappalainen T 2015. Tools and best practices for data processing in allelic expression analysis. Genome Biol 16:195
    [Google Scholar]
  47. 47. 
    Sun W, Hu Y. 2013. eQTL mapping using RNA-seq data. Stat. Biosci. 5:198–219
    [Google Scholar]
  48. 48. 
    Alamancos GP, Agirre E, Eyras E 2014. Methods to study splicing from high-throughput RNA sequencing data. Methods Mol. Biol. 1126:357–97
    [Google Scholar]
  49. 49. 
    van Dam S, Võsa U, van der Graaf A, Franke L, de Magalhães JP 2018. Gene co-expression analysis for functional classification and gene-disease predictions. Brief. Bioinform. 19:575–92
    [Google Scholar]
  50. 50. 
    Khatri P, Sirota M, Butte AJ 2012. Ten years of pathway analysis: current approaches and outstanding challenges. PLOS Comput. Biol. 8:e1002375
    [Google Scholar]
  51. 51. 
    de Leeuw CA, Neale BM, Heskes T, Posthuma D 2016. The statistical properties of gene-set analysis. Nat. Rev. Genet. 17:353–64
    [Google Scholar]
  52. 52. 
    Trapnell C, Pachter L, Salzberg SL 2009. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25:1105–11
    [Google Scholar]
  53. 53. 
    Langmead B, Trapnell C, Pop M, Salzberg SL 2009. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25
    [Google Scholar]
  54. 54. 
    Engström PG, Steijger T, Sipos B, Grant GR, Kahles A et al. 2013. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat. Methods 10:1185–91
    [Google Scholar]
  55. 55. 
    Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C et al. 2013. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21
    [Google Scholar]
  56. 56. 
    Kim D, Langmead B, Salzberg SL 2015. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12:357–60
    [Google Scholar]
  57. 57. 
    Liao Y, Smyth GK, Shi W 2013. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res 41:e108
    [Google Scholar]
  58. 58. 
    Wu TD, Watanabe CK. 2005. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21:1859–75
    [Google Scholar]
  59. 59. 
    Lin H-N, Hsu W-L. 2017. DART: a fast and accurate RNA-seq mapper with a partitioning strategy. Bioinformatics 34:190–97
    [Google Scholar]
  60. 60. 
    Sedlazeck FJ, Rescheneder P, von Haeseler A 2013. NextGenMap: fast and accurate read mapping in highly polymorphic genomes. Bioinformatics 29:2790–91
    [Google Scholar]
  61. 61. 
    Medina I, Tárraga J, Martínez H, Barrachina S, Castillo MI et al. 2016. Highly sensitive and ultrafast read mapping for RNA-seq analysis. DNA Res 23:93–100
    [Google Scholar]
  62. 62. 
    Baruzzo G, Hayer KE, Kim EJ, Di Camillo B, FitzGerald GA, Grant GR 2017. Simulation-based comprehensive benchmarking of RNA-seq aligners. Nat. Methods 14:135–39
    [Google Scholar]
  63. 63. 
    Bonfert T, Kirner E, Csaba G, Zimmer R, Friedel CC 2015. ContextMap 2: fast and accurate context-based RNA-seq mapping. BMC Bioinform 16:122
    [Google Scholar]
  64. 64. 
    Wu TD, Nacu S. 2010. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26:873–81
    [Google Scholar]
  65. 65. 
    Aken BL, Ayling S, Barrell D, Clarke L, Curwen V et al. 2016. The Ensembl gene annotation system. Database 2016:baw093
    [Google Scholar]
  66. 66. 
    Liao Y, Smyth GK, Shi W 2014. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923–30
    [Google Scholar]
  67. 67. 
    Anders S, Pyl PT, Huber W 2015. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31:166–69
    [Google Scholar]
  68. 68. 
    Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L 2011. Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol 12:R22
    [Google Scholar]
  69. 69. 
    Hansen KD, Brenner SE, Dudoit S 2010. Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res 38:e131
    [Google Scholar]
  70. 70. 
    Liu X, Shi X, Chen C, Zhang L 2015. Improving RNA-Seq expression estimation by modeling isoform- and exon-specific read sequencing rate. BMC Bioinform 16:332
    [Google Scholar]
  71. 71. 
    Love MI, Hogenesch JB, Irizarry RA 2016. Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation. Nat. Biotechnol. 34:1287–91
    [Google Scholar]
  72. 72. 
    Robert C, Watson M. 2015. Errors in RNA-Seq quantification affect genes of relevance to human disease. Genome Biol 16:177
    [Google Scholar]
  73. 73. 
    Li B, Dewey CN. 2011. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform 12:323
    [Google Scholar]
  74. 74. 
    Soneson C, Love MI, Robinson MD 2015. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4:1521
    [Google Scholar]
  75. 75. 
    Trapnell C, Roberts A, Goff L, Pertea G, Kim D et al. 2012. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7:562–78
    [Google Scholar]
  76. 76. 
    Xing Y, Yu T, Wu YN, Roy M, Kim J, Lee C 2006. An expectation-maximization algorithm for probabilistic reconstructions of full-length isoforms from splice graphs. Nucleic Acids Res 34:3150–60
    [Google Scholar]
  77. 77. 
    Jiang H, Wong WH. 2009. Statistical inferences for isoform expression in RNA-Seq. Bioinformatics 25:1026–32
    [Google Scholar]
  78. 78. 
    Li B, Ruotti V, Stewart RM, Thomson JA, Dewey CN 2010. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics 26:493–500
    [Google Scholar]
  79. 79. 
    Turro E, Su S-Y, Gonçalves Â, Coin LJM, Richardson S, Lewin A 2011. Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads. Genome Biol 12:R13
    [Google Scholar]
  80. 80. 
    Richard H, Schulz MH, Sultan M, Nürnberger A, Schrinner S et al. 2010. Prediction of alternative isoforms from exon expression levels in RNA-Seq experiments. Nucleic Acids Res 38:e112
    [Google Scholar]
  81. 81. 
    Nicolae M, Mangul S, Măndoiu II, Zelikovsky A 2011. Estimation of alternative splicing isoform frequencies from RNA-Seq data. Algorithms Mol. Biol. 6:9
    [Google Scholar]
  82. 82. 
    Mezlini AM, Smith EJM, Fiume M, Buske O, Savich GL et al. 2013. iReckon: simultaneous isoform discovery and abundance estimation from RNA-seq data. Genome Res 23:519–29
    [Google Scholar]
  83. 83. 
    Zakeri M, Srivastava A, Almodaresi F, Patro R 2017. Improved data-driven likelihood factorizations for transcript abundance estimation. Bioinformatics 33:i142–51
    [Google Scholar]
  84. 84. 
    Roberts A, Pachter L. 2013. Streaming fragment assignment for real-time analysis of sequencing experiments. Nat. Methods 10:71–73
    [Google Scholar]
  85. 85. 
    Cappé O, Moulines E. 2009. On-line expectation–maximization algorithm for latent data models. J. R. Stat. Soc. B 71:593–613
    [Google Scholar]
  86. 86. 
    Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G et al. 2010. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28:511–15
    [Google Scholar]
  87. 87. 
    Li W, Feng J, Jiang T 2011. IsoLasso: a LASSO regression approach to RNA-Seq based transcriptome assembly. J. Comput. Biol. 18:1693–707
    [Google Scholar]
  88. 88. 
    Canzar S, Andreotti S, Weese D, Reinert K, Klau GW 2016. CIDANE: comprehensive isoform discovery and abundance estimation. Genome Biol 17:16
    [Google Scholar]
  89. 89. 
    Maretty L, Sibbesen JA, Krogh A 2014. Bayesian transcriptome assembly. Genome Biol 15:501
    [Google Scholar]
  90. 90. 
    Shi X, Wang X, Wang T-L, Hilakivi-Clarke L, Clarke R, Xuan J 2018. SparseIso: a novel Bayesian approach to identify alternatively spliced isoforms from RNA-seq data. Bioinformatics 34:56–63
    [Google Scholar]
  91. 91. 
    Pertea M, Pertea GM, Antonescu CM, Chang T-C, Mendell JT, Salzberg SL 2015. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33:290–95
    [Google Scholar]
  92. 92. 
    Tomescu AI, Kuosmanen A, Rizzi R, Mäkinen V 2013. A novel min-cost flow method for estimating transcript expression with RNA-Seq. BMC Bioinform 14:Suppl. 5S15
    [Google Scholar]
  93. 93. 
    Bernard E, Jacob L, Mairal J, Vert J-P 2014. Efficient RNA isoform identification and quantification from RNA-Seq data with network flows. Bioinformatics 30:2447–55
    [Google Scholar]
  94. 94. 
    Shao M, Kingsford C. 2017. Accurate assembly of transcripts through phase-preserving graph decomposition. Nat. Biotechnol. 35:1167–69
    [Google Scholar]
  95. 95. 
    Glaus P, Honkela A, Rattray M 2012. Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics 28:1721–28
    [Google Scholar]
  96. 96. 
    SEQC/MAQC-III Consort 2014. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 32:903–14
    [Google Scholar]
  97. 97. 
    Hensman J, Papastamoulis P, Glaus P, Honkela A, Rattray M 2015. Fast and accurate approximate inference of transcript expression from RNA-seq data. Bioinformatics 31:3881–89
    [Google Scholar]
  98. 98. 
    Nariai N, Hirose O, Kojima K, Nagasaki M 2013. TIGAR: transcript isoform abundance estimation method with gapped alignment of RNA-Seq data by variational Bayesian inference. Bioinformatics 29:2292–99
    [Google Scholar]
  99. 99. 
    Amari S-I, Nagaoka H. 2000. Methods of Information Geometry transl. D Harada. Transl. Math. Monogr. 191 Oxford: Am. Math. Soc.
  100. 100. 
    Patro R, Mount SM, Kingsford C 2014. Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat. Biotechnol. 32:462–64
    [Google Scholar]
  101. 101. 
    Varadhan R, Roland C. 2004. Squared extrapolation methods (SQUAREM): a new class of simple and efficient numerical schemes for accelerating the convergence of the EM algorithm Work. Pap. 63 Johns Hopkins Univ. Dep. Biostat. Baltimore, MD: https://biostats.bepress.com/jhubiostat/paper63/
  102. 102. 
    Zhang Z, Wang W. 2014. RNA-Skim: a rapid method for RNA-Seq quantification at transcript level. Bioinformatics 30:i283–92
    [Google Scholar]
  103. 103. 
    Bray NL, Pimentel H, Melsted P, Pachter L 2016. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34:525–27
    [Google Scholar]
  104. 104. 
    Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C 2017. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14:417–19
    [Google Scholar]
  105. 105. 
    Foulds J, Boyles L, DuBois C, Smyth P, Welling M 2013. Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining R Ghani, TE Senator, P Bradley, R Parekh, J He446–54 New York: Assoc. Comput. Mach.
    [Google Scholar]
  106. 106. 
    Ju CJ-T, Li R, Wu Z, Jiang J-Y, Yang Z, Wang W 2017. Fleximer: accurate quantification of RNA-Seq via variable-length k-mers. Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics263–72 New York: Assoc. Comput. Mach.
    [Google Scholar]
  107. 107. 
    Kanitz A, Gypas F, Gruber AJ, Gruber AR, Martin G, Zavolan M 2015. Comparative assessment of methods for the computational inference of transcript isoform abundance from RNA-seq data. Genome Biol 16:150
    [Google Scholar]
  108. 108. 
    Germain P-L, Vitriolo A, Adamo A, Laise P, Das V, Testa G 2016. RNAontheBENCH: computational and empirical resources for benchmarking RNAseq quantification and differential expression methods. Nucleic Acids Res 44:5054–67
    [Google Scholar]
  109. 109. 
    Teng M, Love MI, Davis CA, Djebali S, Dobin A et al. 2016. A benchmark for RNA-seq quantification pipelines. Genome Biol 17:74
    [Google Scholar]
  110. 110. 
    Zhang C, Zhang B, Lin L-L, Zhao S 2017. Evaluation and comparison of computational tools for RNA-seq isoform quantification. BMC Genom 18:583
    [Google Scholar]
  111. 111. 
    Prakash C, Haeseler AV. 2017. An enumerative combinatorics model for fragmentation patterns in RNA sequencing provides insights into nonuniformity of the expected fragment starting-point and coverage profile. J. Comput. Biol. 24:200–12
    [Google Scholar]
  112. 112. 
    Jones DC, Kuppusamy KT, Palpant NJ, Peng X, Murry CE et al. 2016. Isolator: accurate and stable analysis of isoform-level expression in RNA-Seq experiments. bioRxiv 088765. https://doi.org/10.1101/088765
    [Crossref]
  113. 113. 
    Soneson C, Love MI, Patro R, Hussain S, Malhotra D, Robinson MD 2018. A junction coverage compatibility score to quantify the reliability of transcript abundance estimates and annotation catalogs. Life Sci. Alliance 2e201800175
  114. 114. 
    Efron B, Hastie T. 2016. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science New York: Cambridge Univ. Press, 1st ed.
  115. 115. 
    Tusher VG, Tibshirani R, Chu G 2001. Significance analysis of microarrays applied to the ionizing radiation response. PNAS 98:5116–21
    [Google Scholar]
  116. 116. 
    Smyth GK. 2004. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3:3
    [Google Scholar]
  117. 117. 
    Bourgon R, Gentleman R, Huber W 2010. Independent filtering increases detection power for high-throughput experiments. PNAS 107:9546–51
    [Google Scholar]
  118. 118. 
    Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y 2008. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 18:1509–17
    [Google Scholar]
  119. 119. 
    Robinson MD, Oshlack A. 2010. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11:R25
    [Google Scholar]
  120. 120. 
    Anders S, Huber W. 2010. Differential expression analysis for sequence count data. Genome Biol 11:R106
    [Google Scholar]
  121. 121. 
    Dillies M-A, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M et al. 2013. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief. Bioinform. 14:671–83
    [Google Scholar]
  122. 122. 
    Hansen KD, Irizarry RA, Wu Z 2012. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics 13:204–16
    [Google Scholar]
  123. 123. 
    Risso D, Schwartz K, Sherlock G, Dudoit S 2011. GC-content normalization for RNA-Seq data. BMC Bioinform 12:480
    [Google Scholar]
  124. 124. 
    Lovén J, Orlando DA, Sigova AA, Lin CY, Rahl PB et al. 2012. Revisiting global gene expression analysis. Cell 151:476–82
    [Google Scholar]
  125. 125. 
    Risso D, Ngai J, Speed TP, Dudoit S 2014. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32:896–902
    [Google Scholar]
  126. 126. 
    Taruttis F, Feist M, Schwarzfischer P, Gronwald W, Kube D et al. 2017. External calibration with Drosophila whole-cell spike-ins delivers absolute mRNA fold changes from human RNA-Seq and qPCR data. Biotechniques 62:53–61
    [Google Scholar]
  127. 127. 
    Hicks SC, Okrah K, Paulson JN, Quackenbush J, Irizarry RA, Bravo HC 2018. Smooth quantile normalization. Biostatistics 19:185–98
    [Google Scholar]
  128. 128. 
    Vallejos CA, Risso D, Scialdone A, Dudoit S, Marioni JC 2017. Normalizing single-cell RNA sequencing data: challenges and opportunities. Nat. Methods 14:565–71
    [Google Scholar]
  129. 129. 
    Love MI, Huber W, Anders S 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550
    [Google Scholar]
  130. 130. 
    Robinson MD, McCarthy DJ, Smyth GK 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–40
    [Google Scholar]
  131. 131. 
    Hardcastle TJ, Kelly KA. 2010. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinform 11:422
    [Google Scholar]
  132. 132. 
    Leng N, Dawson JA, Thomson JA, Ruotti V, Rissman AI et al. 2013. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics 29:1035–43
    [Google Scholar]
  133. 133. 
    Robinson MD, Smyth GK. 2007. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23:2881–87
    [Google Scholar]
  134. 134. 
    McCarthy DJ, Chen Y, Smyth GK 2012. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res 40:4288–97
    [Google Scholar]
  135. 135. 
    Himes BE, Jiang X, Wagner P, Hu R, Wang Q et al. 2014. RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells. PLOS ONE 9:e99625
    [Google Scholar]
  136. 136. 
    Love MI, Anders S, Kim V, Huber W 2016. RNA-Seq workflow: gene-level exploratory analysis and differential expression. F1000Research 4:1070
    [Google Scholar]
  137. 137. 
    Robinson MD, Smyth GK. 2008. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics 9:321–32
    [Google Scholar]
  138. 138. 
    Soneson C, Delorenzi M. 2013. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinform 14:91
    [Google Scholar]
  139. 139. 
    Zhou X, Lindsay H, Robinson MD 2014. Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic Acids Res 42:e91
    [Google Scholar]
  140. 140. 
    Cox DR, Reid N. 1987. Parameter orthogonality and approximate conditional inference. J. R. Stat. Soc. B 49:1–39
    [Google Scholar]
  141. 141. 
    Chen Y, Lun ATL, Smyth GK 2014. Differential expression analysis of complex RNA-seq experiments using edgeR. Statistical Analysis of Next Generation Sequencing Data S Datta, D Nettleton51–74 Cham, Switz.: Springer Int.
    [Google Scholar]
  142. 142. 
    Wu H, Wang C, Wu Z 2013. A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data. Biostatistics 14:232–43
    [Google Scholar]
  143. 143. 
    Nelder JA, Wedderburn RWM. 1972. Generalized linear models. J. R. Stat. Soc. A 135:370–84
    [Google Scholar]
  144. 144. 
    Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57:289–300
    [Google Scholar]
  145. 145. 
    Benjamini Y, Yekutieli D. 2001. The control of the false discovery rate in multiple testing under depencency. Ann. Stat. 29:1165–88
    [Google Scholar]
  146. 146. 
    Lund SP, Nettleton D, McCarthy DJ, Smyth GK 2012. Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates. Stat. Appl. Genet. Mol. Biol. 11:5
    [Google Scholar]
  147. 147. 
    Di Y, Schafer DW, Cumbie JS, Chang JH 2011. The NBP negative binomial model for assessing differential gene expression from RNA-Seq. Stat. Appl. Genet. Mol. Biol. 10:24
    [Google Scholar]
  148. 148. 
    van de Wiel MA, Leday GGR, Pardo L, Rue H, van der Vaart AW, van Wieringen WN 2013. Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors. Biostatistics 14:113–28
    [Google Scholar]
  149. 149. 
    van de Wiel MA, Neerincx M, Buffart TE, Sie D, Verheul HMW 2014. ShrinkBayes: a versatile R-package for analysis of count-based sequencing data in complex study designs. BMC Bioinform 15:116
    [Google Scholar]
  150. 150. 
    Rue H, Martino S, Chopin N 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. B 71:319–92
    [Google Scholar]
  151. 151. 
    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:R29
    [Google Scholar]
  152. 152. 
    Li J, Tibshirani R. 2013. Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data. Stat. Methods Med. Res. 22:519–36
    [Google Scholar]
  153. 153. 
    Stephens M. 2016. False discovery rates: a new deal. Biostatistics 18:275–94
    [Google Scholar]
  154. 154. 
    Zhu A, Ibrahim JG, Love MI 2018. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics In press. https://doi.org/10.1093/bioinformatics/bty895
    [Crossref]
  155. 155. 
    Leek JT, Storey JD. 2007. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLOS Genet 3:12
    [Google Scholar]
  156. 156. 
    Leek JT. 2014. svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res 42:e161
    [Google Scholar]
  157. 157. 
    Finner H, Roters M. 2001. On the false discovery rate and expected type I errors. Biomet. J. 43:985–1005
    [Google Scholar]
  158. 158. 
    McCarthy DJ, Smyth GK. 2009. Testing significance relative to a fold-change threshold is a TREAT. Bioinformatics 25:765–71
    [Google Scholar]
  159. 159. 
    Chen Y, Lun ATL, Smyth GK 2016. From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Research 5:1438
    [Google Scholar]
  160. 160. 
    Di Y, Emerson SC, Schafer DW, Kimbrel JA, Chang JH 2013. Higher order asymptotics for negative binomial regression inferences from RNA-sequencing data. Stat. Appl. Genet. Mol. Biol. 12:49–70
    [Google Scholar]
  161. 161. 
    Storey JD. 2003. The positive false discovery rate: a Bayesian interpretation and the q-value. Ann. Stat. 31:2013–35
    [Google Scholar]
  162. 162. 
    Ignatiadis N, Klaus B, Zaugg JB, Huber W 2016. Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nat. Methods 13:577–80
    [Google Scholar]
  163. 163. 
    Efron B. 2004. Large-scale simultaneous hypothesis testing. J. Am. Stat. Assoc. 99:96–104
    [Google Scholar]
  164. 164. 
    Efron B. 2007. Size, power and false discovery rates. Ann. Stat 35:1351–77
    [Google Scholar]
  165. 165. 
    Van den Berge K, Soneson C, Robinson MD, Clement L 2017. stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression and differential transcript usage. Genome Biol 18:151
    [Google Scholar]
  166. 166. 
    Heller R, Manduchi E, Grant GR, Ewens WJ 2009. A flexible two-stage procedure for identifying gene sets that are differentially expressed. Bioinformatics 25:1019–25
    [Google Scholar]
  167. 167. 
    Kakaradov B, Xiong HY, Lee LJ, Jojic N, Frey BJ 2012. Challenges in estimating percent inclusion of alternatively spliced junctions from RNA-seq data. BMC Bioinform 13:Suppl. 6S11
    [Google Scholar]
  168. 168. 
    Turro E, Astle WJ, Tavaré S 2014. Flexible analysis of RNA-seq data using mixed effects models. Bioinformatics 30:180–88
    [Google Scholar]
  169. 169. 
    Papastamoulis P, Rattray M. 2018. A Bayesian model selection approach for identifying differentially expressed transcripts from RNA sequencing data. J. R. Stat. Soc. C 67:3–23
    [Google Scholar]
  170. 170. 
    Pimentel H, Bray NL, Puente S, Melsted P, Pachter L 2017. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat. Methods 14:687–89
    [Google Scholar]
  171. 171. 
    Blekhman R, Marioni JC, Zumbo P, Stephens M, Gilad Y 2010. Sex-specific and lineage-specific alternative splicing in primates. Genome Res 20:180–89
    [Google Scholar]
  172. 172. 
    Purdom E, Simpson KM, Robinson MD, Conboy JG, Lapuk AV, Speed TP 2008. FIRMA: a method for detection of alternative splicing from exon array data. Bioinformatics 24:1707–14
    [Google Scholar]
  173. 173. 
    Anders S, Reyes A, Huber W 2012. Detecting differential usage of exons from RNA-seq data. Genome Res 22:2008–17
    [Google Scholar]
  174. 174. 
    Soneson C, Matthes KL, Nowicka M, Law CW, Robinson MD 2016. Isoform prefiltering improves performance of count-based methods for analysis of differential transcript usage. Genome Biol 17:12
    [Google Scholar]
  175. 175. 
    Love MI, Soneson C, Patro R 2018. Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification. F1000Research 7:952
    [Google Scholar]
  176. 176. 
    Nowicka M, Robinson MD. 2016. DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics. F1000Research 5:1356
    [Google Scholar]
  177. 177. 
    Li YI, Knowles DA, Humphrey J, Barbeira AN, Dickinson SP et al. 2017. Annotation-free quantification of RNA splicing using LeafCutter. Nat. Genet. 50:151–58
    [Google Scholar]
  178. 178. 
    Papastamoulis P, Rattray M. 2017. Bayesian estimation of differential transcript usage from RNA-seq data. Stat. Appl. Genet. Mol. Biol. 16:367–86
    [Google Scholar]
  179. 179. 
    Froussios K, Mourão K, Simpson GG, Barton GJ, Schurch NJ 2017. Identifying differential isoform abundance with RATs: a universal tool and a warning. bioRxiv 132761. https://doi.org/10.1101/132761
    [Crossref]
  180. 180. 
    Shen S, Park JW, Lu Z-X, Lin L, Henry MD et al. 2014. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. PNAS 111:E5593–601
    [Google Scholar]
  181. 181. 
    Trincado JL, Entizne JC, Hysenaj G, Singh B, Skalic M et al. 2018. SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions. Genome Biol 19:40
    [Google Scholar]
  182. 182. 
    Yi L, Pimentel H, Bray NL, Pachter L 2018. Gene-level differential analysis at transcript-level resolution. Genome Biol 19:53
    [Google Scholar]
  183. 183. 
    Hicks SC, Townes FW, Teng M, Irizarry RA 2017. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics 19:562–78
    [Google Scholar]
  184. 184. 
    Marinov GK, Williams BA, McCue K, Schroth GP, Gertz J et al. 2014. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res 24:496–510
    [Google Scholar]
  185. 185. 
    Svensson V, Vento-Tormo R, Teichmann SA 2018. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13:599–604
    [Google Scholar]
  186. 186. 
    Wagner A, Regev A, Yosef N 2016. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34:1145–60
    [Google Scholar]
  187. 187. 
    Moor AE, Itzkovitz S. 2017. Spatial transcriptomics: paving the way for tissue-level systems biology. Curr. Opin. Biotechnol. 46:126–33
    [Google Scholar]
  188. 188. 
    Kumar P, Tan Y, Cahan P 2017. Understanding development and stem cells using single cell-based analyses of gene expression. Development 144:17–32
    [Google Scholar]
  189. 189. 
    Kang HM, Subramaniam M, Targ S, Nguyen M, Maliskova L et al. 2018. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36:89–94
    [Google Scholar]
  190. 190. 
    Paulson KG, Voillet V, McAfee MS, Hunter DS, Wagener FD et al. 2018. Acquired cancer resistance to combination immunotherapy from transcriptional loss of class I HLA. Nat. Commun. 9:3868
    [Google Scholar]
  191. 191. 
    Giladi A, Amit I. 2018. Single-cell genomics: a stepping stone for future immunology discoveries. Cell 172:14–21
    [Google Scholar]
  192. 192. 
    Trapnell C. 2015. Defining cell types and states with single-cell genomics. Genome Res 25:1491–98
    [Google Scholar]
  193. 193. 
    Sandberg R. 2014. Entering the era of single-cell transcriptomics in biology and medicine. Nat. Methods 11:22–24
    [Google Scholar]
  194. 194. 
    Soneson C, Robinson MD. 2018. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15:255–61
    [Google Scholar]
  195. 195. 
    Jaakkola MK, Seyednasrollah F, Mehmood A, Elo LL 2017. Comparison of methods to detect differentially expressed genes between single-cell populations. Brief. Bioinform. 18:735–43
    [Google Scholar]
  196. 196. 
    Kharchenko PV, Silberstein L, Scadden DT 2014. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11:740–42
    [Google Scholar]
  197. 197. 
    Finak G, McDavid A, Yajima M, Deng J, Gersuk V et al. 2015. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA-seq data. Genome Biol 16:278
    [Google Scholar]
  198. 198. 
    Van den Berge K, Perraudeau F, Soneson C, Love MI, Risso D et al. 2018. Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications. Genome Biol 19:24
    [Google Scholar]
  199. 199. 
    Risso D, Perraudeau F, Gribkova S, Dudoit S, Vert J-P 2018. A general and flexible method for signal extraction from single-cell RNA-seq data. Nat. Commun. 9:284
    [Google Scholar]
  200. 200. 
    Eling N, Richard AC, Richardson S, Marioni JC, Vallejos CA 2018. Correcting the mean-variance dependency for differential variability testing using single-cell RNA sequencing data. Cell Syst 7:284–94.e12
    [Google Scholar]
  201. 201. 
    Korthauer KD, Chu L-F, Newton MA, Li Y, Thomson J et al. 2016. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biol 17:222
    [Google Scholar]
  202. 202. 
    Treangen TJ, Salzberg SL. 2012. Repetitive DNA and next-generation sequencing: computational challenges and solutions. Nat. Rev. Genet. 13:36–46
    [Google Scholar]
  203. 203. 
    Weirather JL, de Cesare M, Wang Y, Piazza P, Sebastiano V et al. 2017. Comprehensive comparison of Pacific Biosciences and Oxford Nanopore Technologies and their applications to transcriptome analysis. F1000Research 6:100
    [Google Scholar]
  204. 204. 
    Steijger T, Abril JF, Engström PG, Kokocinski F RGASP Consort. et al. 2013. Assessment of transcript reconstruction methods for RNA-seq. Nat. Methods 10:1177–84
    [Google Scholar]
  205. 205. 
    Tilgner H, Grubert F, Sharon D, Snyder MP 2014. Defining a personal, allele-specific, and single-molecule long-read transcriptome. PNAS 111:9869–74
    [Google Scholar]
  206. 206. 
    Gonzalez-Garay ML. 2016. Introduction to isoform sequencing using Pacific Biosciences Technology (Iso-Seq). Transcriptomics and Gene Regulation J Wu141–60 Dordrecht, Neth.: Springer Neth.
    [Google Scholar]
  207. 207. 
    Laver T, Harrison J, O'Neill PA, Moore K, Farbos A et al. 2015. Assessing the performance of the Oxford Nanopore Technologies MinION. Biomol. Detect. Quantif. 3:1–8
    [Google Scholar]
  208. 208. 
    Ross MG, Russ C, Costello M, Hollinger A, Lennon NJ et al. 2013. Characterizing and measuring bias in sequence data. Genome Biol 14:R51
    [Google Scholar]
  209. 209. 
    Teng JLL, Yeung ML, Chan E, Jia L, Lin CH et al. 2017. PacBio but not Illumina technology can achieve fast, accurate and complete closure of the high GC, complex Burkholderia pseudomallei two-chromosome genome. Front. Microbiol. 8:1448
    [Google Scholar]
  210. 210. 
    Eid J, Fehr A, Gray J, Luong K, Lyle J et al. 2009. Real-time DNA sequencing from single polymerase molecules. Science 323:133–38
    [Google Scholar]
  211. 211. 
    Levene MJ, Korlach J, Turner SW, Foquet M, Craighead HG, Webb WW 2003. Zero-mode waveguides for single-molecule analysis at high concentrations. Science 299:682–86
    [Google Scholar]
  212. 212. 
    Travers KJ, Chin C-S, Rank DR, Eid JS, Turner SW 2010. A flexible and efficient template format for circular consensus sequencing and SNP detection. Nucleic Acids Res 38:e159
    [Google Scholar]
  213. 213. 
    Carneiro MO, Russ C, Ross MG, Gabriel SB, Nusbaum C, DePristo MA 2012. Pacific Biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genom 13:375
    [Google Scholar]
  214. 214. 
    Wang Y, Yang Q, Wang Z 2014. The evolution of nanopore sequencing. Front. Genet. 5:449
    [Google Scholar]
  215. 215. 
    Quick J, Quinlan AR, Loman NJ 2014. A reference bacterial genome dataset generated on the MinION™ portable single-molecule nanopore sequencer. Gigascience 3:22
    [Google Scholar]
  216. 216. 
    Garalde DR, Snell EA, Jachimowicz D, Sipos B, Lloyd JH et al. 2018. Highly parallel direct RNA sequencing on an array of nanopores. Nat. Methods 15:201–6
    [Google Scholar]
  217. 217. 
    Smith AM, Jain M, Mulroney L, Garalde DR, Akeson M 2017. Reading canonical and modified nucleotides in 16S ribosomal RNA using nanopore direct RNA sequencing. bioRxiv 132274. http://doi.org/10.1101/132274
    [Crossref]
  218. 218. 
    Sharon D, Tilgner H, Grubert F, Snyder M 2013. A single-molecule long-read survey of the human transcriptome. Nat. Biotechnol. 31:1009–14
    [Google Scholar]
  219. 219. 
    Oikonomopoulos S, Wang YC, Djambazian H, Badescu D, Ragoussis J 2016. Benchmarking of the Oxford Nanopore MinION sequencing for quantitative and qualitative assessment of cDNA populations. Sci. Rep. 6:31602
    [Google Scholar]
  220. 220. 
    Abdel-Ghany SE, Hamilton M, Jacobi JL, Ngam P, Devitt N et al. 2016. A survey of the sorghum transcriptome using single-molecule long reads. Nat. Commun. 7:11706
    [Google Scholar]
  221. 221. 
    Au KF, Sebastiano V, Afshar PT, Durruthy JD, Lee L et al. 2013. Characterization of the human ESC transcriptome by hybrid sequencing. PNAS 110:E4821–30
    [Google Scholar]
  222. 222. 
    Treutlein B, Gokce O, Quake SR, Südhof TC 2014. Cartography of neurexin alternative splicing mapped by single-molecule long-read mRNA sequencing. PNAS 111:E1291–99
    [Google Scholar]
  223. 223. 
    Rhoads A, Au KF. 2015. PacBio sequencing and its applications. Genom. Proteom. Bioinform. 13:278–89
    [Google Scholar]
  224. 224. 
    Marchet C, Lecompte L, Da Silva C, Cruaud C, Aury JM et al. 2017. De novo clustering of long reads by gene from transcriptomics data. Nucleic Acids Res 47e2
  225. 225. 
    Workman RE, Myrka AM, Wong GW, Tseng E, Welch KC Jr., Timp W 2018. Single-molecule, full-length transcript sequencing provides insight into the extreme metabolism of the ruby-throated hummingbird Archilochus colubris. Gigascience 7:1–12
    [Google Scholar]
  226. 226. 
    An D, Cao HX, Li C, Humbeck K, Wang W 2018. Isoform sequencing and state-of-art applications for unravelling complexity of plant transcriptomes. Genes 9:43
    [Google Scholar]
  227. 227. 
    Gordon SP, Tseng E, Salamov A, Zhang J, Meng X et al. 2015. Widespread polycistronic transcripts in fungi revealed by single-molecule mRNA sequencing. PLOS ONE 10:e0132628
    [Google Scholar]
  228. 228. 
    Li H. 2018. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34:3094–100
    [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-072018-021255
Loading
/content/journals/10.1146/annurev-biodatasci-072018-021255
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error