Advertisement

Transcriptomic Approaches for Muscle Biology and Disorders

  • Poching Liu
  • Surajit Bhattacharya
  • Yi-Wen ChenEmail author
Chapter
Part of the Methods in Physiology book series (METHPHYS)

Abstract

Transcriptomics approaches have been advancing quickly in the past 20 years. Technologies including various arrays and sequencing technologies are used to determine expression of RNA transcripts in cells and tissues. While the earlier approaches focus on the messenger RNA (mRNA), all other types of RNA transcripts, such as micro RNAs and non-coding RNAs, can be easily studied using current approaches. For skeletal muscle research, investigators use transcriptomics approaches to study basic muscle biology; molecular responses to physiological and environmental stimuli; effects of aging on muscles; disease mechanism of muscle disorders; molecular changes in muscles of non-muscle diseases; and molecular responses to therapeutic. This chapter focuses on current approaches and platforms used in the studies. Platform selections and limitations as well as data analysis will be discussed. Emerging approaches such as single cell profiling, single nucleus profiling, modified RNA profiling, and spatial transcription profiling are described in the chapter.

References

  1. 1.
    Li, Z., et al. (2018). Systematic transcriptome-wide analysis of mRNA-miRNA interactions reveals the involvement of miR-142-5p and its target (FOXO3) in skeletal muscle growth in chickens. Molecular Genetics and Genomics: MGG, 293, 69–80.  https://doi.org/10.1007/s00438-017-1364-7.CrossRefPubMedGoogle Scholar
  2. 2.
    Li, R., et al. (2019). Characterization and expression profiles of muscle transcriptome in Schizothoracine fish, Schizothorax prenanti. Gene, 685, 156–163.  https://doi.org/10.1016/j.gene.2018.10.070.CrossRefPubMedGoogle Scholar
  3. 3.
    Cote, L. E., Simental, E., & Reddien, P. W. (2019). Muscle functions as a connective tissue and source of extracellular matrix in planarians. Nature Communications, 10, 1592.  https://doi.org/10.1038/s41467-019-09539-6.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Burniston, J. G., et al. (2013). Gene expression profiling of gastrocnemius of “minimuscle” mice. Physiological Genomics, 45, 228–236.  https://doi.org/10.1152/physiolgenomics.00149.2012. physiolgenomics.00149.2012 [pii].CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Pietu, G., et al. (1996). Novel gene transcripts preferentially expressed in human muscles revealed by quantitative hybridization of a high density cDNA array. Genome Research, 6, 492–503.CrossRefGoogle Scholar
  6. 6.
    Chen, Y. W., Hubal, M. J., Hoffman, E. P., Thompson, P. D., & Clarkson, P. M. (2003). Molecular responses of human muscle to eccentric exercise. Journal of Applied Physiology, 95, 2485–2494.CrossRefGoogle Scholar
  7. 7.
    Chen, Y. W., et al. (2002). Response of rat muscle to acute resistance exercise defined by transcriptional and translational profiling. The Journal of Physiology, 545, 27–41.CrossRefGoogle Scholar
  8. 8.
    Bonafiglia, J. T., Menzies, K. J., & Gurd, B. J. (2019). Gene expression variability in human skeletal muscle transcriptome responses to acute resistance exercise. Experimental Physiology, 104, 625–629.  https://doi.org/10.1113/EP087436.CrossRefPubMedGoogle Scholar
  9. 9.
    Turner, D. C., Seaborne, R. A., & Sharples, A. P. (2019). Comparative transcriptome and methylome analysis in human skeletal muscle anabolism, hypertrophy and epigenetic memory. Scientific Reports, 9, 4251.  https://doi.org/10.1038/s41598-019-40787-0.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Dickinson, J. M., et al. (2018). Transcriptome response of human skeletal muscle to divergent exercise stimuli. Journal of Applied Physiology (1985), 124, 1529–1540.  https://doi.org/10.1152/japplphysiol.00014.2018.CrossRefGoogle Scholar
  11. 11.
    Mahmassani, Z. S., et al. (2019). Age-dependent skeletal muscle transcriptome response to bed rest-induced atrophy. Journal of Applied Physiology (1985), 126, 894–902.  https://doi.org/10.1152/japplphysiol.00811.2018.CrossRefGoogle Scholar
  12. 12.
    Vechin, F. C., et al. (2019). Low-intensity resistance training with partial blood flow restriction and high-intensity resistance training induce similar changes in skeletal muscle transcriptome in elderly humans. Applied Physiology, Nutrition, and Metabolism = Physiologie appliquee, nutrition et metabolisme, 44, 216–220.  https://doi.org/10.1139/apnm-2018-0146.CrossRefPubMedGoogle Scholar
  13. 13.
    Chen, Y. W., et al. (2005). Early onset of inflammation and later involvement of TGFbeta in Duchenne muscular dystrophy. Neurology, 65, 826–834.CrossRefGoogle Scholar
  14. 14.
    Dadgar, S., et al. (2014). Asynchronous remodeling is a driver of failed regeneration in Duchenne muscular dystrophy. The Journal of Cell Biology, 207, 139–158.  https://doi.org/10.1083/jcb.201402079. jcb.201402079 [pii].CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Sharma, V., Harafuji, N., Belayew, A., & Chen, Y. W. (2013). DUX4 differentially regulates transcriptomes of human rhabdomyosarcoma and mouse C2C12 cells. PLoS One, 8, e64691.  https://doi.org/10.1371/journal.pone.0064691. PONE-D-13-08552 [pii].CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Dixit, M., et al. (2007). DUX4, a candidate gene of facioscapulohumeral muscular dystrophy, encodes a transcriptional activator of PITX1. Proceedings of the National Academy of Sciences of the United States of America, 104, 18157–18162.CrossRefGoogle Scholar
  17. 17.
    Wang, E. T., et al. (2019). Transcriptome alterations in myotonic dystrophy skeletal muscle and heart. Human Molecular Genetics, 28, 1312–1321.  https://doi.org/10.1093/hmg/ddy432.CrossRefPubMedGoogle Scholar
  18. 18.
    Chen, Y. W., Zhao, P., Borup, R., & Hoffman, E. P. (2000). Expression profiling in the muscular dystrophies: Identification of novel aspects of molecular pathophysiology. The Journal of Cell Biology, 151, 1321–1336.CrossRefGoogle Scholar
  19. 19.
    Zhang, N., et al. (2018). Dynamic transcriptome profile in db/db skeletal muscle reveal critical roles for long noncoding RNA regulator. The International Journal of Biochemistry and Cell Biology, 104, 14–24.  https://doi.org/10.1016/j.biocel.2018.08.013.CrossRefPubMedGoogle Scholar
  20. 20.
    Scott, L. J., et al. (2016). The genetic regulatory signature of type 2 diabetes in human skeletal muscle. Nature Communications, 7, 11764.  https://doi.org/10.1038/ncomms11764.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Gallagher, I. J., et al. (2012). Suppression of skeletal muscle turnover in cancer cachexia: Evidence from the transcriptome in sequential human muscle biopsies. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research, 18, 2817–2827.  https://doi.org/10.1158/1078-0432.CCR-11-2133.CrossRefGoogle Scholar
  22. 22.
    Chen, Y. W., et al. (2017). Molecular signatures of differential responses to exercise trainings during rehabilitation. Biomedical Genetics and Genomics, 2.  https://doi.org/10.15761/BGG.1000127.
  23. 23.
    Boehler, J. F., et al. (2017). Effect of endurance exercise on microRNAs in myositis skeletal muscle—A randomized controlled study. PLoS One, 12, e0183292.  https://doi.org/10.1371/journal.pone.0183292.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Benoit, B., et al. (2017). Fibroblast growth factor 19 regulates skeletal muscle mass and ameliorates muscle wasting in mice. Nature Medicine, 23, 990–996.  https://doi.org/10.1038/nm.4363.CrossRefPubMedGoogle Scholar
  25. 25.
    Wu, J., et al. (2014). Ribogenomics: The science and knowledge of RNA. Genomics, Proteomics and Bioinformatics, 12, 57–63.  https://doi.org/10.1016/j.gpb.2014.04.002.CrossRefPubMedGoogle Scholar
  26. 26.
    Varemo, L., et al. (2016). Proteome- and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes. Cell Reports, 14, 1567.  https://doi.org/10.1016/j.celrep.2016.01.054.CrossRefPubMedGoogle Scholar
  27. 27.
    Lundberg, E., et al. (2010). Defining the transcriptome and proteome in three functionally different human cell lines. Molecular Systems Biology, 6, 450.  https://doi.org/10.1038/msb.2010.106.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Vogel, C., & Marcotte, E. M. (2012). Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nature Reviews. Genetics, 13, 227–232.  https://doi.org/10.1038/nrg3185.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Nie, L., Wu, G., Brockman, F. J., & Zhang, W. (2006). Integrated analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: Zero-inflated Poisson regression models to predict abundance of undetected proteins. Bioinformatics, 22, 1641–1647.  https://doi.org/10.1093/bioinformatics/btl134.CrossRefPubMedGoogle Scholar
  30. 30.
    Greenbaum, D., Colangelo, C., Williams, K., & Gerstein, M. (2003). Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biology, 4, 117.  https://doi.org/10.1186/gb-2003-4-9-117.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Margulies, M., et al. (2005). Genome sequencing in microfabricated high-density picolitre reactors. Nature, 437, 376–380.  https://doi.org/10.1038/nature03959.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Heather, J. M., & Chain, B. (2016). The sequence of sequencers: The history of sequencing DNA. Genomics, 107, 1–8.  https://doi.org/10.1016/j.ygeno.2015.11.003.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Schena, M., Shalon, D., Davis, R. W., & Brown, P. O. (1995). Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270, 467–470.CrossRefGoogle Scholar
  34. 34.
    DeRisi, J., et al. (1996). Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nature Genetics, 14, 457–460.  https://doi.org/10.1038/ng1296-457.CrossRefPubMedGoogle Scholar
  35. 35.
    Dalma-Weiszhausz, D. D., Warrington, J., Tanimoto, E. Y., & Miyada, C. G. (2006). The affymetrix GeneChip platform: An overview. Methods in Enzymology, 410, 3–28.  https://doi.org/10.1016/S0076-6879(06)10001-4.CrossRefPubMedGoogle Scholar
  36. 36.
    Kostek, M. C., et al. (2007). Gene expression responses over 24 h to lengthening and shortening contractions in human muscle: Major changes in CSRP3, MUSTN1, SIX1, and FBXO32. Physiological Genomics, 31, 42–52.CrossRefGoogle Scholar
  37. 37.
    Borup, R. H., et al. (2002). Development and production of an oligonucleotide MuscleChip: Use for validation of ambiguous ESTs. BMC Bioinformatics, 3, 33.CrossRefGoogle Scholar
  38. 38.
    Fan, J. B., et al. (2006). Illumina universal bead arrays. Methods in Enzymology, 410, 57–73.  https://doi.org/10.1016/S0076-6879(06)10003-8.CrossRefPubMedGoogle Scholar
  39. 39.
    Kotorashvili, A., et al. (2012). Effective DNA/RNA co-extraction for analysis of microRNAs, mRNAs, and genomic DNA from formalin-fixed paraffin-embedded specimens. PLoS One, 7, e34683.  https://doi.org/10.1371/journal.pone.0034683.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Kibriya, M. G., et al. (2010). Analyses and interpretation of whole-genome gene expression from formalin-fixed paraffin-embedded tissue: An illustration with breast cancer tissues. BMC Genomics, 11, 622.  https://doi.org/10.1186/1471-2164-11-622.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Wolber, P. K., Collins, P. J., Lucas, A. B., De Witte, A., & Shannon, K. W. (2006). The agilent in situ-synthesized microarray platform. Methods in Enzymology, 410, 28–57.  https://doi.org/10.1016/S0076-6879(06)10002-6.CrossRefPubMedGoogle Scholar
  42. 42.
    Wu, L., Brady, L., Shoffner, J., & Tarnopolsky, M. A. (2018). Next-generation sequencing to diagnose muscular dystrophy, rhabdomyolysis, and HyperCKemia. The Canadian Journal of Neurological Sciences. Le journal canadien des sciences neurologiques, 45, 262–268.  https://doi.org/10.1017/cjn.2017.286.CrossRefPubMedGoogle Scholar
  43. 43.
    Nigro, V., & Piluso, G. (2012). Next generation sequencing (NGS) strategies for the genetic testing of myopathies. Acta myologica: Myopathies and Cardiomyopathies: Official Journal of the Mediterranean Society of Myology, 31, 196–200.Google Scholar
  44. 44.
    Hestand, M. S., et al. (2010). Tissue-specific transcript annotation and expression profiling with complementary next-generation sequencing technologies. Nucleic Acids Research, 38, e165.  https://doi.org/10.1093/nar/gkq602.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Colangelo, V., et al. (2014). Next-generation sequencing analysis of miRNA expression in control and FSHD myogenesis. PLoS One, 9, e108411.  https://doi.org/10.1371/journal.pone.0108411.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Cardoso, T. F., et al. (2017). RNA-seq based detection of differentially expressed genes in the skeletal muscle of Duroc pigs with distinct lipid profiles. Scientific Reports, 7, 40005.  https://doi.org/10.1038/srep40005.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Pennisi, E. (2010). Genomics. Semiconductors inspire new sequencing technologies. Science, 327, 1190.  https://doi.org/10.1126/science.327.5970.1190.CrossRefPubMedGoogle Scholar
  48. 48.
    Tripathi, A. K., et al. (2014). Transcriptomic dissection of myogenic differentiation signature in caprine by RNA-Seq. Mechanisms of Development, 132, 79–92.  https://doi.org/10.1016/j.mod.2014.01.001.CrossRefPubMedGoogle Scholar
  49. 49.
    Parmakelis, A., Kotsakiozi, P., Kontos, C. K., Adamopoulos, P. G., & Scorilas, A. (2017). The transcriptome of a “sleeping” invader: De novo assembly and annotation of the transcriptome of aestivating Cornu aspersum. BMC Genomics, 18, 491.  https://doi.org/10.1186/s12864-017-3885-1.CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Possidonio, A. C., et al. (2014). Cholesterol depletion induces transcriptional changes during skeletal muscle differentiation. BMC Genomics, 15, 544.  https://doi.org/10.1186/1471-2164-15-544.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Cartolano, M., Huettel, B., Hartwig, B., Reinhardt, R., & Schneeberger, K. (2016). cDNA Library Enrichment of Full Length Transcripts for SMRT Long Read Sequencing. PLoS One, 11, e0157779.  https://doi.org/10.1371/journal.pone.0157779.CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Chen, S. Y., Deng, F., Jia, X., Li, C., & Lai, S. J. (2017). A transcriptome atlas of rabbit revealed by PacBio single-molecule long-read sequencing. Scientific Reports, 7, 7648.  https://doi.org/10.1038/s41598-017-08138-z.CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Masonbrink, R. E., et al. (2019). An annotated genome for Haliotis rufescens (Red Abalone) and resequenced green, pink, pinto, black, and white abalone species. Genome Biology and Evolution, 11, 431–438.  https://doi.org/10.1093/gbe/evz006.CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Loman, N. J., & Watson, M. (2015). Successful test launch for nanopore sequencing. Nature Methods, 12, 303–304.  https://doi.org/10.1038/nmeth.3327.CrossRefPubMedGoogle Scholar
  55. 55.
    Mikheyev, A. S., & Tin, M. M. (2014). A first look at the Oxford nanopore MinION sequencer. Molecular Ecology Resources, 14, 1097–1102.  https://doi.org/10.1111/1755-0998.12324.CrossRefPubMedGoogle Scholar
  56. 56.
    Ayub, M., & Bayley, H. (2012). Individual RNA base recognition in immobilized oligonucleotides using a protein nanopore. Nano Letters, 12, 5637–5643.  https://doi.org/10.1021/nl3027873.CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Johnson, S. S., Zaikova, E., Goerlitz, D. S., Bai, Y., & Tighe, S. W. (2017). Real-time DNA sequencing in the Antarctic dry valleys using the Oxford nanopore sequencer. Journal of Biomolecular Techniques: JBT, 28, 2–7.  https://doi.org/10.7171/jbt.17-2801-009.CrossRefPubMedGoogle Scholar
  58. 58.
    McIntyre, A. B. R., et al. (2016). Nanopore sequencing in microgravity. NPJ Microgravity, 2, 16035.  https://doi.org/10.1038/npjmgrav.2016.35.CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Runtuwene, L. R., Tuda, J. S. B., Mongan, A. E., & Suzuki, Y. (2019). On-site MinION sequencing. Advances in Experimental Medicine and Biology, 1129, 143–150.  https://doi.org/10.1007/978-981-13-6037-4_10.CrossRefPubMedGoogle Scholar
  60. 60.
    Walter, M. C., et al. (2017). MinION as part of a biomedical rapidly deployable laboratory. Journal of Biotechnology, 250, 16–22.  https://doi.org/10.1016/j.jbiotec.2016.12.006.CrossRefPubMedGoogle Scholar
  61. 61.
    Jain, M., Olsen, H. E., Paten, B., & Akeson, M. (2016). The Oxford nanopore MinION: Delivery of nanopore sequencing to the genomics community. Genome Biology, 17, 239.  https://doi.org/10.1186/s13059-016-1103-0.CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Narola, J., Pandey, S. N., Glick, A., & Chen, Y. W. (2013). Conditional expression of TGF-beta1 in skeletal muscles causes endomysial fibrosis and myofibers atrophy. PLoS One, 8, e79356.  https://doi.org/10.1371/journal.pone.0079356. PONE-D-13-27811 [pii].CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Cho, D. S., & Doles, J. D. (2017). Single cell transcriptome analysis of muscle satellite cells reveals widespread transcriptional heterogeneity. Gene, 636, 54–63.  https://doi.org/10.1016/j.gene.2017.09.014.CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Zeng, W., et al. (2016). Single-nucleus RNA-seq of differentiating human myoblasts reveals the extent of fate heterogeneity. Nucleic Acids Research, 44, e158.  https://doi.org/10.1093/nar/gkw739.CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Dell’Orso, S., et al. (2019). Single cell analysis of adult mouse skeletal muscle stem cells in homeostatic and regenerative conditions. Development, 146.  https://doi.org/10.1242/dev.174177.
  66. 66.
    Winokur, S. T., et al. (2003). Expression profiling of FSHD muscle supports a defect in specific stages of myogenic differentiation. Human Molecular Genetics, 12, 2895–2907.CrossRefGoogle Scholar
  67. 67.
    Lemmers, R. J., et al. (2010). A unifying genetic model for facioscapulohumeral muscular dystrophy. Science, 329, 1650–1653.  https://doi.org/10.1126/science.1189044.CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Snider, L., et al. (2010). Facioscapulohumeral dystrophy: Incomplete suppression of a retrotransposed gene. PLoS Genetics, 6, e1001181.  https://doi.org/10.1371/journal.pgen.1001181.CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Jones, T. I., et al. (2015). Individual epigenetic status of the pathogenic D4Z4 macrosatellite correlates with disease in facioscapulohumeral muscular dystrophy. Clinical Epigenetics, 7, 37.  https://doi.org/10.1186/s13148-015-0072-6.CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Himeda, C. L., et al. (2014). Myogenic enhancers regulate expression of the facioscapulohumeral muscular dystrophy-associated DUX4 gene. Molecular and Cellular Biology, 34, 1942–1955.  https://doi.org/10.1128/MCB.00149-14.CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    van den Heuvel, A., et al. (2019). Single-cell RNA sequencing in facioscapulohumeral muscular dystrophy disease etiology and development. Human Molecular Genetics, 28, 1064–1075.  https://doi.org/10.1093/hmg/ddy400.CrossRefPubMedGoogle Scholar
  72. 72.
    Ritchie, M. E., et al. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43, e47–e47.  https://doi.org/10.1093/nar/gkv007.CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Gautier, L., Cope, L., Bolstad, B. M., & Irizarry, R. A. (2004). affy – Analysis of Affymetrix GeneChip data at the probe level. Bioinformatics, 20, 307–315.  https://doi.org/10.1093/bioinformatics/btg405.CrossRefPubMedGoogle Scholar
  74. 74.
    Dunning, M. J., Smith, M. L., Ritchie, M. E., & Tavare, S. (2007). beadarray: R classes and methods for Illumina bead-based data. Bioinformatics, 23, 2183–2184.  https://doi.org/10.1093/bioinformatics/btm311.CrossRefPubMedGoogle Scholar
  75. 75.
    Bolstad, B. M., Irizarry, R. A., Astrand, M., & Speed, T. P. (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 19, 185–193.  https://doi.org/10.1093/bioinformatics/19.2.185.CrossRefGoogle Scholar
  76. 76.
    Carvalho, B. S., & Irizarry, R. A. (2010). A framework for oligonucleotide microarray preprocessing. Bioinformatics, 26, 2363–2367.  https://doi.org/10.1093/bioinformatics/btq431.CrossRefPubMedPubMedCentralGoogle Scholar
  77. 77.
    Warnes, G. R., Bolker, B., Bonebakker, L., Gentleman, R., Huber, W., Liaw, A., Lumley, T., et al. (2009). gplots: Various R programming tools for plotting data. R package version 2.Google Scholar
  78. 78.
    Student. (1908). The probable error of a mean. Biometrika.Google Scholar
  79. 79.
    Fisher, R. (1919). A. XV.—The correlation between relatives on the supposition of Mendelian inheritance. Transactions of the Royal Society of Edinburgh, 52, 399–433.  https://doi.org/10.1017/S0080456800012163.CrossRefGoogle Scholar
  80. 80.
    Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57, 289–300.  https://doi.org/10.2307/2346101.CrossRefGoogle Scholar
  81. 81.
    Bonferroni, C. (1936). Teoria statistica delle classi e calcolo delle probabilità. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze, 8, 3–62.Google Scholar
  82. 82.
    Schadt, E. E., Turner, S., & Kasarskis, A. (2010). A window into third-generation sequencing. Human Molecular Genetics, 19, R227–R240.  https://doi.org/10.1093/hmg/ddq416.CrossRefPubMedGoogle Scholar
  83. 83.
    Eisenstein, M. (2012). Oxford nanopore announcement sets sequencing sector abuzz. Nature Biotechnology, 30, 295–296.  https://doi.org/10.1038/nbt0412-295.CrossRefPubMedGoogle Scholar
  84. 84.
    Kim, D., et al. (2013). TopHat2: Accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology, 14, R36.  https://doi.org/10.1186/gb-2013-14-4-r36.CrossRefPubMedPubMedCentralGoogle Scholar
  85. 85.
    Trapnell, C., et al. (2012). Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nature Protocols, 7, 562–578.  https://doi.org/10.1038/nprot.2012.016.CrossRefPubMedPubMedCentralGoogle Scholar
  86. 86.
    Trapnell, C., et al. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology, 28, 511–515.  https://doi.org/10.1038/nbt.1621.CrossRefPubMedPubMedCentralGoogle Scholar
  87. 87.
    Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods, 9, 357–359.  https://doi.org/10.1038/nmeth.1923.CrossRefPubMedPubMedCentralGoogle Scholar
  88. 88.
    Adjeroh, D., Bell, T., & Mukherjee, A. (2008). The Burrows-Wheeler Transform: Data compression, suffix arrays, and pattern matching. New York: Springer.CrossRefGoogle Scholar
  89. 89.
    Ferragina, P., & Manzini, G. (2001). An experimental study of a compressed index. Information Sciences, 135, 13–28.  https://doi.org/10.1016/S0020-0255(01)00098-6.CrossRefGoogle Scholar
  90. 90.
    Dobin, A., et al. (2013). STAR: Ultrafast universal RNA-seq aligner. Bioinformatics (Oxford, England), 29, 15–21.  https://doi.org/10.1093/bioinformatics/bts635.CrossRefGoogle Scholar
  91. 91.
    Kim, D., Langmead, B., & Salzberg, S. L. (2015). HISAT: A fast spliced aligner with low memory requirements. Nature Methods, 12, 357–360.  https://doi.org/10.1038/nmeth.3317.CrossRefPubMedPubMedCentralGoogle Scholar
  92. 92.
    Pertea, M., Kim, D., Pertea, G. M., Leek, J. T., & Salzberg, S. L. (2016). Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nature Protocols, 11, 1650–1667.  https://doi.org/10.1038/nprot.2016.095.CrossRefPubMedPubMedCentralGoogle Scholar
  93. 93.
    Pertea, M., et al. (2015). StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nature Biotechnology, 33, 290–295.  https://doi.org/10.1038/nbt.3122.CrossRefPubMedPubMedCentralGoogle Scholar
  94. 94.
    Frazee, A. C., et al. (2015). Ballgown bridges the gap between transcriptome assembly and expression analysis. Nature Biotechnology, 33, 243–246.  https://doi.org/10.1038/nbt.3172.CrossRefPubMedPubMedCentralGoogle Scholar
  95. 95.
    Li, H., et al. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics (Oxford, England), 25, 2078–2079.  https://doi.org/10.1093/bioinformatics/btp352.CrossRefGoogle Scholar
  96. 96.
    Wang, L., Wang, S., & Li, W. (2012). RSeQC: Quality control of RNA-seq experiments. Bioinformatics, 28, 2184–2185.  https://doi.org/10.1093/bioinformatics/bts356.CrossRefPubMedGoogle Scholar
  97. 97.
    Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods, 5, 621–628.  https://doi.org/10.1038/nmeth.1226.CrossRefPubMedGoogle Scholar
  98. 98.
    Li, B., Ruotti, V., Stewart, R. M., Thomson, J. A., & Dewey, C. N. (2010). RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics, 26, 493–500.  https://doi.org/10.1093/bioinformatics/btp692.CrossRefPubMedGoogle Scholar
  99. 99.
    Li, B., & Dewey, C. N. (2011). RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics, 12, 323.  https://doi.org/10.1186/1471-2105-12-323.CrossRefPubMedPubMedCentralGoogle Scholar
  100. 100.
    Dempster, A. P., Laird, N. M., & Rubin, D. B. (1976). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 1–38.CrossRefGoogle Scholar
  101. 101.
    Anders, S., Pyl, P. T., & Huber, W. (2015). HTSeq—A Python framework to work with high-throughput sequencing data. Bioinformatics (Oxford, England), 31, 166–169.  https://doi.org/10.1093/bioinformatics/btu638.CrossRefGoogle Scholar
  102. 102.
    Liao, Y., Smyth, G. K., & Shi, W. (2014). featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 30, 923–930.  https://doi.org/10.1093/bioinformatics/btt656.CrossRefPubMedPubMedCentralGoogle Scholar
  103. 103.
    Lawrence, M., et al. (2013). Software for computing and annotating genomic ranges. PLoS Computational Biology, 9, e1003118.  https://doi.org/10.1371/journal.pcbi.1003118.CrossRefPubMedPubMedCentralGoogle Scholar
  104. 104.
    Soneson, C., Love, M. I., & Robinson, M. D. (2015). Differential analyses for RNA-seq: Transcript-level estimates improve gene-level inferences. F1000Research, 4, 1521.  https://doi.org/10.12688/f1000research.7563.1.CrossRefPubMedGoogle Scholar
  105. 105.
    Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139–140.  https://doi.org/10.1093/bioinformatics/btp616.CrossRefPubMedPubMedCentralGoogle Scholar
  106. 106.
    Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15, 550.  https://doi.org/10.1186/s13059-014-0550-8.CrossRefPubMedPubMedCentralGoogle Scholar
  107. 107.
    Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society. Series A (General), 135, 370.  https://doi.org/10.2307/2344614.CrossRefGoogle Scholar
  108. 108.
    Wald, A. (1945). Sequential tests of statistical hypotheses. The Annals of Mathematical Statistics, 16, 117–186.  https://doi.org/10.1214/aoms/1177731118.CrossRefGoogle Scholar
  109. 109.
    Feng, J., et al. (2012). GFOLD: A generalized fold change for ranking differentially expressed genes from RNA-seq data. Bioinformatics, 28, 2782–2788.  https://doi.org/10.1093/bioinformatics/bts515.CrossRefPubMedGoogle Scholar
  110. 110.
    Tarazona, S., et al. (2015). Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. Nucleic Acids Research, 43, e140.  https://doi.org/10.1093/nar/gkv711.CrossRefPubMedPubMedCentralGoogle Scholar
  111. 111.
    Eberwine, J., et al. (1992). Analysis of gene expression in single live neurons. Proceedings of the National Academy of Sciences of the United States of America, 89, 3010–3014.  https://doi.org/10.1073/pnas.89.7.3010.CrossRefPubMedPubMedCentralGoogle Scholar
  112. 112.
    Hwang, B., Lee, J. H., & Bang, D. (2018). Single-cell RNA sequencing technologies and bioinformatics pipelines. Experimental and Molecular Medicine, 50, 96.  https://doi.org/10.1038/s12276-018-0071-8.CrossRefPubMedGoogle Scholar
  113. 113.
    Van Der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.Google Scholar
  114. 114.
    Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology, 36, 411–420.  https://doi.org/10.1038/nbt.4096.CrossRefPubMedPubMedCentralGoogle Scholar
  115. 115.
    Gatto, S., Puri, P. L., & Malecova, B. (2017). Single cell gene expression profiling of skeletal muscle-derived cells. Methods in Molecular Biology, 1556, 191–219.  https://doi.org/10.1007/978-1-4939-6771-1_10.CrossRefPubMedGoogle Scholar
  116. 116.
    Banerji, C. R. S., et al. (2017). PAX7 target genes are globally repressed in facioscapulohumeral muscular dystrophy skeletal muscle. Nature Communications, 8, 2152.  https://doi.org/10.1038/s41467-017-01200-4.CrossRefPubMedPubMedCentralGoogle Scholar
  117. 117.
    Stahl, P. L., et al. (2016). Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science, 353, 78–82.  https://doi.org/10.1126/science.aaf2403.CrossRefGoogle Scholar
  118. 118.
    Saletore, Y., et al. (2012). The birth of the Epitranscriptome: Deciphering the function of RNA modifications. Genome Biology, 13, 175.  https://doi.org/10.1186/gb-2012-13-10-175.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© The American Physiological Society 2019

Authors and Affiliations

  • Poching Liu
    • 1
  • Surajit Bhattacharya
    • 2
  • Yi-Wen Chen
    • 3
    • 4
    Email author
  1. 1.DNA Sequencing and Genomics Core, National Heart, Lung and Blood InstituteNational Institutes of HealthBethesdaUSA
  2. 2.Center for Genetic Medicine ResearchChildren’s National Medical CenterWashington, DCUSA
  3. 3.Center for Genetic Medicine Research, Children’s National Health SystemChildren’s Research InstituteWashington, DCUSA
  4. 4.Department of Genomics and Precision Medicine, School of Medicine and Health ScienceGeorge Washington UniversityWashington, DCUSA

Personalised recommendations