Introduction to Isoform Sequencing Using Pacific Biosciences Technology (Iso-Seq)

Part of the Translational Bioinformatics book series (TRBIO, volume 9)


Alternative RNA splicing is a known phenomenon, but we still do not have a complete catalog of isoforms that explain variability in the human transcriptome. We have made significant progress in developing methods to study variability of the transcriptome, but we are far away of having a complete picture of the transcriptome. The initial methods to study gene expression were based on cloning of cDNAs and Sanger sequencing. The strategy was labor-intensive and expensive. With the development of microarrays, different methods based on exon arrays and tiling arrays provided valuable information about RNA expression. However, the microarray presented significant limitations. Most of the limitations became apparent by 2005, but it was not until 2008 that an alternative method to study the transcriptome was developed. RNA Sequencing using next-generation sequencing (RNA-Seq) quickly became the technology of choice for gene expression profiling. Recently, the precision and sensitivity of RNA-Seq have come into question, especially for transcriptome reconstruction. This chapter will describe a relatively new method, “Isoform Sequencing” (Iso-Seq). Iso-Seq was developed by Pacific Biosciences (PacBio), and it is capable of identifying new isoforms with extraordinary precision due to its long-read technology. The technique to create libraries is straightforward, and the PacBio RS II instrument generates the information in hours. The bioinformatics analysis is performed using the freely available SMRT® Portal software. The SMRT® Portal is easy to use and capable of performing all the steps necessary to analyze the raw data and to generate high-quality full-length isoforms. For the universal acceptance of the Iso-Seq method, the capacity of the SMRT® Cells needs to improve at least 10- to 100-fold to make the system affordable and attractive to users.


Isoform Pacific biosciences Iso-Seq Pacbio SMRT RNA-Seq 


  1. 1.
    Abdullah-Sayani A, Bueno-de-Mesquita JM, van de Vijver MJ. Technology Insight: tuning into the genetic orchestra using microarrays–limitations of DNA microarrays in clinical practice. Nat Clin Pract Oncol. 2006;3:501–16. doi: 10.1038/ncponc0587.CrossRefPubMedGoogle Scholar
  2. 2.
    Agarwal A, et al. Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays. BMC Genom. 2010;11:383. doi: 10.1186/1471-2164-11-383.CrossRefGoogle Scholar
  3. 3.
    Alwine JC, Kemp DJ, Stark GR. Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes. Proc Natl Acad Sci USA. 1977;74:5350–4.PubMedCentralCrossRefPubMedGoogle Scholar
  4. 4.
    Au KF, et al. Characterization of the human ESC transcriptome by hybrid sequencing. Proc Natl Acad Sci USA. 2013;110:E4821–30. doi: 10.1073/pnas.1320101110.PubMedCentralCrossRefPubMedGoogle Scholar
  5. 5.
    Ayub M, Bayley H. Individual RNA base recognition in immobilized oligonucleotides using a protein nanopore. Nano Lett. 2012;12:5637–43. doi: 10.1021/nl3027873.PubMedCentralCrossRefPubMedGoogle Scholar
  6. 6.
    Bottomly D, et al. Evaluating gene expression in C57BL/6 J and DBA/2 J mouse striatum using RNA-Seq and microarrays. PLoS ONE. 2011;6:e17820. doi: 10.1371/journal.pone.0017820.PubMedCentralCrossRefPubMedGoogle Scholar
  7. 7.
    Carneiro MO, Russ C, Ross MG, Gabriel SB, Nusbaum C, DePristo MA. Pacific biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genom. 2012;13:375. doi: 10.1186/1471-2164-13-375.CrossRefGoogle Scholar
  8. 8.
    Chaisson MJ, et al. Resolving the complexity of the human genome using single-molecule sequencing. Nature. 2015;517:608–11. doi: 10.1038/nature13907.PubMedCentralCrossRefPubMedGoogle Scholar
  9. 9.
    Chin CS, et al. The origin of the Haitian cholera outbreak strain. N Engl J Med. 2011;364:33–42. doi: 10.1056/NEJMoa1012928.PubMedCentralCrossRefPubMedGoogle Scholar
  10. 10.
  11. 11.
    Cock PJ, Fields CJ, Goto N, Heuer ML, Rice PM. The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res. 2010;38:1767–71. doi: 10.1093/nar/gkp1137.PubMedCentralCrossRefPubMedGoogle Scholar
  12. 12.
    Dobin A, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21. doi: 10.1093/bioinformatics/bts635.PubMedCentralCrossRefPubMedGoogle Scholar
  13. 13.
    Draghici S, Khatri P, Eklund AC, Szallasi Z. Reliability and reproducibility issues in DNA microarray measurements. Trends Genet. 2006;22:101–9. doi: 10.1016/j.tig.2005.12.005.PubMedCentralCrossRefPubMedGoogle Scholar
  14. 14.
    Eid J, et al. Real-time DNA sequencing from single polymerase molecules. Science. 2009;323:133–8. doi: 10.1126/science.1162986.CrossRefPubMedGoogle Scholar
  15. 15.
    English AC, et al. Mind the gap: upgrading genomes with Pacific Biosciences RS long-read sequencing technology. PLoS ONE. 2012;7:e47768. doi: 10.1371/journal.pone.0047768.PubMedCentralCrossRefPubMedGoogle Scholar
  16. 16.
    Engstrom PG, et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat Methods. 2013;10:1185–91. doi: 10.1038/nmeth.2722.PubMedCentralCrossRefPubMedGoogle Scholar
  17. 17.
    Flusberg BA, et al. Direct detection of DNA methylation during single-molecule, real-time sequencing. Nat Methods. 2010;7:461–5. doi: 10.1038/nmeth.1459.PubMedCentralCrossRefPubMedGoogle Scholar
  18. 18.
    Gonzalez D, Kozdon JB, McAdams HH, Shapiro L, Collier J. The functions of DNA methylation by CcrM in Caulobacter crescentus: a global approach. Nucleic Acids Res. 2014;42:3720–35. doi: 10.1093/nar/gkt1352.PubMedCentralCrossRefPubMedGoogle Scholar
  19. 19.
    HDF_group. HDFS file format. 2015.
  20. 20.
  21. 21.
    Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12:357–60. doi: 10.1038/nmeth.3317.CrossRefPubMedGoogle Scholar
  22. 22.
    Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14:R36. doi: 10.1186/gb-2013-14-4-r36.PubMedCentralCrossRefPubMedGoogle Scholar
  23. 23.
    Korlach J, et al. Real-time DNA sequencing from single polymerase molecules. Methods Enzymol. 2010;472:431–55. doi: 10.1016/S0076-6879(10)72001-2.CrossRefPubMedGoogle Scholar
  24. 24.
    Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9. doi: 10.1038/nmeth.1923.PubMedCentralCrossRefPubMedGoogle Scholar
  25. 25.
    Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25. doi: 10.1186/gb-2009-10-3-r25.PubMedCentralCrossRefPubMedGoogle Scholar
  26. 26.
    Larsen PA, Smith TP. Application of circular consensus sequencing and network analysis to characterize the bovine IgG repertoire. BMC Immunol. 2012;13:52. doi: 10.1186/1471-2172-13-52.PubMedCentralCrossRefPubMedGoogle Scholar
  27. 27.
    Li JJ, Jiang CR, Brown JB, Huang H, Bickel PJ. Sparse linear modeling of next-generation mRNA sequencing (RNA-Seq) data for isoform discovery and abundance estimation. Proc Natl Acad Sci USA. 2011;108:19867–72. doi: 10.1073/pnas.1113972108.PubMedCentralCrossRefPubMedGoogle Scholar
  28. 28.
    Life_Technologies. Manual for Trizol Plus. 2015a.
  29. 29.
    Life_Technologies. Manual or Poly(A)Purist™ MAG Kit. 2015b.
  30. 30.
    Lister R, O’Malley RC, Tonti-Filippini J, Gregory BD, Berry CC, Millar AH, Ecker JR. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell. 2008;133:523–36. doi: 10.1016/j.cell.2008.03.029.PubMedCentralCrossRefPubMedGoogle Scholar
  31. 31.
    Loman NJ, Quinlan AR. Poretools: a toolkit for analyzing nanopore sequence data. Bioinformatics. 2014;30:3399–401. doi: 10.1093/bioinformatics/btu555.PubMedCentralCrossRefPubMedGoogle Scholar
  32. 32.
    Martin JA, Wang Z. Next-generation transcriptome assembly. Nat Rev Genet. 2011;12:671–82. doi: 10.1038/nrg3068.CrossRefPubMedGoogle Scholar
  33. 33.
    Mooney M, McWeeney S. Data integration and reproducibility for high-throughput transcriptomics. Int Rev Neurobiol. 2014;116:55–71. doi: 10.1016/B978-0-12-801105-8.00003-5.CrossRefPubMedGoogle Scholar
  34. 34.
    Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5:621–8. doi: 10.1038/nmeth.1226.CrossRefPubMedGoogle Scholar
  35. 35.
    Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science. 2008;320:1344–9. doi: 10.1126/science.1158441.PubMedCentralCrossRefPubMedGoogle Scholar
  36. 36.
    Nagaraj SH, Gasser RB, Ranganathan S. A hitchhiker’s guide to expressed sequence tag (EST) analysis. Briefings Bioinform. 2007;8:6–21. doi: 10.1093/bib/bbl015.CrossRefGoogle Scholar
  37. 37.
    Nawy T. End-to-end RNA sequencing. Nat Methods. 2013;10(12):1144-1145 10:1144-1145.Google Scholar
  38. 38.
    Pacific_Biosciences. bas.h5 reference guide. 2015a.
  39. 39.
    Pacific_Biosciences. Metadata output guide. 2015b.
  40. 40.
    Pacific_Biosciences. PacBio consumables reagents. 2015c.
  41. 41.
    Pacific_Biosciences. PacBio datasets. 2015d.
  42. 42.
    Pacific_Biosciences. PacBio DevNet. 2015e.
  43. 43.
    Pacific_Biosciences. PacBio SMRT Cells. 2015f.
  44. 44.
    Pacific_Biosciences. PacBio SMRT Sample Prep web site. 2015g.
  45. 45.
    Pacific_Biosciences. PacBio software. 2015h.
  46. 46.
    Parkinson J, Blaxter M. Expressed sequence tags: an overview. Methods Mol Biol. 2009;533:1–12. doi: 10.1007/978-1-60327-136-3_1.CrossRefPubMedGoogle Scholar
  47. 47.
    Quick J, Quinlan AR, Loman NJ. A reference bacterial genome dataset generated on the MinION portable single-molecule nanopore sequencer. Gigascience. 2014;3:22. doi: 10.1186/2047-217X-3-22.PubMedCentralCrossRefPubMedGoogle Scholar
  48. 48.
    Roberts A, Pimentel H, Trapnell C, Pachter L. Identification of novel transcripts in annotated genomes using RNA-Seq. Bioinformatics. 2011;27:2325–9. doi: 10.1093/bioinformatics/btr355.CrossRefPubMedGoogle Scholar
  49. 49.
    Roy NC, Altermann E, Park ZA, McNabb WC. A comparison of analog and next-generation transcriptomic tools for mammalian studies. Brief Funct Genomics. 2011;10:135–50. doi: 10.1093/bfgp/elr005.CrossRefPubMedGoogle Scholar
  50. 50.
    Sage_Science. The BluePippin System. 2015a.
  51. 51.
    Sage_Science. The SageELF. 2015b.
  52. 52.
    Sharon D, Tilgner H, Grubert F, Snyder M. A single-molecule long-read survey of the human transcriptome. Nat Biotechnol. 2013;31:1009–14. doi: 10.1038/nbt.2705.PubMedCentralCrossRefPubMedGoogle Scholar
  53. 53.
    Steijger T, et al. Assessment of transcript reconstruction methods for RNA-seq. Nat Methods. 2013;10:1177–84. doi: 10.1038/nmeth.2714.CrossRefPubMedGoogle Scholar
  54. 54.
    Steinbock LJ, Radenovic A. The emergence of nanopores in next-generation sequencing. Nanotechnology. 2015;26:074003. doi: 10.1088/0957-4484/26/7/074003.CrossRefPubMedGoogle Scholar
  55. 55.
    Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25:1105–11. doi: 10.1093/bioinformatics/btp120.PubMedCentralCrossRefPubMedGoogle Scholar
  56. 56.
    Trapnell C, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28:511–5. doi: 10.1038/nbt.1621.PubMedCentralCrossRefPubMedGoogle Scholar
  57. 57.
    Walter NA, McWeeney SK, Peters ST, Belknap JK, Hitzemann R, Buck KJ. SNPs matter: impact on detection of differential expression. Nat Methods. 2007;4:679–80. doi: 10.1038/nmeth0907-679.PubMedCentralCrossRefPubMedGoogle Scholar
  58. 58.
    Wang K, et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 2010;38:e178. doi: 10.1093/nar/gkq622.PubMedCentralCrossRefPubMedGoogle Scholar
  59. 59.
    Wang L, Feng Z, Wang X, Wang X, Zhang X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics. 2010;26:136–8. doi: 10.1093/bioinformatics/btp612.CrossRefPubMedGoogle Scholar
  60. 60.
    Wang L, Si Y, Dedow LK, Shao Y, Liu P, Brutnell TP. A low-cost library construction protocol and data analysis pipeline for Illumina-based strand-specific multiplex RNA-seq. PLoS One. 2011;6:e26426. doi: 10.1371/journal.pone.0026426.PubMedCentralCrossRefPubMedGoogle Scholar
  61. 61.
    Weber AP, Weber KL, Carr K, Wilkerson C, Ohlrogge JB. Sampling the Arabidopsis transcriptome with massively parallel pyrosequencing. Plant Physiol. 2007;144:32–42. doi: 10.1104/pp.107.096677.PubMedCentralCrossRefPubMedGoogle Scholar
  62. 62.
    Wilhelm BT, et al. Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature. 2008;453:1239–43. doi: 10.1038/nature07002.CrossRefPubMedGoogle Scholar
  63. 63.
    Wu TD, Nacu S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics. 2010;26:873–81. doi: 10.1093/bioinformatics/btq057.PubMedCentralCrossRefPubMedGoogle Scholar
  64. 64.
    Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008;18:821–9. doi: 10.1101/gr.074492.107.PubMedCentralCrossRefPubMedGoogle Scholar
  65. 65.
    Zheng CL, Kawane S, Bottomly D, Wilmot B. Analysis considerations for utilizing RNA-Seq to characterize the brain transcriptome. Int Rev Neurobiol. 2014;116:21–54. doi: 10.1016/B978-0-12-801105-8.00002-3.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Center for Molecular Imaging. The Brown Foundation Institute of Molecular Medicine for the Prevention of Human DiseasesThe University of Texas, Health Science Center at HoustonHoustonUSA

Personalised recommendations