Advertisement

Transcriptome Profiling Strategies

Chapter
First Online:
  • 1.5k Downloads

Abstract

With the rapid development of high-speed DNA sequencing technologies, it became feasible to sequence deeply into cDNA libraries prepared from RNA samples. Such cDNA libraries can benefit from the development of full-length cDNA cloning technologies providing means to obtain sequence information on the entire RNA transcripts or their selected 5′ end. Comprehensive overviews on transcriptomes can be obtained today by combination of those new sequencing technologies with large-scale cDNA library preparation forming the basis to different approaches for transcriptome profiling.

In this chapter, we describe the use of full-length cDNA preparations in combination with shotgun sequencing in mRNA profiling (so-called RNA-Seq methods for “RNA sequencing”) and RNA-Seq profiling starting directly from RNA. Moreover, we describe the use of cap analysis gene expression (CAGE) for high-throughput mRNA detection and determination of transcription start sites (TSS) on the genome level. Here we applied “nanoCAGE", which uses template switching in the 5′ end selection step, to obtain CAGE data from very small amounts of RNA. We compare the sequencing data obtained by the three different library preparation methods and give directions for a bioinformatics pipeline used for their analysis. Examples are taken from our studies on the transcriptional regulation of gene expression during behavioral maturation of worker honey bees (Apis mellifera) to advise on transcriptome profiling strategies.

Keywords

Transcriptome profiling cDNA RNA sequencing CAGE CAGEscan Bioinformatics RNA-Seq nanoCAGE Full-length cDNA 

List of Abbreviations

CAGE

Cap analysis gene expression

CPCC

Cophenetic correlation coefficient

DEGs

Differentially expressed genes

EST

Expressed sequence tag

FPKM

Fragments per kilobase per million reads

GO

Gene ontology

MAPQ

Mapping quality

RNA-Seq

RNA sequencing

RPKM

Reads per kilobase per million

TMM

Trimmed mean of M-values

TPM

Tags per million

TSSs

Transcription start sites

This is a preview of subscription content, log in to check access.

Notes

Acknowledgment

We want to express our great thanks to Adam R. Hamilton, Yulia A. Medvedeva, Tanvir Alam, Intikhab Alam, Magbubah Essack, Boris Umylny, Boris R. Jankovic, Nicholas L. Naeger, Makoto Suzuki, and Gene E. Robinson for their great support for our honey bee project, which would have not been possible without working together with them. We further want to thank Charles Plessy and Piero Carninci for their support and encouragement for using CAGE.

Supplementary material

Open image in new windowFig. QG4.1 Open image in new windowFig. QG4.2

References

  1. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11(10):R106. doi: 10.1186/gb-2010-11-10-r106 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Anders S, McCarthy DJ, Chen Y, Okoniewski M, Smyth GK, Huber W, Robinson MD (2013) Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat Protoc 8(9):1765–1786. doi: 10.1038/nprot.2013.099 CrossRefPubMedGoogle Scholar
  3. Anders S, Pyl PT, Huber W (2015) HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31(2):166–169. doi: 10.1093/bioinformatics/btu638 CrossRefPubMedPubMedCentralGoogle Scholar
  4. Auer PL, Doerge RW (2010) Statistical design and analysis of RNA sequencing data. Genetics 185(2):405–416. doi: 10.1534/genetics.110.114983 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A (2013) NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res 41(Database issue):D991–D995. doi: 10.1093/nar/gks1193 CrossRefPubMedPubMedCentralGoogle Scholar
  6. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30(15):2114–2120. doi: 10.1093/bioinformatics/btu170 CrossRefPubMedPubMedCentralGoogle Scholar
  7. Consortium EP (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489(7414):57–74. doi: 10.1038/nature11247 CrossRefGoogle Scholar
  8. Consortium F, the RP, Clst, Forrest AR, Kawaji H, Rehli M, Baillie JK, de Hoon MJ, Haberle V, Lassmann T, Kulakovskiy IV, Lizio M, Itoh M, Andersson R, Mungall CJ, Meehan TF, Schmeier S, Bertin N, Jorgensen M, Dimont E, Arner E, Schmidl C, Schaefer U, Medvedeva YA, Plessy C, Vitezic M, Severin J, Semple C, Ishizu Y, Young RS, Francescatto M, Alam I, Albanese D, Altschuler GM, Arakawa T, Archer JA, Arner P, Babina M, Rennie S, Balwierz PJ, Beckhouse AG, Pradhan-Bhatt S, Blake JA, Blumenthal A, Bodega B, Bonetti A, Briggs J, Brombacher F, Burroughs AM, Califano A, Cannistraci CV, Carbajo D, Chen Y, Chierici M, Ciani Y, Clevers HC, Dalla E, Davis CA, Detmar M, Diehl AD, Dohi T, Drablos F, Edge AS, Edinger M, Ekwall K, Endoh M, Enomoto H, Fagiolini M, Fairbairn L, Fang H, Farach-Carson MC, Faulkner GJ, Favorov AV, Fisher ME, Frith MC, Fujita R, Fukuda S, Furlanello C, Furino M, Furusawa J, Geijtenbeek TB, Gibson AP, Gingeras T, Goldowitz D, Gough J, Guhl S, Guler R, Gustincich S, Ha TJ, Hamaguchi M, Hara M, Harbers M, Harshbarger J, Hasegawa A, Hasegawa Y, Hashimoto T, Herlyn M, Hitchens KJ, Ho Sui SJ, Hofmann OM, Hoof I, Hori F, Huminiecki L, Iida K, Ikawa T, Jankovic BR, Jia H, Joshi A, Jurman G, Kaczkowski B, Kai C, Kaida K, Kaiho A, Kajiyama K, Kanamori-Katayama M, Kasianov AS, Kasukawa T, Katayama S, Kato S, Kawaguchi S, Kawamoto H, Kawamura YI, Kawashima T, Kempfle JS, Kenna TJ, Kere J, Khachigian LM, Kitamura T, Klinken SP, Knox AJ, Kojima M, Kojima S, Kondo N, Koseki H, Koyasu S, Krampitz S, Kubosaki A, Kwon AT, Laros JF, Lee W, Lennartsson A, Li K, Lilje B, Lipovich L, Mackay-Sim A, Manabe R, Mar JC, Marchand B, Mathelier A, Mejhert N, Meynert A, Mizuno Y, de Lima Morais DA, Morikawa H, Morimoto M, Moro K, Motakis E, Motohashi H, Mummery CL, Murata M, Nagao-Sato S, Nakachi Y, Nakahara F, Nakamura T, Nakamura Y, Nakazato K, van Nimwegen E, Ninomiya N, Nishiyori H, Noma S, Noma S, Noazaki T, Ogishima S, Ohkura N, Ohimiya H, Ohno H, Ohshima M, Okada-Hatakeyama M, Okazaki Y, Orlando V, Ovchinnikov DA, Pain A, Passier R, Patrikakis M, Persson H, Piazza S, Prendergast JG, Rackham OJ, Ramilowski JA, Rashid M, Ravasi T, Rizzu P, Roncador M, Roy S, Rye MB, Saijyo E, Sajantila A, Saka A, Sakaguchi S, Sakai M, Sato H, Savvi S, Saxena A, Schneider C, Schultes EA, Schulze-Tanzil GG, Schwegmann A, Sengstag T, Sheng G, Shimoji H, Shimoni Y, Shin JW, Simon C, Sugiyama D, Sugiyama T, Suzuki M, Suzuki N, Swoboda RK, ‘t Hoen PA, Tagami M, Takahashi N, Takai J, Tanaka H, Tatsukawa H, Tatum Z, Thompson M, Toyodo H, Toyoda T, Valen E, van de Wetering M, van den Berg LM, Verado R, Vijayan D, Vorontsov IE, Wasserman WW, Watanabe S, Wells CA, Winteringham LN, Wolvetang E, Wood EJ, Yamaguchi Y, Yamamoto M, Yoneda M, Yonekura Y, Yoshida S, Zabierowski SE, Zhang PG, Zhao X, Zucchelli S, Summers KM, Suzuki H, Daub CO, Kawai J, Heutink P, Hide W, Freeman TC, Lenhard B, Bajic VB, Taylor MS, Makeev VJ, Sandelin A, Hume DA, Carninci P, Hayashizaki Y (2014) A promoter-level mammalian expression atlas. Nature 507(7493):462–470. doi: 10.1038/nature13182 CrossRefGoogle Scholar
  9. da Huang W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57. doi: 10.1038/nprot.2008.211 CrossRefGoogle Scholar
  10. de Klerk E, ‘t Hoen PA (2015) Alternative mRNA transcription, processing, and translation: insights from RNA sequencing. Trends Genet 31(3):128–139. doi: 10.1016/j.tig.2015.01.001 CrossRefPubMedGoogle Scholar
  11. de Klerk E, den Dunnen JT, ‘t Hoen PA (2014) RNA sequencing: from tag-based profiling to resolving complete transcript structure. Cell Mol Life Sci 71(18):3537–3551. doi: 10.1007/s00018-014-1637-9 CrossRefPubMedPubMedCentralGoogle Scholar
  12. Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, Keime C, Marot G, Castel D, Estelle J, Guernec G, Jagla B, Jouneau L, Laloe D, Le Gall C, Schaeffer B, Le Crom S, Guedj M, Jaffrezic F, French StatOmique C (2013) A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform 14(6):671–683. doi: 10.1093/bib/bbs046 CrossRefPubMedGoogle Scholar
  13. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15–21. doi: 10.1093/bioinformatics/bts635 CrossRefPubMedPubMedCentralGoogle Scholar
  14. Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z (2009) GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10:48. doi: 10.1186/1471-2105-10-48 CrossRefPubMedPubMedCentralGoogle Scholar
  15. Elsik CG, Worley KC, Bennett AK, Beye M, Camara F, Childers CP, de Graaf DC, Debyser G, Deng J, Devreese B, Elhaik E, Evans JD, Foster LJ, Graur D, Guigo R, HGSC production teams, Hoff KJ, Holder ME, Hudson ME, Hunt GJ, Jiang H, Joshi V, Khetani RS, Kosarev P, Kovar CL, Ma J, Maleszka R, Moritz RF, Munoz-Torres MC, Murphy TD, Muzny DM, Newsham IF, Reese JT, Robertson HM, Robinson GE, Rueppell O, Solovyev V, Stanke M, Stolle E, Tsuruda JM, Vaerenbergh MV, Waterhouse RM, Weaver DB, Whitfield CW, Wu Y, Zdobnov EM, Zhang L, Zhu D, Gibbs RA, Honey Bee Genome Sequencing C (2014) Finding the missing honey bee genes: lessons learned from a genome upgrade. BMC Genomics 15:86. doi: 10.1186/1471-2164-15-86 CrossRefPubMedPubMedCentralGoogle Scholar
  16. Frith MC, Valen E, Krogh A, Hayashizaki Y, Carninci P, Sandelin A (2008) A code for transcription initiation in mammalian genomes. Genome Res 18(1):1–12. doi: 10.1101/gr.6831208 CrossRefPubMedPubMedCentralGoogle Scholar
  17. Harbers M (2008) The current status of cDNA cloning. Genomics 91(3):232–242. doi: 10.1016/j.ygeno.2007.11.004 CrossRefPubMedGoogle Scholar
  18. Huang S, Zhang J, Li R, Zhang W, He Z, Lam TW, Peng Z, Yiu SM (2011) SOAPsplice: genome-wide ab initio detection of splice junctions from RNA-Seq data. Front Genet 2:46. doi: 10.3389/fgene.2011.00046 CrossRefPubMedPubMedCentralGoogle Scholar
  19. Kawaji H, Lizio M, Itoh M, Kanamori-Katayama M, Kaiho A, Nishiyori-Sueki H, Shin JW, Kojima-Ishiyama M, Kawano M, Murata M, Ninomiya-Fukuda N, Ishikawa-Kato S, Nagao-Sato S, Noma S, Hayashizaki Y, Forrest AR, Carninci P, Consortium F (2014) Comparison of CAGE and RNA-seq transcriptome profiling using clonally amplified and single-molecule next-generation sequencing. Genome Res 24(4):708–717. doi: 10.1101/gr.156232.113 CrossRefPubMedPubMedCentralGoogle Scholar
  20. Khamis AM, Hamilton AR, Medvedeva YA, Alam T, Alam I, Essack M, Umylny B, Jankovic BR, Naeger NL, Suzuki M, Harbers M, Robinson GE, Bajic VB (2015) Insights into the transcriptional architecture of behavioral plasticity in the honey bee Apis mellifera. Sci Rep 5:11136CrossRefPubMedPubMedCentralGoogle Scholar
  21. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14(4):R36. doi: 10.1186/gb-2013-14-4-r36 CrossRefPubMedPubMedCentralGoogle Scholar
  22. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9(4):357–359. doi: 10.1038/nmeth.1923 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 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(3):R25. doi: 10.1186/gb-2009-10-3-r25 CrossRefPubMedPubMedCentralGoogle Scholar
  24. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14):1754–1760. doi: 10.1093/bioinformatics/btp324 CrossRefPubMedPubMedCentralGoogle Scholar
  25. Liao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30(7):923–930. doi: 10.1093/bioinformatics/btt656 CrossRefPubMedGoogle Scholar
  26. Liu Y, Zhou J, White KP (2014) RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30(3):301–304. doi: 10.1093/bioinformatics/btt688 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Lizio M, Harshbarger J, Shimoji H, Severin J, Kasukawa T, Sahin S, Abugessaisa I, Fukuda S, Hori F, Ishikawa-Kato S, Mungall CJ, Arner E, Baillie JK, Bertin N, Bono H, de Hoon M, Diehl AD, Dimont E, Freeman TC, Fujieda K, Hide W, Kaliyaperumal R, Katayama T, Lassmann T, Meehan TF, Nishikata K, Ono H, Rehli M, Sandelin A, Schultes EA, ‘t Hoen PA, Tatum Z, Thompson M, Toyoda T, Wright DW, Daub CO, Itoh M, Carninci P, Hayashizaki Y, Forrest AR, Kawaji H, Consortium F (2015) Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol 16:22. doi: 10.1186/s13059-014-0560-6 CrossRefPubMedPubMedCentralGoogle Scholar
  28. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. doi: 10.1186/s13059-014-0550-8 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17(1):10–12, doi: 10.14806/ej.17.1.200CrossRefGoogle Scholar
  30. Murata M, Nishiyori-Sueki H, Kojima-Ishiyama M, Carninci P, Hayashizaki Y, Itoh M (2014) Detecting expressed genes using CAGE. Methods Mol Biol 1164:67–85. doi: 10.1007/978-1-4939-0805-9_7 CrossRefPubMedGoogle Scholar
  31. Plessy C, Bertin N, Takahashi H, Simone R, Salimullah M, Lassmann T, Vitezic M, Severin J, Olivarius S, Lazarevic D, Hornig N, Orlando V, Bell I, Gao H, Dumais J, Kapranov P, Wang H, Davis CA, Gingeras TR, Kawai J, Daub CO, Hayashizaki Y, Gustincich S, Carninci P (2010) Linking promoters to functional transcripts in small samples with nanoCAGE and CAGEscan. Nat Methods 7(7):528–534. doi: 10.1038/nmeth.1470 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140. doi: 10.1093/bioinformatics/btp616 CrossRefPubMedPubMedCentralGoogle Scholar
  33. Salimullah M, Sakai M, Plessy C, Carninci P (2011) NanoCAGE: a high-resolution technique to discover and interrogate cell transcriptomes. Cold Spring Harbor protocols 2011(1):pdb prot5559. doi:  10.1101/pdb.prot5559 Google Scholar
  34. Sims D, Sudbery I, Ilott NE, Heger A, Ponting CP (2014) Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet 15(2):121–132. doi: 10.1038/nrg3642 CrossRefPubMedGoogle Scholar
  35. Supek F, Bosnjak M, Skunca N, Smuc T (2011) REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One 6(7):e21800. doi: 10.1371/journal.pone.0021800 CrossRefPubMedPubMedCentralGoogle Scholar
  36. Takahashi H, Kato S, Murata M, Carninci P (2012a) CAGE (cap analysis of gene expression): a protocol for the detection of promoter and transcriptional networks. Methods Mol Biol 786:181–200. doi: 10.1007/978-1-61779-292-2_11 CrossRefPubMedPubMedCentralGoogle Scholar
  37. Takahashi H, Lassmann T, Murata M, Carninci P (2012b) 5′ end-centered expression profiling using cap-analysis gene expression and next-generation sequencing. Nat Protoc 7(3):542–561. doi: 10.1038/nprot.2012.005 CrossRefPubMedPubMedCentralGoogle Scholar
  38. Thorvaldsdottir H, Robinson JT, Mesirov JP (2013) Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14(2):178–192. doi: 10.1093/bib/bbs017 CrossRefPubMedPubMedCentralGoogle Scholar
  39. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7(3):562–578. doi: 10.1038/nprot.2012.016 CrossRefPubMedPubMedCentralGoogle Scholar
  40. Wang L, Wang S, Li W (2012) RSeQC: quality control of RNA-seq experiments. Bioinformatics 28(16):2184–2185. doi: 10.1093/bioinformatics/bts356 CrossRefPubMedGoogle Scholar
  41. Yendrek CR, Ainsworth EA, Thimmapuram J (2012) The bench scientist’s guide to statistical analysis of RNA-Seq data. BMC Res Notes 5:506. doi: 10.1186/1756-0500-5-506 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research CenterKing Abdullah University of Science and Technology (KAUST)ThuwalSaudi Arabia
  2. 2.Division of Genomic TechnologiesRIKEN Center for Life Science TechnologiesYokohamaJapan

Personalised recommendations

Cite chapter

Buy options