Signal Sequencing for Gene Expression Profiling

Part of the Applied Bioinformatics and Biostatistics in Cancer Research book series (ABB)


Over the past decade, advances in DNA sequencing technologies have made sequencing entire genomes a reality. The ever-expanding size and detail of the genomic data has created a solid framework for the rapid development of sensitive, high throughput gene expression profiling techniques. In this chapter, we discuss, in detail, the ways in which SAGE and MPSS signal sequencing methods have been used to conduct thorough comparative gene expression profiles, the advantages these methods have over traditional expression profiling techniques (i.e. microarrays), and their potential to significantly contribute to understanding the perturbed signaling networks of cancer. Because there are many factors that greatly influence the quality of the data produced by sequencing based expression profiling, the specifics of approaches used in data analysis and the factors to consider when mapping signal sequence data to the transcriptome or genome are presented to, hopefully, help researchers in their current and future gene expression profiling research. We use gene expression data from prostate and ovarian cancer to illustrate the power these technologies hold for generating “deep” and sensitive (i.e. a wide dynamic range) expression profiles, and, finally, we discuss the development of the next generation of sequencing technologies and their application to deciphering the cancer transcriptome. High throughput technologies coupled with a broad, systems-based approach to understanding disease will substantially aid in the development of clinical tools for disease diagnosis and prognosis and will undoubtedly contribute to the design of novel and efficacious therapeutics.


LNCaP Cell Generation Sequencing Technology Unigene Cluster Classic Cloning Androgen Independence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Agaton C, Unneberg P, Sievertzon M, Holmberg A, Ehn M, Larsson M, Odeberg J, Uhlen M, Lundeberg J (2002) Gene expression analysis by signature pyrosequencing. Gene 289:31–39.PubMedCrossRefGoogle Scholar
  2. Audic S, Claverie JM (1997) The significance of digital gene expression profiles. Genome Res 7:986–995.PubMedGoogle Scholar
  3. Bainbridge MN, Warren RL, Hirst M, Romanuik T, Zeng T, Go A, Delaney A, Griffith M, Hickenbotham M, Magrini V, Mardis ER, Sadar MD, Siddiqui AS, Marra MA, Jones SJ (2006) Analysis of the prostate cancer cell line LNCaP transcriptome using a sequencing-by-synthesis approach. BMC Genomics 7:246.PubMedCrossRefGoogle Scholar
  4. Bala P, Georgantas RW III, Sudhir D, Suresh M, Shanker K, Vrushabendra BM, Civin CI, Pandey A (2005) TAGmapper: a web-based tool for mapping SAGE tags. Gene 364:123–129.PubMedCrossRefGoogle Scholar
  5. Bianchetti L, Wu Y, Guerin E, Plewniak F, Poch O (2007) SAGETTARIUS: a program to reduce the number of tags mapped to multiple transcripts and to plan SAGE sequencing stages. Nucleic Acids Res 35(18):e122.PubMedCrossRefGoogle Scholar
  6. Boon K, Osorio EC, Greenhut SF, Schaefer CF, Shoemaker J, Polyak K, Morin PJ, Buetow KH, Strausberg RL, De Souza SJ, Riggins GJ (2002) An anatomy of normal and malignant gene expression. Proc Natl Acad Sci U S A 99:11287–11292.PubMedCrossRefGoogle Scholar
  7. Brenner S, Johnson M, Bridgham J, Golda G, Lloyd DH, Johnson D, Luo S, McCurdy S, Foy M, Ewan M, Roth R, George D, Eletr S, Albrecht G, Vermaas E, Williams SR, Moon K, Burcham T, Pallas M, DuBridge RB, Kirchner J, Fearon K, Mao J, Corcoran K (2000a) Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nat Biotechnol 18:630–634.PubMedCrossRefGoogle Scholar
  8. Brenner S, Williams SR, Vermaas EH, Storck T, Moon K, McCollum C, Mao JI, Luo S, Kirchner JJ, Eletr S, DuBridge RB, Burcham T, Albrecht G (2000b) In vitro cloning of complex mixtures of DNA on microbeads: physical separation of differentially expressed cDNAs. Proc Natl Acad Sci U S A 97:1665–1670.PubMedCrossRefGoogle Scholar
  9. Bussemakers MJ, van Bokhoven A, Verhaegh GW, Smit FP, Karthaus HF, Schalken JA, Debruyne FM, Ru N, Isaacs WB (1999) DD3: a new prostate-specific gene, highly overexpressed in prostate cancer. Cancer Res 59:5975–5979.PubMedGoogle Scholar
  10. Chang GT, Blok LJ, Steenbeek M, Veldscholte J, van Weerden WM, van Steenbrugge GJ, Brinkmann AO (1997) Differentially expressed genes in androgen-dependent and -independent prostate carcinomas. Cancer Res 57:4075–4081.PubMedGoogle Scholar
  11. Chen G, Gharib TG, Huang CC, Taylor JM, Misek DE, Kardia SL, Giordano TJ, Iannettoni MD, Orringer MB, Hanash SM, Beer DG (2002) Discordant protein and mRNA expression in lung adenocarcinomas. Mol Cell Proteomics 1:304–313.PubMedCrossRefGoogle Scholar
  12. Cloonan N, Grimmond SM (2008) Transcriptome content and dynamics at single-nucleotide resolution. Genome Biol 9:234.PubMedCrossRefGoogle Scholar
  13. Cloonan N, Forrest AR, Kolle G, Gardiner BB, Faulkner GJ, Brown MK, Taylor DF, Steptoe AL, Wani S, Bethel G, Robertson AJ, Perkins AC, Bruce SJ, Lee CC, Ranade SS, Peckham HE, Manning JM, McKernan KJ, Grimmond SM (2008) Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods 5:613–619.PubMedCrossRefGoogle Scholar
  14. Debes JD, Tindall DJ (2004) Mechanisms of androgen-refractory prostate cancer. N Engl J Med 351:1488–1490.PubMedCrossRefGoogle Scholar
  15. Forrest AR, Carninci P (2009) Whole genome transcriptome analysis. RNA Biol 6:107–112.PubMedCrossRefGoogle Scholar
  16. Goldstein DB, Cavalleri GL (2005) Genomics: understanding human diversity. Nature 437:1241–1242.PubMedCrossRefGoogle Scholar
  17. Greenlee RT, Murray T, Bolden S, Wingo PA (2000) Cancer statistics, 2000. CA Cancer J Clin 50:7–33.PubMedCrossRefGoogle Scholar
  18. Greller LD, Tobin FL (1999) Detecting selective expression of genes and proteins. Genome Res 9:282–296.PubMedGoogle Scholar
  19. Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R (1999) Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 17:994–999.PubMedCrossRefGoogle Scholar
  20. Hassan R, Remaley AT, Sampson ML, Zhang J, Cox DD, Pingpank J, Alexander R, Willingham M, Pastan I, Onda M (2006) Detection and quantitation of serum mesothelin, a tumor marker for patients with mesothelioma and ovarian cancer. Clin Cancer Res 12:447–453.PubMedCrossRefGoogle Scholar
  21. Hough CD, Sherman-Baust CA, Pizer ES, Montz FJ, Im DD, Rosenshein NB, Cho KR, Riggins GJ, Morin PJ (2000) Large-scale serial analysis of gene expression reveals genes differentially expressed in ovarian cancer. Cancer Res 60:6281–6287.PubMedGoogle Scholar
  22. Hu YF, Ren ZY, Li YF, Sun HX, Chang YS, Su CB, Wang RZ, Zuo J, Fang FD (2002) Serial analysis of gene expression in the pituitary adenomas and para-tumor normal pituitary tissues. Zhongguo Yi Xue Ke Xue Yuan Xue Bao 24:611–615.PubMedGoogle Scholar
  23. Isaacs JT (1999) The biology of hormone refractory prostate cancer. Why does it develop? Urol Clin North Am 26:263–273.PubMedCrossRefGoogle Scholar
  24. Kal AJ, van Zonneveld AJ, Benes V, van den Berg M, Koerkamp MG, Albermann K, Strack N, Ruijter JM, Richter A, Dujon B, Ansorge W, Tabak HF (1999) Dynamics of gene expression revealed by comparison of serial analysis of gene expression transcript profiles from yeast grown on two different carbon sources. Mol Biol Cell 10:1859–1872.PubMedGoogle Scholar
  25. Kim YJ, Boyd A, Athey BD, Patel JM (2005) miBLAST: scalable evaluation of a batch of nucleotide sequence queries with BLAST. Nucleic Acids Res 33:4335–4344.PubMedCrossRefGoogle Scholar
  26. Lal A, Lash AE, Altschul SF, Velculescu V, Zhang L, McLendon RE, Marra MA, Prange C, Morin PJ, Polyak K, Papadopoulos N, Vogelstein B, Kinzler KW, Strausberg RL, Riggins GJ (1999) A public database for gene expression in human cancers. Cancer Res 59:5403–5407.PubMedGoogle Scholar
  27. Lash AE, Tolstoshev CM, Wagner L, Schuler GD, Strausberg RL, Riggins GJ, Altschul SF (2000) SAGEmap: a public gene expression resource. Genome Res 10:1051–1060.PubMedCrossRefGoogle Scholar
  28. Levy S, Sutton G, Ng PC, Feuk L, Halpern AL, Walenz BP, Axelrod N, Huang J, Kirkness EF, Denisov G, Lin Y, Macdonald JR, Pang AW, Shago M, Stockwell TB, Tsiamouri A, Bafna V, Bansal V, Kravitz SA, Busam DA, Beeson KY, McIntosh TC, Remington KA, Abril JF, Gill J, Borman J, Rogers YH, Frazier ME, Scherer SW, Strausberg RL, Venter JC (2007) The diploid genome sequence of an individual human. PLoS Biol 5:e254.PubMedCrossRefGoogle Scholar
  29. Lin B, White JT, Lu W, Xie T, Utleg AG, Yan X, Yi EC, Shannon P, Khrebtukova I, Lange PH, Goodlett DR, Zhou D, Vasicek TJ, Hood L (2005) Evidence for the presence of disease-perturbed networks in prostate cancer cells by genomic and proteomic analyses: a systems approach to disease. Cancer Res 65:3081–3091.PubMedGoogle Scholar
  30. Madden SL, Galella EA, Zhu J, Bertelsen AH, Beaudry GA (1997) SAGE transcript profiles for p53-dependent growth regulation. Oncogene 15:1079–1085.PubMedCrossRefGoogle Scholar
  31. Man MZ, Wang X, Wang Y (2000) POWER_SAGE: comparing statistical tests for SAGE experiments. Bioinformatics 16:953–959.PubMedCrossRefGoogle Scholar
  32. Margulies EH, Innis JW (2000) eSAGE: managing and analysing data generated with serial analysis of gene expression (SAGE). Bioinformatics 16:650–651.PubMedCrossRefGoogle Scholar
  33. Matsumura H, Reich S, Ito A, Saitoh H, Kamoun S, Winter P, Kahl G, Reuter M, Kruger DH, Terauchi R (2003) Gene expression analysis of plant host–pathogen interactions by SuperSAGE. Proc Natl Acad Sci U S A 100:15718–15723.PubMedCrossRefGoogle Scholar
  34. Norambuena T, Malig R, Melo F (2007) SAGExplore: a web server for unambiguous tag mapping in serial analysis of gene expression oriented to gene discovery and annotation. Nucleic Acids Res 35:W163–168.PubMedCrossRefGoogle Scholar
  35. Patel BJ, Pantuck AJ, Zisman A, Tsui KH, Paik SH, Caliliw R, Sheriff S, Wu L, deKernion JB, Tso CL, Belldegrun AS (2000) CL1-GFP: an androgen independent metastatic tumor model for prostate cancer. J Urol 164:1420–1425.PubMedCrossRefGoogle Scholar
  36. Peters DG, Kudla DM, Deloia JA, Chu TJ, Fairfull L, Edwards RP, Ferrell RE (2005) Comparative gene expression analysis of ovarian carcinoma and normal ovarian epithelium by serial analysis of gene expression. Cancer Epidemiol Biomarkers Prev 14:1717–1723.PubMedCrossRefGoogle Scholar
  37. Porter D, Yao J, Polyak K (2006) SAGE and related approaches for cancer target identification. Drug Discov Today 11:110–118.PubMedCrossRefGoogle Scholar
  38. Pylouster J, Senamaud-Beaufort C, Saison-Behmoaras TE (2005) WEBSAGE: a web tool for visual analysis of differentially expressed human SAGE tags. Nucleic Acids Res 33:W693–695.PubMedCrossRefGoogle Scholar
  39. Reinartz J, Bruyns E, Lin JZ, Burcham T, Brenner S, Bowen B, Kramer M, Woychik R (2002) Massively parallel signature sequencing (MPSS) as a tool for in-depth quantitative gene expression profiling in all organisms. Brief Funct Genomic Proteomic 1:95–104.PubMedCrossRefGoogle Scholar
  40. Richterich P (1998) Estimation of errors in “raw” DNA sequences: a validation study. Genome Res 8:251–259.PubMedGoogle Scholar
  41. Romualdi C, Bortoluzzi S, D’Alessi F, Danieli GA (2003) IDEG6: a web tool for detection of differentially expressed genes in multiple tag sampling experiments. Physiol Genomics 12:159–162.PubMedGoogle Scholar
  42. Ronaghi M, Karamohamed S, Pettersson B, Uhlen M, Nyren P (1996) Real-time DNA sequencing using detection of pyrophosphate release. Anal Biochem 242:84–89.PubMedCrossRefGoogle Scholar
  43. Ronaghi M, Uhlen M, Nyren P (1998) A sequencing method based on real-time pyrophosphate. Science 281(363):365.Google Scholar
  44. Saha S, Sparks AB, Rago C, Akmaev V, Wang CJ, Vogelstein B, Kinzler KW, Velculescu VE (2002) Using the transcriptome to annotate the genome. Nat Biotechnol 20:508–512.PubMedCrossRefGoogle Scholar
  45. Silva AP, De Souza JE, Galante PA, Riggins GJ, De Souza SJ, Camargo AA (2004) The impact of SNPs on the interpretation of SAGE and MPSS experimental data. Nucleic Acids Res 32:6104–6110.PubMedCrossRefGoogle Scholar
  46. Stewart JJ, White JT, Yan X, Collins S, Drescher CW, Urban ND, Hood L, Lin B (2006) Proteins associated with Cisplatin resistance in ovarian cancer cells identified by quantitative proteomic technology and integrated with mRNA expression levels. Mol Cell Proteomics 5:433–443.PubMedGoogle Scholar
  47. Stolovitzky GA, Kundaje A, Held GA, Duggar KH, Haudenschild CD, Zhou D, Vasicek TJ, Smith KD, Aderem A, Roach JC (2005) Statistical analysis of MPSS measurements: application to the study of LPS-activated macrophage gene expression. Proc Natl Acad Sci U S A 102:1402–1407.PubMedCrossRefGoogle Scholar
  48. Tian B, Hu J, Zhang H, Lutz CS (2005) A large-scale analysis of mRNA polyadenylation of human and mouse genes. Nucleic Acids Res 33:201–212.PubMedCrossRefGoogle Scholar
  49. Tso CL, McBride WH, Sun J, Patel B, Tsui KH, Paik SH, Gitlitz B, Caliliw R, van Ophoven A, Wu L, deKernion J, Belldegrun A (2000) Androgen deprivation induces selective outgrowth of aggressive hormone- refractory prostate cancer clones expressing distinct cellular and molecular properties not present in parental androgen-dependent cancer cells. Cancer J Sci Am 6:220–233.Google Scholar
  50. Untergasser G, Koch HB, Menssen A, Hermeking H (2002) Characterization of epithelial senescence by serial analysis of gene expression: identification of genes potentially involved in prostate cancer. Cancer Res 62:6255–6262.PubMedGoogle Scholar
  51. Vaarala MH, Porvari K, Kyllonen A, Vihko P (2000) Differentially expressed genes in two LNCaP prostate cancer cell lines reflecting changes during prostate cancer progression. Lab Invest 80:1259–1268.PubMedCrossRefGoogle Scholar
  52. van Kampen AH, van Schaik BD, Pauws E, Michiels EM, Ruijter JM, Caron HN, Versteeg R, Heisterkamp SH, Leunissen JA, Baas F, van der Mee M (2000) USAGE: a web-based approach towards the analysis of SAGE data. Serial analysis of gene expression. Bioinformatics 16:899–905.PubMedCrossRefGoogle Scholar
  53. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW (1995) Serial analysis of gene expression. Science 270:484–487.PubMedCrossRefGoogle Scholar
  54. Waghray A, Feroze F, Schober MS, Yao F, Wood C, Puravs E, Krause M, Hanash S, Chen YQ (2001) Identification of androgen-regulated genes in the prostate cancer cell line LNCaP by serial analysis of gene expression and proteomic analysis. Proteomics 1:1327–1338.PubMedCrossRefGoogle Scholar
  55. Walter-Yohrling J, Cao X, Callahan M, Weber W, Morgenbesser S, Madden SL, Wang C, Teicher BA (2003) Identification of genes expressed in malignant cells that promote invasion. Cancer Res 63:8939–8947.PubMedGoogle Scholar
  56. Xu LL, Su YP, Labiche R, Segawa T, Shanmugam N, McLeod DG, Moul JW, Srivastava S (2001) Quantitative expression profile of androgen-regulated genes in prostate cancer cells and identification of prostate-specific genes. Int J Cancer 92:322–328.PubMedCrossRefGoogle Scholar
  57. Zhang L, Zhou W, Velculescu VE, Kern SE, Hruban RH, Hamilton SR, Vogelstein B, Kinzler KW (1997) Gene expression profiles in normal and cancer cells. Science 276:1268–1272.PubMedCrossRefGoogle Scholar
  58. Zhang H, Lee JY, Tian B (2005) Biased alternative polyadenylation in human tissues. Genome Biol 6:R100.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of UrologyUniversity of WashingtonSeattleUSA
  2. 2.Zhejiang-California International Nanosystems InstituteHangzhouChina

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