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Gene Expression Analysis: Current Methods

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Molecular Pathology in Cancer Research

Abstract

Cancer is a genetic disease characterised by multiple heterogeneous genetic and epigenetic changes. Recent studies have identified extensive heterogeneity between and within tumours [1–3]. The genes in a cell need to be studied as a functioning collective in order to tease apart and understand the myriad different levels of processes and interactions that are coordinated towards the common goal of assuring vital functioning of a cell. The study of the transcriptome of cancer cells, a fundamental link between genotype and phenotype, is essential to understanding the complexity of cancer evolution.

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References

  1. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883–892

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Park SY, Gonen M, Kim HJ, Michor F, Polyak K (2010) Cellular and genetic diversity in the progression of in situ human breast carcinomas to an invasive phenotype. J Clin Invest 120(2):636–644

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, Carter SL, Stewart C, Mermel CH, Roberts SA et al (2013) Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499:214–218

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Kevil CG, Walsh L, Laroux FS, Kalogeris T, Grisham MB, Alexander JS (1997) An improved, rapid Northern protocol. Biochem Biophys Res Commun 238(2):277–279

    Article  CAS  PubMed  Google Scholar 

  5. VanGuilder HD, Vrana KE, Freeman WM (2008) Twenty-five years of quantitative PCR for gene expression analysis. Biotechniques 44(5):619–626

    Article  CAS  PubMed  Google Scholar 

  6. Becker-Andre M, Hahlbrock K (1989) Absolute mRNA quantification using the polymerase chain reaction (PCR). A novel approach by a PCR aided transcript titration assay (PATTY). Nucleic Acids Res 17(22):9437–9446

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Noonan KE, Beck C, Holzmayer TA, Chin JE, Wunder JS, Andrulis IL, Gazdar AF, Willman CL, Griffith B, Vonhoff DD et al (1990) Quantitative-analysis of MDR1 (multidrug resistance) gene-expression in human tumors by polymerase chain-reaction. Proc Natl Acad Sci U S A 87(18):7160–7164

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wang J, Lin M, Crenshaw A, Hutchinson A, Hicks B, Yeager M, Berndt S, Huang W-Y, Hayes RB, Chanock SJ et al (2009) High-throughput single nucleotide polymorphism genotyping using nanofluidic dynamic arrays. BMC Genomics 10

    Google Scholar 

  9. Thiel CT, Kraus C, Rauch A, Ekici AB, Rautenstrauss B, Reis A (2003) A new quantitative PCR multiplex assay for rapid analysis of chromosome 17p11.2-12 duplications and deletions leading to HMSN/HNPP. Eur J Hum Genet 11(2):170–178

    Article  CAS  PubMed  Google Scholar 

  10. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW (1995) Serial analysis of gene-expression. Science 270(5235):484–487

    Article  CAS  PubMed  Google Scholar 

  11. Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene-expression patterns with a complementary-DNA microarray. Science 270(5235):467–470

    Article  CAS  PubMed  Google Scholar 

  12. Lashkari DA, DeRisi JL, McCusker JH, Namath AF, Gentile C, Hwang SY, Brown PO, Davis RW (1997) Yeast microarrays for genome wide parallel genetic and gene expression analysis. Proc Natl Acad Sci U S A 94(24):13057–13062

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Perou C, Sorlie T, Eisen M, van de Rijn M, Jeffrey S, Rees C, Pollack J, Ross D, Johnsen H, Akslen L et al (2000) Molecular portraits of human breast tumours. Nature 406:747–752

    Article  CAS  PubMed  Google Scholar 

  14. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS et al (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 98(19):10869–10874

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T et al (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351:2817–2826

    Article  CAS  PubMed  Google Scholar 

  16. van ‘t Veer LJ, Dai HY, van de Vijver MJ, He YDD, Hart AAM, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871):530–536

    Google Scholar 

  17. Bueno-de-Mesquita JM, van Harten WH, Retel VP, van’t Veer LJ, van Dam FSAM, Karsenberg K, Douma KFL, van Tinteren H, Peterse JL, Wesseling J et al (2007) Use of 70-gene signature to predict prognosis of patients with node-negative breast cancer: a prospective community-based feasibility study (RASTER). Lancet Oncol 8(12):1079–1087

    Google Scholar 

  18. Klang SH, Hammerman A, Liebermann N, Efrat N, Doberne J, Hornberger J (2010) Economic implications of 21-gene breast cancer risk assay from the perspective of an Israeli-managed health-care organization. Value Health 13(4):381–387

    Article  PubMed  Google Scholar 

  19. Partin JF, Mamounas EP (2011) Impact of the 21-gene recurrence score assay compared with standard clinicopathologic guidelines in adjuvant therapy selection for node-negative, estrogen receptor-positive breast cancer. Ann Surg Oncol 18(12):3399–3406

    Article  PubMed  Google Scholar 

  20. Kapronov P, Sementchenko VI, Gingeras TR (2003) Beyond expression profiling: next generation uses of high density oligonucleotide arrays. Brief Funct Genomic Proteomic 2(1):47–56

    Article  Google Scholar 

  21. Hacia JG, Collins FS (1999) Mutational analysis using oligonucleotide microarrays. J Med Genet 36(10):730–736

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Bertone P, Stolc V, Royce TE, Rozowsky JS, Urban AE, Zhu X, Rinn JL, Tongprasit W, Samanta M, Weissman S et al (2004) Global identification of human transcribed sequences with genome tiling arrays. Science 306(5705):2242–2246

    Article  CAS  PubMed  Google Scholar 

  23. Manak JR, Dike S, Sementchenko V, Kapranov P, Biemar F, Long J, Cheng J, Bell I, Ghosh S, Piccolboni A et al (2006) Biological function of unannotated transcription during the early development of Drosophila melanogaster. Nat Genet 38(10):1151–1158

    Article  CAS  PubMed  Google Scholar 

  24. Ishida H, Yagi T, Tanaka M, Tokuda Y, Kamoi K, Hongo F, Kawauchi A, Nakano M, Miki T, Tashiro K (2013) Identification of a novel gene by whole human genome tiling array. Gene 516(1):33–38

    Article  CAS  PubMed  Google Scholar 

  25. Coman D, Gruissem W, Hennig L (2013) Transcript profiling in Arabidopsis with genome tiling microarrays. Methods Mol Biol 1067:35–49

    Article  CAS  PubMed  Google Scholar 

  26. Mockler TC, Ecker JR (2005) Applications of DNA tiling arrays for whole-genome analysis. Genomics 85(1):1–15

    Article  CAS  PubMed  Google Scholar 

  27. Wong CW, Albert TJ, Vega VB, Norton JE, Cutler DJ, Richmond TA, Stanton LW, Liu ET, Miller LD (2004) Tracking the evolution of the SARS coronavirus using high-throughput, high-density resequencing arrays. Genome Res 14(3):398–405

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Liu C, Aronow BJ, Jegga AG, Wang N, Miethke A, Mourya R, Bezerra JA (2007) Novel resequencing chip customized to diagnose mutations in patients with inherited syndromes of intrahepatic cholestasis. Gastroenterology 132(1):119–126

    Article  CAS  PubMed  Google Scholar 

  29. Kothiyal P, Cox S, Ebert J, Husami A, Kenna MA, Greinwald JH, Aronow BJ, Rehm HL (2010) High-throughput detection of mutations responsible for childhood hearing loss using resequencing microarrays. BMC Biotechnol 10

    Google Scholar 

  30. Fokstuen S, Munoz A, Melacini P, Iliceto S, Perrot A, Oezcelik C, Jeanrenaud X, Rieubland C, Farr M, Faber L et al (2011) Rapid detection of genetic variants in hypertrophic cardiomyopathy by custom DNA resequencing array in clinical practice. J Med Genet 48(8):572–576

    Article  CAS  PubMed  Google Scholar 

  31. Bertone P, Trifonov V, Rozowsky JS, Schubert F, Emanuelsson O, Karro J, Kao MY, Snyder M, Gerstein M (2006) Design optimization methods for genomic DNA tiling arrays. Genome Res 16(2):271–281

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Jurka J, Kapitonov VV, Pavlicek A, Klonowski P, Kohany O, Walichiewicz J (2005) Repbase update, a database of eukaryotic repetitive elements. Cytogenet Genome Res 110(1-4):462–467

    Article  CAS  PubMed  Google Scholar 

  33. Wang XW, Seed B (2003) Selection of oligonucleotide probes for protein coding sequences. Bioinformatics 19(7):796–802

    Article  CAS  PubMed  Google Scholar 

  34. Sorek R, Cossart P (2010) Prokaryotic transcriptomics: a new view on regulation, physiology and pathogenicity. Nat Rev Genet 11(1):9–16

    Article  CAS  PubMed  Google Scholar 

  35. Morin RD, Bainbridge M, Fejes A, Hirst M, Krzywinski M, Pugh TJ, McDonald H, Varhol R, Jones SJM, Marra MA (2008) Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. Biotechniques 45(1):81–94

    Google Scholar 

  36. Shah SP, Roth A, Goya R, Oloumi A, Ha G, Zhao Y, Turashvili G, Ding J, Tse K, Haffari G et al (2012) The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486:395–399

    CAS  PubMed  Google Scholar 

  37. Morin RD, Mendez-Lago M, Mungall AJ, Goya R, Mungall KL, Corbett RD, Johnson NA, Severson TM, Chiu R, Field M et al (2011) Frequent mutation of histone-modifying genes in non-Hodgkin lymphoma. Nature 476(7360):298–303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ehrenreich A (2006) DNA microarray technology for the microbiologist: an overview. Appl Microbiol Biotechnol 73(2):255–273

    Article  CAS  PubMed  Google Scholar 

  39. Taton TA, Mirkin CA, Letsinger RL (2000) Scanometric DNA array detection with nanoparticle probes. Science 289(5485):1757–1760

    Article  CAS  PubMed  Google Scholar 

  40. Pease AC, Solas D, Sullivan EJ, Cronin MT, Holmes CP, Fodor SPA (1994) Light-generated oligonucleotide arrays for rapid DNA-sequence analysis. Proc Natl Acad Sci U S A 91(11):5022–5026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Fodor SPA, Read JL, Pirrung MC, Stryer L, Lu AT, Solas D (1991) Light-directed, spatially addressable parallel chemical synthesis. Science 251(4995):767–773

    Article  CAS  PubMed  Google Scholar 

  42. Hughes TR, Mao M, Jones AR, Burchard J, Marton MJ, Shannon KW, Lefkowitz SM, Ziman M, Schelter JM, Meyer MR et al (2001) Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat Biotechnol 19(4):342–347

    Article  CAS  PubMed  Google Scholar 

  43. Sanchez-Cabo F, Rainer J, Dopazo A, Trajanoski Z, Hackl H (2011) Insights into global mechanism and disease by gene expression profiling. Methods Mol Biol 719:269–298

    Article  CAS  PubMed  Google Scholar 

  44. Yang YH, Speed T (2002) Design issues for cDNA microarray experiments. Nat Rev Genet 3(8):579–588

    CAS  PubMed  Google Scholar 

  45. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA et al (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring with specific chromosomal translocations. Science 286(286):531–537

    Article  CAS  PubMed  Google Scholar 

  46. Dobbin K, Simon R (2002) Comparison of microarray designs for class comparison and class discovery. Bioinformatics 18(11):1438–1445

    Article  CAS  PubMed  Google Scholar 

  47. Churchill GA (2002) Fundamentals of experimental design for cDNA microarrays. Nat Genet 32:490–495

    Article  CAS  PubMed  Google Scholar 

  48. Rosenzweig BA, Pine PS, Domon OE, Morris SM, Chen JJ, Sistare FD (2004) Dye-bias correction in dual-labeled cDNA microarray gene expression measurements. Environ Health Perspect 112(4):480–487

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Patterson TA, Lobenhofer EK, Fulmer-Smentek SB, Collins PJ, Chu TM, Bao WJ, Fang H, Kawasaki ES, Hager J, Tikhonova IR et al (2006) Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project. Nat Biotechnol 24(9):1140–1150

    Article  CAS  PubMed  Google Scholar 

  50. Peixoto B, Vencio R, Egidio C, Mota-Vieira L, Verjovski-Almeida S, Reis E (2006) Evaluation of reference-based two-color methods for measurement of gene expression ratios using spotted cDNA microarrays. BMC Genomics 7(1):35

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Kerr M, Churchill G (2001) Experimental design for gene expression microarrays. Biostatistics 2:183–201

    Article  CAS  PubMed  Google Scholar 

  52. Teo ZL, McQueen-Miscamble L, Turner K, Martinez G, Madakashira B, Dedhar S, Robinson ML, de Iongh RU (2014) Integrin linked kinase (ILK) is required for lens epithelial cell survival, proliferation and differentiation. Exp Eye Res 121:130–142

    Article  CAS  PubMed  Google Scholar 

  53. Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M et al (2001) Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci 98(24):13790–13795

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Steger D, Berry D, Haider S, Horn M, Wagner M, Stocker R, Loy A (2011) Systematic spatial bias in DNA microarray hybridization is caused by probe spot position-dependent variability in lateral diffusion. PLoS One 6(8), e23727

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Ritchie ME, Silver J, Oshlack A, Holmes M, Diyagama D, Holloway A, Smyth GK (2007) A comparison of background correction methods for two-colour microarrays. Bioinformatics 23(20):2700–2707

    Article  CAS  PubMed  Google Scholar 

  56. Lockhart DJ, Dong HL, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang CW, Kobayashi M, Horton H et al (1996) Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 14(13):1675–1680

    Article  CAS  PubMed  Google Scholar 

  57. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4(2):249–264

    Article  PubMed  Google Scholar 

  58. Naef F, Lim DA, Patil N, Magnasco M (2002) DNA hybridization to mismatched templates: a chip study. Phys Rev E Stat Nonlin Soft Matter Phys 65(4 Pt 1):040902

    Google Scholar 

  59. McGee M, Chen Z (2006) Parameter estimation for the exponential-normal convolution model for background correction of affymetrix GeneChip data. Stat Appl Genet Mol Biol 5

    Google Scholar 

  60. Neuvial P, Hupe P, Brito I, Liva S, Manie E, Brennetot C, Radvanyi F, Aurias A, Barillot E (2006) Spatial normalization of array-CGH data. BMC Bioinformatics 7:264

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Suarez-Farinas M, Pellegrino M, Wittkowski KM, Magnasco MO (2005) Harshlight: a “corrective make-up” program for microarray chips. BMC Bioinformatics 6:294

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Yang Y, Dudoit S, Luu P, Lin D, Peng V, Ngai J, Speed T (2002) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 30, e15

    Article  PubMed  PubMed Central  Google Scholar 

  63. Dudoit S, Yang YH, Callow MJ, Speed TP (2002) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat Sin 12(1):111–139

    Google Scholar 

  64. Bolstad BM, Irizarry RA, Åstrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2):185–193

    Article  CAS  PubMed  Google Scholar 

  65. Goryachev AB, Macgregor PF, Edwards AM (2001) Unfolding of microarray data. J Comput Biol 8(4):443–461

    Article  CAS  PubMed  Google Scholar 

  66. Kerr MK, Martin M, Churchill GA (2000) Analysis of variance for gene expression microarray data. J Comput Biol 7(6):819–837

    Article  CAS  PubMed  Google Scholar 

  67. Berger J, Hautaniemi S, Jarvinen A-K, Edgren H, Mitra S, Astola J (2004) Optimized LOWESS normalization parameter selection for DNA microarray data. BMC Bioinformatics 5(1):194

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Cleveland WS (1979) Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 74(368):829–836

    Article  Google Scholar 

  69. Cleveland WS (1981) Lowess – a program for smoothing scatterplots by robust locally weighted regression. Am Stat 35(1):54

    Article  Google Scholar 

  70. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 31(4), e15

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Petri T, Berchtold E, Zimmer R, Friedel C (2012) Detection and correction of probe-level artefacts on microarrays. BMC Bioinformatics 13(1):114

    Article  PubMed  PubMed Central  Google Scholar 

  72. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge YC, Gentry J et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10)

    Google Scholar 

  73. Wilson CL, Miller CJ (2005) Simpleaffy: a BioConductor package for Affymetrix Quality Control and data analysis. Bioinformatics 21(18):3683–3685

    Article  CAS  PubMed  Google Scholar 

  74. GeneSpring GX Software [http://www.chem.agilent.com]

  75. Kauffmann A, Gentleman R, Huber W (2009) arrayQualityMetrics—a bioconductor package for quality assessment of microarray data. Bioinformatics 25(3):415–416

    Google Scholar 

  76. Cui XQ, Churchill GA (2003) Statistical tests for differential expression in cDNA microarray experiments. Genome Biol 4(4):210

    Google Scholar 

  77. Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 98(9):5116–5121

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Lonnstedt I, Speed T (2002) Replicated microarray data. Stat Sin 12(1):31–46

    Google Scholar 

  79. Smyth GK (2005) Limma: linear models for microarray data. In: Gentleman R, Carey VJ, Huber W, Irizarry RA, Dudoit S (eds) Bioinformatics and computational biology solution using R and bioconductor. Springer, New York, pp 397–420

    Google Scholar 

  80. Smyth GK (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3(Article 3): Article 3

    Google Scholar 

  81. Wu H, Kerr MK, Cui XQ, Churchill GA (2003) MAANOVA: a software package for the analysis of spotted cDNA microarray experiments. In: Parmigiani G, Garret ES, Irizarry RA, Zeger SL (eds) The analysis of gene expression data: an overview of methods and software, 1st edn. Springer, London, pp 313–342

    Google Scholar 

  82. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate – a practical and powerful approach to multiple testing. J R Stat Soc Series B Methodol 57(1):289–300

    Google Scholar 

  83. Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29(4):1165–1188

    Article  Google Scholar 

  84. Sturn A, Quackenbush J, Trajanoski Z (2002) Genesis: cluster analysis of microarray data. Bioinformatics 18(1):207–208

    Article  CAS  PubMed  Google Scholar 

  85. Hartigan JA, Wong MA (1979) A K-means clustering algorithm. Appl Stat 28(1):100–108

    Article  Google Scholar 

  86. Shannon W, Culverhouse R, Duncan J (2003) Analyzing microarray data using cluster analysis. Pharmacogenomics 4(1):41–52

    Article  CAS  PubMed  Google Scholar 

  87. Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1–2):273–324

    Article  Google Scholar 

  88. Inza I, Larranaga P, Blanco R, Cerrolaza AJ (2004) Filter versus wrapper gene selection approaches in DNA microarray domains. Artif Intell Med 31(2):91–103

    Article  PubMed  Google Scholar 

  89. Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27

    Google Scholar 

  90. De’ath G, Fabricius KE (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81(11):3178–3192

    Google Scholar 

  91. Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16(10):906–914

    Article  CAS  PubMed  Google Scholar 

  92. Michaelsen J (1987) Cross-validation in statistical climate forecast models. J Clim Appl Meteorol 26(11):1589–1600

    Article  Google Scholar 

  93. Dupuy A, Simon RM (2007) Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst 99(2):147–157

    Article  PubMed  Google Scholar 

  94. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25(1):25–29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Nishimura D (2001) BioCarta. Biotech Software & Internet Report 2(3):4

    Article  Google Scholar 

  97. Mlecnik B, Scheideler M, Hackl H, Hartler J, Sanchez-Cabo F, Trajanoski Z (2005) PathwayExplorer: web service for visualizing high-throughput expression data on biological pathways. Nucleic Acids Res 33:W633–W637

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Mootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E et al (2003) PGC-1[alpha]-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34(3):267–273

    Article  CAS  PubMed  Google Scholar 

  99. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43):15545–15550

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Allison DB, Cui XQ, Page GP, Sabripour M (2006) Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 7(1):55–65

    Article  CAS  PubMed  Google Scholar 

  101. Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, Collins PJ, de Longueville F, Kawasaki ES, Lee KY et al (2006) The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 24(9):1151–1161

    Article  CAS  PubMed  Google Scholar 

  102. ‘t Hoen PAC, Friedländer MR, Almlöf J, Sammeth M, Pulyakhina I, Anvar SY, Laros JFJ, Buermans HPJ, Karlberg O, Brännvall M et al (2013) Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories. Nat Biotechnol 31:1015–1022

    Google Scholar 

  103. Steijger T, Abril JF, Engström PG, Kokocinski F, Akerman M, Alioto T, Ambrosini G, Antonarakis SE, Behr J, Bertone P et al (2013) Assessment of transcript reconstruction methods for RNA-seq. Nat Methods 10:1177–1184

    Article  CAS  PubMed  Google Scholar 

  104. Griffith M, Griffith OL, Mwenifumbo J, Goya R, Morrissy AS, Morin RD, Corbett R, Tang MJ, Hou Y-CC, Pugh TJ et al (2010) Alternative expression analysis by RNA sequencing. Nat Methods 7:843–847

    Article  CAS  PubMed  Google Scholar 

  105. Maher CA, Kumar-Sinha C, Cao X, Kalyana-Sundaram S, Han B, Jing X, Sam L, Barrette T, Palanisamy N, Chinnaiyan AM (2009) Transcriptome sequencing to detect gene fusions in cancer. Nature 458:97–101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Joensuu H, Roberts PJ, Sarlomo-Rikala M, Andersson LC, Tervahartiala P, Tuveson D, Silberman S, Capdeville R, Dimitrijevic S, Druker B et al (2001) Effect of the tyrosine kinase inhibitor STI571 in a patient with a metastatic gastrointestinal stromal tumor. N Engl J Med 344:1052–1056

    Article  CAS  PubMed  Google Scholar 

  107. Kwak EL, Bang Y-J, Camidge DR, Shaw AT, Solomon B, Maki RG, Ou S-HI, Dezube BJ, Jänne PA, Costa DB et al (2010) Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N Engl J Med 363:1693–1703

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Jiang L, Schlesinger F, Davis CA, Zhang Y, Li R, Salit M, Gingeras TR, Oliver B (2011) Synthetic spike-in standards for RNA-seq experiments. Genome Res 21:1543–1551

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M (2008) The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320:1344–1349

    Google Scholar 

  110. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Ojesina AI, Lichtenstein L, Freeman SS, Pedamallu CS, Imaz-Rosshandler I, Pugh TJ, Cherniack AD, Ambrogio L, Cibulskis K, Bertelsen B et al (2013) Landscape of genomic alterations in cervical carcinomas. Nature 506:371–375

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  112. Kannan K, Wang L, Wang J, Ittmann MM, Li W, Yen L (2011) Recurrent chimeric RNAs enriched in human prostate cancer identified by deep sequencing. Proc Natl Acad Sci U S A 108:9172–9177

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Eswaran J, Cyanam D, Mudvari P, Reddy SDN, Pakala SB, Nair SS, Florea L, Fuqua SAW, Godbole S, Kumar R (2012) Transcriptomic landscape of breast cancers through mRNA sequencing. Sci Rep 2:264

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  114. Cronin M, Pho M, Dutta D, Stephans JC, Shak S, Kiefer MC, Esteban JM, Baker JB (2004) Measurement of gene expression in archival paraffin-embedded tissues: development and performance of a 92-gene reverse transcriptase-polymerase chain reaction assay. Am J Pathol 164:35–42

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Norton N, Sun Z, Asmann YW, Serie DJ, Necela BM, Bhagwate A, Jen J, Eckloff BW, Kalari KR, Thompson KJ et al (2013) Gene expression, single nucleotide variant and fusion transcript discovery in archival material from breast tumors. PLoS One 8, e81925

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. Sinicropi D, Qu K, Collin F, Crager M, Liu M-L, Pelham RJ, Pho M, Dei Rossi A, Jeong J, Scott A et al (2012) Whole transcriptome RNA-Seq analysis of breast cancer recurrence risk using formalin-fixed paraffin-embedded tumor tissue. PLoS One 7, e40092

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. von der Haar T (2008) A quantitative estimation of the global translational activity in logarithmically growing yeast cells. BMC Syst Biol 2:87

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  118. Warner JR (1999) The economics of ribosome biosynthesis in yeast. Trends Biochem Sci 24:437–440

    Article  CAS  PubMed  Google Scholar 

  119. Thore S, Mayer C, Sauter C, Weeks S, Suck D (2003) Crystal structures of the Pyrococcus abyssi Sm core and its complex with RNA. Common features of RNA binding in archaea and eukarya. J Biol Chem 278:1239–1247

    Google Scholar 

  120. Kiss T (2001) Small nucleolar RNA-guided post-transcriptional modification of cellular RNAs. EMBO J 20:3617–3622

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Tsai M-C, Manor O, Wan Y, Mosammaparast N, Wang JK, Lan F, Shi Y, Segal E, Chang HY (2010) Long noncoding RNA as modular scaffold of histone modification complexes. Science 329:689–693

    Google Scholar 

  122. Brantl S (2007) Regulatory mechanisms employed by cis-encoded antisense RNAs. Curr Opin Microbiol 10:102–109

    Article  CAS  PubMed  Google Scholar 

  123. Lustig AJ (1999) Crisis intervention: the role of telomerase. Proc Natl Acad Sci U S A 96:3339–3341

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Ahmad K, Henikoff S (2002) Epigenetic consequences of nucleosome dynamics. Cell 111:281–284

    Article  CAS  PubMed  Google Scholar 

  125. Tariq MA, Kim HJ, Jejelowo O, Pourmand N (2011) Whole-transcriptome RNAseq analysis from minute amount of total RNA. Nucleic Acids Res 39, e120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Morlan JD, Qu K, Sinicropi DV (2012) Selective depletion of rRNA enables whole transcriptome profiling of archival fixed tissue. PLoS One 7, e42882

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Zhulidov PA, Bogdanova EA, Shcheglov AS, Vagner LL, Khaspekov GL, Kozhemyako VB, Matz MV, Meleshkevitch E, Moroz LL, Lukyanov SA et al (2004) Simple cDNA normalization using kamchatka crab duplex-specific nuclease. Nucleic Acids Res 32, e37

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  128. Kingston RE (2001) Preparation of poly(A) + RNA. In: Ausubel FM et al (eds) Current protocols in molecular biology. Wiley, New York (Chapter 4, Unit 4.5)

    Google Scholar 

  129. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5:621–628

    Article  CAS  PubMed  Google Scholar 

  130. Cui P, Lin Q, Ding F, Xin C, Gong W, Zhang L, Geng J, Zhang B, Yu X, Yang J et al (2010) A comparison between ribo-minus RNA-sequencing and polyA-selected RNA-sequencing. Genomics 96:259–265

    Article  CAS  PubMed  Google Scholar 

  131. Zeng W, Mortazavi A (2012) Technical considerations for functional sequencing assays. Nat Immunol 13:802–807

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Adiconis X, Borges-Rivera D, Satija R, DeLuca DS, Busby MA, Berlin AM, Sivachenko A, Thompson DA, Wysoker A, Fennell T et al (2013) Comparative analysis of RNA sequencing methods for degraded or low-input samples. Nat Methods 10:623–629

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M, Gassmann M, Lightfoot S, Menzel W, Granzow M, Ragg T (2006) The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol 7:3

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  134. Dunn TA, Fedor H, Isaacs WB, De Marzo AM, Luo J (2009) Genome-wide expression analysis of recently processed formalin-fixed paraffin embedded human prostate tissues. Prostate 69:214–218

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. von Ahlfen S, Missel A, Bendrat K, Schlumpberger M (2007) Determinants of RNA quality from FFPE samples. PLoS One 2, e1261

    Article  CAS  Google Scholar 

  136. Ozsolak F, Platt AR, Jones DR, Reifenberger JG, Sass LE, McInerney P, Thompson JF, Bowers J, Jarosz M, Milos PM (2009) Direct RNA sequencing. Nature 461:814–818

    Article  CAS  PubMed  Google Scholar 

  137. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Huang R, Jaritz M, Guenzl P, Vlatkovic I, Sommer A, Tamir IM, Marks H, Klampfl T, Kralovics R, Stunnenberg HG et al (2011) An RNA-Seq strategy to detect the complete coding and non-coding transcriptome including full-length imprinted macro ncRNAs. PLoS One 6, e27288

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Levin JZ et al (2010) Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat Methods 7(9):709–715

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Ozsolak F, Milos PM (2011) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12:87–98

    Article  CAS  PubMed  Google Scholar 

  141. The Cancer Genome Atlas Research Network (2012) Comprehensive genomic characterization of squamous cell lung cancers. Nature 489:519–525

    Article  PubMed Central  CAS  Google Scholar 

  142. Tilgner H, Grubert F, Sharon D, Snyder MP (2014) Defining a personal, allele-specific, and single-molecule long-read transcriptome. Proc Natl Acad Sci U S A 111:9869–9874

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Tarazona S, García-Alcalde F, Dopazo J, Ferrer A, Conesa A (2011) Differential expression in RNA-seq: a matter of depth. Genome Res 21:2213–2223

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Levin JZ, Berger MF, Adiconis X, Rogov P, Melnikov A, Fennell T, Nusbaum C, Garraway LA, Gnirke A (2009) Targeted next-generation sequencing of a cancer transcriptome enhances detection of sequence variants and novel fusion transcripts. Genome Biol 10:R115

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  145. Mercer TR, Gerhardt DJ, Dinger ME, Crawford J, Trapnell C, Jeddeloh JA, Mattick JS, Rinn JL (2012) Targeted RNA sequencing reveals the deep complexity of the human transcriptome. Nat Biotechnol 30:99–104

    Article  CAS  Google Scholar 

  146. Halvardson J, Zaghlool A, Feuk L (2013) Exome RNA sequencing reveals rare and novel alternative transcripts. Nucleic Acids Res 41, e6

    Article  CAS  PubMed  Google Scholar 

  147. Jiang H, Lei R, Ding S-W, Zhu S (2014) Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics 15:182

    Article  PubMed  PubMed Central  Google Scholar 

  148. Engström PG, Steijger T, Sipos B, Grant GR, Kahles A, Alioto T, Behr J, Bertone P, Bohnert R, Campagna D et al (2013) Systematic evaluation of spliced alignment programs for RNA-seq data. Nat Methods 10:1185–1191

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  149. Fonseca NA, Rung J, Brazma A, Marioni JC (2012) Tools for mapping high-throughput sequencing data. Bioinformatics 28:3169–3177

    Google Scholar 

  150. Wang K, Singh D, Zeng Z, Coleman SJ, Huang Y, Savich GL, He X, Mieczkowski P, Grimm SA, Perou CM et al (2010) MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res 38, e178

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  151. Flicek P, Amode MR, Barrell D, Beal K, Brent S, Carvalho-Silva D, Clapham P, Coates G, Fairley S, Fitzgerald S (2012) Ensembl 2012. Nucleic Acids Res 40:D84–D90

    Article  CAS  PubMed  Google Scholar 

  152. Pruitt KD, Tatusova T, Maglott DR (2007) NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 35:D61–D65

    Article  CAS  PubMed  Google Scholar 

  153. Thierry-Mieg D, Thierry-Mieg J (2006) AceView: a comprehensive cDNA-supported gene and transcripts annotation. Genome Biol 7:1–14

    Article  PubMed  Google Scholar 

  154. Su Z, Łabaj PP, Li S, Thierry-Mieg J, Thierry-Mieg D, Shi W, Wang C, Schroth GP et al (2014) A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat Biotechnol

    Google Scholar 

  155. Wu P-Y, Phan JH, Wang MD (2013) Assessing the impact of human genome annotation choice on RNA-seq expression estimates. BMC Bioinformatics 14(Suppl 1):S8

    Google Scholar 

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Teo, Z.L., Savas, P., Loi, S. (2016). Gene Expression Analysis: Current Methods. In: Lakhani, S., Fox, S. (eds) Molecular Pathology in Cancer Research. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-6643-1_6

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