Next Generation Microarray Bioinformatics pp 3-17

Part of the Methods in Molecular Biology book series (MIMB, volume 802) | Cite as

A Primer on the Current State of Microarray Technologies

  • Alexander J. Trachtenberg
  • Jae-Hyung Robert
  • Azza E. Abdalla
  • Andrew Fraser
  • Steven Y. He
  • Jessica N. Lacy
  • Chiara Rivas-Morello
  • Allison Truong
  • Gary Hardiman
  • Lucila Ohno-Machado
  • Fang Liu
  • Eivind Hovig
  • Winston Patrick Kuo
Protocol

Abstract

DNA microarray technology has been used for genome-wide gene expression studies that incorporate molecular genetics and computer science analyses on massive levels. The availability of microarrays permit the simultaneous analysis of tens of thousands of genes for the purposes of gene discovery, disease diagnosis, improved drug development, and therapeutics tailored to specific disease processes. In this chapter, we provide an overview on the current state of common microarray technologies and platforms. Since many genes contribute to normal functioning, research efforts are moving from the search for a disease-specific gene to the understanding of the biochemical and molecular functioning of a variety of genes whose disrupted interaction in complicated networks can lead to a disease state. The field of microarrays has evolved over the past decade and is now standardized with a high level of quality control, while providing a relatively inexpensive and reliable alternative to studying various aspects of gene expression.

Key words

Microarrays Gene expression One dye Two dye High throughput QRT-PCR Cross platform 

References

  1. 1.
    Gentleman RC, Carey VJ, Bates DM et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80.PubMedCrossRefGoogle Scholar
  2. 2.
    Kulesh DA, Clive DR, Zarlenga DS et al (1987) Identification of interferon-modulated proliferation-related cDNA sequences. Proc Natl Acad Sci U S A 84: 8453–8457.PubMedCrossRefGoogle Scholar
  3. 3.
    Kuo WP, Liu F, Trimarchi J et al (2006) A sequence-oriented comparison of gene expression measurements across different hybridization-based technologies. Nat Biotechnol 24:832–840.PubMedCrossRefGoogle Scholar
  4. 4.
    Fodor SP, Read JL, Pirrung MC et al (1991) Light-directed, spatially addressable parallel chemical synthesis. Science 251:767–773.PubMedCrossRefGoogle Scholar
  5. 5.
    Lausted C, Dahl T, Warren C et al (2004) POSaM: a fast, flexible, open-source, inkjet oligonucleotide synthesizer and microarrayer. Genome Biol 5:R58.PubMedCrossRefGoogle Scholar
  6. 6.
    Baum M, Bielau S, Rittner N et al (2003) Validation of a novel, fully integrated and flexible microarray benchtop facility for gene expression profiling. Nucleic Acids Res 31:e151.PubMedCrossRefGoogle Scholar
  7. 7.
    Ruano JM, Benoit VV, Aitchison JS et al (2000) Flame hydrolysis deposition of glass on silicon for the integration of optical and microfluidic devices. Anal Chem 72: 1093–1097.PubMedCrossRefGoogle Scholar
  8. 8.
    Benoit V, Steel A, Torres M et al (2001) Evaluation of three-dimensional microchannel glass biochips for multiplexed nucleic acid fluorescence hybridization assays. Anal Chem 73:2412–2420.PubMedCrossRefGoogle Scholar
  9. 9.
    Hokaiwado N, Asamoto M, Tsujimura K et al (2004) Rapid analysis of gene expression changes caused by liver carcinogens and chemopreventive agents using a newly developed three-dimensional microarray system. Cancer Sci 95: 123–130.PubMedCrossRefGoogle Scholar
  10. 10.
    Fare TL, Coffey EM, Dai H, et al (2003) Effects of atmospheric ozone on microarray data quality. Anal Chem 75:4672–4675.PubMedCrossRefGoogle Scholar
  11. 11.
    Quinn MC, Wilson DJ, Young F et al (2009) The chemiluminescence based Ziplex automated workstation focus array reproduces ovarian cancer Affymetrix GeneChip expression profiles. J Transl Med 7:55.PubMedCrossRefGoogle Scholar
  12. 12.
    Gunderson KL, Kruglyak S, Graige MS et al (2004) Decoding randomly ordered DNA arrays. Genome Res 14:870–877.PubMedCrossRefGoogle Scholar
  13. 13.
    Bond GL, Hu W, Levine A (2005) A single nucleotide polymorphism in the MDM2 gene: from a molecular and cellular explanation to clinical effect. Cancer Res 65:5481–5484.PubMedCrossRefGoogle Scholar
  14. 14.
    Guilford P, Hopkins J, Harraway J et al (1998) E-cadherin germline mutations in familial gastric cancer. Nature 392:402–405.PubMedCrossRefGoogle Scholar
  15. 15.
    Imyanitov EN (2009) Gene polymorphisms, apoptotic capacity and cancer risk. Hum Genet 125:239–246.PubMedCrossRefGoogle Scholar
  16. 16.
    Lindblad-Toh K, Tanenbaum DM, Daly MJ et al (2000) Loss-of-heterozygosity analysis of small-cell lung carcinomas using single-nucleotide polymorphism arrays. Nat Biotechnol 18:1001–1005.PubMedCrossRefGoogle Scholar
  17. 17.
    Reddy EP (1983) Nucleotide sequence analysis of the T24 human bladder carcinoma oncogene. Science 220:1061–1063.PubMedCrossRefGoogle Scholar
  18. 18.
    Tuna M, Knuutila S, Mills GB (2009) Uniparental disomy in cancer. Trends Mol Med 15:120–128.PubMedCrossRefGoogle Scholar
  19. 19.
    Mockler TC, Chan S, Sundaresan A et al (2005) Applications of DNA tiling arrays for whole-genome analysis. Genomics 85:1–15.PubMedCrossRefGoogle Scholar
  20. 20.
    Nonne N, Ameyar-Zazoua M, Souidi M et al (2010) Tandem affinity purification of miRNA target mRNAs (TAP-Tar). Nucleic Acids Res 38:e20.PubMedCrossRefGoogle Scholar
  21. 21.
    Wheeler DL, Church DM, Lash AE et al (2001) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 29:11–16.PubMedCrossRefGoogle Scholar
  22. 22.
    Brazma A, Parkinson H, Sarkans U et al (2003) ArrayExpress – a public repository for microarray gene expression data at the EBI. Nucleic Acids Res 31:68–71.PubMedCrossRefGoogle Scholar
  23. 23.
    Brooksbank C, Camon E, Harris MA et al (2003) The European Bioinformatics Institute’s data resources. Nucleic Acids Res 31:43–50.PubMedCrossRefGoogle Scholar
  24. 24.
    Ball CA, Sherlock G, Parkinson H et al (2002) Standards for microarray data. Science 298:539.Google Scholar
  25. 25.
    Ikeo K, Ishi-i J, Tamura T et al (2003) CIBEX: center for information biology gene expression database. C R Biol 326:1079–1082.PubMedCrossRefGoogle Scholar
  26. 26.
    Brazma A, Hingamp P, Quackenbush J et al (2001) Minimum information about a microarray experiment (MIAME) – toward standards for microarray data. Nat Genet 29:365–371.PubMedCrossRefGoogle Scholar
  27. 27.
    Kuo WP, Jenssen TK, Butte AJ et al (2002) Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics 18: 405–412.PubMedCrossRefGoogle Scholar
  28. 28.
    Mecham BH, Klus GT, Strovel J et al (2004) Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements. Nucleic Acids Res 32:e74.PubMedCrossRefGoogle Scholar
  29. 29.
    Carter SL, Eklund AC, Mecham BH et al (2005) Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements. BMC Bioinformatics 6:107.PubMedCrossRefGoogle Scholar
  30. 30.
    Bammler T, Beyer RP, Bhattacharya S et al (2005) Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods 2: 351–356.PubMedCrossRefGoogle Scholar
  31. 31.
    Larkin JE, Frank BC, Gavras H et al (2005) Independence and reproducibility across microarray platforms. Nat Methods 2:337–344.PubMedCrossRefGoogle Scholar
  32. 32.
    Wang H, He X, Band M et al (2005) A study of inter-lab and inter-platform agreement of DNA microarray data. BMC Genomics 6:71.PubMedCrossRefGoogle Scholar
  33. 33.
    Zhu B, Ping G, Shinohara Y et al (2005) Comparison of gene expression measurements from cDNA and 60-mer oligonucleotide microarrays. Genomics 85:657–665.PubMedCrossRefGoogle Scholar
  34. 34.
    Barnes M, Freudenberg J, Thompson S et al (2005) Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platforms. Nucleic Acids Res 33:5914–5923.PubMedCrossRefGoogle Scholar
  35. 35.
    Sherlock G (2005) Of fish and chips. Nat Methods 2:329–330.PubMedCrossRefGoogle Scholar
  36. 36.
    Casciano DA, Woodcock J (2006) Empowering microarrays in the regulatory setting. Nat Biotechnol 24:1103.PubMedCrossRefGoogle Scholar
  37. 37.
    Shi L, Reid LH, Jones WD et al (2006) The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 24:1151–1161.PubMedCrossRefGoogle Scholar
  38. 38.
    Geiss GK, Bumgarner RE, Birditt B et al (2008) Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol 26:317–325.PubMedCrossRefGoogle Scholar
  39. 39.
    Spurgeon SL, Jones RC, Ramakrishnan R (2008) High throughput gene expression measurement with real time PCR in a microfluidic dynamic array. PLoS One 3:e1662.PubMedCrossRefGoogle Scholar
  40. 40.
    Robertson G, Hirst M, Bainbridge M et al (2007) Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat Methods 4:651–657.PubMedCrossRefGoogle Scholar
  41. 41.
    Park PJ (2009) ChIP-seq: advantages and challenges of a maturing technology. Nat Rev Genet 10:669–680.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Alexander J. Trachtenberg
    • 1
  • Jae-Hyung Robert
    • 2
  • Azza E. Abdalla
    • 3
  • Andrew Fraser
    • 4
  • Steven Y. He
    • 5
  • Jessica N. Lacy
    • 1
  • Chiara Rivas-Morello
    • 1
  • Allison Truong
    • 6
  • Gary Hardiman
    • 4
  • Lucila Ohno-Machado
    • 7
  • Fang Liu
    • 8
    • 9
  • Eivind Hovig
    • 10
  • Winston Patrick Kuo
    • 1
    • 2
  1. 1.Harvard Catalyst – Laboratory for Innovative Translational TechnologiesHarvard Medical SchoolBostonUSA
  2. 2.Department of Developmental BiologyHarvard School of Dental MedicineBostonUSA
  3. 3.Department of BiologyUniversity of South CarolinaColumbiaUSA
  4. 4.Department of Allergy and InflammationBIDMCBostonUSA
  5. 5.Department of MedicineUniversity of California San DiegoSan DiegoUSA
  6. 6.Department of BiologyUniversity of California Los AngelesLos AngelesUSA
  7. 7.Division of Biomedical InformaticsUniversity of California San DiegoSan DiegoUSA
  8. 8.Department of Tumor BiologyInstitute for Cancer Research, Norwegian Radium HospitalMontebelloNorway
  9. 9.PubGene ASVinderenNorway
  10. 10.Departments of Tumor Biology and Medical InformaticsInstitute for Cancer Research, Norwegian Radium HospitalMontebelloNorway

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