Spotted Long Oligonucleotide Arrays for Human Gene Expression Analysis (original) (raw)

  1. Andrea Barczak1,
  2. Madeleine Willkom Rodriguez1,
  3. Kristina Hanspers2,
  4. Laura L. Koth1,
  5. Yu Chuan Tai3,
  6. Benjamin M. Bolstad3,
  7. Terence P. Speed4,5, and
  8. David J. Erle1,6
  9. 1 Department of Medicine, University of California, San Francisco, San Francisco, California 94143, USA
  10. 2 Gladstone Institute of Cardiovascular Disease, San Francisco, California 94141, USA
  11. 3 Group in Biostatistics, University of California, Berkeley, California 94720, USA
  12. 4 Department of Statistics, University of California, Berkeley, California 94720, USA
  13. 5 Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville, Vic 3050, Australia

Abstract

DNA microarrays produced by deposition (or `spotting')of a single long oligonucleotide probe for each gene may be an attractive alternative to other types of arrays. We produced spotted oligonucleotide arrays using two large collections of ∼70-mer probes, and used these arrays to analyze gene expression in two dissimilar human RNA samples. These samples were also analyzed using arrays produced by in situ synthesis of sets of multiple short (25-mer)oligonucleotides for each gene (Affymetrix GeneChips). We compared expression measurements for 7344 genes that were represented in both long oligonucleotide probe collections and the in situ-synthesized 25-mer arrays. We found strong correlations (r = 0.8–0.9)between relative gene expression measurements made with spotted long oligonucleotide probes and in situ-synthesized 25-mer probe sets. Spotted long oligonucleotide arrays were suitable for use with both unamplified cDNA and amplified RNA targets, and are a cost-effective alternative for many functional genomics applications. Most previously reported evaluations of microarray technologies have focused on expression measurements made on a relatively small number of genes. The approach described here involves far more gene expression measurements and provides a useful method for comparing existing and emerging techniques for genome-scale expression analysis.

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