Decoding Randomly Ordered DNA Arrays
- Kevin L. Gunderson1,3,
- Semyon Kruglyak1,3,
- Michael S. Graige1,
- Francisco Garcia1,
- Bahram G. Kermani1,
- Chanfeng Zhao1,
- Diping Che1,
- Todd Dickinson1,
- Eliza Wickham1,
- Jim Bierle1,
- Dennis Doucet1,
- Monika Milewski2,
- Robert Yang1,
- Chris Siegmund1,
- Juergen Haas,
- Lixin Zhou1,
- Arnold Oliphant1,
- Jian-Bing Fan1,
- Steven Barnard1, and
- Mark S. Chee1,4
Abstract
We have developed a simple and efficient algorithm to identify each member of a large collection of DNA-linked objects through the use of hybridization, and have applied it to the manufacture of randomly assembled arrays of beads in wells. Once the algorithm has been used to determine the identity of each bead, the microarray can be used in a wide variety of applications, including single nucleotide polymorphism genotyping and gene expression profiling. The algorithm requires only a few labels and several sequential hybridizations to identify thousands of different DNA sequences with great accuracy. We have decoded tens of thousands of arrays, each with 1520 sequences represented at ∼30-fold redundancy by up to ∼50,000 beads, with a median error rate of <1 × 10-4 per bead. The approach makes use of error checking codes and provides, for the first time, a direct functional quality control of every element of each array that is manufactured. The algorithm can be applied to any spatially fixed collection of objects or molecules that are associated with specific DNA sequences.
Footnotes
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Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.2255804. Article published online before print in April 2004.
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↵3 These authors contributed equally to this work.
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↵4 Corresponding author. E-MAIL mchee{at}illumina.com; FAX (858) 202-4680.
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- Accepted January 29, 2004.
- Received December 8, 2003.
- Cold Spring Harbor Laboratory Press
