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Decoding Randomly Ordered DNA Arrays
Author(s) -
Kevin L. Gunderson,
Semyon Kruglyak,
Michael S. Graige,
Francisco GarcíaGarcía,
Bahram G. Kermani,
Chanfeng Zhao,
Diping Che,
Todd A. Dickinson,
Eliza Wickham,
Jim Bierle,
Dennis Doucet,
Monika Milewski,
Robert Yang,
Chris Siegmund,
Juergen Haas,
Lixin Zhou,
Arnold Oliphant,
Jian-Bing Fan,
Steven Barnard,
Mark S. Chee
Publication year - 2004
Publication title -
genome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.556
H-Index - 297
eISSN - 1549-5469
pISSN - 1088-9051
DOI - 10.1101/gr.2255804
Subject(s) - biology , redundancy (engineering) , genotyping , algorithm , dna microarray , dna , computational biology , decoding methods , sequencing by hybridization , error detection and correction , genetics , dna sequencing , computer science , gene , gene expression , dna sequencer , genotype , operating system
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 approximately 30-fold redundancy by up to approximately 50,000 beads, with a median error rate of <1 x 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.

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