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Nucleic Acids Research 2005 33(3):e31; doi:10.1093/nar/gni027
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Published online 18 February 2005

© The Author 2005. Published by Oxford University Press. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oupjournals.org


Methods Online

A sequence-based identification of the genes detected by probesets on the Affymetrix U133 plus 2.0 array

Jeremy Harbig, Robert Sprinkle and Steven A. Enkemann*

H. Lee Moffitt Cancer Center and Research Institute and the University of South Florida Tampa Florida 33612, USA

*To whom correspondence should be addressed. Tel: +1 813 745 9033; Fax: +1 813 979 7265; Email: enkemasa{at}moffitt.usf.edu

Received November 3, 2004. Revised January 10, 2005. Accepted January 20, 2005.

One of the biggest problems facing microarray experiments is the difficulty of translating results into other microarray formats or comparing microarray results to other biochemical methods. We believe that this is largely the result of poor gene identification. We re-identified the probesets on the Affymetrix U133 plus 2.0 GeneChip array. This identification was based on the sequence of the probes and the sequence of the human genome. Using the BLAST program, we matched probes with documented and postulated human transcripts. This resulted in the redefinition of approximately 37% of the probes on the U133 plus 2.0 array. This updated identification specifically points out where the identification is complicated by cross-hybridization from splice variants or closely related genes. More than 5000 probesets detect multiple transcripts and therefore the exact protein affected cannot be readily concluded from the performance of one probeset alone. This makes naming difficult and impacts any downstream analysis such as associating gene ontologies, mapping affected pathways or simply validating expression changes. We have now automated the sequence-based identification and can more appropriately annotate any array where the sequence on each spot is known.


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