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

Abstract

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.

Gene expression profiling reveals that the regulation of estrogen-responsive element-independent genes by 17β-estradiol-estrogen receptor β is uncoupled from the induction of phenotypic changes in cell models

“…Although we also conducted microarray data analysis based on probe sets that relied on earlier genome and transcriptome annotation (Gautier et al 2004, Dai et al 2005, Harbig et al 2005) and posted at http://dbb. urmc.rochester.edu/labs/muyan/ArrayAddendum.…”

“…urmc.rochester.edu/labs/muyan/ArrayAddendum. htm, we present here the data analysis using reorganized and updated probe sets (Gautier et al 2004, Dai et al 2005, Harbig et al 2005) based on the up-to-date genome, cDNA/EST clustering, and single nucleotide polymorphism information through web-based custom GeneChip library files (Chip Definition Files or CDFs, http://arrayanalysis.mbni.med.umich.edu) which increase accuracy and reduce false discovery rates (Dai et al 2007). Following UniGene transformation, the data sets were subjected to the N-statistic test (Klebanov et al 2006) in conjunction with the stepdown Westfall & Young (1993) procedure controlling the familywise error rate (FWER) at a level of 0 .…”

Suppression of Death Receptor-mediated Apoptosis by 1,25-Dihydroxyvitamin D3 Revealed by Microarray Analysis

“…A gene was considered to be consistently regulated if the same gene expression profile was obtained in all three experiments performed with the U133A chips and detected by all probesets. Gene identification was based on the sequence of the probes used on the arrays (13).…”

Gene expression arrays as a tool to unravel mechanisms of normal tissue radiation injury and prediction of response

“…Users had to trust the manufacturer that a given probe actually quantified a specific transcript. In reality, many arrays used a substantial number of incorrect probes (wrong clones or misidentified ones) and a surprisingly large portion of the probes was not present in high-quality sequence databases such as RefSeq [35][36][37][38][39][40] . Apart from probe design issues, the discrepancies between an intended probe sequence and the actual sequence spotted on the microarray also deserve some attention.…”