%0 Journal Article %1 Li2011 %A Li, Q %A Birkbak, N J %A Gyorffy, B %A Szallasi, Z %A Eklund, A C %D 2011 %J BMC Bioinformatics %K JetSet Microarray %P 474-474 %R 10.1186/1471-2105-12-474 %T Jetset: selecting the optimal microarray probe set to represent a gene %U http://www.ncbi.nlm.nih.gov/pubmed/22172014 %V 12 %X Interpretation of gene expression microarrays requires a mapping from probe set to gene. On many Affymetrix gene expression microarrays, a given gene may be detected by multiple probe sets, which may deliver inconsistent or even contradictory measurements. Therefore, obtaining an unambiguous expression estimate of a pre-specified gene can be a nontrivial but essential task.We developed scoring methods to assess each probe set for specificity, splice isoform coverage, and robustness against transcript degradation. We used these scores to select a single representative probe set for each gene, thus creating a simple one-to-one mapping between gene and probe set. To test this method, we evaluated concordance between protein measurements and gene expression values, and between sets of genes whose expression is known to be correlated. For both test cases, we identified genes that were nominally detected by multiple probe sets, and we found that the probe set chosen by our method showed stronger concordance.This method provides a simple, unambiguous mapping to allow assessment of the expression levels of specific genes of interest.

@article{Li2011, abstract = {Interpretation of gene expression microarrays requires a mapping from probe set to gene. On many Affymetrix gene expression microarrays, a given gene may be detected by multiple probe sets, which may deliver inconsistent or even contradictory measurements. Therefore, obtaining an unambiguous expression estimate of a pre-specified gene can be a nontrivial but essential task.We developed scoring methods to assess each probe set for specificity, splice isoform coverage, and robustness against transcript degradation. We used these scores to select a single representative probe set for each gene, thus creating a simple one-to-one mapping between gene and probe set. To test this method, we evaluated concordance between protein measurements and gene expression values, and between sets of genes whose expression is known to be correlated. For both test cases, we identified genes that were nominally detected by multiple probe sets, and we found that the probe set chosen by our method showed stronger concordance.This method provides a simple, unambiguous mapping to allow assessment of the expression levels of specific genes of interest.}, added-at = {2012-09-14T17:14:34.000+0200}, author = {Li, Q and Birkbak, N J and Gyorffy, B and Szallasi, Z and Eklund, A C}, biburl = {https://www.bibsonomy.org/bibtex/26b61db0bda5426bcc00c350896d1a1bc/sbsad}, description = {Jetset: selecting the optimal microarray ... [BMC Bioinformatics. 2011] - PubMed - NCBI}, doi = {10.1186/1471-2105-12-474}, file = {:/bioinfo/users/bsadacca/Biblio/MicroArray/JetSet_Affy.pdf:PDF}, groups = {public}, interhash = {37face5a49416d15b23164bd3d554152}, intrahash = {6b61db0bda5426bcc00c350896d1a1bc}, journal = {BMC Bioinformatics}, keywords = {JetSet Microarray}, pages = {474-474}, pmid = {22172014}, timestamp = {2012-09-18T11:48:53.000+0200}, title = {Jetset: selecting the optimal microarray probe set to represent a gene}, url = {http://www.ncbi.nlm.nih.gov/pubmed/22172014}, username = {sbsad}, volume = 12, year = 2011 }