Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data

By Manhong Dai, Pinglang Wang, Andrew D. Boyd, G. Kostov, B. Athey, Edward G. Jones, W. Bunney, Richard M. Myers, T. Speed, Huda Akil and 2 more. Nucleic Acids Research, 2005

1,910 citations recorded by the source corpus.

Genome-wide expression profiling is a powerful tool for implicating novel gene ensembles in cellular mechanisms of health and disease. The most popular platform for genome-wide expression profiling is the Affymetrix GeneChip. However, its selection of probes relied on earlier genome and transcriptome annotation which is significantly different from current knowledge. The resultant informatics problems have a profound impact on analysis and interpretation the data. Here, we address these critical issues and offer a solution. We identified several classes of problems at the individual probe level in the existing annotation, under the assumption that current genome and transcriptome databases are more accurate than those used for GeneChip design. We then reorganized probes on more than a dozen popular GeneChips into gene-, transcript- and exon-specific probe sets in light of up-to-date genome, cDNA/EST clustering and single nucleotide polymorphism information. Comparing analysis results between the original and the redefined probe sets reveals ∼30–50% discrepancy in the genes previously identified as differentially expressed, regardless of analysis method. Our results demonstrate that the original Affymetrix probe set definitions are inaccurate, and many conclusions derived from past GeneChip analyses may be significantly flawed. It will be beneficial to re-analyze existing GeneChip data with updated probe set definitions.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5ca1d583e53ca84287a8767b7289dbd6fc4f46ed

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Citations in the corpus

Silencing of microRNA families by seed-targeting tiny LNAs (2011, methodology): “The low-level analysis was done using Chip Definition Files where probes were reorganized into Ensembl gene-specific probe se”
xCell: digitally portraying the tissue cellular heterogeneity landscape (2017, methodology): “The analysis was performed using custom CDF files downloaded from Brainarray [38].”
Sox1 marks an activated neural stem/progenitor cell in the hippocampus (2012, methodology): “Arrays were normalized using Robust Multichip Average (RMA) and the latest probe map to MM9 RefSeq genes (Irizarry et al., 2003; Dai et al., 2005).”
Reuse of public genome-wide gene expression data (2012, background): “…can be used to update probe set annotation, to build unique gene level probe sets, to remove ambiguously mapped probes and also to remove individual probes targeting sequence-covering single-nucleotide polymorphisms (SNPs), which could cause a matching probe to be a mismatch for many samples 103 .”
Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines (2014, methodology): “We followed the same set of steps to preprocess the docetaxel, cisplatin and erlotinib/sorafenib clinical trial gene expression data (using the appropriate BrainArray CDF file in each case).”
Chipster: user-friendly analysis software for microarray and other high-throughput data (2011, methodology): “It has been shown that a significant number of probes on several Affymetrix and Illumina arrays map to different genes than indicated by the manufacturer [13-16].”
Genome Majority Vote Improves Gene Predictions (2011, positive): “Changes in gene boundaries also affect microarray design and interpretation of microarray data [4].”
Jetset: selecting the optimal microarray probe set to represent a gene (2011, methodology): “We also evaluated the performance of two alternative probe set definitions: the Brainarray “ hgu133ahsen-trezgcdf ” and the GATExplorer “ genemapperh-gu133acdf ” , both of which redefine probe sets such that each queries an individual gene [4,8].”
Strategies for aggregating gene expression data: The collapseRows R function (2011, background): “First, several sequence-based strategies for reannotating microarray probe identifiers based on updated genomic sequences have been proposed [24-27].”
The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors (2012, methodology): “For Affymetrix arrays, data were normalized from raw CEL files using MAS5 with a custom chip definition file-mapping array oligonucleotides to Entrez gene identifiers (48).”
Improved human disease candidate gene prioritization using mouse phenotype (2007, methodology): “The annotations were created with a custom chip description file Hs133A_Hs_REFSEQ_8.cdf [52] to account for recent advances in human genomics, followed by per gene median normalization.”
FoxA1 translates epigenetic signatures into enhancer-driven lineage-specific transcription. (2008, neutral): “Gene expression data were normalized and summarized using RMA (Irizarry et al., 2003) and updated RefSeq probeset definitions (Dai et al., 2005).”
Reproducibility of computational workflows is automated using continuous analysis (2017, methodology): “Dai et al. 13 publish and maintain BrainArray Custom CDF, a popular source of probe set description files that is routinely updated (http:// brainarray.mbni.med.umich.edu/brainarray/Database/CustomCDF/ genomic_curated_CDF.asp).”