Methodology article
Identifier mapping performance for integrating transcriptomics and proteomics experimental results
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* Corresponding author: Roger S Day day01@pitt.edu
1 Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261 USA
2 Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15260 USA
3 Women's Health Integrated Research Center at Inova Health System, Inova Fairfax Hospital Campus, 3300 Gallows Road, Falls Church, VA 22042 USA
4 Windber Research Institute, 620 Seventh Street, Windber, PA 15963 USA
5 Division of Gynecologic Oncology, Walter Reed Army Medical Center, Washington, D.C. 20307 USA
BMC Bioinformatics 2011, 12:213 doi:10.1186/1471-2105-12-213
Published: 27 May 2011Abstract
Background
Studies integrating transcriptomic data with proteomic data can illuminate the proteome more clearly than either separately. Integromic studies can deepen understanding of the dynamic complex regulatory relationship between the transcriptome and the proteome. Integrating these data dictates a reliable mapping between the identifier nomenclature resultant from the two high-throughput platforms. However, this kind of analysis is well known to be hampered by lack of standardization of identifier nomenclature among proteins, genes, and microarray probe sets. Therefore data integration may also play a role in critiquing the fallible gene identifications that both platforms emit.
Results
We compared three freely available internet-based identifier mapping resources for mapping UniProt accessions (ACCs) to Affymetrix probesets identifications (IDs): DAVID, EnVision, and NetAffx. Liquid chromatography-tandem mass spectrometry analyses of 91 endometrial cancer and 7 noncancer samples generated 11,879 distinct ACCs. For each ACC, we compared the retrieval sets of probeset IDs from each mapping resource. We confirmed a high level of discrepancy among the mapping resources. On the same samples, mRNA expression was available. Therefore, to evaluate the quality of each ACC-to-probeset match, we calculated proteome-transcriptome correlations, and compared the resources presuming that better mapping of identifiers should generate a higher proportion of mapped pairs with strong inter-platform correlations. A mixture model for the correlations fitted well and supported regression analysis, providing a window into the performance of the mapping resources. The resources have added and dropped matches over two years, but their overall performance has not changed.
Conclusions
The methods presented here serve to achieve concrete context-specific insight, to support well-informed decisions in choosing an ID mapping strategy for "omic" data merging.