title: Meta-analysis for protein identification: a case study on yeast data.
abstract: Large amounts of mass spectrometry (MS) proteomics data are now publicly available; however, little attention has been given to how to best combine these data and assess the error rates for protein identification. The objective of this article is to show how variation in the type and amount of data included with each study impacts coverage of the yeast proteome and estimation of the false discovery rate (FDR). Our analysis of a subset of the publicly available yeast data showed that failure to reevaluate the FDR when combining protein IDs from different experiments resulted in an underestimation of the FDR by approximately threefold. A worst-case approximation of the FDR was only slightly larger than estimating the FDR by randomized database matches. The use of a weighted model to emphasize the most informative experimental data provided an increase in the number of IDs at a 1% FDR when compared to other meta-analysis approaches. Also, using an FDR higher than 1% results in a very high rate of false discoveries for IDs above the 1% threshold. Ideally, raw MS data will be made publicly available for complete and consistent reanalysis. In the circumstance that raw data is not available, determining a combined FDR on the basis of the worst-case estimation provides a reasonable approximation of the FDR. When combining experimental results, adding additional experiments results in diminishing and in some cases negative returns on protein identifications. It may be beneficial to include only those experiments generating the most unique identifications due to solid experimental design and sensitive instrumentation.
authors.affiliation: Bioinformatics & High-throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington 98101, USA.
authors.forename: Roger
authors.initials: R
authors.lastname: Higdon
authors.forename: Winston
authors.initials: W
authors.lastname: Haynes
authors.forename: Eugene
authors.initials: E
authors.lastname: Kolker
mesh_terms: D030562:Databases, Protein; D013058:Mass Spectrometry / Q000295:instrumentation / Q000379:methods; D040901:Proteomics / Q000379:methods*; D029701:Saccharomyces cerevisiae Proteins / Q000032:analysis* / Q000235:genetics
chemical_list: D029701:Saccharomyces cerevisiae Proteins