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Significance analysis of spectral count data in label-free shotgun proteomics.

ABSTRACT: Spectral counting has become a commonly used approach for measuring protein abundance in label-free shotgun proteomics. At the same time, the development of data analysis methods has lagged behind. Currently most studies utilizing spectral counts rely on simple data transforms and posthoc corrections of conventional signal-to-noise ratio statistics. However, these adjustments can neither handle the bias toward high abundance proteins nor deal with the drawbacks due to the limited number of replicates. We present a novel statistical framework (QSpec) for the significance analysis of differential expression with extensions to a variety of experimental design factors and adjustments for protein properties. Using synthetic and real experimental data sets, we show that the proposed method outperforms conventional statistical methods that search for differential expression for individual proteins. We illustrate the flexibility of the model by analyzing a data set with a complicated experimental design involving cellular localization and time course.

SUBMITTER: Choi H

PROVIDER: S-EPMC2596341 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

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Significance analysis of spectral count data in label-free shotgun proteomics.

Choi Hyungwon H Fermin Damian D Nesvizhskii Alexey I AI

Molecular & cellular proteomics : MCP 20080720 12

Spectral counting has become a commonly used approach for measuring protein abundance in label-free shotgun proteomics. At the same time, the development of data analysis methods has lagged behind. Currently most studies utilizing spectral counts rely on simple data transforms and posthoc corrections of conventional signal-to-noise ratio statistics. However, these adjustments can neither handle the bias toward high abundance proteins nor deal with the drawbacks due to the limited number of repli ...[more]

PMID: 18644780

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Project description:Shotgun proteomics provides the most powerful analytical platform for global inventory of complex proteomes using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and allows a global analysis of protein changes. Nevertheless, sampling of complex proteomes by current shotgun proteomics platforms is incomplete, and this contributes to variability in assessment of peptide and protein inventories by spectral counting approaches. Thus, shotgun proteomics data pose challenges in comparing proteomes from different biological states. We developed an analysis strategy using quasi-likelihood Generalized Linear Modeling (GLM), included in a graphical interface software package (QuasiTel) that reads standard output from protein assemblies created by IDPicker, an HTML-based user interface to query shotgun proteomic data sets. This approach was compared to four other statistical analysis strategies: Student t test, Wilcoxon rank test, Fisher's Exact test, and Poisson-based GLM. We analyzed the performance of these tests to identify differences in protein levels based on spectral counts in a shotgun data set in which equimolar amounts of 48 human proteins were spiked at different levels into whole yeast lysates. Both GLM approaches and the Fisher Exact test performed adequately, each with their unique limitations. We subsequently compared the proteomes of normal tonsil epithelium and HNSCC using this approach and identified 86 proteins with differential spectral counts between normal tonsil epithelium and HNSCC. We selected 18 proteins from this comparison for verification of protein levels between the individual normal and tumor tissues using liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM-MS). This analysis confirmed the magnitude and direction of the protein expression differences in all 6 proteins for which reliable data could be obtained. Our analysis demonstrates that shotgun proteomic data sets from different tissue phenotypes are sufficiently rich in quantitative information and that statistically significant differences in proteins spectral counts reflect the underlying biology of the samples.

Project description:BACKGROUND:Although a great deal of rice proteomic research has been conducted, there are relatively few studies specifically addressing the rice grain proteome. The existing rice grain proteomic researches have focused on the identification of differentially expressed proteins or monitoring protein expression patterns during grain filling stages. RESULTS:Proteins were extracted from rice grains 10, 20, and 30 days after flowering, as well as from fully mature grains. By merging all of the identified proteins in this study, we identified 4,172 non-redundant proteins with a wide range of molecular weights (from 5.2 kDa to 611 kDa) and pI values (from pH 2.9 to pH 12.6). A Genome Ontology category enrichment analysis for the 4,172 proteins revealed that 52 categories were enriched, including the carbohydrate metabolic process, transport, localization, lipid metabolic process, and secondary metabolic process. The relative abundances of the 1,784 reproducibly identified proteins were compared to detect 484 differentially expressed proteins during rice grain development. Clustering analysis and Genome Ontology category enrichment analysis revealed that proteins involved in the metabolic process were enriched through all stages of development, suggesting that proteome changes occurred even in the desiccation phase. Interestingly, enrichments of proteins involved in protein folding were detected in the desiccation phase and in fully mature grain. CONCLUSION:This is the first report conducting comprehensive identification of rice grain proteins. With a label free shotgun proteomic approach, we identified large number of rice grain proteins and compared the expression patterns of reproducibly identified proteins during rice grain development. Clustering analysis, Genome Ontology category enrichment analysis, and the analysis of composite expression profiles revealed dynamic changes of metabolisms during rice grain development. Interestingly, we detected that proteins involved in glycolysis, TCA-cycle, lipid metabolism, and proteolysis accumulated at higher levels in fully mature grain compared to grain developing stages, suggesting that the accumulation of these proteins during the desiccation stage may be associated with the preparation of proteins required in germination.

Dataset Information

Significance analysis of spectral count data in label-free shotgun proteomics.

Publications

Significance analysis of spectral count data in label-free shotgun proteomics.

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