1. Candidate prioritization for low-abundant differentially expressed proteins in 2D-DIGE datasets.
- Author
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Nandal UK, Vlietstra WJ, Byrman C, Jeeninga RE, Ringrose JH, van Kampen AH, Speijer D, and Moerland PD
- Subjects
- Cells, Cultured, HIV Infections virology, HIV-1 metabolism, Humans, Peptide Fragments analysis, T-Lymphocytes virology, Electrophoresis, Gel, Two-Dimensional methods, HIV Infections metabolism, Proteins analysis, Proteomics methods, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization methods, T-Lymphocytes metabolism, Two-Dimensional Difference Gel Electrophoresis methods
- Abstract
Background: Two-dimensional differential gel electrophoresis (2D-DIGE) provides a powerful technique to separate proteins on their isoelectric point and apparent molecular mass and quantify changes in protein expression. Abundantly available proteins in spots can be identified using mass spectrometry-based approaches. However, identification is often not possible for low-abundant proteins., Results: We present a novel computational approach to prioritize candidate proteins for unidentified spots. Our approach exploits noisy information on the isoelectric point and apparent molecular mass of a protein spot in combination with functional similarities of candidate proteins to already identified proteins to select and rank candidates. We evaluated our method on a 2D-DIGE dataset comparing protein expression in uninfected and HIV-1 infected T-cells. Using leave-one-out cross-validation, we show that the true-positive rate for the top-5 ranked proteins is 43.8%., Conclusions: Our approach shows good performance on a 2D-DIGE dataset comparing protein expression in uninfected and HIV-1 infected T-cells. We expect our method to be highly useful in (re-)mining other 2D-DIGE experiments in which especially the low-abundant protein spots remain to be identified.
- Published
- 2015
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