Title |
Candidate prioritization for low-abundant differentially expressed proteins in 2D-DIGE datasets
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Published in |
BMC Bioinformatics, January 2015
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DOI | 10.1186/s12859-015-0455-x |
Pubmed ID | |
Authors |
Umesh K Nandal, Wytze J Vlietstra, Carsten Byrman, Rienk E Jeeninga, Jeffrey H Ringrose, Antoine HC van Kampen, Dave Speijer, Perry D Moerland |
Abstract |
BackgroundTwo-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.ResultsWe 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%.ConclusionsOur 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. |
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