Candidate prioritization for low-abundant differentially expressed proteins in 2D-DIGE datasets

U.K. Nandal, W.J. Vlietstra, C. Byrman, R.E. Jeeninga, J.H. Ringrose, A.H.C. van Kampen, D. Speijer, P.D. Moerland

Research output: Contribution to JournalArticleAcademicpeer-review

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.
Original languageEnglish
JournalBMC Bioinformatics
DOIs
Publication statusPublished - 2015

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