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- Title
Candidate prioritization for low-abundant differentially expressed proteins in 2D-DIGE datasets.
- Authors
Nandal, Umesh K.; Vlietstra, Wytze J.; Byrman, Carsten; Jeeninga, Rienk E.; Ringrose, Jeffrey H.; van Kampen, Antoine HC; Speijer, Dave; Moerland, Perry D.
- 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.
- Subjects
PROTEIN research; GEL electrophoresis; ISOELECTRIC point; MOLECULAR weights; MASS spectrometry; T cells; HIV infections
- Publication
BMC Bioinformatics, 2015, Vol 16, Issue 1, p1
- ISSN
1471-2105
- Publication type
Article
- DOI
10.1186/s12859-015-0455-x