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

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 co

SUBMITTER: Nandal UK 

PROVIDER: S-EPMC4384356 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature