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

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• 作者：Umesh K Nandal (1)
  Wytze J Vlietstra (1)
  Carsten Byrman (1)
  Rienk E Jeeninga (2)
  Jeffrey H Ringrose (2)
  Antoine HC van Kampen (1) (3)
  Dave Speijer (4)
  Perry D Moerland (1)
  
  1. Bioinformatics Laboratory ; Academic Medical Center ; University of Amsterdam ; PO Box 22700 ; DE Amsterdam ; 1100 ; The Netherlands
  2. Laboratory of Experimental Virology ; Department of Medical Microbiology ; Center for Infection and Immunity Amsterdam (CINIMA) ; Academic Medical Center ; University of Amsterdam ; PO Box 22700 ; DE Amsterdam ; 1100 ; The Netherlands
  3. Biosystems Data Analysis Group ; University of Amsterdam ; Science Park 9041098 ; XH Amsterdam ; The Netherlands
  4. Department of Medical Biochemistry ; Academic Medical Center ; University of Amsterdam ; PO Box 22700 ; DE Amsterdam ; 1100 ; The Netherlands
  
• 关键词：Prioritization ; Proteomics ; 2D ; DIGE ; Network
• 刊名：BMC Bioinformatics
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：16
• 期：1
• 全文大小：1,399 KB
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• 刊物主题：Bioinformatics; Microarrays; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Combinatorial Libraries; Algorithms;
• 出版者：BioMed Central
• ISSN：1471-2105

文摘

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.