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Candidate prioritization for low-abundant differentially expressed proteins in 2D-DIGE datasets
Umesh K Nandal1, Wytze J Vlietstra2, Carsten Byrman3
1Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, PO Box 22700, DE Amsterdam, 1100, The Netherlands. u.k.nandal@amc.uva.nl.
BMC Bioinformatics
|January 29, 2015
Summary
This study introduces a new computational method to identify low-abundance proteins in two-dimensional differential gel electrophoresis (2D-DIGE) experiments. The approach improves protein identification, particularly for challenging low-abundance spots in HIV-1 infected T-cells.
Area of Science:
- Proteomics
- Computational Biology
- Biochemistry
Background:
- Two-dimensional differential gel electrophoresis (2D-DIGE) is a key technique for protein separation and expression quantification.
- Identifying low-abundance proteins in 2D-DIGE spots remains a significant challenge in proteomics.
Purpose of the Study:
- To develop and evaluate a novel computational approach for prioritizing candidate proteins in unidentified 2D-DIGE spots.
- To enhance the identification of low-abundance proteins in complex biological samples.
Main Methods:
- A computational method was developed integrating protein spot information (isoelectric point, molecular mass) with functional similarity to known proteins.
- The approach was validated using a 2D-DIGE dataset comparing uninfected and HIV-1 infected T-cells.
- Leave-one-out cross-validation was employed to assess the method's performance.
Main Results:
- The novel computational approach effectively prioritizes candidate proteins for unidentified spots.
- A true-positive rate of 43.8% was achieved for the top-5 ranked proteins in the evaluated dataset.
- The method demonstrated robust performance on a dataset comparing uninfected and HIV-1 infected T-cells.
Conclusions:
- The developed computational method shows significant promise for improving protein identification in 2D-DIGE.
- This approach is expected to be valuable for re-analyzing existing 2D-DIGE experiments, especially for identifying previously elusive low-abundance proteins.
