Verification of single-peptide protein identifications by the application of complementary database search algorithms

Data produced from the MudPIT analysis of yeast (S. cerevisiae) and rice (O. sativa) were used to develop a technique to validate single-peptide protein identifications using complementary database search algorithms. This results in a considerable reduction of overall false-positive rates for protei...

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Bibliographic Details
Published in:Journal of biomolecular techniques 2006-12, Vol.17 (5), p.327-332
Main Authors: Rohrbough, James G, Breci, Linda, Merchant, Nirav, Miller, Susan, Haynes, Paul A
Format: Article
Language:English
Subjects:
Algorithms
Databases, Protein
Oryza - chemistry
Peptides - analysis
Peptides - chemistry
Plant Proteins - analysis
Plant Proteins - chemistry
Proteins - analysis
Proteins - chemistry
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Summary:Data produced from the MudPIT analysis of yeast (S. cerevisiae) and rice (O. sativa) were used to develop a technique to validate single-peptide protein identifications using complementary database search algorithms. This results in a considerable reduction of overall false-positive rates for protein identifications; the overall false discovery rates in yeast are reduced from near 25% to less than 1%, and the false discovery rate of yeast single-peptide protein identifications becomes negligible. This technique can be employed by laboratories utilizing a SEQUEST-based proteomic analysis platform, incorporating the XTandem algorithm as a complementary tool for verification of single-peptide protein identifications. We have achieved this using open-source software, including several data-manipulation software tools developed in our laboratory, which are freely available to download.
ISSN:1524-0215