Verification of single-peptide protein identifications by the application of complementary database search algorithms. |
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Authors: | James G Rohrbough Linda Breci Nirav Merchant Susan Miller Paul A Haynes |
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Institution: | Department of Biochemistry and Molecular Biophysics, The University of Arizona, Tucson, USA. |
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Abstract: | 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. |
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Keywords: | Database search algorithms protein identification SEQUEST criteria tandem mass spectrometry XTandem single-peptide verification |
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