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| A statistical model for identifying proteins by tandem mass spectrometry |
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| Author(s): Nesvizhskii AI, Keller A, Kolker E, Aebersold R |
| Source: ANALYTICAL CHEMISTRY Volume: 75 Issue: 17 Pages: 4646-4658 Published: SEP 1 2003 |
| Times Cited: 606 References: 45 |
| Abstract: A statistical model is presented for computing probabilities that proteins are present in a sample on the basis of peptides assigned to tandem mass (MS/MS) spectra acquired from a proteolytic digest of the sample. Peptides that correspond to more than a single protein in the sequence database are apportioned among all corresponding proteins, and a minimal protein list sufficient to account for the observed peptide assignments is derived using the expectation-maximization algorithm. Using peptide assignments to spectra generated from a sample of 18 purified proteins, as well as complex H. influenzae and Halobacterium samples, the model is shown to produce probabilities that are accurate and have high power to discriminate correct from incorrect protein identifications. This method allows filtering of large-scale proteomics data sets with predictable sensitivity and false positive identification error rates. Fast, consistent, and transparent, it provides a standard for publishing large-scale protein identification data sets in the literature and for comparing the results obtained from different experiments. |
| Document Type: Article |
| Language: English |
| Reprint Address: Nesvizhskii, AI (reprint author), Inst Syst Biol, 1441 N 34th St, Seattle, WA 98103 USA |
Addresses:
1. Inst Syst Biol, Seattle, WA 98103 USA |
| Publisher: AMER CHEMICAL SOC, 1155 16TH ST, NW, WASHINGTON, DC 20036 USA |
| Subject Category: Chemistry, Analytical |
| IDS Number: 719FE |
| ISSN: 0003-2700 |
| DOI: 10.1021/ac0341261 |
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| | | | | | | | | Record from Web of Science® | | | | | | | | | |