Name
Abstract | ||
|---|---|---|
In mass spectrometry-based proteomics, the statistical significance of a peptide-spectrum or protein-spectrum match is an important indicator of the correctness of the peptide or protein identification. In bottom-up mass spectrometry, probabilistic models, such as the generating function method, have been successfully applied to compute the statistical significance of peptide-spectrum matches for short peptides containing no post-translational modifications. As top-down mass spectrometry, which often identifies intact proteins with post-translational modifications, becomes available in many laboratories, the estimation of statistical significance of top-down protein identification results has come into great demand.In this paper, we study an extended generating function method for accurately computing the statistical significance of protein-spectrum matches with post-translational modifications. Experiments show that the extended generating function method achieves high accuracy in computing spectral probabilities and false discovery rates.The extended generating function method is a non-trivial extension of the generating function method for bottom-up mass spectrometry. It can be used to choose the correct protein-spectrum match from several candidate protein-spectrum matches for a spectrum, as well as separate correct protein-spectrum matches from incorrect ones identified from a large number of tandem mass spectra. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1186/1471-2164-15-S1-S9 | BMC Genomics |
Keywords | Field | DocType |
proteins,microarrays,computational biology,proteomics,tandem mass spectrometry | Generating function,Tandem,Proteomics,Biology,Mass spectrum,Tandem mass spectrometry,Mass spectrometry,Probabilistic logic,Bioinformatics,DNA microarray | Journal |
Volume | Issue | ISSN |
15 Suppl 1 | S-1 | 1471-2164 |
Citations | PageRank | References |
1 | 0.38 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaowen Liu | 1 | 88 | 6.51 |
| Matthew W Segar | 2 | 1 | 0.72 |
| Shuai Cheng Li | 3 | 184 | 30.25 |
| Sangtae Kim | 4 | 142 | 26.44 |