Improvement of OMSSA for High Accuracy MS/MS Data

PSM (peptide-spectrum-match) scoring is a key step in peptide identification from MS/MS data. The development of high accuracy mass spectrometers brings a challenge to PSM scoring, especially to score calibration. The change of precursor mass tolerance from low accuracy to high accuracy reduces the...

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Bibliographic Details
Published in:Journal of biomolecular techniques 2014-05, Vol.25 (Suppl), p.S32-S32
Main Authors: Azencott, R., Hawke, D.H., Kong, A.
Format: Article
Language:English
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Poster Abstracts
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Summary:PSM (peptide-spectrum-match) scoring is a key step in peptide identification from MS/MS data. The development of high accuracy mass spectrometers brings a challenge to PSM scoring, especially to score calibration. The change of precursor mass tolerance from low accuracy to high accuracy reduces the number of candidate peptides for one spectrum; therefore, a calibration technique that uses the empirical distribution of candidate PSM scores is questionable. Through examples, we show that OMSSA (Open Mass Spectrometry Search Algorithm) outperforms other open-source software on high accuracy MS/MS data. This is most likely due to the fact that the scoring method in OMSSA does not rely on empirical score distributions. To further improve its performance, we incorporated a new scoring method based on matched intensities of candidate PSMs into OMSSA. The most important feature of the new scoring method is that the score distribution, estimated by Monte Carlo simulation, is also independent of empirical PSM score distributions. With this new score method, the performance of OMSSA has been improved. Using test datasets we have achieved results similar to or better than that of the gold-standard search tool, Mascot.
ISSN:1524-0215
1943-4731