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Activity Number:
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154
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #310386 |
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Title:
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Statistical Methods for Peptide Identification Using Tandem Mass Spectrometry Through Sequence Database Searching
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Author(s):
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Qunhua Li*+ and Matthew Stephens
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Companies:
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University of Washington and The University of Chicago
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Address:
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4747 30th Ave NE 155, Seattle, WA, 98105,
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Keywords:
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mixture models ; EM algorithm ; mass spectrometry ; proteomics ; peptide identification
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Abstract:
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Peptide identification using tandem mass spectra through database searching is a key component in proteomic research. In this study, we developed a model-based scoring algorithm for peptide identification using mixture models. It effectively takes account of the spectral noise and incorporates the spectral intensity. In addition, our approach also provides a natural confidence measure for assessing the uncertainty of the score. We will present our results on several public available benchmark datasets.
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