Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry | Zendy

Lukas Käll | Zendy; John D. Storey | Zendy; William Stafford Noble | Zendy

Open Access

Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry

Author(s) -

Lukas Käll,

John D. Storey,

William Stafford Noble

Publication year - 2008

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btn294

Subject(s) - parametric statistics , computer science , robustness (evolution) , semiparametric model , posterior probability , logistic regression , statistics , mathematics , artificial intelligence , machine learning , chemistry , bayesian probability , biochemistry , gene

A mass spectrum produced via tandem mass spectrometry can be tentatively matched to a peptide sequence via database search. Here, we address the problem of assigning a posterior error probability (PEP) to a given peptide-spectrum match (PSM). This problem is considerably more dif.cult than the related problem of estimating the error rate associated with a large collection of PSMs. Existing methods for estimating PEPs rely on a parametric or semiparametric model of the underlying score distribution.

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