What question did this study set out to answer?

To develop a statistical model that computes the probability of protein presence in a sample based on peptide assignments from tandem mass spectrometry spectra.

July 15, 2003Analytical Chemistry

A Statistical Model for Identifying Proteins by Tandem Mass Spectrometry

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To develop a statistical model that computes the probability of protein presence in a sample based on peptide assignments from tandem mass spectrometry spectra.
Apportioned shared peptides matching multiple database entries and derived a minimal protein list using the expectation-maximization algorithm.
Tested the statistical framework on tandem mass spectrometry spectra generated from an 18-protein purified mixture as well as complex Haemophilus influenzae and Halobacterium samples.
Produced accurate probabilities with high discriminatory power to differentiate correct from incorrect protein identifications across simple and complex mixtures.
Established a standardized method for filtering large-scale proteomics datasets with predictable sensitivity and controlled false-positive error rates.

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Implication

Validation study demonstrates accurate statistical protein identification across bacterial and purified proteome samples, enabling standardized false-positive filtering in mass spectrometry.

Cite This Study

Nesvizhskii et al. (2003) studied this question.

synapsesocial.com/papers/69d57425def4cefba5c22a77 https://doi.org/10.1021/ac0341261

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Implication

Validation study demonstrates accurate statistical protein identification across bacterial and purified proteome samples, enabling standardized false-positive filtering in mass spectrometry.

A Statistical Model for Identifying Proteins by Tandem Mass Spectrometry

Key Points

Discussion

Implication

Cite This Study

A Statistical Model for Identifying Proteins by Tandem Mass Spectrometry

Key Points

Discussion

Implication

Cite This Study

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