Quality control metrics for LC-MS feature detection tools demonstrated on Saccharomyces cerevisiae proteomic profiles.
J Proteome Res
; 5(7): 1527-34, 2006 Jul.
Article
em En
| MEDLINE
| ID: mdl-16823959
Quantitative proteomic profiling using liquid chromatography-mass spectrometry is emerging as an important tool for biomarker discovery, prompting development of algorithms for high-throughput peptide feature detection in complex samples. However, neither annotated standard data sets nor quality control metrics currently exist for assessing the validity of feature detection algorithms. We propose a quality control metric, Mass Deviance, for assessing the accuracy of feature detection tools. Because the Mass Deviance metric is derived from the natural distribution of peptide masses, it is machine- and proteome-independent and enables assessment of feature detection tools in the absence of completely annotated data sets. We validate the use of Mass Deviance with a second, independent metric that is based on isotopic distributions, demonstrating that we can use Mass Deviance to identify aberrant features with high accuracy. We then demonstrate the use of independent metrics in tandem as a robust way to evaluate the performance of peptide feature detection algorithms. This work is done on complex LC-MS profiles of Saccharomyces cerevisiae which present a significant challenge to peptide feature detection algorithms.
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Base de dados:
MEDLINE
Assunto principal:
Saccharomyces cerevisiae
/
Espectrometria de Massas
/
Cromatografia Líquida
/
Proteínas de Saccharomyces cerevisiae
/
Proteômica
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
J Proteome Res
Assunto da revista:
BIOQUIMICA
Ano de publicação:
2006
Tipo de documento:
Article
País de afiliação:
Estados Unidos